Grid-Based Navigation for Autonomous
基于车载激光雷达的点云匹配定位
摘要定位是自动驾驶车辆自主导航技术的关键问题之一。
保持稳定、精确、实时的定位能够保证自动驾驶车辆行驶的安全。
自动驾驶车辆的定位方式主要有：基于全球卫星导航系统(Global Navigation Satellite System, GNSS)的定位方式，基于激光雷达点云地图匹配的定位方式等。
目前在自动驾驶领域，全球卫星导航系统是使用最广泛、应用最成熟的定位技术，自动驾驶车辆依靠GNSS定位技术能够在大多数场景下完成精确定位与导航，但仍然存在卫星信号被周边环境的建筑物和树木等障碍物遮挡导致定位失效或者偏移等状况。
目前为了提高定位精度，利用GNSS中的实时差分定位技术（Real-time Kinematic, RTK）结合惯性导航系统（Inertial Navigation System, INS）进行多传感器融合定位，以满足自动驾驶车辆高精度定位的需求。
但是，在复杂地形环境下对定位的干扰依然不可避免，因此在自动驾驶领域基于全球卫星导航系统的定位方式在地形环境复杂的场景下仍受到很大的限制。
车载激光雷达采集的点云地图不仅能够提供全方位的道路特征信息，同时，能够为自动驾驶车辆实现厘米级定位提供数据基础。
基于点云地图匹配的定位技术能够不受天气、地形等环境因素的影响，在定位测算中保持相互独立，在复杂环境下替代GNSS定位，为自动驾驶车辆的安全行驶提供保障。
为此，本文对基于点云地图匹配的定位技术进行研究，并对经典的点云匹配算法进行改进，对比不同算法在复杂环境下的定位精度和效率，验证基于点云地图匹配的定位技术的可行性。
本文的研究数据选取于目前国际上最大的自动驾驶场景数据集(KITTI)中的城市环境点云数据。
首先，对点云数据进行预处理，利用LOAM算法预先构建出点云地图，作为匹配定位实验的基础数据，利用PCL库中的滤波算法对点云数据中存在的噪声点进行过滤优化，增强匹配定位的稳定性；然后，分别利用迭代最近点(Iterative Closest Points，ICP)算法和正态分布变换(Normal Distribution Transform，NDT)算法进行单帧点云与点云地图的匹配定位实验，分析两种算法的稳定性、精度以及效率；最后，在当前完成匹配定位的基础上进行研究和算法改进，利用Gauss-Newton 法改进ICP算法，同时利用K-D树加速查找对应点，并且和NDT算法进行集成，定义为NDT-ICP算法，进行匹配定位实验。
脉冲星测量技术用于深空探测器自主导航的原理及方法
脉冲星测量技术用于深空探测 器自主导航的原理及方法
1/76
脉冲星的发现
2/76
脉冲星机制
当一颗恒星变成超新星时，经过激烈变化后，留下满天膨胀的气体和微 小物质，余下的核心直径只有几十到十几公里。超新星的内爆非常强烈，恒 星原子里的质子和电子被紧紧地压缩 在一起，抵消了它们的电荷，形成 中子。这种中子星可以达到水密度 的1014倍，有着极强的磁场，可以 非常快速地旋转。因为磁轴不与旋 转轴重合，二者一般具有一定的 夹角，当脉冲星高速旋转时，辐 射束将沿着磁场两极方向被抛出 ，随着脉冲星的自转，该辐射束 周期性扫过探测器的视界，形成 脉冲。
11/76
脉冲星导航技术研究历程
12/76
研究历程
a) 脉冲星导航思想的萌芽阶段 1. 脉冲星导航思想最早于 20 世纪 70 年代提出。 2. 1971 年，Reichley，Downs 和Morris 首次描述了把射电脉冲星作为时钟的思 想。 3. 1974 年，Downs 在文献《Interplanetary Navigation Using Pulsation Radio Source》中提出一种基于射电脉冲星信号进行行星际导航的思想，标志着脉 冲星导航思想的初步形成。但由于脉冲星的射电信号强度较弱，宇宙中的射 电信号噪声强度大，导航中需要至少 25m 口径的天线接收信号，因此该方法 很难在工程中实现。 4. 20 世纪 70 年代后期，天文观测在 X 射线波段能量范围 1~20keV、频率范围 2.5×1017 ~ 4.8×1017Hz的进展，促进了对 X 射线脉冲星特性的研究。 5. 1980 年 Downs 和 Reichley 提出测量脉冲星脉冲到达时间的技术。 1981 年 Chester 和Butman 在国际上第一次正式提出利用 X 射线脉冲星进行航天器导 航的思想。
网格英文作文模板
网格英文作文模板Title: The Grid: A Comprehensive Essay。
Introduction:The grid, in its essence, embodies a structured system of intersecting lines that form squares or rectangles. This concept permeates various domains, ranging from urban planning to digital design. In this essay, we delve into the multifaceted nature of the grid, exploring its significance and applications across diverse fields.Historical Perspective:The grid finds its roots in ancient civilizations, where it served as a fundamental tool for organizing space and resources. For instance, the grid layout of cities like Mohenjo-Daro in the Indus Valley Civilization reflects meticulous urban planning. Similarly, the grid-based design of the Roman army camps facilitated efficient troopmovement and communication.Urban Planning:In modern times, urban planners utilize the grid as a blueprint for city development. Grid-based street layouts, such as those seen in Manhattan, promote accessibility and navigation. Moreover, the grid enables efficient allocation of utilities and infrastructure, contributing to sustainable urban living.Architecture:Architects leverage the grid as a design framework to create aesthetically pleasing and functional structures. The grid serves as a guide for spatial organization, facilitating the harmonious arrangement of building elements. From classical temples to contemporary skyscrapers, the grid influences architectural compositions across epochs.Graphic Design:In graphic design, the grid serves as a foundational principle for layout and composition. Designers employ grid systems to establish visual hierarchy, alignment, and consistency in various media formats. Whether designing a magazine spread or a website interface, adherence to the grid enhances readability and user experience.Information Technology:In the realm of information technology, the grid takes on a new dimension with the advent of grid computing and data visualization. Grid computing harnesses distributed computing resources to solve complex problems efficiently. Additionally, data visualization techniques like grid maps and treemaps offer insights into large datasets, aiding decision-making processes.Art:Artists explore the concept of the grid as both a formal structure and a metaphorical motif. From PietMondrian's iconic grid-based paintings to Sol LeWitt's conceptual grid drawings, artists experiment with thegrid's geometric precision and conceptual implications. The grid serves as a vehicle for artistic expression,reflecting themes of order, repetition, and control.Conclusion:In conclusion, the grid permeates various facets of human endeavor, serving as a fundamental framework for organization and expression. Whether in urban planning, architecture, design, technology, or art, the grid continues to shape our world in profound ways. Its versatility and ubiquity underscore its enduring relevance in an ever-evolving society.。
自主式水下机器人的导航系统设计及算法研究的开题报告
自主式水下机器人的导航系统设计及算法研究的开题报告一、研究背景水下机器人是一种能够在海洋、湖泊、河流等水体中自主航行、获取信息、完成任务的智能化设备。
随着科技的不断发展，水下机器人已成为海洋探测、水下搜救、海底勘探等领域的重要工具。
而在水下机器人中，导航系统是其最重要的部分之一。
传统的GPS导航技术在水下并不能很好地应用，因为水下水草、岩石、潮汐等复杂环境会干扰信号传输，导致导航不准确。
因此，自主式水下机器人的导航系统相比其他智能机器人的导航系统更复杂，不同地形、潮汐、地形和流动速度变化都要考虑进去。
因此，如何设计一种能够应对复杂环境的自主水下机器人导航系统成为该研究领域的重点。
二、研究内容本研究旨在设计一种能够在复杂水下环境下进行自主航行的导航系统，同时开发相应的算法来提高导航精度。
具体研究内容包括：1. 根据水下机器人的性能和任务需求，选择合适的传感器、电子设备和通讯系统，设计自主式水下机器人的硬件系统。
2. 结合机器人在水下环境中的运动模型和水质环境模型，设计自主式水下机器人的导航算法。
该算法应涵盖环境感知、路径规划和控制三个方面，能够实现机器人的自主航行、感知避障和规避水流的能力。
3. 根据设计的导航算法，实现相应的控制软件，测试验证机器人自主航行、路径规划、避障和水流规避等性能。
三、研究意义自主式水下机器人的导航系统研究具有重要意义，主要表现在以下几个方面：1. 对水下机器人导航技术的研究和探讨具有重要的学术价值，可以为智能机器人导航系统的研究提供借鉴。
2. 自主式水下机器人的导航系统能够应用于海洋探测、水下搜救、水下勘探等领域，具有广阔的应用前景和市场前景。
3. 设计的导航系统对于提高水下机器人的自主控制能力、增强其适应水下环境的能力具有重要意义。
四、研究方法本研究主要采用以下方法：1. 文献综述：通过查阅资料掌握国内外自主式水下机器人的导航系统发展现状、技术瓶颈和解决方法等内容，总结相关算法和实现方式。
移动机器人路径规划和导航(英文)
Autonomous Mobile Robots, Chapter 6
6.2.1
Road-Map Path Planning: Voronoi Diagram
• Easy executable: Maximize the sensor readings • Works also for map-building: Move on the Voronoi edges
© R. Siegwart, I. Nourbakhsh
Autonomous Mobile Robots, Chapter 6
6.2.1
Road-Map Path Planning: Adaptive Cell Decomposition
© R. Siegwart, I. Nourbakhsh
Autonomous Mobile Robots, Chapter 6
© R. Siegwart, I. Nourbakhsh
Autonomous Mobile Robots, Chapter 6
6.2.1
Road-Map Path Planning: Voronoi, Sysquake Demo
© R. Siegwart, I. Nourbakhsh
Autonomous Mobile Robots, Chapter 6
Ø Topological or metric or a mixture between both.
• First step:
Ø Representation of the environment by a road-map (graph), cells or a potential field. The resulting discrete locations or cells allow then to use standard planning algorithms.
使用AI技术进行船舶自动导航的步骤与技巧
使用AI技术进行船舶自动导航的步骤与技巧一、引言船舶自动导航是利用人工智能（AI）技术实现的一种新型自动导航系统。
它可以通过感知环境、规划路径和控制运动等一系列操作，使船只能够在复杂的海洋环境中高效安全地行驶。
本文将介绍使用AI技术进行船舶自动导航的步骤与技巧。
二、数据收集与预处理数据是AI技术的基础，而在船舶自动导航中也不例外。
首先，我们需要收集大量关于海洋环境、水深、岸线、其他船只等方面的数据。
这些数据可以来自卫星图像、雷达监测设备、传感器等多个来源。
然后，我们需要对这些数据进行预处理，包括数据清洗、去噪和标准化等操作，以确保数据的质量和可用性。
三、环境感知在船舶自动导航中，准确地感知周围环境是非常重要的。
为了实现这一目标，我们可以借助计算机视觉和传感器技术。
计算机视觉可以通过图像识别和目标检测等技术，实时获取船只周围的环境信息。
而传感器技术可以用来感知海洋水流、海浪高度、风速等关键指标。
通过综合分析这些数据，我们可以构建一个全面准确的环境感知模型。
四、路径规划与优化一旦我们获得了准确的环境感知信息，下一步就是进行路径规划与优化。
在船舶自动导航中，路径规划是指确定最佳的行驶路线，以达到目的地或完成特定任务。
而路径优化则是通过数学建模和算法求解等方法，对路径进行进一步改进和优化。
在这个过程中，我们需要考虑多种因素，包括海洋环境、其他船只、安全距离等，并且需要充分利用AI技术的强大计算能力。
五、动作控制与自主决策路径规划完成后，接下来就是实际的动作控制与自主决策阶段。
这涉及到如何根据预定的路线和目标，在现实环境中进行运动控制和决策调整。
为了做到这一点，我们可以使用机器学习（ML）和深度强化学习（DRL）等AI技术。
通过对历史数据的学习和模型训练，船舶可以逐渐提高自身的决策能力和运动控制准确性。
六、实时监控与反馈调整在船舶自动导航过程中，实时监控和反馈调整是必不可少的环节。
通过传感器获取实时数据，并根据当前环境做出相应的反馈调整，可以有效保证船只的安全性和稳定性。
MEMS惯导-单目视觉里程计组合导航技术研究
MEMS惯导-单目视觉里程计组合导航技术研究MEMS惯导/单目视觉里程计组合导航技术研究摘要：本文主要研究了MEMS惯导和单目视觉里程计组合导航技术。
MEMS惯导是一种高精度、低成本的惯性导航技术，而单目视觉里程计是一种基于相机视觉的位姿估计技术。
MEMS惯导和单目视觉里程计的组合可以互补各自的优点，提高导航的精度和鲁棒性。
首先，本文对MEMS惯导和单目视觉里程计的原理和特点进行了介绍，并对其存在的问题进行了分析。
然后，本文提出了一种基于卡尔曼滤波器的MEMS惯导/单目视觉里程计组合导航算法。
该算法将MEMS惯导和单目视觉里程计的位姿估计结果进行融合，得到更加准确和可靠的导航结果。
最后，本文进行了实验验证，结果表明，该算法在多种复杂环境下均能取得较好的导航精度和鲁棒性。
关键词：MEMS惯导；单目视觉里程计；组合导航；卡尔曼滤波器；导航精度；鲁棒性。
Abstract:This paper mainly studies the MEMS inertial navigation and monocular visual odometry combined navigation technology. MEMS inertial navigation is a high-precision and low-cost inertial navigation technology, while monocular visual odometry is a position and attitude estimation technology based on camera vision. The combination of MEMS inertial navigation and monocular visual odometry can complement each other's advantages and improve the accuracy and robustness of navigation.Firstly, this paper introduces the principles and characteristics of MEMS inertial navigation and monocular visual odometry, and analyzes the problems existing in them. Then, this paper proposes a MEMS inertial navigation/monocular visual odometry combined navigation algorithm based on Kalman filter. The algorithm fuses the position and attitude estimation results of MEMS inertial navigation and monocular visual odometry to obtain more accurate and reliable navigation results. Finally, this paper conducts experimental verification, and the results show that the algorithm can achieve good navigation accuracy and robustness in various complex environments.Keywords: MEMS inertial navigation; monocular visualodometry; combined navigation; Kalman filter; navigation accuracy; robustnessIn recent years, MEMS inertial navigation and monocular visual odometry have become popular among researchers as they provide accurate and low-cost navigation solutions. However, each approach has its limitations. MEMS inertial navigation suffers fromdrift errors, while monocular visual odometry is susceptible to lighting changes, occlusions, andmotion blur. To overcome these limitations,researchers have proposed a combined navigation approach that fuses the results of the two methods.One such approach is the Kalman filter-based algorithm, which integrates the measurements from MEMS inertial sensors and monocular vision to estimate the position and attitude of the system. The algorithm caneffectively suppress the drift errors of the inertial navigation system using the visual measurements as a reference, while compensating for the scale drifterror of the monocular visual odometry using theinertial measurements. Additionally, the algorithm can handle the nonlinearities and uncertainties of the navigation system and provide a more accurate and reliable navigation solution.To verify the effectiveness of the proposed algorithm, experimental tests were conducted in various complex environments. These tests included indoor and outdoor environments with different lighting conditions, as well as environments with obstacles and sudden movements. The results showed that the algorithm could achieve good navigation accuracy and robustness even in these challenging conditions.In conclusion, the combination of MEMS inertial navigation and monocular visual odometry using a Kalman filter-based algorithm is a promising approach to provide accurate and reliable navigation solutions. The algorithm can effectively address the limitations of both methods and is suitable for various complex environments. Future research should explore the application of this approach in specific fields, such as autonomous driving and robotics, to further evaluate its potentialOne potential application of this approach is in the field of autonomous driving. With the increasing demand for self-driving cars, accurate navigation becomes crucial for ensuring the safety and efficiency of the vehicle. By combining MEMS inertial navigation and monocular visual odometry, the proposed algorithm can provide precise location and orientationinformation for the autonomous vehicle. With the help of the Kalman filter, the algorithm can effectively correct errors and improve the overall accuracy of the navigation system.Another potential application is in the field of robotics. Many robotic systems require accurate positioning and orientation information to perform tasks such as mapping, exploration, and manipulation. By using the proposed approach, robotic systems can achieve higher precision and reliability in navigation, leading to improved performance and efficiency.However, there are still some challenges that need to be addressed. For example, the accuracy of the visual odometry system can be affected by external factors such as lighting conditions and camera calibration. The MEMS IMU system can also suffer from drift due to the accumulation of errors over time. To overcomethese challenges, researchers can explore the use of advanced sensor fusion techniques and machine learning algorithms.In summary, the combination of MEMS inertialnavigation and monocular visual odometry using a Kalman filter-based algorithm holds great potentialfor providing accurate and reliable navigationsolutions in various applications. Further researchand development in this area are needed to address the challenges and fully exploit the benefits of this approachOne area where the combination of MEMS inertial navigation and monocular visual odometry could prove particularly valuable is in autonomous vehicles.Autonomous vehicles rely on accurate and reliable navigation to operate safely and efficiently. While GPS is the primary navigation system used today, ithas limitations, such as poor performance in urban environments and susceptibility to jamming or spoofing.MEMS inertial navigation and monocular visual odometry offer an alternative or complementary approach to GPS-based navigation for autonomous vehicles. By using highly accurate inertial sensors and cameras to measure vehicle motion and track landmarks, these systems can provide precise and reliable position and orientation information.One of the key advantages of using these technologiesin combination is their redundancy. MEMS inertial navigation can provide accurate position andorientation estimates over short periods of time, buterrors can accumulate over longer periods due to drift. Monocular visual odometry can help correct theseerrors by providing additional position andorientation estimates based on image data.However, using these technologies in an autonomous vehicle setting presents several challenges. For example, the vehicle may encounter scenarios where the camera cannot see sufficient landmarks to track its position accurately. Additionally, environmentalfactors such as lighting conditions and weather can also affect the performance of visual odometry.To overcome these challenges, advanced algorithms and sensor fusion techniques, such as deep learning and Kalman filtering, can be used to optimize the performance of the system. For example, a deeplearning-based object recognition algorithm could be trained to identify and track specific landmarks that are more robust to changes in environmental conditions.Another potential application for MEMS inertial navigation and visual odometry is in robotics. For example, in warehouse automation, robots that can navigate accurately and efficiently can help improve the speed and productivity of operations whilereducing costs.Overall, the combination of MEMS inertial navigation and monocular visual odometry has significantpotential for a wide range of applications. Continued research and development in this area will be critical to realizing the full benefits of these technologies in practical settingsIn conclusion, MEMS inertial navigation and monocular visual odometry are powerful technologies that can be used together for various applications, such as autonomous vehicles, drones, virtual reality, and robotics. They can improve accuracy, reliability, and efficiency while reducing costs. Continued research and development in this area is essential to fully unlock the potential of these technologies inpractical settings。
无人驾驶英语PPT
Motion Planning
It determines the specific actions, including acceleration, braking, and steering, that the vehicle needs to take along the planned path
Global Path Planning
This technique plans a complete route for the vehicle from the start to the destination, considering all possible traffic scenarios and objectives
要点二
Semantic Segmentation
It allows the vehicle to understand the scene in detail by assigning semantic means to different parts of the environment
要点三
3D Reconstruction
Level 4
High automation The vehicle can handle most or all driving tasks without human intervention, but limited to specific geographic regions and weather conditions.
Public transportation
Autonomous vehicles can be used for shared rides, shuttle services, or even fully automated bus systems, providing effective and sustainable transportation options for urban areas
Integrating Grid-Based and Topological Maps for Mobile Robot Navigation
Integrating Grid-Based and Topological Maps for Mobile Robot NavigationSebastian Thrun Computer Science Department Carnegie Mellon University Pittsburgh,PA15213Arno B¨ucken Institut f¨u r InformatikUniversit¨a t BonnD-53117Bonn,GermanyIn:Proceedings of the Thirteenth National Conference on Artificial IntelligenceAAAI,Portland,Oregon,August1996AbstractResearch on mobile robot navigation has produced two ma-jor paradigms for mapping indoor environments:grid-basedand topological.While grid-based methods produce accu-rate metric maps,their complexity often prohibits efficientplanning and problem solving in large-scale indoor environ-ments.Topological maps,on the other hand,can be usedmuch more efficiently,yet accurate and consistent topolog-ical maps are considerably difficult to learn in large-scaleenvironments.This paper describes an approach that integrates bothparadigms:grid-based and topological.Grid-based mapsare learned using artificial neural networks and Bayesian in-tegration.Topological maps are generated on top of thegrid-based maps,by partitioning the latter into coherentregions.By combining both paradigms—grid-based andtopological—,the approach presented here gains the best ofboth worlds:accuracy/consistency and efficiency.The pa-per gives results for autonomously operating a mobile robotequipped with sonar sensors in populated multi-room envi-ronments.IntroductionTo efficiently carry out complex missions in indoor environ-ments,autonomous mobile robots must be able to acquire and maintain models of their environments.The task of ac-quiring models is difficult and far from being solved.The following factors impose practical limitations on a robot’s ability to learn and use accurate models:1.Sensors.Sensors often are not capable to directly mea-sure the quantity of interest(such as the exact location of obstacles).2.Perceptual limitations.The perceptual range of mostsensors is limited to a small range close to the robot.To acquire global information,the robot has to actively explore its environment.3.Sensor noise.Sensor measurements are typically cor-rupted by noise,the distribution of which is often un-known(it is rarely Gaussian).4.Drift/slippage.Robot motion is inaccurate.Odometricerrors accumulate over time.plexity and dynamics.Robot environments arecomplex and dynamic,making it principally impossible to maintain exact models.6.Real-time requirements.Time requirements often de-mand that the internal model must be simple and easily ac-cessible.For example,fine-grain CAD models are often disadvantageous if actions must be generated in real-time.Recent research has produced two fundamental paradigms for modeling indoor robot environments:the grid-based (metric)paradigm and the topological paradigm.Grid-based approaches,such as those proposed by Moravec/Elfes (Moravec1988)and many others,represent environments by evenly-spaced grids.Each grid cell may,for exam-ple,indicate the presence of an obstacle in the correspond-ing region of the environment.Topological approaches, such a those described in(Engelson&McDermott1992; Kortenkamp&Weymouth1994;Kuipers&Byun1990; Matari´c1994;Pierce&Kuipers1994),represent robot en-vironments by graphs.Nodes in such graphs correspond to distinct situations,places,or landmarks(such as doorways). They are connected by arcs if there exists a direct path be-tween them.Both approaches to robot mapping exhibit orthogonal strengths and weaknesses.Occupancy grids are considerably easy to construct and to maintain even in large-scale envi-ronments(Buhmann et al.1995;Thrun&B¨u cken1996). Since the intrinsic geometry of a grid corresponds directly to the geometry of the environment,the robot’s position within its model can be determined by its position and orientation in the real world—which,as shown below,can be deter-mined sufficiently accurately using only sonar sensors,in environments of moderate size.As a pleasing consequence, different positions for which sensors measure the same values (i.e.,situations that look alike)are naturally disambiguated in grid-based approaches.This is not the case for topological approaches,which determine the position of the robot relative to the model based on landmarks or distinct sensory features. For example,if the robot traverses two places that look alike, topological approaches often have difficulty determining if these places are the same or not(particularly if these places have been reached via different paths).Also,since sensory input usually depends strongly on the view-point of the robot, topological approaches may fail to recognize geometrically nearby places.On the other hand,grid-based approaches suffer from their enormous space and time complexity.This is because the resolution of a grid must befine enough to capture every im-portant detail of the pactness in a key advantage of topological representations.Topological maps are usu-ally more compact,since their resolution is determined by the complexity of the environment.Consequently,they per-mit fast planning,facilitate interfacing to symbolic planners and problem-solvers,and provide more natural interfaces for human instructions.Since topological approaches usuallyGrid-based approachespermits efficient planning,low space complexity(res-olution depends on the com-plexity of the environment)does not require accurate de-termination of the robot’spositionconvenient represen-tation for symbolic planners,problem solvers,natural lan-guage interfacesdifficult to construct andmaintain in larger environ-mentsrecognition of places(basedon landmarks)often am-biguous,sensitive to thepoint of viewmay yield suboptimal paths Table1:Comparison of grid-based and topological approaches to map building.manufacturer(Real World Interface,Inc.)as part of the reg-ular navigation software.Grid-Based MapsThe metric maps considered here are two-dimensional,dis-crete occupancy grids,as originally proposed in(Elfes1987;Moravec1988)and since implemented successfully in vari-ous systems.Each grid-cell in the map has an occu-pancy value attached,which measures the subjective beliefwhether or not the center of the robot can be moved to thecenter of that cell(i.e.,the occupancy map models the con-figuration space of the robot,see e.g.,(Latombe1991)). This section describes the four major components of our ap-proach to building grid-based maps(see also(Thrun1993)): (1)sensor interpretation,(2)integration,(3)position esti-mation,and(4)exploration.Examples of metric maps are shown in various places in this paper.Sensor InterpretationTo build metric maps,sensor reading must be“translated”into occupancy values for each grid cell.The idea here is to train an artificial neural network using Back-Propagation to map sonar measurements to occupancy val-ues.The input to the network consists of the four sensor readings closest to,along with two values that encode in polar coordinates relative to the robot(angle to the first of the four sensors,and distance).The output target for the network is1,if is occupied,and0otherwise. Training examples can be obtained by operating a robot in a known environment and recording its sensor readings;notice that each sonar scan can be used to construct many training examples for different-coordinates.In our implemen-tation,training examples are generated with a mobile robot simulator.Figure2shows three examples of sonar scans along with their neural network interpretation.The darker a value in the circular region around the robot,the larger the occupancy value computed by the network.Figures2a&b depict sit-uations in a corridor.Situations such as the one shown in Figure2c—that defy simple interpretation—are typical for cluttered indoor environments.Integration Over TimeSonar interpretations must be integrated over time,to yield asingle,consistent map.To do so,it is convenient to interpret(a)(b)(c)Figure2:Sensor interpretation:Three example sonar scans(toprow)and local occupancy maps(bottom row),generated by theneural network.111Here denotes the prior probability for occupancy(which,if set to0.5,can be omitted in this equation).Noticethat this formula can be used to update occupancy valuesincrementally.An example map of a competition ring con-structed at the1994AAAI autonomous robot competition isshown in Figure3.Position EstimationThe accuracy of the metric map depends crucially on thealignment of the robot with its map.Unfortunately,slippageand drift can have devastating effects on the estimation ofthe robot position.Identifying and correcting for slippageand drift is therefore imperative for grid-based approaches torobot navigation(Feng,Borenstein,&Everett1994;Rencken1993).Figure4gives an example that illustrates the importanceof position estimation in grid-based robot mapping.In Fig-ure4a,the position is determined solely based on dead-23.1meters32.2metersFigure3:Grid-based map,constructed at the1994AAAI au-tonomous mobile robot competition.(a)(b)Figure 4:Map constructed without (a)and with (b)the position estimation mechanism described in this paper.Figure 5:Autonomous exploration.(a)Exploration values,com-puted by value iteration.White regions are completely unexplored.By following the grey-scale gradient,the robot moves to the next unexplored area on a minimum-cost path.(b)Actual path traveled during autonomous exploration,along with the resulting metric map.The large black rectangle in (a)indicates the global wall orientation wall .a Voronoi diagramb critical pointc critical lined topological regionse fV1V2V3V4V5V6V7topological graph Figure 6:Extracting topological maps.(a)Metric map,(b)V oronoi diagram,(c)critical points,(d)critical lines,(e)topological regions,and (f)the topological graph.ure 7b depicts the critical lines (the critical points are on the intersections of critical lines and the Voronoi diagram).The resulting partitioning and the topological graph are shown in Figure 7c&d.As can be seen,the map has been partitioned into 67regions.Performance ResultsTopological maps are abstract representations of metric maps.As is generally the case for abstract representations and abstract problem solving,there are three criteria for as-sessing the appropriateness of the abstraction:consistency ,loss ,and efficiency .Two maps are consistent with each other if every solution (plan)in one of the maps can be represented as a solution in the other map.The loss measures the loss in performance (path length),if paths are planned in the more abstract,topological map as opposed to the grid-based map.Efficiency measures the relative time complexity of problem solving (planning).Typically,when using abstract models,efficiency is traded off with consistency and performance loss.ConsistencyThe topological map is always consistent with the grid-based map.For every abstract plan generated using the topologi-cal map,there exists a corresponding plan in the grid-based map (in other words,the abstraction has the downward solu-tion property (Russell &Norvig 1995)).Conversely,every path that can be found in the grid-based map has an abstract representation which is a admissible plan in the topological map (upward solution property ).Notice that although con-sistency appears to be a trivial property of the topological maps,not every topological approach proposed in the lit-erature generates maps that would be consistent with their corresponding metric representation.(a)Grid-based map(b)Topological regionsFigure8:Another example of a map.map.In other words,planning on the topological level in-creases the efficiency by more than three orders of magnitude, while inducing a performance loss of only1.82%.The map shown in Figure8,which is smaller but was recoded with a higher resolution,consists of20,535explored grid cells and22topological regions.On average,paths in the grid-based map lead through84.8cells,while the average length of a topological plan is4.82(averaged over 1,928,540systematically generated pairs of points).Here the complexity reduction is even larger.Planning using the metric map is16104more expensive than planning with the topological map.While these numbers are empirical and only correct for the particular maps investigated here,we conjecture that the relative quotient is roughly correct for other maps as well.It should be noted that the compactness topological maps allows us to exhaustively pre-compute and memorize all plans connecting two nodes.Our example maps contain 67(22)nodes,hence there are only2,211(231)different plans that are easily generated and memorized.If a new path planning problem arrives,topological planning amounts to looking up the correct plan.The reader may also notice that topological plans often do not directly translate into motion commands.In(Thrun& B¨u cken1996),a local“triplet planner”is described,which generates cost-optimal plans for triplets of adjacent topo-logical regions.As shown there,triplet plans can also be pre-computed exhaustively,but they are not necessarily op-timal,hence cause some small additional performance loss (1.42%and1.19%for the maps investigated here).DiscussionThis paper proposes an integrated approach to mapping in-door robot environments.It combines the two major ex-isting paradigms:grid-based and topological.Grid-based maps are learned using artificial neural networks and Bayes’rule.Topological maps are generated by partitioning the grid-based map into critical regions.Building occupancy maps is a fairly standard procedure, which has proven to yield robust maps at various research sites.To the best of our knowledge,the maps exhibited in this paper are significantly larger than maps constructed from sonar sensors by other researchers.The most important as-pect of this research,however,is the way topological graphs are constructed.Previous approaches have constructed topo-logical maps from scratch,memorizing only partial metric information along the way.This often led to problems of dis-ambiguation(e.g.,different places that look alike),and prob-lems of establishing correspondence(e.g.,different views of the same place).This paper advocates to integrate both,grid-based and topological maps.As a direct consequence,differ-ent places are naturally disambiguated,and nearby locations are detected as such.In the integrated approach,landmarks play only an indirect role,through the grid-based position es-timation mechanisms.Integration of landmark information over multiple measurements at multiple locations is auto-matically done in a consistent way.Visual landmarks,which often come to bear in topological approaches,can certainly be incorporated into the current approach,to further improve the accuracy of position estimation.In fact,sonar sensors can be understood as landmark detectors that indirectly—through the grid-based map—help determine the actual position in the topological map(cf.(Simmons&Koenig1995)).One of the key empirical results of this research con-cerns the cost-benefit analysis of topological representations. While grid-based maps yield more accurate control,planning with more abstract topologicalmaps is several orders of mag-nitude more efficient.A large series of experiments showed that in a map of moderate size,the efficiency of planning can be increased by three to four orders of magnitude,while the loss in performance is negligible(e.g.,1.82%).We believe that the topological maps described here will enable us to control an autonomous robot in multiplefloors in our univer-sity building—complex mission planning in environments of that size was completely intractable with our previous methods.A key disadvantage of grid-based methods,which is inher-ited by the approach presented here,is the need for accurately determining the robot’s position.Since the difficulty of po-sition control increases with the size of the environment,one might be inclined to think that grid-based approaches gener-ally scale poorly to large-scale environments(unless they are provided with an accurate map).Although this argument is convincing,we are optimistic concerning the scaling proper-ties of the approach taken here.The largest cycle-free map that was generated with this approach was approximately 100meters long;the largest single cycle measured approx-imately58by20meters.We are not aware of any purely topological approach to robot mapping that would have been demonstrated to be capable of producing consistent maps of comparable size.Moreover,by using more accurate sensors (such as laser rangefinders),and by re-estimating robot po-sitions backwards in time(which would be mathematically straightforward,but is currently not implemented because of its enormous computational complexity),we believe that maps can be learned and maintained for environments that are an order of magnitude larger than those investigated here.AcknowledgmentThe authors wish to thank the RHINO mobile robot group at the University of Bonn,in particular W.Burgard,A.Cremers,D.Fox, M.Giesenschlag,T.Hofmann,and W.Steiner,and the XA VIER mobile robot group at CMU.We also thank T.Ihle for pointing out an error in a previous version of this paper.This research is sponsored in part by the National Science Foun-dation under award IRI-9313367,and by the Wright Laboratory, Aeronautical Systems Center,Air Force Materiel Command,USAF, and the Advanced Research Projects Agency(ARPA)under grant number F33615-93-1-1330.The views and conclusions contained in this document are those of the author and should not be interpreted as necessarily representing official policies or endorsements,either expressed or implied,of NSF,Wright Laboratory or the United States Government.ReferencesBuhmann,J.;Burgard,W.;Cremers,A.B.;Fox,D.;Hofmann,T.; Schneider,F.;Strikos,J.;and Thrun,S.1995.The mobile robot Rhino.AI Magazine16(1).Crowley,J.1989.World modeling and position estimation for a mobile robot using ultrasonic ranging.In Proceedings1989IEEE International Conference on Robotics and Automation.Dean,T.L.,and Boddy,M.1988.An analysis of time-dependent planning.In Proceeding Seventh NCAI,AAAI.Elfes,A.1987.Sonar-based real-world mapping and navigation. IEEE Journal of Robotics and Automation3(3):249–265. Engelson,S.,and McDermott,D.1992.Error correction in mobile robot map learning.In Proceedings1992IEEE International Conference on Robotics and Automation.Feng,L.;Borenstein,J.;and Everett,H.1994.“where am I?”sensors and methods for autonomous mobile robot positioning. TR UM-MEAM-94-12,University of Michigan at Ann Arbor. Fox,D.;Burgard,W.;and Thrun,S.1995.The dynamic window approach to collision avoidance.TR IAI-TR-95-13,University of Bonn.Hinkel,R.,and Knieriemen,T.1988.Environment perception with a laser radar in a fast moving robot.In Proceedings Sympo-sium on Robot Control.Howard,R.A.1960.Dynamic Programming and Markov Pro-cesses.MIT Press.Kortenkamp,D.,and Weymouth,T.1994.Topological mapping for mobile robots using a combination of sonar and vision sensing. In Proceedings Twelfth NCAI,AAAI.Kuipers,B.,and Byun,Y.-T.1990.A robot exploration and mapping strategy based on a semantic hierarchy of spatial repre-sentations.TR,University of Texas at Austin.Latombe,J.-C.1991.Robot Motion Planning.Kluwer Academic Publishers.Matari´c,M.J.1994.Interaction and intelligent behavior.Techni-cal Report AI-TR-1495,MIT,AI-Lab.Moravec,H.P.1988.Sensor fusion in certainty grids for mobile robots.AI Magazine61–74.Nilsson,N.J.1982.Principles of Artificial Intelligence.Springer Publisher.Pearl,J.1988.Probabilistic reasoning in intelligent systems: networks of plausible inference.Morgan Kaufmann Publishers. Pierce,D.,and Kuipers,B.1994.Learning to explore and build maps.In Proceedings Twelfth NCAI,AAAI.Rencken,W.1993.Concurrent localisation and map building for mobile robots using ultrasonic sensors.In Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems. Russell,S.,and Norvig,P.1995.Artificial Intelligence:A Modern Approach.Prentice Hall.Schiele,B.,and Crowley,J.1994.A comparison of position es-timation techniques using occupancy grids.In Proceedings IEEE International Conference on Robotics and Automation. Simmons,R.,and Koenig,S.1995.Probabilistic robot navigation in partially observable environments.In Proceedings IJCAI-95. Thrun,S.,and B¨u cken,A.1996.Learning maps for indoor mobile robot navigation.TR CMU-CS-96-121,Carnegie Mellon University.Thrun,S.1993.Exploration and model building in mobile robot domains.In Proceedings ICNN-93,IEEE NN Council.。
导航制导与控制英语作文
导航制导与控制英语作文In recent years, navigation, guidance, and control systems have become increasingly important in various fields, including aviation, maritime, and automotive industries. These systems play a crucial role in ensuring the safety, efficiency, and accuracy of transportation. In this article, we will explore the concept of navigation, guidance, and control, their applications, and the advancements in these technologies.Navigation refers to the process of determining the position, direction, and route of a vehicle or vessel. It involves the use of various instruments, such as GPS (Global Positioning System), radar, and compass, to gather data and calculate the vehicle's position relative to a reference point. With the advancements in satellite technology, GPS has become the most commonly used navigation system. It provides accurate positioning information, enabling vehicles to navigate through unfamiliar territories with ease.Guidance, on the other hand, involves providing instructions or recommendations to the vehicle or vessel to follow a specific route or path. It utilizes the data collected from the navigation system to guide the vehicle along the desired trajectory. Guidance systems can be autonomous, where the vehicle makes decisions on its own based on predefined algorithms, or they can be remotely controlled by a human operator. These systems are crucial in ensuring that vehicles stay on track and avoid obstacles or hazards.Control is the final component of the navigation, guidance, and control system. It involves the manipulation of the vehicle's actuators, such as engines, rudders, or thrusters, to maintain stability, speed, and direction. Control systems use feedback from sensors to continuously adjust the vehicle's parameters and keep it within the desired operating limits. For example, in aviation, autopilot systems control the aircraft's altitude, heading, and speed, reducing the workload of the pilots and ensuring a smooth and safe flight.These navigation, guidance, and control systems have numerous applications across various industries. In aviation, they are essential for ensuring safe takeoff, landing, and navigation during flight. They also enable aircraft to fly in adverse weather conditions, reducing the risk of accidents. In maritime transportation, navigation systems help shipsnavigate through narrow channels, avoid collisions, and reach their destinations efficiently. In the automotive industry, navigation and guidance systems are integrated into vehicles to provide turn-by-turn directions, real-time traffic updates, and assistance in parking.Advancements in technology have significantly improved the performance and capabilities of navigation, guidance, and control systems. Modern navigation systems can provide highly accurate positioning information, even in challenging environments such as dense urban areas or deep oceans. They can also integrate with other sensors, such as cameras and lidar, to provide a comprehensive view of the surroundings and detect potential obstacles. Furthermore, the development of artificial intelligence and machine learning algorithms has enabled autonomous navigation, where vehicles can make intelligent decisions based on real-time data.In conclusion, navigation, guidance, and control systems are essential in ensuring the safety, efficiency, and accuracy of transportation. These systems rely on advanced technologies such as GPS, radar, and artificial intelligence to provide accurate positioning, guide vehicles along desired trajectories, and maintain control. With continuous advancements in technology, these systems are becoming more sophisticated and capable, revolutionizing the way we navigate and control vehicles in various industries.。
无人飞艇的基于计算机视觉导航和预设航线跟踪控制
Vol.33,No.3ACTA AUTOMATICA SINICA March,2007 Computer Vision-based Navigation and Predefined Track Following Control of a Small Robotic AirshipXIE Shao-Rong1LUO Jun1RAO Jin-Jun1GONG Zhen-Bang1Abstract For small robotic airships,it is required that the airship should be capable of following a predefined track.In this paper, computer vision-based navigation and optimal fuzzy control strategies for the robotic airship are proposed.Firstly,visual navigation based on natural landmarks of the environment is introduced.For example,when the airship isflying over a city,buildings can be used as visual beacons whose geometrical properties are known from the digital map or a geographical information system(GIS). Then a geometrical methodology is adopted to extract information about the orientation and position of the airship.In order to keep the airship on a predefined track,a fuzzyflight control system is designed,which uses those data as its input.And genetic algorithms(GAs),a general-purpose global optimization method,are utilized to optimize the membership functions of the fuzzy controller.Finally,the navigation and control strategies are validated.Key words Visual navigation,flight control,predefined track following,robotic airship.1IntroductionSmall airships are aerial robots built from a lightweight envelope for buoyancy and a propelling system housed in a gondola.The fact that theflight of airships is based on buoyancy is one of their main advantages.Small airships outperform sub-miniaturefixed-wing vehicles(airplanes) and rotary-wing aircrafts(helicopters)in stability,oper-ation safety,endurance,payload to weight ratio[1],etc.So they will surelyfind uses in[2∼3]anti-terrorism,traffic ob-servation,advertising,aerophotogrammetry,climate moni-toring,locale of calamity watching,surveillance over man-made structures and archaeological sites,as well as estab-lishment of emergency telecommunication relay platforms. For these missions,it is demanded that the airship is ca-pable of autonomously following predefined track,which consists of autonomous navigation andflight control.Con-sequently,they are recently becoming a focus of research.The accomplishments of the above tasks make visual sen-sors(like CCD cameras)a natural choice for their sensory apparatuses.Visual sensors are necessary not only to the performances data acquisition as part of the mission such as taking pictures of predefined spots,but also to autonomous navigation of the small airship,supplying data in situations where conventional,well-established aerial navigation tech-niques,like those using inertial,GPS and other kinds of dead-reckoning systems,are not adequate.There have been important developments in the area of visual navigation for mobile robots in recent years.Among those more successful are the ones that use navigation based on visual landmarks[4].For aerial robots,though previ-ous work on visual servoing has comprised the stabiliza-tion problem[5∼6]and vertical landing[7]using small indoor blimps and helicopters,hovering solution[8]and a strategy for line-following tasks[9∼11]using outdoor robotic airships, visual navigation of aerial robots is much less explored[12]. Usually,autonomous navigation of UAVs relies on inertial navigation system(INS),GPS,DGPS,etc.,which are tra-ditional and well-established in navigation of aircraft in general.It is clearly understood that vision is in itself a very hard problem and solutions to some specific issues are Received August12,2005;in revised form February16,2006 Supported by National Natural Science Foundation of P.R.China(50405046,60605028),Shanghai Project of International Coopera-tion(045107031)and the Program for Excellent Young Teachers of Shanghai(04Y0HB094).1.School of Mechatronics Engineering and Automation,Shanghai University,Shanghai200072,P.R.ChinaDOI:10.1360/aas-007-0286Fig.1The airship in Shanghai University restricted to constraints either in the environment or in the visual system itself.Nevertheless,visual navigation could be of great advantages when it comes to aerial vehicles in the aforementioned situations.In the present paper,visual navigation of a small robotic airship based on natural landmarks already existent in the environment is introduced.The vision system is able to track those visual beacons.For example,buildings can be used as visual beacons when the airship isflying over a city. According to the digital map or the geographical informa-tion system(GIS),their geometrical properties are known. Then a geometrical methodology can extract information about orientation and position of the airship.And in order to keep the airship on a predefined track,an optimal fuzzy flight control system is designed,which uses that data as its input.2Dynamic characteristics and control architecture of thesmall robotic airshipThe prototype of the robotic unmanned blimp we devel-oped is shown as Fig.1.The platform has a length of11 m,a maximal diameter of3m,and a volume of50m3.It is equipped with two engines on both sides of the gondola, and has four control surfaces at the stern,arranged in a ‘+’shape.Its useful payload capacity is around15kg at sea level.It canflight with a maximum speed of about60 km/h.The mathematical,reasonable and relatively simple lin-ear dynamic model of the small robotic airship is readily analyzed and realized.The airship dynamics indicates that the state parameters involved in longitudinal and lateralNo.3XIE Shao-Rong et al.:Computer Vision-based Navigation and Predefined Track Following Control ...287Fig.2Architecture of control and navigation systemmotions are weakly dependent.So the system can be split into two subsystems in the following way.1)S long =[X,Z,θ]T and S long =[U,W,Q ]T to describe the dynamics within the longitudinal plane,the control in-puts being δe and δt .2)S lat =[Y,φ,ψ]and X lat =[V,P,R ]T to describe the dynamics within the lateral plane,the control input being δr .The body axes are fixed in the vehicle with the origin O at the center of volume (CV),the OX axis is coincident with the axis of symmetry of the envelope,and the OXZ plane coincides with the longitudinal plane of symmetry of the blimp.(φ,θ,ψ)denote three Euler angles.The airship linear and angular velocities are given by (U,V,W )and (P,Q,R ),respectively.The airship dynamics model shows that:1)The rolling corresponding mode is structurally stable.2)The longitu-dinal and lateral control can be viewed as decoupled.3)An airship has more nonlinearities than ordinary aircraft due to the added mass.According to that decoupled lateral and longitudinal dy-namics model,the control architecture of the system is pre-sented in Fig.2.In this architecture three independent controllers are uti-lized as follows.1)A proportional-integral controller for the longitudinal velocity v acting on the throttle deflection δt ;2)a heading controller acting on the rudder deflection δr ;3)a controller for height and pitch acting on the elevator deflection δe .The navigation and mission planner is designed to pro-vide longitudinal velocity reference V ref height reference H ref and heading reference Ψref .In a specific mission flight,,H ref and the waypoints are predefined by the user.As the airship position is motional,the planner should be computed in real-time for the heading controller.3Visual navigation methodo logy3.1Navigation principle based on visual beacon [12]Visual beacons denote calibration objects with known visual and geometrical properties.Formally,the beacon vi-sually assigns a set {P 0,P 1,P 2,P 3}of characteristic pointswhere the distances of the form Pi − P j ,0≤i <j <n are known.Depending on the number and disposition of the charac-teristic points,it is possible to use an image of the beacon -acquired by an onboard camera with known parameters (fo-cus,resolution,CCD physical dimensions)-to estimate the position and orientation of that camera,andconsequentlyFig.3Image projection of the vertices of a tetrahedral beacon B over the image plane of camera Cof the airship,in relation to the visual beacon.Fig.3illustrates the geometrical construct of image pro-jection.Let C be a camera with focal point F .Let B be a visual beacon with a set of 4non-coplanar characteristic points {P 0,P 1,P 2,P 3}.Let {p 0,p 1,p 2,p 3}be the copla-nar points corresponding to the image projections of the characteristic points of B over the image plane of C .Let Vi =P i −p i ,0≤i <4,be the light-ray-path vectors going from the points p i to the corresponding P i passing through F ,and v i =F −p i ,0≤i <4,the vectors in the same direction of V i ,but going just until F .Once the vectors Vi are found,the position and orienta-tion of C can be determined.Since the distances between the points P i are known and vectors v i are determinable if the points p i are known,the following equation system (1)can be specified,where D i,j = P i −P j ,0≤i <j ≤3,is the distance between points P i and P j .The unknowns ofthe system are λ0,λ1,λ2,λ3and V i =λi v i .Expanding themodulus operations on the left-hand side of the equation,we have a nonlinear system with six quadratic equations and four unknowns as follows:8>>>>><>>>>>: λ0 v 0−λ1 v 1 =D 0,1 λ0 v 0−λ2 v 2 =D 0,2 λ0 v 0−λ3 v 3 =D 0,3λ1 v 1−λ2 v 2 =D 1,2 λ1 v 1−λ3 v 3 =D 1,3 λ2 v 2−λ3 v 3 =D 2,3(1)The existence of the six equations guarantees one solution.Therefore,a visual beacon with tetrahedral topology -that is,having four non-coplanar characteristic points -guar-288ACTAAUTOMATICA SINICA Vol.33antees a uniquesolution to the values Vi ,hence a unique position and orientation to the camera for the point set p i determined in an image.However,tetrahedral -and therefore tridimensional -beacons are more difficult to construct and reproduce than the bidimensional ones;in particular,practical applications of autonomous airships,where the distances involved could be large and hence the visual beacon,seem to favor the use of bi-dimensional ones.A bi-dimensional beacon would have to have a minimum of three characteristic points to make possible the determination of position and orientation of the camera since with points less than thrice the number of solutions found for position and orientation would be in-finite.Nonetheless,a triangular beacon would imply in an equation system just three quadratic equations,in a way the number of solutions for a given projection of character-istic points on the image plane would be 2or 4.That is,for a given image of a triangular beacon,there would be two or four possible positions /orientations of the beacon with the same characteristic point projections found in the image.However,this ambiguity can be removed if distortions in the vertex markers,caused by perspective projection,are taken into account.Observing the apparent size of each marker,it is possible to determine the ratios between their distances and thus to choose one among the several solu-tions.3.2Implementation of Visual NavigationAccording to the above principle,it is very important that the point set p i be determined by digital image pro-cessing method for implementation visual navigation.Be-cause p i is the point corresponding to the image projection of the characteristic point of natural visual beacon over the image plane of C ,feature-based approaches are ideal for picking up feature points of natural beacons.They are successfully carried out in computer vision.For example,when the airship is flying over a city,buildings can be used as visual beacons,as their feature points easily are seg-mented in images.According to the digital map of the city or the geographical information system (GIS),their geo-metrical properties are known.In general,they are shown in a graphical interface (Fig.4block 3,see next page).The camera coordinate system {C }is presented in first place.That system is an orthonormal basis with the CCD matrix center as the origin,X axis parallel to the CCD width,Y axis parallel to the CCD height and Z axis co-incident with the camera axis (line perpendicular to the image plane passing through the focal point),pointing to-ward the back of the camera.On the other hand,{B }is the world coordinate system.The geometrical methodology used here for computing estimations of position and orientation of the airship from an onboard camera is simple.Since the onboard camera is assumed to be installed at the bottom of the airship gondola,pointing downwards,and the X −Y plane of {B }is parallel to the image plane,the yaw orientation is easily determined.4Optimal fuzzy control system4.1Heading controllerThe control block of the heading controller is shown inFig.5.The heading controller consists of a rule based fuzzy controller and an integrator.The integrator (Fig.5block (b))is used to include the integral of the error as a third input to the heading con-trollers to compensate for setpoint error caused by unbal-Fig.5Heading controller block diagramFig.6The membership functions for the fuzzy inputanced forces and other disturbances.The integrator is reset at zero on each change of setpoint.Because integration only occurs for small values of error,the problems of integrator windup are avoided and at the same time the setpoint is eliminated.The fuzzy controller (Fig.5block (a))is the main part of the heading controller.Its inputs are heading error and heading error rate ,and the output is δr .K e and K c are normalized from the universes of discourse of the inputs to the range of [-1,1].The universe of discourse of output deflection is limited in [-30deg,30deg]by the actual mech-anism of the control surfaces,so K d =30.Seven fuzzy sets are defined for each input variable,as shown in Fig.6,where x 1=0.1and x i =0.3(i =2,3,···,7)for the initial design.The rule base is built as shown in Table 1.4.2Optimization of Fuzzy ControllerSince the rule base and membership functions of fuzzy set are determined by designers imprecisely,the quality of con-trol may be not that good.So a tuning operation is needed for the fuzzy control system.In fact,this operation is a process of optimization.Genetic algorithms (GAs),known to be robust general-purpose global optimization method,are utilized to optimize the membership functions of fuzzy controller.Considering Fig.6,the membership functions of two fuzzy input variables are determined by parameters x =(x 1,x 2,...,x 14)of a controller,where x 1,x 2,...,x 7for er-ror and x 7,x 8,...,x 14for error rate.In this approach con-straint conditions are inducted to guarantee that all fuzzy sets are in the universes of discourse.g 1=7X i =1x i −2≤0(2)g 2=14X i =8x i −2≤0(3)where g 1and g 2are functions of constraint.No.3XIE Shao-Rong et al.:Computer Vision-based Navigation and Predefined Track Following Control...2891)COM setting;2)A/D data;3)Digital map andflight trajectory;4)GPS data;5)Command editor;6)Error prompt;7)Flightdata;8)Control inputs;9)Flight attitudeFig.4The human-machine interface of ground stationTable1Fuzzy rule baseEC\E NB NM NS Z P S P M P BNB-0.8333-0.8333-0.6333-0.5-0.3333-0.16670NM-0.8333-0.6333-0.5-0.3333-0.166700.1667NS-0.6333-0.5-0.3333-0.166700.16670.3333Z-0.5-0.3333-0.166700.1667-0.33330.5P S-0.3333-0.166700.16670.33330.50.6333P M-0.166700.16670.33330.50.63330.8333P B00.16670.33330.50.63330.83330.8333In traditional GAs,the optimization problems with con-straint conditions are converted into the ones without con-straint conditions using penalty functions.But it’s not easyto determine the penalty coefficients.When the penalty co-efficients are small,some individuals out of the searchingspace may have highfitness,so the GAs may get the wrongresults.Whereas,when they are too huge,the differencesamong individuals are weak,so its hard for the selectionoperator of GAs to select valid individuals with highfit-ness.Obviously,the traditional GAs are expected to beimproved for the constraint optimization problems.A selection operator of GAs based on direct comparisonapproach is presented.Step1.A function measuring the degree of the individ-ual violating the s.t.is defined.For example,v(x)=−ε+JXj=1g j(x)(4)whereεis a small positive constant.Step2.Choose two individuals,say,x1and x2,from previous generation randomly.Step3.Select one to the next generation according to the two rules:if v(x1)and v(x2)have the same signs,the one with smaller objective function value is selected;or if v(x1)and v(x2)have different signs,say,v(x1)<0,then x1is selected.Repeat Step2and Step3till the next generation has enough individuals.This operator treats constraint condition not by penalty functions but by direct comparison,so the advantages of GAs are preserved.Additionally,because it takes the effect of invalid solutions into consideration,the searching ability of GAs is also augmented.Based on6DOF nonlinear dynamic model of the robotic airship system,the simulation and optimization program is developed in MATLAB environment.The optimal mem-bership functions of heading error and heading error rate are shown in Fig.7.Considering the step input of head-ing error,the airship responses under the optimal fuzzy heading controller and the initial controller are shown in290ACTA AUTOMATICA SINICA Vol.33Fig.7The optimal membership functions of input variable of heading controller.Fig.8.Obviously,the optimal fuzzy heading controller spends much shorter time than the initial one,and over-shoot is avoided.5Verification of the navigation and control strategiesIn theflight experiment,for safety consideration,the elevator and throttle were under manual control to keep the altitude and the cruise speed.The rudder was controlled by the ANN autonomous control system,and it could also be switched to human operator control in take-offand landing phase and in the case of danger.When the trim airspeed is8m/s,the tracking error and deflections of rudder are shown in Fig.9.Because of the large time constant and large virtual mass of airship,about Fig.8Responses of theinitial fuzzy controller and the optimal fuzzy controllerFig.9Tracking error and deflections of rudder55m tracking errors occurred in two sharp angles despite that the saturate control of rudder(+/-30deg)is acted. The results manifest that the strategies are feasible,and the system can track mission path with satisfactory precision. 6ConclusionThis paper presents computer vision-based navigation and predefined track following control for small robotic air-ship.The vision system is able to track those visual beacons already existent in the environment.For example,build-ings can be used as visual beacons when an airship isflying over a city.According to the digital map or the geographi-cal information system(GIS),their geometrical properties are known.Then a geometrical methodology can extract information about orientation and position of the airship. And in order to keep the airship on a predefined track,a fuzzyflight control system is designed,which uses that data as its inputs.And genetic algorithms(GAs)are utilized to optimize the membership functions of the fuzzy controller.References1Elfes A,Bueno S S,Bergerman M,Paiva E C,Ramos J G,Azinheira J R.Robotic airships for exploration of plane-tary bodies with an atmosphere:Autonomy challenges.Au-tonomous Robots,2003,14(2∼3):147∼1642Elfes A,Bueno S S,Bergerman M,Ramos J G,Gomes S B V.Project AURORA:Development of an autonomous un-manned remote monitoring robotic airship.Journal of the Brazilian Computer Society.1998,4(3):70∼783Kantora George,Wettergreen David.Collection of environ-mental data from an airship platform.Sensor Fusion and De-centralized Control in Robotic Systems.2001,4571:76∼83 4Becker C.Reliable navigation using landmarks.In:Proceed-ings of the IEEE International Conference on Robotics and Automation.Nagoya,Japan,1995,1:401∼4065Zhang Hong,Ostrowski J.P.Visual servoing with dynam-ics:Control of an unmanned blimp.In:Proceedings of the IEEE International Conference on Robotics and Automa-tion.Michigan,USA,1999,1:618∼6236Hamel Tarek,Mahony Robert.Visual servoing of an under-actuated dynamic rigid-body system:An image-based ap-proach.IEEE Transactions on Robotics and Automation, 2002,18(2):187∼1987Shakernia Omid,Yi Ma,Koo T J,Hespanha J,Sastry S S.Vision guided landing of an unmanned aerial vehicle.In: Proceeding of the IEEE38th Conference on Decision and control.Arizon,USA,1999,4:4143∼41488Azinheira J R,Rives,P,Carvalho J R H,Silvera G F,de Paiva E C,Bueno S S.Visual servo control for the hover-ing of all outdoor robotic airship.In:Proceedings of the IEEE International Conference on Robotics and Automa-tion.Washington,USA,2002,3:2787∼2792No.3XIE Shao-Rong et al.:Computer Vision-based Navigation and Predefined Track Following Control (291)9Rives Patrick,Azinheira J R.Linear structures following by an airship using vanishing point and horizon line in a vi-sual servoing scheme.In:Proceedings of the2004IEEE In-ternational Conference on Robotics and Automation.New Orleans,USA,2004,1:255∼26010Silveira G F,Garvalho J R H,Rives P,Azinheira J R,Bueno S S,Madrid M K.Optimal visual servoed guidance of out-door autonomous robotic airships.In:Proceedings of the America Control A,2002,779∼78411Silveira G F,Garvalho J R H,Rives P,Azinheira J R, Bueno S S,Madrid M K.Line following visual servoing for aerial robots combined with complementary sensors.In:Pro-ceedings of the11th International Conference on Advanced Robotics.Portugal,2003,1160∼116512Coelho L S,Campos M F.Pose estimation of autonomous dirigibles using artificial landmarks.In;Proceedings of XII Brazilian Symposium on Computer Graphics and Image Pro-cessing.1999,161∼170XIE Shao-Rong Received her Ph.D.degree from Intelligent Machine Instituteat Tianjin University in2001.From2001to2003she worked as a postdoctoral fel-low in Shanghai University.Now she isan associate professor at the same uni-versity.Her research interest covers com-puter vision and intelligent control.Cor-responding author of this paper.E-mail:srxie@ LUO Jun Associate professor in the school of mechatronics engineering and au-tomation at Shanghai University.He re-ceived his Ph.D.degree from the Research Institute of Robotics at Shanghai Jiaotong University in2000.From2000to2002he worked as a postdoctoral fellow in Shang-hai University.His research interest covers telerobotics and special robotics.E-mail: luojun@RAO Jin-Jun Ph.D.candidate in the School of mechatronics engineering and au-tomation at Shanghai University.His re-search interest includesflight control of a small robotic airship.E-mail:mr-jjrao@GONG Zhen-Bang Professor at Shanghai University.His research interest covers precision mechanical system and advanced robot.E-mail: zhbgong@。
SLAM应用领域
SLAM的应用领域自同时定位与地图构建被意识到并提出有严密数学基础的方案起，在很多领域被成功地工程实现和应用。
比如，外星球探索[4–7]，采矿自动化与安全[8–11]，水下探测与深海勘探[1, 2, 12–14]，无人机导航与自治[15–17]以及灾难现场搜寻与营救[18–20]。
在这些对环境缺乏先验知识的场合，同时定位与地图构建的引入是实现移动机器人自治的唯一手段，它使移动机器人能够估计自身方位和周围环境特征的方位和几何轮廓，进而合理决策与规划动作和路径。
在民用领域，同时定位与地图构建可使移动车辆在GPS无法正常工作的环境下实现定位[21–24]，并追踪辨识动态车辆与行人[3, 24, 25]，为实现智能避障，辅助驾驶和自导航提供了可能。
陆地自主车(ALV: Autonomous Land Vehicle)之一是移动机器人重要应用领域，2005年美国国防部高级研究计划局(DARPA)举办的Grand Challenge比赛，由著名专家S.Thrun领导团队开发的Stanley自主车[26]仅用不到7小时完成了142英里的自主行使任务。
我国对地面智能移动机器人的研究起步较晚，但是也取得了很大的进展[27]。
20世纪80年代末，国家“八六三”计划自动化领域智能移动机器人主题确定立项进行遥控驾驶的防核化侦察车的研制;几乎同时国家部分部委也在规划“八五”预研中的智能移动机器人技术研究。
真正取得突破性进展的是“八五”期间研制成功的我国第一辆样车ATB-1(Autonomous Test Bed-1)，该车由南京理工大学、国防科技大学、清华大学、浙江大学和北京理工大学联合研制。
在19%年演示中，该车各项性能都达到了较高的标准;在“八五”的基础上，我国在“九五”期间研制了第二代自主地面车ATB-2;到目前为止，第三代自主车已经研制成功，并已通过鉴定，现在正在进行第四代的自主车的研制工作。
另外，国内具有代表性的系统还有: 清华大学研制的THMR-v[28]，国防科技大学研制的CITA VT-IV[29]及其与中国一汽合作研究的“红旗”自主轿车，吉林大学研制的JLUIV系列实验车[30]以及西安交通大学研制的Springrobot实验车[31]等。
基于局部几何-拓扑地图的地下矿自动驾驶定位导航方法
基于局部几何−拓扑地图的地下矿自动驾驶定位导航方法刘仕杰， 邹渊， 张旭东（北京理工大学 机械与车辆学院，北京 100081）摘要：无人驾驶技术在提高效率、节省成本、减少安全隐患等方面具有巨大优势。
针对目前地下环境中定位导航方案实施难度大、成本高、构建地图耗时长等问题，提出了一种基于局部几何−拓扑地图的地下矿自动驾驶定位导航方法。
设计了一种局部几何−拓扑地图，井下环境的路网主体结构由拓扑地图表示，该地图上定义了巷道（边）和交叉路口（节点），在每个节点中存储以该节点为中心构建的局部几何地图，用以实现节点处的精确定位。
提出了一种基于局部几何−拓扑地图的定位方法，使用基于激光雷达的交叉路口检测算法与交叉路口定位算法进行车辆全局定位。
设计了一种基于自适应模型预测控制（MPC ）的轨迹跟随算法，保证车辆在交叉路口大曲率转向时的路径跟踪精度。
使用三维物理仿真平台构建了地下矿的仿真环境与车辆仿真模型，仿真结果表明：该方法能够实现地下矿自动驾驶定位导航功能，在各种类型交叉路口的定位误差均在0.2 m 以内，可以满足自动驾驶的定位精度要求；在整个行驶过程中车辆始终保持较为平稳的行驶状态和较小的跟踪误差。
与目前依赖于5G 、UWB 等技术的定位导航方法相比，该方法仅依赖于激光雷达与惯性测量单元2种车身传感器，在控制设备成本上具有极大优势。
关键词：地下矿山；自动驾驶；定位导航；局部几何−拓扑地图；自适应模型预测控制；轨迹跟随中图分类号：TD524 文献标志码：AA localization and navigation method for underground mine autonomous drivingbased on local geometric topology mapLIU Shijie, ZOU Yuan, ZHANG Xudong(School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)Abstract : Unmanned driving technology has enormous advantages in improving efficiency, saving costs and reducing safety hazards. In the current implementation of localization and navigation solutions in underground environments, there are problems of implementation difficulties, high costs, and time-consuming construction of maps. In order to solve the above problems, a localization and navigation method for underground mine autonomous driving based on local geometric topology map is proposed. A local geometric topology map has been designed. The main structure of the underground environment road network is represented by a topology map. The map defines roadways (sides) and intersections (nodes), and stores a local geometric map built around the node in each node to achieve precise positioning at the node. A localization method based on local geometric topology map is proposed, which uses a LiDAR-based intersection detection algorithm and intersection localization algorithm for global vehicle localization. A trajectory-following algorithm based on adaptive model predictive control (MPC) has been designed to ensure the path-tracking precision of vehicles turning at high收稿日期：2023-02-02；修回日期：2023-08-15；责任编辑：胡娴。
用于自动驾驶系统的路径规划技术研究
摘要摘要自动驾驶是目前科技领域的一个前沿热点技术，在谷歌、特斯拉、Uber等科技公司的刺激下，科研机构和汽车制造厂商纷纷调配资源来加快该技术相关的研发工作。
路径规划技术是自动驾驶系统中必不可少的技术模块。
路径规划依赖于高精度地图，规划车体行驶的最优路线，实现从起始点到目标点的任务需求。
路径规划技术也被广泛运用在游戏线路搜索、扫地机器人、物流配送、仓储巡检等场景中，因此便需要路径规划算法能够适应更为复杂的环境地图，同时又能节省时间成本。
从算法本身特点及环境复杂度出发，采取对单一算法的改进和多种算法的融合方法，对路径规划技术的研究具有重要意义。
本文从全局路径规划和局部路径规划两部分着手，分别对这两部分的算法进行研究。
根据全局路径规划A*算法的原理，仿真对比了A*算法中常用的四种不同启发函数的寻路效果，同时提出一种改进的加权曼哈顿距离启发函数，仿真验证该启发函数提高了A*算法的寻路效率，在搜索到的路径长度，搜索时间和搜索的结点数量方面均具有优越性；根据局部路径规划DWA算法的原理，对该算法评价函数中三项评价指标的权重值如何选取进行了仿真分析，通过将采用加权曼哈顿距离的A*算法和DWA算法融合，使融合后的算法尽可能贴近全局最优路径，有效避免了DWA算法容易陷入局部最优的缺点，同时缩短了算法的运行时间。
根据自动驾驶系统的关键技术组成，对机器人底盘和激光雷达进行选型，搭建了无人车硬件平台；以ROS系统的工作框架和基本特点为基础，对导航包组织框架各模块作用的详细分析，研究了move_base包进行路径规划算法验证的具体方法，配置了本文研究的算法插件和机器人可视化模型；实验部分通过对move_base包中关键参数的配置，分别搭建静态和动态实验环境，完成路径规划的实验验证与结果分析，证明本文研究的算法具有一定的实用性。
关键词：全局规划，局部规划，ROS，最优路径，运行时间AbstractAbstractAutonomous driving is a hot frontier technology in the field of science and technology at present. Spurred by Google, tesla, Uber and other technology companies, scientific research institutions and automobile manufacturers have deployed resources to accelerate the research and development related to this technology. Path planning technology is an essential technology module in the automatic driving system. Path planning relies on high-precision map to plan the optimal route for the vehicle, so as to achieve the task requirements from the starting point to the target point. Path planning technology is also widely used in game route search, sweeping robot, logistics distribution, warehouse inspection and other scenarios. Therefore, path planning algorithm is required to be able to adapt to more complex environment map and save time cost. Starting from the characteristics of the algorithm itself and the complexity of the environment, it is of great significance to study the path planning technology by improving the single algorithm and integrating multiple algorithms.This paper studies the algorithms of global path planning and local path planning respectively. According to the principle of global path planning A* algorithm, the simulation compares the path planning effect of four different heuristic functions commonly used in A* algorithm, and puts forward an improved heuristic function with weighted Manhattan distance. The simulation verifies that the heuristic function improves the path planning efficiency of A* algorithm, and has advantages in the length of the searched path, the search time and the number of nodes searched. According to the principle of local path planning DWA algorithm, how to select the weight of three evaluation indexes of the evaluation function of this algorithm is simulated and analyzed.By fusing A* algorithm with weighted Manhattan distance and DWA algorithm, the fusing algorithm is made as close as possible to the global optimal path, which effectively avoids the shortcoming that DWA algorithm is prone to fall into local optimal, and meanwhile reduces the running time of the algorithm.According to the key technology composition of the automatic driving system, the robot chassis and lidar are selected, and the hardware platform of the unmanned vehicle is built;Based on the working framework and basic characteristics of ROS用于自动驾驶系统的路径规划技术研究system, the function of each module of navigation stack organization framework was analyzed in detail, and the specific method of move_base package for path planning algorithm verification was studied. The algorithm plug-in and robot visualization model studied in this paper were configured.In the experimental part, the key parameters in move_base package were configured, the static and dynamic experimental environments were set up, and the experimental verification and result analysis of path planning were completed, which proves that the algorithm studied in this paper has certain practicability.Key Words: Global path planning, Local path planning, ROS, Optimal path, Running timeIV目录目录第1章绪论 (1)1.1 课题研究背景与意义 (1)1.2 自动驾驶技术发展现状 (2)1.2.1 国外自动驾驶发展现状 (2)1.2.2 国内自动驾驶发展现状 (4)1.3 路径规划技术的发展概况 (5)1.3.1 传统算法 (5)1.3.2 智能算法 (6)1.3.3 启发式算法 (7)1.3.4 路径规划技术发展趋势 (8)1.4 论文章节安排 (8)第2章全局路径规划算法 (10)2.1 引言 (10)2.2 A*算法原理 (10)2.3 A*算法实验仿真 (12)2.4 改进的启发函数 (15)2.5 本章小结 (17)第3章局部路径规划算法 (19)3.1 引言 (19)3.2 DWA算法原理 (19)3.2.1 车体运动模型建立 (19)3.2.3 速度采样 (20)3.2.4 评价函数 (22)3.3 DWA算法仿真 (24)3.4 全局规划算法与局部规划算法的融合 (29)3.4.1 DWA算法存在的缺点 (29)3.4.2 A*算法与DWA算法的融合 (29)3.4.3 融合A*算法的DWA算法实验仿真 (30)3.5 本章小结 (34)用于自动驾驶系统的路径规划技术研究第4章机器人自动驾驶系统搭建 (35)4.1 引言 (35)4.2 硬件平台搭建 (35)4.2.1 机器人平台 (35)4.2.2 激光雷达 (38)4.3 软件系统设计 (41)4.3.1 ROS架构 (41)4.3.2 导航包模块构成 (42)4.3.2 全局路径规划算法扩展 (46)4.3.3 局部路径规划算法扩展 (47)4.3.4 机器人可视化模型的建立 (48)4.4 本章小结 (50)第5章路径规划实验验证 (51)5.1 引言 (51)5.2 move_base包参数配置 (51)5.2.1 通用文件配置 (52)5.2.2 全局规划文件配置 (52)5.2.3 本地规划器配置 (53)5.2.4 局部规划文件配置 (54)5.3 实验结果及分析 (54)5.3.1 静态环境下实验结果 (54)5.3.2 动态环境下实验结果 (56)5.3.3 实验结果分析 (58)5.4本章小结 (60)第6章总结与展望 (62)参考文献 (65)致谢 (69)作者简历及攻读学位期间发表的学术论文与研究成果 (71)第1章绪论第1章绪论1.1 课题研究背景与意义在智慧交通和人工智能技术飞快发展的今天，自动驾驶系统通过去除人力化成本，为人们的生活带来更多的便捷。
移动机器人路径规划和导航(英文)
• First step:
Ø Representation of the environment by a road-map (graph), cells or a potential field. The resulting discrete locations or cells allow then to use standard planning algorithms.
© R. Siegwart, I. Nourbakhsh
Autonomous Mobile Robots, Chapter 6
6.2.1
Potential Field Path Planning
• Robot is treated as a point under the influence of an artificial potential field.
6.2.1
Road-Map Path Planning: Cell Decomposition
• Divide space into simple, connected regions called cells • Determine which open sells are adjacent and construct a connectivity graph • Find cells in which the initial and goal configuration (state) lie and search for a path in the connectivity graph to join them. • From the sequence of cells found with an appropriate search algorithm, compute a path within each cell.
define grid motion的用法 -回复
define grid motion的用法-回复Grid motion refers to the movement of an object along a grid system. It is commonly used in various fields such as computer graphics, robotics, and games. This article will delve into the concept of grid motion, its applications, and provide a step-by-step guide on how to implement it.Introduction to Grid Motion:Grid motion involves moving an object along a grid, which is typically composed of evenly spaced rows and columns. Each square within the grid represents a discrete position that the object can occupy. By restricting the movement to these predefined positions, grid motion allows for precise control and facilitates various computational tasks.Applications of Grid Motion:1. Computer Graphics: Grid motion is widely used in computer graphics to render animations and simulate movement. It provides a straightforward way to control objects' positions by simply moving them to adjacent grid cells. The grid-based approach simplifies collision detection and pathfinding algorithms, making them more efficient.2. Robotics: Grid motion plays a crucial role in the navigation of robots. By defining a grid map of the robot's environment, it becomes easier to plan paths and avoid obstacles. Grid-based mapping allows the robot to move from one cell to another in a systematic manner, ensuring safe and efficient navigation.3. Games: Many games utilize grid motion to implement movement mechanics. From turn-based strategy games like chess and checkers to real-time strategy games and puzzle games, the grid system provides a well-defined framework for character movement and interactive gameplay.Implementing Grid Motion:Step 1: Define the GridTo implement grid motion, we first need to define a grid system. This involves determining the size of each grid cell and creating a grid structure to store data. In programming, this can be achieved using arrays, matrices, or even specialized data structures specifically designed for grids.Step 2: Define Object PositionsNext, we assign an initial position to the object within the grid. This position will be represented by a set of coordinates that correspond to the grid cells. These coordinates can be integers, where the origin (0,0) is typically the top-left corner of the grid.Step 3: Handle User InputsTo enable user interaction, we need to develop a mechanism to interpret user inputs and respond accordingly. For example, in a game, this can involve capturing keyboard or mouse events to move the object in response to user commands.Step 4: Restrict Movement to Grid CellsTo enforce grid motion, we must ensure that the object can only move to adjacent grid cells. This can be achieved by defining a set of rules that restrict movement, such as only allowing horizontal or vertical movement and disallowing diagonal movement.Step 5: Update Object PositionDepending on the desired behavior, we update the object's position accordingly. If the user wants to move left, we decrement the x-coordinate. If the user wants to move up, we decrement the y-coordinate. Conversely, if the user wants to move right or down,we increment the corresponding coordinate.Step 6: Collision Detection and PathfindingFor more advanced grid motion requirements, additional logic may be needed. This can involve implementing collision detection algorithms to prevent objects from moving through walls or other obstacles. Pathfinding algorithms can also be employed to calculate the shortest path between two grid cells, enabling more intelligent and efficient movement.Conclusion:Grid motion is a versatile concept with numerous practical applications. Its simplicity and precision make it ideal for various fields, including computer graphics, robotics, and gaming. By following the step-by-step guide outlined in this article, developers can easily implement grid motion to enhance their projects and create immersive and interactive experiences.。
地磁导航 标准
地磁导航标准Magnetic navigation is a type of navigation that uses the Earth's magnetic field to determine the direction of travel. 地磁导航是一种利用地球磁场确定旅行方向的导航方式。
This technology has been used for centuries by both humans and animals to find their way in unfamiliar territory. 这项技术几个世纪以来一直被人类和动物用来在陌生领域中找到自己的位置。
One of the key advantages of magnetic navigation is that it does not rely on external infrastructure such as GPS satellites or cell towers. 地磁导航的一个关键优势是它不依赖于GPS 卫星或基站等外部基础设施。
Instead, it relies on the Earth's magnetic field, which is always present and available for use. 相反，它依赖于地球的磁场，这个磁场一直存在并可供使用。
This makes magnetic navigation a reliable and cost-effective option for many applications. 这使得地磁导航成为许多应用的可靠且经济实惠的选择。
One of the most common uses of magnetic navigation is in marine navigation, where compasses are used to determine the direction of travel. 地磁导航最常见的用途之一是在海上导航，航海罗盘被用来确定旅行方向。
室内移动机器人自主导航系统设计
室内移动机器人自主导航系统设计DOI :10.19557/ki.1001-9944.2021.06.008林义忠,马凯(广西大学机械工程学院,南宁530004)摘要:该文设计了一种基于机器人操作系统(ROS )的移动机器人自主导航系统,利用深度相机获取环境的深度信息并将其转换为伪激光数据,基于该伪激光数据以及里程计信息通过Gmapping 算法构建环境二维栅格地图,并结合A *算法和动态窗口法完成移动机器人的路径规划。
实验结果表明,在实验室环境中,机器人可以绕过障碍物,自主的运动到目标点,初步验证了该自主导航系统的可行性。
关键词:移动机器人;深度相机;栅格地图;自主导航中图分类号:TP242文献标识码:A文章编号:1001⁃9944(2021)06⁃0038⁃05Design of Autonomous Navigation System for Indoor Mobile RobotLIN Yi ⁃zhong ,MA Kai(College of Mechanical Engineering ,Guangxi University ,Nanning 530004,China )Abstract :This paper designs an autonomous navigation system for mobile robots based on the robot operating system (ROS ),which uses a depth camera to obtain the depth information of the environment and converts it into pseudo laser data.Based on the pseudo laser data and odometer information ,it is constructed by the gmapping algorithm the two ⁃dimensional grid map of the environment is combined with the A*algorithm and the dynamic window method to complete the path planning of the mobile robot.The experimental results show that in the laboratory environment ,the robot can bypass obstacles and move to the target point autonomously ,which preliminarily verifies the feasibility of the autonomous navigation system.Key words :mobile robot ;depth camera ;grid map ;autonomous navigation收稿日期:2020-12-25;修订日期:2021-05-06作者简介:林义忠(1964—),男,博士,研究方向为移动机器人及AGV 、工业机器人检测与控制、机器视觉与智能机器人;马凯(1993—),男,硕士研究生,研究方向为智能机器人。
科研潜力英语自我介绍
科研潜力英语自我介绍**English Version**Hello, my name is John Smith, and I am thrilled to have the opportunity to introduce myself and highlight my research potential. I am currently a Ph.D. candidate in the field of Computer Science, specializing in Artificial Intelligence and Machine Learning. My academic journey has been a blend of rigorous theoretical study and hands-on practical experience, which has prepared me well for the challenges of research.My research interests lie at the intersection of AI and robotics, particularly in developing intelligent systems that can autonomously navigate and interact with their environments. I am particularly fascinated by the idea of creating robots that can learn from experience, adapt to new situations, and collaborate effectively with humans.During my graduate studies, I have conducted several independent research projects that have allowed me to gain valuable insights into the field. One of my mostsignificant projects involved developing a deep learningalgorithm for object recognition in complex environments. This project required me to design and implement a neural network architecture that could efficiently process large amounts of image data and accurately classify objects. Through this project, I honed my skills in programming, data analysis, and algorithm design.Beyond my academic work, I have also had the opportunity to collaborate with industry partners on applied research projects. These experiences have provided me with a unique perspective on the practical challenges and opportunities in AI research. For example, I worked with a robotics company to develop a machine learning-based system for autonomous navigation. This project taught me the importance of translating theoretical ideas into practical solutions that can be effectively implemented in real-world scenarios.My academic achievements and research experiences have been recognized by my peers and mentors. I have received several awards for my research contributions, including the Best Paper Award at an international conference on AI and Robotics. These achievements are a testament to mydedication to research and my ability to produce impactful work.I am confident that my strong academic background, hands-on research experiences, and passion for AI research make me an ideal candidate for further exploration in this field. I am excited about the opportunities that lie ahead and look forward to contributing to the advancement of AI and robotics research.**Chinese Version**大家好，我叫约翰·史密斯，很荣幸有机会介绍自己并突出我的科研潜力。
双激光雷达反光柱导航算法
双激光雷达反光柱导航算法英文回答：Dual LIDAR Reflector Navigation Algorithm.Introduction.In recent years, light detection and ranging (LIDAR) technology has been widely used in the field of autonomous navigation. LIDAR sensors can accurately measure the distance between the sensor and surrounding objects by emitting laser pulses and receiving the reflected signals. This information can be used to create a detailed map of the environment, which can then be used for navigation.Dual LIDAR Reflector Navigation Algorithm.The dual LIDAR reflector navigation algorithm is a novel algorithm that uses two LIDAR sensors to improve the accuracy and robustness of navigation. The algorithm isbased on the principle of triangulation, which is a technique used to determine the location of an object by measuring the angles between it and two known points.In the dual LIDAR reflector navigation algorithm, the two LIDAR sensors are mounted on a mobile platform, such as a robot or a car. The sensors are pointed in opposite directions, so that they can scan the environment in a 360-degree field of view.When the mobile platform moves, the LIDAR sensors continuously scan the environment and collect data on the surrounding objects. The data is processed by the navigation algorithm, which uses the principle of triangulation to calculate the position and orientation of the mobile platform.The dual LIDAR reflector navigation algorithm has several advantages over traditional navigation algorithms. First, it is more accurate because it uses two LIDAR sensors to measure the distance between the mobile platform and surrounding objects. Second, it is more robust becauseit can operate in environments with poor lighting or visibility. Third, it is more efficient because it can process data from two LIDAR sensors simultaneously.Applications.The dual LIDAR reflector navigation algorithm has a wide range of applications, including:Autonomous navigation for robots and cars.Mapping and surveying.Construction and mining.Agriculture and forestry.中文回答：双激光雷达反光柱导航算法。
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
Grid-Based Navigation for Autonomous, Mobile RobotsCarsten BUSCHMANN, Florian MÜLLER and Stefan FISCHERInstitute of Operating Systems and Networks, Technical University of BraunschweigBraunschweig, Germany, e-mail: (buschmann, fmueller, fischer)@ibr.cs.tu-bs.deAbstract - Navigation is a major challenge for autonomous, mobile robots. The problem can basically be divided into positioning and path planning. In this paper we present an approach which we call grid-based navigation. Though we also propose a scheme for path finding, we focus on positioning. Our approach uses minimal environmental infrastructure and only two light sensors on the mobile device itself. Starting out from a predefined tile and orientation in the grid, the mobile robot can autonomously head for destination tiles in the grid. On its way it determines the current location in the grid using a finite state machine by picking up line-crossing events with its sensors.1IntroductionA key ability needed by an autonomous, mobile robot is the possibility to navigate through the space. The problem can basically be decomposed into positioning and path planning. Though we also propose a scheme for the latter, we clearly focus on the detection of the current position by monitoring grid crossing events by using two light sensors.Especially if the robot is severely resource-constrained, simple schemes are favourable to elaborated algorithms. Rather simple sensors and actuators as well as a limited computing platform also demand simple, robust techniques due to inaccuracy and the lack of resources.The paper is structured as follows: In section 2, we give an overview over the environment that the robot which is described in the following section needs for navigation. In section 4 we present the underlying algorithm and discuss various details. After that the software structure of the implementation is presented. The paper is concluded by a summary and an outlook to future work.2EnvironmentThe robot navigates on a grid (see Figure 1) which regularly dividesthe ground into square tiles that are identified with Cartesiancoordinates. As mentioned earlier, the robot starts out from apredefined position (e.g. the centre of tile 0,0) and orientation (e.g.North, which means looking up the y-axis). It is sufficient that thefull grid is rectangular and all tiles are the same size. It is notrequired that the robot knows the dimension (n,m) of the grid. It issatisfactory to only request navigation to tiles that really exist.Heading from tile to tile, the robot distinguishes eight drivingFigure 1: The grid directions: north, north-east, east, south-east, south and so on.Depending on the size of the robot and the sensitivity of theluminance sensors, different tile sizes and contrasts between plane and grid are possible. We used a grid made out of black self-adhesive tape of about 2 cm width on white ground with tiles of about 45 by 45 cm.3The RobotThe robot requires only very basic sensing and actuation capabilities. All that is needed are two ground-observing light sensors attached in a line orthogonal to the driving direction at the bottom of the robot, and two individually controllable wheels. For precise positioning (see Figure 3) the light sensors (A) are attached between the wheels (B) to make sure the sensor readings represent the wheels’ location. As shown in section 4, the distance between wheels and sensors should be as small as possible to facilitate the alignment when crossing the grid orthogonally. At the back, the robots rests on a ball (C) that is spherical seated, allowing the robot to turn on the spot. The distance between the two light sensors should be no smaller than double the grid line width. This ensures that orthogonal grid line crossings where both light sensors get dark simultaneously (see Figure 2a) can be securely distinguished from diagonal crossings at the intersection of grid lines (see Figure 2b). If the sensors are located too close to each other, misinterpretations may appear (see Figure 2c).Figure 2: Consequences of different distances between the light sensorsBasically only binary sensor readings are required; in order to cope with a wider spectrum of readings, an initial calibration on light and dark ground can be used to determine a threshold between the two by calculating the average of both readings. Figure 3 to Figure 5 show the robot we used for experimenting built out of the LEGO Mindstorms Robotics Invention System 2.0 [1].It does not only offer a cheap possibility to experiment with various robots, it also allows for focusing our work on algorithmic aspects without putting much effort into mechanical construction. The entire navigation and positioning code executes on the yellow RCX control unit (D in Figure 5). The PDA shown on the robot in Figure 4 serves solely as a communication gateway to a Wireless LAN Network [2].Nevertheless, the Robotics Invention Systems also yields some disadvantages. The RCX is extremely resource constrained. It offers only 32kB of memory firmware, program code and data. In addition, engines and sensors are rather imprecise, e.g. the driving speed of the robot heavily varies with the remaining battery capacity. All this demands simple, robust algorithms for navigation.Figure 3: Bottom view of the robot with light sensors (A), wheels (B) and ball (C)Figure 4: Robot with PDA for WLANcommunication Figure 5: Top view of the robot including the yellow RCX control unit (D)4AlgorithmThe process of driving to a destination tile always follows the same 5-step-algorithm: (1) calculate the next tile to go to, (2) turn towards the direction of the next tile, (3) start driving and wait for line-crossing events to happen, (4) thereby decide which tile the robot arrived in, and (5) finally determine whether the robot has reached the destination tile. The 5-step-algorithm is also visualized in Figure 6. Determining the next tile is done via a breadth-first search: starting from the current tile, the coordinates of all adjacent tiles that have not been visited on the way to the current destination arestored into a vector together withinformation on its predecessor. Ifthe destination tile is not among thevector entries, again each entry’s alladjacent and not yet considered tilesare stored together with theirpredecessor. The algorithmterminates when the destination tileis in one of the vectors. The path tothat destination tile can then bederived using the chain ofpredecessors stored together withthe coordinates. This rather naiveapproach has potential to beoptimized. An example would be toemploy branch-and-bound-techniques. Nevertheless, in largegrids the applicability of such tree-based techniques is limited. In thiscase greedy algorithms might be analternative. However, such pathfinding issues were not the focus ofour research.Once a path has been derived, therobot turns towards the direction ofthe next tile (which can becalculated from the current and nextcoordinates) by turning both wheelsin different directions for apredefined time corresponding to 45degrees (repeatedly if necessary).Because wheels and sensors areattached in a line, the light sensorswill not “move” but only rotate.The robot then starts driving,waiting for events to occur. Wedistinguish light sensor events andtimeout events. A series of sensorevents corresponding to the crossingof the grid) is always concluded bya timeout event. If no sensor eventshave occurred for a certain timeperiod, it is assumed that the robotFigure 6: The 5-step-algorithm has reached the next tile. The lengthof the interval depends on the sizeof the tiles, the robot dimensions,path deviation and speed. If it is chosen too long (and tiles are small), the robot might already have started to cross the next lines at the opposite side of the new tile. If it is chosen too short, the timeout might occur during the crossing of the grid. For our implementation, we chose a timeout interval of four seconds.Number, kind and order of sensor events determine which tile the robot drove to. Due to path deviations due to wheel slip, different engine powers etc. the robot does not necessarily arrive at the tile it was actually heading for.Basically two ways of crossing the grid can be distinguished: the change to a tile in horizontal (East, West) or vertical (North, South) direction which we will call orthogonal crossing and the change to a tile at north-east, north-west south-east or south-west which we will call diagonal crossing for the rest of this paper.Determining orthogonal crossings is relatively easy: after both sensors have gone dark and light again once and nothing happened afterwards for a while, the robot has reached the next tile. From the order in which the two events occurred a first indication can be drawn on the robot’s deviation from the wanted direction (orthogonal to the grid line).Figure 7: Robot alignment at orthogonal crossingsWe use this fact to adjust the driving direction when crossing the line (see Figure 7). When the robot is heading for an orthogonal line crossing and the first light sensor event occurs, we stop the robot. Let us assume that it was the right sensor that got dark first (a). We then turn the corresponding (right) wheel back to make the robot turn until the according light sensor gets off the line again. The robot then again approaches the line until one light sensor recognizes it (b). This procedure is repeated until both light sensors are on the line simultaneously. By doing so the robot almost perfectly aligns with the grid line (c). Please note that this procedure is only invoked if the robot intentionally performs an orthogonal crossing.When heading for a tile in diagonal direction (e.g. north-east), things are more complicated because five different cases can occur (see Figure 8). Depending on the position in the current tile, driving diagonally can lead to arriving in one out of three tiles that can be distinguished by the order in which the events occur. Please note that the upper left arrow and the lower right arrow denote special cases of an orthogonal crossing: though intending to cross horizontally, the robot effectively performs an orthogonal crossing. Unfortunately, this can only be detected after the robot arrived in tile 1 or 3 respectively.Figure 8: Possible cases when crossing diagonally Figure 9: Finite state machine for determining which tile the robotarrived inWe use a finite state machine (FSM, see Figure 9) to figure out which of the five possible cases occurred: Events indicating that a line was crossed by a light sensor as well as timeouts induce state changes. The order in which these occur determines the destination tile. Non-valid event orders lead to error states which cause the robot to stop at the current position. Since unintended orthogonal crossing can only be detected after the robot arrived in the new tile, no alignment is carried out.Once the robot has reached the next tile, it checks whether it has reached the destination tile. If not, it continues its travel towards the next tile on the way to the destination; otherwise it restarts the 5-step-algorithm for a new destination.Videos showing a demonstration of the algorithm can be watched at http://www.ibr.cs.tu-bs.de/arbeiten/cbuschma/lego-pos/videos.html.5The SoftwareThe software for the robot was developed using the BrickOS operating and programming system for the Robotic Invention System [3]. It is derived from LegOS [4] and offers numerous advantages such as an elaborate firmware, native coding in C/C++, multi threading including semaphores, direct hardware access as well as event based sensor readings and comfortable network routines for the IR interface [5]. In addition, the full code is available as open-source.Figure 10: Software and hardware componentsFigure 10 depicts the major components involved in implementing the algorithm: Ellipses represent hardware whereas boxes stand for software components. The Javastorms interface [6] and the infrared port are not part of the navigation itself but are used for reporting current locations and receiving future destinations from a wireless LAN via the Gateway as described in [2].6Conclusion and Future WorkIn this paper we presented an approach for mobile robot positioning and navigation which we call grid-based navigation. It uses only minimal environmental infrastructure and two light sensors on the robot. Starting out from a predefined location and orientation in the grid, the mobile robot can autonomously head for destination tiles. On the way it determines its location in the grid using a finite state machine by picking up line-crossing events with its sensors. In addition, we demonstrated how we implemented the underlying algorithm in software and robot hardware. Nevertheless, the required grid limits this approach to scenarios where either a grid can be set up or a natural grid exists (e.g. due to a tiled floor). Nevertheless, it is easy to adjust both sensing and algorithm to different scenarios like a chessboard-like floor.In this paper obstacles are not considered at all. A touch sensor could enable the robot to detect obstacles in tiles which then could be marked as blocked.The path finding algorithm described in this paper is far from optimal. Greedy algorithms would allow for quite some improvement. A possible approach is to calculate the next tile to go to whenever the robot reaches a new tile. A possible metric is the line of sight distance to the destination tile. However in combination with obstacles blocking certain tiles this may lead to locally optimal but globally unfavourable paths if obstacles are arranged in a maleficent way. Nevertheless, strategies to escape such problems exist e.g. in the fields of linear optimization or geographic routing.A major cause of errors is the robot’s inaccuracy. Due to its simple construction, the robot shows a hardly predictable behaviour in terms of driving and turning speed as well as path deviation when going straight. Thus we found that it does not make much sense to develop more sophisticated algorithmic approaches. Hence we stuck to simple, qualitative approaches that are robust to such errors. Given a robot that is able to move more exactly and at defined speeds, new possibilities wouldarise. By measuring the time intervals between light sensor events, the angle in which the robot crosses the grid line could be derived.In addition to the navigation software on the robot we also developed a gateway application that resides on a PDA which is mounted on the robot and that allows for giving driving commands in the form of destination coordinates to the robot. Furthermore, the robot can send back its current locationin the grid whenever it reaches a new tile. As future work we plan to replace the PDA by the wireless sensor mote ESB 430/2 developed at the Freie Universität Berlin [7]. We are currently implementing an infrared API to enable communication between the RCX and these devices. The motes will then be able to use the robot for autonomous wandering, e.g. broadcasting their current location periodically and thus providing a location service to a sensor network consisting of additional motes. When the robot comes into the communication range of a sensor mote, the mote gets a first indication on its location. Hearing the robot multiple times from different locations would enable the mote the estimateits position by averaging the locations transmitted by the robot.7AcknowledgementsThis work is part of the project “SoftWare ARchitecture for Mobile self-organising Systems”. The SWARMS project described in [8] is funded by the German Research Foundation (DFG), where it is part of the focal program "Basic Software for Self-Organizing Infrastructures in Networked Mobile Systems" (SPP 1140). For further information, see www.swarms.de.References[1] Website of the Lego Mindstorms Robotics Inventions System 2.0:/eng/default.asp[2] Axel Wegener: WLAN-Gateway for the RCX Robot on a PDA, Semester project, TechnicalUniversity of Braunschweig, 2003[3] Website of the BrickOS project: [4] Website of the LegOS project: /projects/legos[5] Jonathan B. Knudsen: The Unofficial Guide to Lego Mindstorms Robots, O’Reilly, 1999[6] Website of the Javastorms project: [7] Website of the Embedded Sensor Board ESB 430/2: [8] Carsten Buschmann, Stefan Fischer, Norbert Luttenberger and Florian Reuter: Middleware forSwarm-Like Collections of Devices, in IEEE Pervasive Computing Magazine, Vol. 2, No. 4, 2003。