STABLE WALKING OF A 7-DOF BIPED ROBOT
一种7自由度外骨骼上肢康复机器人设计与控制研究
一种7自由度外骨骼上肢康复机器人设计与控制研究刘志辉;孙奕;唐智;袁涛;孔繁磊;王生泽【期刊名称】《东华大学学报（自然科学版）》【年(卷),期】2017(043)004【摘要】Aiming at the limited degree of freedom of exoskeleton rehabilitation robot,the model of upper limb movement was simplified according to the mechanism of upper limb joint movement.An improved 7-DoF exoskeleton upper limb rehabilitation robot structure design was proposed,and the motion support parts of the unit were modeled and analyzed.Electrical design and control research of rehabilitation exercise system were carried out according to safety ergonomics.The results show that the 7-DoF exoskeleton upper limb rehabilitation robot is more suitable for human engineering,the control mechanism is optimized and the safety performance is good,which lays a solid foundation for realizing the real-time control and motion planning in the later stage.%针对外骨骼上肢康复机器人自由度受限的问题,根据上肢关节运动机理对上肢结构进行模型简化,提出一种改进的7自由度外骨骼上肢康复机器人结构设计,并对运动支撑的各部分单元进行结构建模和分析,根据安全人机工程学进行了康复运动系统的电气设计与控制研究.研究结果表明,该7自由度外骨骼上肢康复机器人在结构设计上更加符合人因工学,控制机制优化,安全性能良好,为实现后期的实时控制与运动规划奠定了基础.【总页数】6页(P535-540)【作者】刘志辉;孙奕;唐智;袁涛;孔繁磊;王生泽【作者单位】东华大学机械工程学院,上海201620;上海养志康复医院,上海201620;东华大学机械工程学院,上海201620;东华大学机械工程学院,上海201620;东华大学机械工程学院,上海201620;东华大学机械工程学院,上海201620【正文语种】中文【中图分类】TH122;TP3【相关文献】1.外骨骼式上肢康复机器人及其控制方法研究 [J], 王东岩;李庆玲;杜志江;孙立宁2.一种六自由度上肢康复机器人的结构设计及运动学分析 [J], 曹电锋;杨启志;庄佳奇;姚斌斌3.基于安全性考虑的五自由度外骨骼式上肢康复机器人自适应控制 [J], 康浩博;王建辉;4.基于安全性考虑的五自由度外骨骼式上肢康复机器人自适应控制 [J], 康浩博;王建辉5.外骨骼上肢康复机器人滑模控制策略研究 [J], 管瑞欣因版权原因，仅展示原文概要，查看原文内容请购买。
智能机器人英文文档
Obstacle Avoidance Design for Humanoid RobotBased on Four Infrared SensorsChing-Chang Wong1*, Chi-Tai Cheng1, Kai-Hsiang Huang1, Yu-Ting Yang1,Hsiang-Min Chan1 and Chii-Sheng Yin21Department of Electrical Engineering, Tamkang University,Tamsui, Taiwan 251, R.O.C.2Metal Industries Research & Development Centre,Kaohsiung, Taiwan 811, R.O.C.AbstractAbehavior strategy of humanoid robot for obstacle avoidance based on four infrared sensors is proposed and implemented on an autonomous humanoid robot. A mechanical structure with 26 degrees of freedom is design so that an implemented small-size humanoid robot named TWNHR-III is able to accomplish five walking motions. Three walking experiments are presented to illustrate that the proposed biped structure lets TWNHR-III can move forward, turn, and slip. One electronic compass and four infrared sensors are mounted on TWNHR-III to obtain the head direction of the robot and detect obstacles, respectively. Based on the obtained information from these sensors, a d ecision tree method is proposed to decide one behavior from five movements: walk forward, turn right and left, and slip right and left. Two MATLAB simulations and one real experiment are presented to illustrate that the robot can avoid obstacles autonomously and go to the destination effectively.Key Words: Humanoid Robot, Autonomous Mobile Robot, Obstacle Avoidance, Decision Tree1. IntroductionAlthough the robot has been investigated for many years, there are still many issues to be studied, especially in the humanoid robots [1⎽4]. Hardware and software architectures,walking gait generation, and artificial intelligence are the main research fields of humanoid robots.Robot soccer games are used to encourage the researches on robotics and artificial intelligence (AI). Two international robot soccer associations, RoboCup [5] and FIRA[6], advance this research and hold the international competitions and the international symposiums. Robot soccer games are two teams constituted by several soccer robots to play soccer games under some size restrictions and rules. In the FIRA Cup event, several main categories are organized: the Mi cro-ro bot So ccer t ournament(MiroSot), the Simu lated ro bot So ccer t ournament (SimuroSot),the Robo t So ccer t ournament (RoboSot), and the Hu manoid ro bot So ccer t ournament (HuroSot). In the HuroSot category, the humanoid robot has to detect all information from the game field and decides its strategy by itself. There are many robots in the match field, so the robot must have the ability to avoid the collision with other robots and walk to an appropriate destination.Thus obstacle run is a competition category in the HuroSot league of FIRACup. The main idea of this competition category is used to test the ability of obstacle avoidance of the robot. In general, vision sensors, ultrasonic sensors, and infrared sensors are usually used todetect obstacles in the soccer game [7⎽11].In this paper, a mechanical structure with 26 degrees of freedom is design so that an implemented small-size humanoid robot named TWNHR-III (TaiWaN HumanoidRobot-III) is able to accomplish five walking motions:walk forward, turn right and left, and slip right and left. One digital electronic compass and four infrared sensors are installed on TWNHR-III. Based onthe information obtained from these sensors, a decision tree method is proposed to determine one behavior from these five movements in each decision so that TWNHR-III can avoid obstacles and go to a destination effectively.The rest of this paper is organized as follow s: In Section 2, the system architecture of TWNHR-III is described.In Section 3, a decision tree method for obstacle avoidance is proposed and two simulation results and one practical test on TWNHR-III are described. In Section 4, some conclusions are made.2. System Architecture of TWNHR-IIIThe system architecture of TWNHR-III is described in this section. The height of TWNHR-III is 46 cm and the weight is 3.1 kg with batteries. The frameworks of TWNHR-III are mainly fabricated from aluminum alloy 5052 in order to realize the concepts of light weight, wear-resisting, high stiffness, and wide movable range.Each actuator system of the joint consists of a high torque and a gear. The rotating speed and rotating angle of each joint are designed based on the result of computer program. The mechanical structure and electronic structure of TWNHR-III are described as follows:2.1 Mechanical StructureMechanical structure design is the first step in the humanoid robot design. The degrees of freedom (DOFs) configuration for TWNHR-III is described in Figure 1,where 26 degrees of freedom are implemented and the rotational direction of each joint is defined by using the inertial coordinate system fixed on the ground. There are 2 degrees of freedom on the neck, 2 degrees of freedom on the waist and trunk, 8 degrees of freedom on the arm,and 14 degrees of freedom on the two legs. Aphotograph and some mechanical views of TWNHR-III are respectively described in Figure 2 and Figure 3.Human body mechanism basically comprises bones,joints, muscles, and tendons. It is impossible to replace all of the muscular-skeletal system by current mechanical and electronic components. Therefore, the primary goal of the humanoid robot mechanical design is to let the implemented robot can imitate equivalent human motion. A mechanical structure is designed and implemented so that the implemented humanoid robot can find the ball, walk forward, turn right and left, and slip right and left. The details of the development of the head,waist and trunk, arms, and legs are described as follows:2.1.1 HeadThe 3D mechanism design and DOFs diagram of the head are described in Figure 4, where the head of TWNHR-III has 2 degrees of freedom and each degree is described by the number in (b). The raw and pitch motions are implemented on the head so that it can turn right-and-left and up-and-down. Some corresponding behaviors between human and TWNHR-III in the joints of head are described in Table 1.2.1.2 Waist and TrunkThe 3D mechanism design and DOFs diagram of the waist and trunk are described in Figure 5, where each waist and trunk of TWNHR-III has 2 degrees of freedom and each degree is described by the number in (b). The waist and trunk are designed based on the concept that robot can adjust the trunk motions to compensate for the robot’s walk motion. Some corresponding behaviors between human and TWNHR-III in the joints of waist and trunk are described in Table 2.2.1.3 ArmsThe 3D mechanism design of the arms are described in Figure 6, where each arm of TWNHR-III has 4 degrees of freedom. The arms of the robot are designed based on some behaviors of human’sarms. For example,its arm can hold an object such as a ball. Some corresponding behaviors between human and TWNHR-III in the joints of arms are described in Table 3.2.1.4 LegsIn order to realize the normal walking motion of human,7 degrees of freedom are adopted to implement the joints of one leg. Leg is take great part weight of whole body, due to the knee joint. In order to improve the robust of the leg, two motors are designed and implemented in one knee joint. The 3D mechanism design and DOFs diagram of the legs are described in Figure 7, where each leg of TWNHR-III has 7 degrees of freedom and each degree is described by the number in (b). The legs are designed based on the concept that robot can accomplish the human walking motion. Some corresponding behaviors between human and TWNHR-III in the joints of legs are described in Table 4.2.2. Electronic StructureIn the electronic structure design for TWNHR-III,the system block diagram is described in Figure8, where 26 servomotors with high torques are used as the actuators of the robot. In order to build a fully autonomous vision based humanoid robot, a 16-bit DSP processor with a CMOS sensor is chosen to process the vision image of environment. The image of the field is captured by the CMOS sensor and the position information of the ball and goals is processed and extracted by the DSP processor.One electronic compass and four infrared sensors are mounted on TWNHR-III to obtain the head direction of the robot and detect obstacles, respectively. The installed positions and their detectable ranges of these four infrared sensors are described in Figure 9 and Figure 10, respectively.The electronic compass is mounted on the body to detect the head direction of the robot and the goal direction, respectively. The relative angle of goal direction and robot direction is shown in Figure 11. In the circuit design, the SoPC (System on a Programmable Chip) concept is applied and implemented on a FPGA chip to reduce the complexity of circuit design. The implemented FPGA chip can process the data obtained from the sensors and generate desired pulses to control the angles of servomotors. Many functions are implemented on the FPGAchip to process the data and control the robot so that the weight of the robot is reduced.2.3. Walking ExperimentsA mechanical structure with 26 degrees of freedom is design so that TWNHR-III is able to accomplish five walking motions: walk forward, turn right and left, and slip right and left. Its control method is based on the try and error method. In order to verify the effectiveness ofthe implemented humanoid robot, three basic walking skills: straight walk, turn, and slip are carried out on a horizontal even plane and described as follows:2.3.1 Straight WalkSome snapshots of straight walking of TWNHR-III are shown in Figure 12, where the distance between every white line is 5 cm. Every step of the straight walking is able to move forward 10 cm.2.3.2 TurnSome snapshots of left turning of TWNHR-III are shown in Figure 13, where the angle between every white line is 15 degrees. Each time of the robot turning is able to turn 30 degrees.2.3.3 SlipSome snapshots of right slipping of TWNHR-III are shown in Figure 14, where the distance between every white line is 5 cm. Every step of the robot slipping is able to slip 10 cm.From these experiment results, we can see that the proposed mechanical structure can let TWNHR-III move forward, turn, and slip effectively.3. Obstacle Avoidance3.1 Decision Tree MethodObstacle run is a competition category in the HuroSot league of FIRA Cup. As shown in Figure 15, there is a finish line marked on one side of the playing field. This side of the playing field is called the finish side. The opposite side of the playing field is called the start side. The two other sides are called side lines. Arobot has crossed the finish line when the robot crosses the finish plane and touches the ground in the end zone. During the obstacle run competition, the robot does not allow to touch any obstacles. The robot has to detect obstacles and the direction of the goal line. Adecision tree method based on four infrared sensors is proposed and described in Figure 16. The behavior output of the decision tree is the robot’s five basic motions including go forward, 30 degree right turn, 30 degree left turn, slip right, and slip left. Sixteen behavior situations and their corresponding movements are described in Table 5. The strategy will check the relative behavior from the decision tree before the robot move. In order to let the robot walk toward the goaldirection, the robot will adjust the robot direction to face the goal direction based on the electronic compass informationwhen he robot is in the safe situation (B16 situation).3.2 Simulation and Experiment ResultsIn order to illustrate the proposed method can successfully avoid obstacles and go to the destination,two MATLAB simulations and one real exper iment are presented. Figure 17 and Figure 18 illustrate the obstacle avoidance ability of the robot by MATLAB simulation results. In Figure 17, there is one obstacle on the robot’s way to the goal line. When the robot detects the obstacle, the “slip right”behavior is made by the proposed decision tree method to avoid the obstacle based on the detected behavior situation B10. The robot keeps slipping to the right side, until there is no obstacle in front of the robot. When the detected behavior situation is changed from B10 to B16, the “move to goal line” behavior is made to let the robot walk toward the goal line. In Figure 18, there are two obstacles on the robot’s way to the goal line. The robot also chooses the behavior from the proposed behavior strateg y. At the location of “Safe point”,the robot is already in the safe situation. Therefore, the detected behavior situation is B16 to let the robot walk toward the goal line. As the robot moving forward, it detects the other obstacle. The “slip left” behavi or is made to avoid this obstacle based on the detected behavior situation B9. The detected behavior situation will change to B16 when the robot is away from this obstacle.The computer simulation results in Figure 17 andFigure 18 illustrate that the robot can effectively avoid obstacles and successfully arrive the goal line based on the proposed decision tree method. In the practical test, the proposed decision tree method implemented on the TWNHR-III in a real test ground is discussed. Six sequential image stills of TWNHR-III for a real experiment of obstacle avoidance are shown in Figure 19, where two obstacles are on the robot’s way to the goal line. Once TWNHR-III detects the obstacle via the infrared sensors, the robot will do an appropriate behavior to avoid obstacles. We can see that TWNHR-III can successfully avoid two obstacles by the proposed decision tree method.5. ConclusionThe soccer robot needs to play a soccer game autonomously. Playing soccer game is a good test platform to verify the ability of the designed and implemented robot. There are many robots in thematch field, so the soccer robot must have the ability to avoid the collision with other robots and move to an appropriate destination. Thus the obstacle run is a competition category in the HuroSot league of FIRACup. Some basic walking experiments of TWNHR-III have been presented to illustrate that the proposed mechanical structure with 26 degrees of freedom can let TWNHR-III move forward, turn, and slip. Based on the obtained information from one compass and four infrared sensors installed on TWNHR-III, a decision tree method is proposed. Two MATLAB simulation results and one practical test on TWNHR-III have been presented to illustrate that the proposed decision tree method can let the robot effectively avoid obstacles and successfully go to the destination. One CMOS sensor is installed on TWNHR-III so that it can be a visionbased soccer robot to autonomously find a ball and kick a ball. Moreover, TWNHR-III won champion of the humanoid league in Taiwan Cup 2006. In the future, TWNHR-III will be used to investigate the walking gait and artificial intelligence. For example, some force sensors will be installed on TWNHR-III to study the biped walking control on even or uneven ground. More research on artificial intelligence will be carried on TWNHR-III to make it to be an intelligent robot.AcknowledgementThis research was supported in part by the National Science Council (NSC) of the Republic of China under contract NSC 95-2221-E-032-057-MY3 and the Metal Industries Research & Development Centre (MIRDC), Kaohsiung, Taiwan, Republic of China.References[1] Ogura, Y., Aikawa, H., Shimomura, K., Kondo, H.,Morishima, A., Lim, H. O. and Takanishi, A., “Development of a New Humanoid Robot WABIAN-2,”IEEE Int. Conf. on Robotics and Automation, pp. 835⎽840 (2006).[2] Hirai, K., Hirose, M., Haikawa, Y. and Takenaka, T.,“The Development of Honda Humanoid Robot,” IEEE Int. Conf. on Robotics and Automation, Vol. 2, pp. 1321⎽1326 (1998).[3] Kaneko, K., Kanehiro, F., Kajita, S., Hirukawa, H.,Kawasaki, T., H irata, M., A kachi, K. and Isozumi, T.,“Humanoid Robot HRP-2,” IEEE Int. Conf. on Robotics and Automation, Vol. 2, pp. 1083⎽1090 (2004).[4] Lohmeier, S., Loffler, K., Gienger, M., Ulbrich, H. and Pfeiffer, F., “Computer System and Controlof Biped “Johnnie”,” IEEE Int. Conf. on Robotics and Automation, Vol. 4, pp. 4222⎽4227 (2004).[5] URL: [6] URL: [7] Borenstein, J. and Koren, Y., “Real-Time Obstacle Avoidance for Fact Mobile Robots,” IEEE Tran. On Systems, Man and Cybernetics, Vol. 19, pp. 1179⎽1187 (1989).[8] Crowley, J., “DynamicWorld Modeling for an Intelligent Mobile Robot Using a Rotating Ultra-Sonic Ranging Device,” IEEE Int. Conf. on Robotics and Automation,Vol. 2, pp. 128⎽135 (1985).[9] Rao, N. S. V., “Robot Navigation in Unknown Generalized Polygonal Terrains Using Vision Sensors,” IEEE Tran. on Systems, Man and Cybernetics, Vol. 25, pp. 947⎽962 (1995).[10] Innocenti, C., Mondino, G., Regis, P. and Sandini, G., “Trajectory Planning and Real-Time Control of an Autonomous Mobile Robot Equipped withVision and Ultrasonic Sensors,” IEEE/RSJ/GI Int. Conf. on Intelligent Robots and Systems, Vol. 3, pp. 1861⎽1866 (1994).[11] Wong, C. C., Cheng, C. T., Huang, K. H., Yang, Y. T., Chan, H. M. and Yin, C. S., “Me chanical Design of Small-Size Humanoid Robot TWNHR-3,” The 33rd Annual Conference of the IEEE Industrial Electronics Society, pp. 451⎽454 (2007).Manuscript Received: Jul. 27, 2007Accepted: Aug. 7, 2008258 Ching-Chang Wong et al.。
仿生爬壁滑翔机器人样机的设计与实验研究
仿生爬壁滑翔机器人样机的设计与实验研究黄伟;吴士林;程浩【摘要】针对多足攀爬机器人运动速度低、稳定性差、回收困难等问题,对飞鼠等四足攀爬滑翔生物体态构型和运动机理进行了分析,提取了飞鼠滑翔控制的波动步态模型,提出了一种多模式运动模型.构建了爬壁机器人模型和样机,并在仿生爬壁机器人的基础上初步构建了滑翔机器人样机;利用流体仿真软件FLUENT,确定了滑翔机器人升阻力系数、俯仰力矩与俯仰角之间的关系,以及稳定滑翔速度等参数;通过滑翔实验对仿真实验结果进行了验证.研究结果表明:滑翔机器人可以通过翼膜形状改变其气动特性,实现俯仰、滚转、偏航等姿态调整,维持滑翔过程的稳定性和有效性.【期刊名称】《机电工程》【年(卷),期】2019(036)007【总页数】5页(P761-765)【关键词】爬壁机器人;滑翔机器人;滑翔轨迹;波动步态【作者】黄伟;吴士林;程浩【作者单位】青海交通职业技术学院机械工程系,青海西宁810028;北京航空航天大学机器人研究所,北京100191;浙江省机械工业情报研究所,浙江杭州310009【正文语种】中文【中图分类】TH122;TP2420 引言当前，社会生产、服务、环保等方面对爬壁机器人的需求越来越大[1-2]。
仿生爬壁机器人的快速回收是该类机器人研究的一个重点。
若能使机器人具备自然界中某些蜥蜴类爬行动物的无动力定向滑降能力，可以大幅提高回收效率，且更具经济性[3]。
美国哈佛大学MIRKO等[4]从蝙蝠、蝴蝶、蝗虫等仿生研究获得启发，针对不同的打开方式研制了EPFL系列机器人，该类机器人兼具跳跃和滑翔能力，具有质量轻、过渡效果好等特点；佛罗里达州立大学BACHMANN等[5]研制的MMALV 机器人采用了轮腿复合构型和可展开翼面，翼展30 cm，兼具滑翔和地面行走能力，该机器人重450 g，前端装有螺旋桨提供飞行动力，可从6 m高度由地面爬行过渡到飞行状态；美国斯坦福大学的ALEXIS等[6]设计了一款能够飞行、滑翔和附着壁面的多运动模式机器人，该机器人通过螺旋桨获得飞行速度，采用固定翼方式产生飞行和滑翔动力，机器人足端有微刺结构，能够附着竖直粗糙墙面，从而能够完成由空中飞行到墙面潜伏。
双Schatz机构爬行机器人
双Schatz机构爬行机器人姚燕安;姚舜;刘超【摘要】以经典的Schatz机构为基础单元提出了一种新概念爬行移动机器人.基于Schatz机构的结构特点与运动分析,提出将两个Schatz机构组合成移动机器人的构型方案,对杆件的外形及电机的安装位置进行设计.通过分析将其置于平面内的移动机理,提出通过控制两个电机的相位差来控制其移动的控制方案,使用动力学仿真软件建立虚拟样机在设定的场地内进行仿真实验,探索移动规律.基于移动规律验证控制其移动的控制方案的可行性,进而研究控制机器人完成定点运动的控制策略,通过实验验证了设计制作样机理论分析及研究的正确性.【期刊名称】《北京交通大学学报》【年(卷),期】2015(039)004【总页数】6页(P12-17)【关键词】爬行机器人;Schatz机构;空间6R机构【作者】姚燕安;姚舜;刘超【作者单位】北京交通大学机械与电子控制工程学院,北京100044;北京交通大学机械与电子控制工程学院,北京100044;北京交通大学机械与电子控制工程学院,北京100044;上海交通大学机械系统与振动国家重点实验室,上海200240【正文语种】中文【中图分类】TH122连杆机构形式多样、结构紧凑、运动形式灵活,将其应用于移动机器人具有独特的技术优势与发展潜力.文献[1-3]作者研制一种步行机器人,组成其腿部机构的是平行四边形机构,其足端可在垂直平面内作平移运动.Liu等[4]研制以滚动的方式实现移动的平面四杆机构、球面四杆机构和空间Benett机构.刘超等[5]研制了一种基于空间RCCR四杆机构的内外双足型式的步行机器人.田耀斌等[6]研制了一种单自由度概率翻转移动连杆机构,该机构为一种空间八杆机构,其外形为几何学上的双三角锥.郝艳玲等[7]研制了一种基于空间的十四杆机构外形为4个四边形机构空间正交的滚动机器人.Gifford等[8]设计了一种基于空间二十六杆机构的多面体移动机器人,并在越障性能方面显示出优势.上述基于连杆机构的机器人移动方式新颖,但是控制其移动的方式方法较为复杂.本文作者探索将Schatz机构应用于移动机器人.Schatz机构是一种经典的空间连杆机构[9],最早由Schatz提出,Brat[10]、Baker[11]和Lee等[12-16]对其进行了分析,物料混合机是该机构的成功工业应用案例.本文作者将两个Schatz机构组合成一款具备爬行能力的移动机器人,研究其构型方案、移动规律及控制策略.该机器人构型新颖、控制方式方法简单.1.1 构型方案经典的Schatz机构是由6个杆件和6个转动副组成的单自由度空间连杆机构[9],将各个杆件和转动副命名如图1所示.转动副J1和J6的转动轴向相互平行,其余相邻转动副两两垂直.变量L1～L6分别表示杆1～杆6的长度,可得[9]在物料混合机的经典应用案例中,将Schatz机构的杆1用作机架,用物料桶替换杆4,杆2在电机的驱动下绕转动副J2轴线高速转动,使物料桶在空间翻滚,将物料混合. 如图1,在转动副J1的中心位置建立坐标系Oxyz.其x轴轴线与杆1平行,z轴轴线与杆2平行,其方向如图1所示,通过右手螺旋定则确定y轴轴线和方向.用θ来表示杆2相对于杆1的角位移,即转动副J1的角位移,称其为动力位移.图1所示状态中,Schatz机构处于初始状态,y轴正方向指向地面,此时动力角位移θ为0.用C和D表示转动副J3和J4的位置,使用DH法得到其相对于坐标系Oxyz 的位置坐标当Cy＜0且Dy＞0时,Schatz机构以D点为支点,即此时θ满足当Cy＞0且Dy＜0时,Schatz机构以C点为支点,即此时θ满足可知当Schatz机构的θ从0增长到π时,其完成一个运动周期.现将两个Schatz机构通过连接杆件的方式组合,如图2所示.将两个Schatz机构分别称为闭环Ⅰ和闭环Ⅱ,由电机Ⅰ和电机Ⅱ直接驱动的杆件分别称为驱动杆1和驱动杆2,同时这两个电机作为两个闭环的两个转动副,其他转动副命名如图2(a)所示.将该组合中两个在空间作翻滚运动的杆件称为足1和足2,如图2(b)所示.在闭环1的驱动杆1处建立坐标系O0x0y0z0,坐标原点O0位于转动副中心,z0轴正方向与驱动杆1的方向平行,x0轴正方向如图2(b)所示,y0轴正方向可通过右手螺旋定则确定.驱动杆1与驱动杆2相对于坐标系O0x0y0z0中的相对角位移分别为θ1和θ2.此方案中即两个电机输出的转速相等,方向相同.1.2 移动机理足1和足2在电机的驱动下在空间翻滚,产生带动机器人移动的动力.当θ1和θ2取不同数值时,闭环所处的姿态不同,足1和足2产生驱动力的大小和方向也不同,所以当θ1和θ2之差取不同值时,机器人的运动轨迹也不同,称θ1和θ2之差为相位差δ,即将该机器人按照图2(b)所示姿态置于地面,即y0轴正方向垂直地面向下.其运动时,足1和足2作翻滚运动,其杆件的两端会与地面产生碰撞和摩擦,即会有对地面相对滑动的趋势,进而产生作为动力的摩擦力.由于δ取不同值时,足1和足2产生的动力不相同,因而拟采取仿真实验的方法,建立仿真模型,探索其在δ取不同值时的移动规律.仿真环境中建立机器人仿真模型,如图3所示.将动摩擦系数和静摩擦系数均设为1,驱动杆1和驱动杆2的转速为30 r/min,各个杆件材料均匀密度相同.机器人仿真模型其他尺寸参数如表1所示.在点O0处建立方向向量s,与z0轴正方向相同.以δ=0时机器人的移动轨迹为例,说明仿真数据的含义.在50个移动周期之后,该机器人沿向左弯曲的弧线AB,从A点出发行进到B点,将其移动轨迹用虚线标出,如图4所示.通过测量可得到虚线的圆弧半径r、s、转向角度α,以及行进的起始点之间的距离d(A与B之间的距离),将δ取不同值时机器人的移动轨迹数据记录并分析,如表2所示.为了便于分析仿真实验数据,定义两个参数.1)为了鉴别机器人移动轨迹与直线的相似程度,定义直行系数λ为式中:d'为计算距离;l为轨迹圆弧长度.当机器人在实验中行进方向转动角度不超过180°时,d'=d;当其行进方向转动角度超过180°时,d'=2r.机器人移动轨迹与直线的接近程度与λ成正比.2)为了分析该机器人的转向效果,定义在单位时间内机器人行进方向改变的角度为单位转角μ仿真实验中机器人的移动轨迹会根据相位差δ的变化为半径不同的向左或向右弯曲的圆弧或直线.通过仿真实验研究,发现机器人的移动轨迹随着相位差δ的变化而变化的规律,因此可以采用控制驱动两个闭环的电机之间δ的方法实现控制其在平面内行进和转向. 由于该机器人需在平面内完成移动,并完成到达指定目标点的任务,将其控制策略分为两个阶段,调整行进方位和行进.1)在调整行进方位的阶段中,主要目的是尽可能快地控制其调整行进方向向量s,使其指向目标位置点.通过表2中的数据可以看出在电机转速一定的前提下,当δ=50°,μ最大,达到了11.24°,此时其转向速度最大.此外,当相位差δ取50°时其移动路径圆弧的半径最小,说明此时移动装置在最小的空间内,以最快的速度调整行进方向.所以,选择50°为调整行进方位的相位差值.2)在控制机器人行进的阶段中,主要目的是驱动其尽快到达目标位置点.虽然沿直线行进是最快的方式,但是仿真实验中当其沿直线行进时,其行进方向并不与其行进方向矢量s重合,在实际控制中存在很大问题隐患,所以选择通过控制其沿一对最接近于直线的弯曲方向相反的轨迹递进向前,完成行进任务.控制机器人沿一对弯曲方向相反的轨迹曲线行进,包括控制其向右沿弧线行进和向左沿弧线行进.对于向右沿弧线行进的方式,通过对仿真数据的分析和对比,可以看出当δ取120°时,λ达到同向弯曲路径圆弧的最大值0.98,此时移动轨迹与直线的契合度最高,所以选择120°为控制其向右行进的相位差;同理,选择20°为控制其向左沿弧线行进的相位差.基于以上分析,制定基于双Schatz机构爬行机器人的控制流程图如图5所示,其移动过程分为4个步骤.1)获取机器人与目标点的距离d,判定d是否为0:是则到达目标点,完成任务,反之进行步骤2).2)获取机器人当前方向向量s.判定方向向量s是否指向目标点:是则进行步骤4),反之进行步骤3).3)控制该机器人调整自身方位,然后返回步骤2).4)判定目标点与方向向量s的相对位置:若目标点在s的左侧,则控制其向左行进,到达目标点与直行起点连线和轨迹弧线的交点,然后返回步骤1);反之,若目标点在s的右侧或s指向目标点,则控制其向右行进,到达目标点与直行起点连线和轨迹弧线的交点,然后返回步骤1).如图6所示,将双Schatz机构爬行机器人放置于出发点A,B点为目标点,锯齿状线条为该机构质心的轨迹曲线,用来表示其整体移动轨迹.1)调整位姿.初始位姿如图7(a)所示.首先获取行进方向向量s,目标点B在s的左侧,将相位差δ调整为50°,如图7(b)所示.电机驱动该机器人移动向左沿弧线移动改变方向向量方向s到达中间点M,s逆时针旋转,当其到达点M时,s指向B.2)行进.当目标点B不在s的左侧,则控制两个电机相位差δ达到120°,如图7(c)所示.该机器人向右沿弧线行进到达目标点B与直行起点M连线和轨迹弧线的交点N.此时机器人位姿如图7(d)所示,再次判断其方位,目标点B在方向向量s的左侧,则将相位差δ降低到20°,如图7(e)所示.此时,机器人向左沿弧线行进到达目标点B,完成移动任务.设计制作双Schatz机构爬行机器人样机如图8所示.通过将其中两个零件底面设计成圆柱面的方法,降低机身与地面的摩擦系数.此外,将足1和足2的两端边缘设计为直角构造,该设计可以提高其与地面的摩擦系数.样机其余各尺寸参数见表3.电机选用24 V直流电机,额定转速为12 r/min.当两个电机之间的相位差取不同值时,物理样机同样可以沿不同参数的圆弧行进,物理样机的实验结果与仿真结果基本一致,双Schatz机构爬行机器人可实现地面移动和在其移动路径任意一点进行转向的功能.1)提出双Schatz机构爬行机器人的设计方案,分析其移动机理,并初步提出控制方法.其次,建立虚拟样机进行仿真实验研究,发现其移动规律,验证了初步提出的控制方法可行,并制定针对该机器人的控制策略.最后,设计制作物理样机进行实验研究,从实验结果推断理论分析的正确性和控制方案的可行性.2)与已有各类多自由度移动机器人相比,该机器人的动力系统被简化,降低了控制系统的复杂度,并且在低成本、高可靠性方面具有优势,期望此种机器人能够成为移动机器人家族中具有发展潜力的一个新成员.但该机器人会因地面环境的不同而发生移动规律不稳定的情况,其动态特性和控制方法还有待深入研究.【相关文献】[1]Tavolieri C,Ottaviano E,Ceccarelli M,et al.Analysis and design of a 1-DOF leg for walking machines[C]//Proceedings of RAAD’06,15th International Workshop on Robotics,2006. 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机器人的英文PPT
the third stage---intelligent robotics development(1984---?)1999 Sony demonstrated the Intelligent robotics---AIBO2002 iRobot company introduced a vacuum cleaner robot Roomba.2006 Microosoft introduced Microsoft Robotics studio.
The application of robot
A robot which operates semi or fully autonomously to perform services useful to the well being of humans and equipment,excluding manufacturing operations.Care-O-Bot,helps achieve greater independence for elderly or mobility impaired persons from outside help,and therefore contributes to longer remmaining at home.Cleaning robots have entered the rger surfaces as railway ststions,airports,malls,etc.can already be cleaned automatically with full autonomous navigation capability.
Trend of Robot
英语科普环保类阅读试题答案及解析
英语科普环保类阅读试题答案及解析1. February 16, 2014（VOA）JAKARTA — The United States and China say they recognize the need for "urgent action" to address the twin challenges of climate change and the air pollution caused by burning fossil fuels. U.S. Secretary of State John Kerry called for greater political will to address a warming planet.China is the leading producer of greenhouse gases and joins the United States as the largest consumers of energy. So U.S. officials say the world's two biggest economies agreeing to limit emissions and promote energy efficiency in buildings and industry "sends a strong message to the world" that this is an issue that needs to be addressed now.Following that agreement with Chinese leaders, Secretary Kerry told an audience in Indonesia that Washington hopes this "unique partnership" with Beijing will help set an example for global leadership and global seriousness on climate change.Kerry Warns About Global Climate Change"Together we account for roughly 40 percent of the world’s emissions. But this is not just about China and the United States," Kerry said. "It is about every country on Earth doing whatever it can to pursue cleaner and healthier energy sources.Kerry said scientific evidence compels the world to act."It is not a lack of ability that is the problem," Kerry said. "It is a lack of political resolve that is standing in our way.The European Union is calling for a 40 percent cut in carbon emissions by 2030. European Commission President Jose Manuel Barroso says that target is ambitious and affordable."It shows that we are beyond debate where we either have to be green or a defender of industry," Barroso said. "We believe these two issues are not contradictory, but can perfectly go together if handled smartly.Some European parliamentarians and some environmentalists believe the carbon reduction goals do not do enough to encourage cleaner energy. Jason Anderson heads European climate and energy policy for the World Wildlife Fund."We want to make sure that they do not put the brakes on the energy transition that we are seeing now," Anderson said. "Their ambitions for renewable energy would actually slow down the pace of change, which it is completely senseless as it is one of those areas of the economy that is growing right now.Kerry's climate speech was the first in a series of events meant to focus on cutting carbon emissions before talks in Paris next year on coming up with a successor to the 1997 Kyoto Protocol, which was never ratified by the United States. （）419【1】European Commission President Jose Manuel Barroso believes that________.A．The European Union accounts for 40 percent of the world’s emissions.B．we either have to be green or a defender of industry.C．we have to be green and a defender of industry if handled smartly.D．the carbon reduction goals do not do enough to encourage cleaner energy.【答案】C【解析】考查细节理解。
基于ADAMS和MATLAB的两足机器人的步态联合仿真
基于ADAMS和MATLAB的两足机器人的步态联合仿真曹杰昌;张立中;白杨杨【期刊名称】《长春理工大学学报（自然科学版）》【年(卷),期】2016(039)005【摘要】为提高两足机器人的设计效率,降低设计成本,验证步态规划的正确性,在研制两足机器人物理样机前,应对其进行虚拟样机仿真.首先在ADAMS环境中建立简化的两足机器人动力学模型,然后在Matlab/Simulink中建立控制系统,最后利用二者之间的接口实现基于ADAMS和MATLAB的两足机器人的步态联合仿真,在仿真过程中观察两足机器人的行走过程,验证设计方案的正确性,为其物理样机的研制提供依据.%In order to improve the efficiency, reduce the cost of biped robot design and verify the correctness of gait programming, virtual prototype simulation system has been built before developed the physical prototype of biped robot. First, the mechanical dynamic model of biped robot was built simplify in ADAMS. Second, the control systems ware built in Matlab/Simulink. Last, using the interface between them, we can realized the coordinated simulation of gait for biped robot based on ADAMS and MATLAB. Observed the walking of biped robot in the simulation process, verify the accuracy of the design,provide evidence for the development.【总页数】4页(P81-84)【作者】曹杰昌;张立中;白杨杨【作者单位】长春理工大学机电工程学院,长春 130022;长春理工大学机电工程学院,长春 130022;长春理工大学空地激光通信国防重点学科实验室,长春 130022;长春理工大学机电工程学院,长春 130022;长春理工大学空地激光通信国防重点学科实验室,长春 130022【正文语种】中文【中图分类】TP391.7【相关文献】1.四足除草机器人的ADAMS与MATLAB联合仿真 [J], 卢衷正;戈振扬;丁巍2.基于ADAMS的液压驱动四足机器人步态规划与仿真 [J], 庄明;俞志伟;龚达平;许明理;戴振东3.基于ADAMS与MATLAB的四足机器人的trot步态联合仿真 [J], 王建明;赵彦;朱彦防;马宗利4.基于ADAMS的四足机器人trot步态仿真 [J], 纪鹏飞;陈劲杰;宁方宽;史纬朋5.基于Adams的六足爬壁机器人的步态规划与仿真 [J], 魏武;戴伟力因版权原因，仅展示原文概要，查看原文内容请购买。
Bipedal Walking on Rough Terrain Using Manifold Control
Bipedal Walking on Rough Terrain Using Manifold ControlTom Erez and William D.SmartMedia and Machines Lab,Department of Computer Science and EngineeringWashington University in St.Louis,MOetom,wds@Abstract—This paper presents an algorithm for adapting periodic behavior to gradual shifts in task parameters.Since learning optimal control in high dimensional domains is subject to the’curse of dimensionality’,we parametrize the policy only along the limit cycle traversed by the gait,and thus focus the computational effort on a closed one-dimensional manifold,embedded in the high-dimensional state space.We take an initial gait as a departure point,and iterate between modifying the task slightly,and adapting the gait to this modification.This creates a sequence of gaits,each optimized for a different variant of the task.Since every two gaits in this sequence are very similar,the whole sequence spans a two-dimensional manifold,and combining all policies in this 2-manifold provides additional robustness to the system.We demonstrate our approach on two simulations of bipedal robots —the compass gait walker,which is a four-dimensional system, and RABBIT,which is ten-dimensional.The walkers’gaits are adapted to a sequence of changes in the ground slope,and when all policies in the sequence are combined,the walkers can safely traverse a rough terrain,where the incline changes at every step.I.INTRODUCTIONThis paper deals with the general task of augmenting the capacities of legged robots by using reinforcement learn-ing1.The standard paradigm in Control Theory,whereby an optimized reference trajectory is foundfirst,and then a stabilizing controller is designed,can become laborious when a whole range of task variants are considered.Standard algo-rithms of reinforcement learning cannot yet offer compelling alternatives to the control theory paradigm,mostly because of the prohibitive effect of the curse of dimensionality. Legged robots often constitute a high-dimensional system, and standard reinforcement learning methods,with their focus on Markov Decision Processes models,usually cannot overcome the exponential growth in state space volume. Most previous work in machine learning for gait domains required either an exhaustive study of the state space[1], or the use of non-specific optimization techniques,such as genetic algorithms[2].In this paper,we wish to take a first step towards efficient reinforcement learning in high-dimensional domains by focusing on periodic tasks.We make the observation that while legged robots have a high-dimensional state space,not every point in the state space represents a viable pose.By definition,a proper gait would always converge to a stable limit cycle,which traces a closed one-dimensional manifold embedded in the 1The interested reader is encouraged to follow the links mentioned in the footnotes to section IV to see movies of our simulations high-dimensional state space.This is true for any system performing a periodic task,regardless of the size of its state space(see also[3],section3.1,and[4],figure19,for a validation of this point in the model discussed below).This observation holds a great promise:a controller that can keep the system close to one particular limit cycle despite minor perturbations(i.e.has a non-trivial basin of attraction)is free to safely ignore the entire volume of the state space. Finding such a stable controller is far from trivial,and amounts to creating a stable gait.However,for our purpose, such a controller can be suboptimal,and may be supplied by a human tele-operating the system,by leveraging on passive dynamic properties of the system(as in section IV-A),or by applying control theory tools(as in section IV-B).In all cases,the one-dimensional manifold traced by the gait of a stable controller can be identified in one cycle of the gait, simply by recording the state of the system at every time step. Furthermore,by querying the controller,we can identify the local policy on and around that manifold.With these two provided,we can create a local representation of the policy which generated the gait by approximating the policy only on and around that manifold,like a ring embedded in state space,and this holds true regardless of the dimensionality of the state space.By representing the original control function in a compact way we may focus our computational effort on the relevant manifold alone,and circumvent the curse of dimensionality as such a parametrization does not scale exponentially with the number of dimensions.This opens a door for an array of reinforcement learning methods(such as policy gradient)which may be used to adapt the initial controller to different conditions,and thus augment the capacities of the system.In this article we report two experiments.Thefirst studies the Compass-Gait walker([9],[10],[11],a system known for its capacity to walk without actuation on a small range of downward slopes.The second experiment uses a simulation of the robot RABBIT[3],a biped robot with knees and a torso,but no feet,which has been studied before by the control theory community[5],[4],[6].Thefirst model has a four-dimensional state space,and the second model has10 state dimensions and4action dimensions.By composing together several controllers,each adapted to a different incline,we are able to create a composite controller that can stably traverse a rough terrain going downhill.The same algorithm was successfully applied to the second system too, although the size of that problem would be prohibitive formost reinforcement learning algorithms.In the following wefirst give a short review of previous work in machine learning,and then explain the technical aspects of constructing a manifold controller,as well as the learning algorithms used.We then demonstrate the effec-tiveness of Manifold Control by showing how it is used to augment the capacities of existing controllers in two different models of bipedal walk.We conclude by discussing the potential of our approach,and offer directions for future work.II.P REVIOUS W ORKThe generalfield of gait design has been at the focus of mechanical engineering for many years,and recent years saw an increase in the contribution from the domain of machine learning.For example,Stilman et al.[7]studied an eight-dimensional system of a biped robot with knees, similar to the one studied below.They showed that in their particular case the dimensionality can be reduced through some specific approximations during different phases.Then, they partitioned the entire volume of the reduced state space into a grid,and performed Q-learning using a simulation model of the system’s dynamics.The result was a robot walker that can handle a range of slopes around the level horizontal plane.In addition,there is a growing interest in recent years in gaits that can effectively take advantage of the passive dynamics(see the review by Collins et al.[8]for a thorough coverage).Tedrake[9]discusses several versions the com-pass gait walker which were built and analyzed.Controllers for the compass gait based on an analytical treatment of the system equations wasfirst suggested by Goswami et al.[10],who used both hip and ankle actuation.Further results were described by Spong and Bhatia[11],where the case of uneven terrain was also discussed.Ramamoorthy and Kuipers[12]suggested hybrid control of walking over irregular terrain by seeking inspiration from human walking.III.M ANIFOLD C ONTROLA.The Basic Control SchemeThe basic idea in manifold control is to focus the com-putational effort on the limit cycle.Therefore,the policy is approximated using locally activated processing elements (PEs),positioned along the manifold spanned by the limit cycle.Each PE defines a local policy,linear in the position of the state relative to that PE.When the policy is queried with a given state x,the local policy of each PE is calculated as:µi(x)=[1(x−c i)T M T]G i(1) where c i is the location of element i,M is a diagonal matrix which determines the scale of each dimension,and G i is an (n+1)-by-m matrix,where m is the action dimension and n is the number of state space dimensions.G i is made of m columns,one for each action dimension,and each column is an(n+1)-sized gain vector.Thefinal policy u(x)is calculated by mixing the local policies of each PEaccordingFig.1.Illustrations of the models used.On the left,the compass-gait walker:the system’s state is defined by the two legs’angles from the vertical direction and the associated angular velocities,for a total of four dimensions. Thisfigure also depicts the incline of the sloped ground.On the right, RABBIT:the system’s state is defined bt the angle of the torso form the vertical direction,the angles between the thighs and the torso,and the knee angles between the shank and the thigh.This model of RABBIT has ten state dimensions,where at every moment one leg isfixed to the ground, and the other leg is free to swing.to a normalized Gaussian activation function,usingσas a scalar bandwidth term:w i=exp(−(x−c i)T M TσM(x−c i)),(2)u(x)= n i=1w iµitraverse a path of higher value(i.e.collect more rewards,or less cost)along its modified limit cycle.1)Defining the Value Function:In the present work we consider a standard nondiscounted reinforcement learning formulation with afinite time horizon and no terminal costs. More accurately,we define the Value Vπ(x0)of a given state x0under afixed policyπ(x)as:Vπ(x0)= T0r(x t,π(x t))dt(4) where r(x,u)is the reward determined by the current state and the selected action,T is the time horizon,and x t is the solution of the time invariant ordinary differential equation ˙x=f(x,π(x))with the initial condition x=x0,so thatx t= t0f(xτ,π(xτ))dτ.(5) 2)Approximating the Policy Gradient:With C being the locations of the processing elements,and G being the set of their local gains,we make use of a method,due to[14],of piecewise estimation of the gradient of the value function at a given initial state x0with respect to the parameter set G. As Munos showed in[14],from(4)we can write∂V∂G,(6) and for the general form r=r(x,u)we can decompose∂r/∂G as∂r∂u ∂u∂x∂xFig.2.The path used to test the compass gait walker,and an overlay of the walker traversing this path.Note how the step length adapts to the changing incline.the forward swing,but will undergo an inelastic collision with thefloor during the backward swing.At this point it will become the stance leg,and the other leg will be set free to swing.The entire system is placed on a plane that is inclined at a variable angleφfrom the horizon.In the interests of brevity,we omit a complete description of the system dynamics here,referring the interested reader to the relevant literature[15],[10],[11].Although previous work considered actuation both at the hip and the ankle,we chose to study the case of actuation at the hip only.The learning phase in this system was done using simple stochastic gradient ascent,rather than the elaborate policy gradient estimation described in section III-B.2.The initial manifold was sampled at an incline ofφ=−0.05(the initial policy is the zero policy,so there were no approximation errors involved).One shaping iteration consisted of the following:first,G was modified to G tent=G+ηδG, withη=0.1andδG drawn at random from a multinormal distribution with unit covariance.The new policy’s perfor-mance was measured as a sum of all the rewards along20 steps.If the value of this new policy was bigger than the present one,it was adopted,otherwise it was rejected.Then, a newδG was drawn,and the process repeated itself.After 3successful adoptions,the shaping iterations step concluded with a resampling of the new controller,and the incline was decreased by0.01.After10shaping iteration steps,we had controllers that could handle inclines up toφ=−0.14.After another10 iteration steps with the incline increasing by0.005,we had controllers that could handle inclines up toφ=0.0025(a slight uphill).This range is approxmately double the limit of the passive walker[15].Finally,we combined the various controllers into one composite controller.This new controller used1500charts to span a two-dimensional manifold embedded in the four-dimensional state space.The performance of the composite controller was tested on an uneven terrain where the incline was gradually changed fromφ=0toφ=0.15radians, made of“tiles”of variable length whose inclines were0.01 radians apart.figure III-B.2shows an overlay of the walker’s downhill path.A movie of this march is available online.2B.The Uphill-Walking RABBIT RobotWe applied manifold control also to simulations of the legged robot RABBIT,using code from Prof.Jessy Grizzle that is freely available online[16].RABBIT is a biped robot with a torso,two knees and no feet(seefigure1b.),and is actuated at four places:both hip joins(where thighs are actuated against the torso),and both knees(where shanks are actuated against the thighs).The simulation assumes a stance leg with no slippage,and a swing leg that is free to move at all directions until it collides inelastically with the floor,and becomes the stance leg,freeing the other leg to swing.This robot too is modeled as a nonlinear system with impulse effects.Again,we are forced to omit a complete reconstruction of the model’s details,and refer the interested reader to[4],equation8.This model was studied extensively by the control theory community.In particular,an optimal desired signal was derived in[6],and a controller that successfully realizes this signal was presented in[4].However,all the efforts were focused on RABBIT walking on even terrain.We sought a way to augment the capacities of the RABBIT model,and allow it to traverse a rough,uneven terrain.We found that the controller suggested by[4]can easily handle negative (downhill)incline of0.2radiand and more,but cannot handle positive(uphill)inclines.3.Learning started by approximating the policy from[4]as a manifold controller,using400processing elements with a mean distance of about0.03state space length units.The performance of the manifold controller was indistinguishable to the naked eye from the original controller,and perfor-mance,as measured by the performance criterion C3in[6] (the same used by[4]),was only1%worse,probably due to minor approximation errors.The policy gradient was estimated using(6),according to a simple reward model:r(x,u)=10v x hip−1Fig.3.The rough terrain traversed by RABBIT.Since this model has knees,it can walk both uphill and downhill.Note how the step length adapts to the changing terrain.The movie of this parade can be seen at tt http:/2b8sdm,which is a shortcut to the YouTube website.where v xhip is the velocity of the hip joint(where the thighand the torso meet)in the positive direction of the X-axis, and u max is a scalar parameter(in our case,chosen to be 120)that tunes the nonlinear action penalty and promotes energetic efficiency.After the initial manifold controller was created,the system followed a fully automated shaping protocol for 20iterations:at every iteration,∂V/∂G was estimated, andηwasfixed to0.1%of|G|.This small learning rate ensured that we don’t modify the policy too much and lose stability.The modified policy,assumed to be slightly better, was then tested on a slightly bigger incline(the veryfirst manifold controller was tried on an incline of0rad.,and in every iteration we increased the incline in0.003rad.).This small modification to the model parameters ensured that the controller can still walk stably on the incline.If stability was not lost(as was the case in all our iterations),we resampled u(·;C,G new)so that C adj overlapped the limit cycle of the modified system(with the new policy and new incline),and the whole process repeated.This procedure allowed a gradual increase in the system’s maximal stable incline.Figure4depicts the evolution of the stability margins of every ring along the shaping iteration:for every iteration we present an upper(and lower)bound on the incline for which the controller can maintain stability.Thiswas tested by setting a test incline,and allowing the system to run for10 seconds.If no collapse happened by this time,the test incline was raised(lowered),until an incline was found for which the system can no longer maintain stability.As this picture shows,our automated shaping protocol does not maintain a tight control on the stability margins-for most iterations,a modest improvement is recorded.The system’s nonlinearity is well illustrated by the curious case of iteration9,where the same magnitude ofδG causes a massive improvement, despite the fact that the control manifold itself didn’t change dramatically(seefigure5).The converse is also true for some iterations(such as17and18)there is a decrease in the stability margins,but this is not harming the overall effectiveness,since these iterations are using training data obtained at an incline that is very far from the stability Fig.4.Thisfigure shows the inclines for which each iteration could maintain a stable gait on the RABBIT model.The diagonal line shows the incline for which each iteration was trained.Iteration0is the original controller.The initial manifold control approximation degrades most of the stability margin of the original control,but this is quickly regained through adaptation.Note that both the learning rate and the incline change rate were held constant through the entire process.The big jump in iteration 9exemplifies the nonlinearity of the system,as small changes may have unpredictable results,in this case,for the best.margin.Finally,three iterations were composed together,and the resulting controller successfully traversed a rough terrain that included inclines from−0.05to0.15radians.Figure3 shows an overlay image of the rough path.V.C ONCLUSION AND F UTURE W ORKIn this paper we present a compact representation of the policy for periodic tasks,and apply a trajectory-based policy gradient algorithm to it.Most importantly,the methods we present do not scale exponentially with the number of dimensions,and hence allow us to circumvent the curse of dimensionality in the particular case of periodic tasks.By following a gradual shaping process,we are able to create robust controllers that augment the capacities of existing systems in a consistent way.33.54−1.5−0.2anglea n g . v e l.anglea n g . v el.anglea n g . v e l.anglea n g . v el.anglea n g . v e l.anglea n g . v el.anglea n g . v e l.anglea n g . v el.anglea n g . v e l.anglea n g . v el.anglea n g . v e l.anglea n g . v el.anglea n g . v e l.anglea n g . v e l.Fig.5.A projection of the manifold of several stages of the shaping process for the RABBIT model.The top row shows the angle and angular velocity between the torso and the stance thigh,and the bottom row shows the angle and angular velocity of the knee of the swing leg.Every two consecutive iterations are only slightly different from each other.Throughout the entire shaping process,changes accumulate,and new solutions emerge.Manifold control may also be used when the initial con-troller is profoundly suboptimal 4.It is also important to note that the rough terrain was traversed without informing the walker of the current terrain.We may say that the walkers walked blindly on their rough path.This demonstrates how stable a composite manifold controller can be.However,in some practical applications it could be beneficial to represent this important piece of information explicitly,and select the most appropriate ring at every step.We believe that the combination of local learning and careful shaping holds a great promise to many applications of periodic tasks,and hope to demonstrate it through future work on even higher-dimensional systems.Future research directions could include methods that allow second-order convergence,and learning a model of the plant.R EFERENCES[1]M.Stilman,C.G.Atkeson,J.J.Kuffner,and G.Zeglin,“Dynamicprogramming in reduced dimensional spaces:Dynamic planning for robust biped locomotion,”in Proceedings of the 2005IEEE Interna-tional Conference on Robotics and Automation (ICRA 2005),2005,pp.2399–2404.[2]J.Buchli, F.Iida,and A.Ijspeert,“Finding resonance:Adaptivefrequency oscillators for dynamic legged locomotion,”in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).IEEE,2006,pp.3903–3909.[3] C.Chevallereau and P.Sardain,“Design and actuation optimization ofa 4-axes biped robot for walking and running,”in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA),2000.[4] F.Plestan,J.W.Grizzle,E.Westervelt,and G.Abba,“Stable walkingof a 7-dof biped robot,”IEEE Trans.Robot.Automat.,vol.19,no.4,pp.653–668,Aug.2003.4theinterested reader is welcome to see other results of manifold learning on a 14-dimensional system at /2h3qny and /2462j7.[5] C.Sabourin,O.Bruneau,and G.Buche,“Experimental validation ofa robust control strategy for the robot rabbit,”in Proceedings of the IEEE 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com-pass gait biped,”in Proceedings of the 2003IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003),vol.2,2003,pp.1933–1938.[12]S.Ramamoorthy and B.Kuipers,“Qualitative hybrid control ofdynamic bipedal walking,”in Robotics :Science and Systems II ,G.S.Sukhatme,S.Schaal,W.Burgard,and D.Fox,Eds.MIT Press,2007.[13]S.Schaal and C.Atkeson,“constructive incremental learning fromonly local information,”neural computation ,no.8,pp.2047–2084,1998.[14]R.Munos,“Policy gradient in continuous time,”Journal of MachineLearning Research ,vol.7,pp.771–791,2006.[15] A.Goswami,B.Thuilot,and B.Espiau,“Compass-like biped robotpart i:Stability and bifurcation of passive gaits,”INRIA,Tech.Rep.2996,October 1996.[16] E.Westervelt, B.Morris,and J.Grizzle.(2003)Five link walker.IEEE-CDC Workshop:Feedback Control of Biped Walking Robots.[Online].Available:/2znlz2。
毕业论文（设计）基于matlab的双足步行机器人腿部运动模型的建立与运动仿真
诚信声明本人郑重声明：本论文及其研究工作是本人在指导教师的指导下独立完成的，在完成论文时所利用的一切资料均已在参考文献中列出。
本人签名：年月日毕业设计任务书设计题目：基于MATLAB的双足步行机器人腿部运动模型的建立与运动仿真系部：机械工程系专业：机械电子工程学号：112012337学生：指导教师（含职称）：（讲师）专业负责人：1．设计的主要任务及目标1）通过查阅有关资料，了解双足型机器人主要技术参数；2）双足型机器人的腿部模型建立及运动部件设计3）利用Pro/E完成动作的仿真2．设计的基本要求和内容1）双足型机器人的腿部功能选择；2）模型的建立；3）运动的仿真4）完成毕业设计说明书的撰写3．主要参考文献[1] 孙增圻.机器人系统仿真及应[ J ].系统仿真报，1995 ，7( 3 ):23-29.[2] 蒋新松，主编.机器人学导论[ M ].沈阳：辽宁：辽宁科学技术出版社，1994.[3] 蔡自兴.机器人学[ M ].北京：清华大学出版社，2000.[4] 薛定宇，陈阳泉.基于MATLAB/Simulink的系统仿真技术与应用[ M ].北京：清华大学出版社，20024．进度安排设计各阶段名称起止日期1 发放毕业设计题目及选题2015.03.03—2015.03.232 查阅文献，了解研究意义，完成开题报告2015.03.24—2015.04.133 编写说明书，已完成工作，完成中期答辩2015.04.14—2015.05.044 继续编写毕业设计说明书2015.05.01—2015.06.015 提交设计说明书，完成毕业答辩2015.06.02—2015.06.22审核人：年月日基于Matlab的双足步行机器人腿部运动模型的建立与运动仿真摘要：最近几年，双足仿人步行机器人发展很快，有很高的科学研究价值。
步行机器人的运动是模仿人的步行运动的形式，相比其它机器人有更好的灵活性，所以可以完成各种生活中的难度更大的任务，实用价值远高于其它机器人，当然研究难度和控制也相当复杂。
基于三维线性倒立摆的双足机器人步态规划
基于三维线性倒立摆的双足机器人步态规划李龙澍;王唯翔;王凡【摘要】Gait control strategies are an important factor affecting the walking stabiliti of bipedal robots.Offered a kind of gait planning algorithm of biped robot based on 3D linear inverted pendulun.Firstly,simplify the original 3D inverted pendulum model and suppose the location of the ZMP for the starting state of walking cycle.Secondly the function between centroid and time derived from equations of motion.Then simplify gait planning of biped robot to each walking cycle and get the relevant parameters of the functions through initial conditions for eachcycle.Finally ,extend this method to the direction of the gaitplanning.Experimental results demonstrate the feasibility and effectiveness of the approach.%双足机器人的步态控制策略是保证双足稳定行走的重要条件之一.提出一种基于三维线性倒立摆模型的双足机器人步态规划的算法.首先简化了三维倒立摆模型,并且假设了步行周期起始状态的ZMP位置,然后通过运动方程推导出含参数的质心与时间的函数,再将机器人的步态规划简化到每个步行周期,通过每个周期的初始条件获得函数的相关参数,最后将此方法推广到带转向的步态规划中,并应用于实际Robocup3D比赛中.实验结果表明该方法具有可行性和有效性.【期刊名称】《计算机技术与发展》【年(卷),期】2011(021)006【总页数】4页(P66-69)【关键词】双足机器人;步态规划;三维线性倒立摆;Robocup3D【作者】李龙澍;王唯翔;王凡【作者单位】安徽大学计算智能与信号处理教育部重点实验室,安徽合肥230039;安徽大学计算智能与信号处理教育部重点实验室,安徽合肥230039;合肥师范学院计算机科学与技术系,安徽合肥230601【正文语种】中文【中图分类】TP390 引言机器人技术是一门新兴的综合学科,它是计算机技术、电子、机械、自动控制、人工智能等多个领域新技术的综合应用,代表了机电一体化的最新成就,是目前科技发展最活跃的领域之一。
Virtual model control of a biped walking robot
Acknowledgments
This research was supported in part by the O ce of Naval Research, Grant #N00014-93-1-0333 3
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Contents
1 Introduction
2.1 2.2 2.3 2.4 2.5 2.6 1.1 Virtual Model Controllers : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 12 1.2 Summary of Thesis Contents : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 14
Author
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Department of Electrical Engineering and Computer Science August 25, 1995
Accepted by
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F. R. Morgenthaler Chairman, Department Committee on Graduate Theses
Virtual Model Control of a Biped Walking Robot
Certi ed by
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双足机器人平衡控制及步态规划研究
摘要摘要驱动技术，人工智能，高性能计算机等最新技术已经使双足机器人有了粗略模拟人体运动的灵巧性，能够进行舞蹈展示，乐器演奏，与人交谈等。
然而这与投入实际应用所需求的能力还有不小差距。
主要体现在缺乏与人类相近的平衡能力和步伐协调能力，对工作环境要求高，在非结构化环境中适应能力差。
因此，本文以自主研制的双足机器人为研究对象，重点研究了双足机器人的平衡控制，阻抗控制以及步态规划等内容。
本文首先简要介绍了自主研制的双足机器人的软硬件构架，建立了ADAMS 和Gazebo仿真来协助对控制算法性能预测和优化并减少对物理机器人的危险操作。
接着分析了双足机器人的正逆运动学并引入运动学库KDL来简化运动学运算。
稳定的平衡控制对于双足机器人而言在目前还是个不小的挑战。
本文就此研究了两种处理平衡的阻抗调节方案。
一种是基于LQR的固定阻抗模型，这种方案简单有效，但存在易产生振动的问题，本文结合滤波改善了平衡控制效果。
另一种是基于增强学习的自适应阻抗模型。
该方法可以在不知道系统内部动态信息的情况下利用迭代策略在线得到最优解，是对前述LQR方法的进一步优化。
随后本文通过仿真和实验进行了验证并分析了优缺点。
步态规划是机器人运动控制中最基础的一环。
本文从五连杆平面机器人入手对其运动控制进行了研究。
首先采用基于ZMP的多项式拟合法实现了机器人平地行走的步态规划。
然后分析其动力学模型并利用PD控制器进行运动仿真，就仿真中出现双腿支撑阶段跟踪误差较大的问题提出了PD与径向基神经网络混合控制的新策略。
再次通过仿真证实该方案能够减小跟踪误差。
最后，本文利用前述多项式拟合法对实验平台的物理机器人进行静态行走和上楼梯的步态规划。
针对上楼梯的步态规划的特殊性，本文提出了分段拟合来实现各关节的协同规划，并引入了躯干前倾角来辅助身体平衡。
由于时间所限，本文实现了双足机器人的稳定步行实验，上楼梯实验还尚缺稳健性，这将作为下一步的工作。
关键词：双足机器人，平衡控制，步态规划，ADAMS仿真，增强学习IABSTRACTDriving technology, artificial intelligence, high-performance computers and other latest technology has enable bipedal robots to roughly emulate the motor dexterity of humans, able to dance show, musical instruments, and talking. However, this ability still have big gap between putting into practical application. Mainly reflected in the lack of the ability of balance, and the coordination of walking. High demands on the working environment, poor adaptability in unstructured environments. In this paper, the self-developed bipedal humanoid robot is researched, and the balance control, impedance control and gait planning are mainly studied.This paper first introduces the hardware and software architecture of the biped robot, and establishes the ADAMS and Gazebo simulation to assist in the prediction and optimization of the performance of the control algorithm, so as to reduce the risk operation of the physical robot and avoiding the potential risks. Then the forward kinematics and inverse kinematics of the biped robot are analyzed and the kinematic library KDL is introduced to simplify the kinematic operation.Stable balance control is still a challenge for biped robots. In this paper, we present two schemes for impedance adjustment when dealing with the balance. One is the fixed impedance model, which is simple and effective, but there is a problem of vibration, a filter is combined in this paper to improve the balance control effect. The other is an adaptive impedance model based on integral reinforcement learning. This method can obtain the optimal solution online by using the policy iteration without knowing the dynamic information of the system. It is a further optimization of the LQR method. Then the scheme is simulated and experimented, and the advantages and disadvantages are analyzed.Gait planning is the most basic part of robot motion control. First, a simplified five-link planar robot model is established to facilitate the study. Then, the ZMP-based polynomial fitting method is used to realize the gait planning of the robot's horizontal walking. Then the dynamic model is analyzed and the PD controller is used to simulate the motion. A new strategy of PD and RBF neural network hybrid control is proposed to reduce the tracking error during DSP. Again, the simulation results show that the scheme can reduce the tracking error.IIFinally, this paper applies the polynomial fitting method to carry on the static walking and the stairway gait planning of the physical robot of the experimental platform. In view of the particularity of the gait planning of the stairs, this paper proposes a partition fitting to realize the cooperative planning of each joint and introduces the trunk leaning forward to assist the body balance. Due to time constraints, this paper has achieved a stable walking experiment of bipedal robots, and the stair experiment is still lacking in robustness, which will be the next step of the work.Keywords: biped robot, balance control, gait planning, ADAMS simulation, reinforcement learningIII目录第一章绪论 (1)1.1 研究工作的背景与意义 (1)1.2 国内外研究历史和发展态势 (2)1.2.1双足机器人的发展现状 (2)1.2.2双足机器人平衡控制概况 (6)1.2.3机器人阻抗控制概况 (7)1.2.4双足机器人步态规划及运动控制概况 (8)1.3 本文的主要工作 (9)1.4 本论文的结构安排 (10)第二章双足机器人控制系统架构与仿真平台设计 (11)2.1 双足机器人机体结构 (11)2.2 双足机器人控制系统框架设计 (13)2.2.1硬件系统设计 (13)2.2.2控制软件设计 (15)2.3 双足机器人仿真平台的设计 (16)2.3.1机器人系统常用仿真软件 (16)2.3.2ADAMS虚拟样机建模 (17)2.3.3G AZEBO模型建立 (18)2.4 本章小结 (19)第三章双足机器人运动学建模分析 (20)3.1 双足机器人位姿的描述 (20)3.2 正向运动学求解 (21)3.3 逆运动学求解 (22)3.4 五连杆平面机器人的运动仿真 (26)3.4.1开源运动学和动力学库KDL (26)3.4.2基于KDL的双足机器人运动学仿真 (26)3.5 本章小结 (27)第四章双足机器人站姿下的平衡控制 (28)4.1 双足机器人的平衡控制策略 (28)4.2 双足机器人的踝关节平衡策略 (30)IV4.2.1基于倒立摆的固定阻抗模型 (31)4.2.2基于增强学习的自适应阻抗模型 (33)4.3 仿真结果 (38)4.3.1固定阻抗与自适应阻抗仿真结果及对比 (38)4.3.2仿真算法的进一步优化 (41)4.4 实验结果 (43)4.4.1实验设计 (43)4.4.2实验结果与分析 (44)4.5 本章小结 (47)第五章五连杆双足机器人行走步态规划及控制 (48)5.1 步态规划依据和方法 (48)5.1.1步态规划的依据 (48)5.1.2离线步态规划的方法 (49)5.2 五连杆平面机器人模型的建立 (49)5.2.1五连杆模型简介 (50)5.2.2五连杆的运动学与动力学模型 (51)5.3 五连杆机器人的步态规划 (53)5.3.1摆动腿的轨迹规划 (53)5.3.2髋关节的轨迹规划 (55)5.3.3轨迹规划展示 (56)5.4 基于PD控制器的五连杆运动控制 (57)5.4.1PD控制器设计 (58)5.4.2仿真实验结果及分析 (59)5.5 基于RBFNN的五连杆运动控制 (61)5.5.1基于动力学模型的控制分析 (61)5.5.2RBF神经网络控制器设计 (62)5.5.3仿真实验结果及分析 (64)5.6 本章小结 (65)第六章双足机器人步态规划与实验 (66)6.1 双足机器人步态规划的约束 (66)6.2 双足机器人静态行走的步态规划 (66)6.2.1步行准备阶段运动规划 (67)6.2.2周期步行阶段运动规划 (69)V6.2.3步态仿真验证 (71)6.2.4双足机器人步行实验 (73)6.3 双足机器人上楼梯的步态规划 (73)6.3.1起步阶段运动规划 (73)6.3.2上楼梯双腿支撑阶段运动规划 (74)6.3.3跨两层台阶运动规划 (75)6.3.4双足机器人上楼梯仿真及实验 (76)6.4 本章小结 (78)第七章全文总结与展望 (79)7.1 全文总结 (79)7.2 后续工作展望 (80)致谢 (81)参考文献 (82)攻读硕士学位期间取得的成果 (87)VI第一章绪论第一章绪论1.1 研究工作的背景与意义上世纪60年代初，工业机器人和自主移动机器人成为现实，为实现大规模自动化生产，降低制造成本提升产品质量做出了巨大贡献。
会走路的机器人英语作文
会走路的机器人英语作文Title: The Advancements of Walking Robots。
In the realm of technological innovation, the development of walking robots stands as a testament to human ingenuity and perseverance. Walking robots, also known as bipedal robots, have made remarkable strides in recent years, both figuratively and literally. This essay delves into the intricacies of these marvels of engineering, exploring their significance, challenges, and potential applications.Firstly, it is essential to understand the significance of walking robots. These machines mimic the bipedal locomotion of humans, allowing them to navigate diverse terrains with agility and adaptability. Unlike wheeled robots, walking robots possess the ability to traverse uneven surfaces, climb stairs, and negotiate obstacles with relative ease. This capability opens up a myriad of possibilities across various fields, from search and rescueoperations in disaster-stricken areas to exploration missions in extraterrestrial environments.The development of walking robots has not been without its challenges. One of the primary obstacles faced by engineers is achieving stability and balance. The intricate interplay of sensors, actuators, and control algorithms is required to maintain the robot's equilibrium while walking. Moreover, ensuring energy efficiency and robustness poses additional hurdles in the design and implementation phases. Despite these challenges, advancements in materials science, artificial intelligence, and biomechanics have propelledthe evolution of walking robots to new heights.In recent years, walking robots have found applicationin diverse domains, showcasing their versatility and potential impact. In the field of healthcare, exoskeletons equipped with walking capabilities aid individuals with mobility impairments, enabling them to regain independence and improve their quality of life. Similarly, in industrial settings, walking robots are utilized for tasks thatrequire navigation in complex environments, such asinspection, maintenance, and logistics. Furthermore, in the realm of entertainment and education, walking robots serve as engaging platforms for STEM (Science, Technology, Engineering, and Mathematics) learning, captivating audiences with their humanoid movements and interactive features.Looking ahead, the future of walking robots appears promising, with ongoing research and development paving the way for groundbreaking advancements. Enhanced sensor technologies, such as LiDAR (Light Detection and Ranging) and computer vision, enable robots to perceive their surroundings with unprecedented accuracy, enhancing their autonomy and decision-making capabilities. Additionally, advancements in soft robotics and bio-inspired design principles offer novel approaches to overcoming the challenges associated with locomotion and manipulation in complex environments.In conclusion, walking robots represent a remarkable fusion of engineering prowess and scientific innovation. Their ability to navigate and interact with the world in amanner akin to humans holds immense potential for revolutionizing various industries and societal domains. As researchers and engineers continue to push the boundaries of what is possible, walking robots are poised to play an increasingly prominent role in shaping the future of technology and human-machine interaction.。
The_robot_has_magical_legs_of_a_bird_会“鹰爪功”的机器人
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2. Whats the function of the accelerometer on the SNAGs right foot?
A. To balance the SNAG in flight.
B. To change the SNAGs flying speed.
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Scientists have already conducted several tests with the robot in rural areas and on
different surfaces. In all the situations, the SNAG was able to catch objects thrown by hand,
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Βιβλιοθήκη 经过多年的研究，斯坦福大学的一个工程师团队成功制造了一个带有小型
双足行走机器人平衡控制译文
外文资料：Robots1. IntroductionNowadays, the applications of machines and robots to assist human in performing their tasks has become increasingly extensive. In industrial applications, the use of robotics system has reached the level which surpasses human ability in terms of speed and accuracy. On the other hand, in the field of domestic robots or service robots, the developments are still far from perfection. The main factor that distinguishes industrial robots from service robots is their working environment. For a service robot to perfectly perform its tasks, it needs to be able to adapt and cope with the normal human living environment. From the practical point of view, bipedal robot is the most suitable robot structure due to its similarity of physical configuration with human especially in terms of locomotion method. However, the realization of bipedal robot is more challenging compared to other types of mobile robot due the unstable nature of bipedal walking. Therefore, many studies have been carried out especially concerning the stability sensing and control strategies of bipedal robot. The common approach in defining the stability of bipedal robot is by using the “Zero Moment Point” (ZMP) criterion[1]. The simplest implementation of ZMP is to generate the joint trajectories based on the pre-planned walking gait while maintaining the ZMP at the given references, but this approach has a limitation in maintaining the balance if there is any unknown external disturbance [2-5]. Many studies specifically focus on the techniques to monitor the real-time ZMP position from the physical system and used it as the feedback component [6-9]. Takanishi and Kato [7] proposed a method to monitor the ZMP position by measuring the force and moment acting on the robot’s shank by using universal forcemoment sensor. Another method utilizes an array of force sensitive resistor placed on the sole of the robot’s foot to obtain the ground reaction force at different locations of the foot. The reaction forces measured from the sensor array is then used to compute the position of the center of pressure which reflects the position of the ZMP [9]. The inverted pendulum technique is another alternative for analyzing the robot stability [10]. This method monitor the instability by constantly reading the body acceleration and tilt angle by means of accelerometer and gyroscope. However, the readings from both sensors are subject to noise and drift during the operation and the effort to apply filters in correcting the measurements often requires considerable amount of computing power [11]. This paper proposed a novel method for sensing and stability control of bipedal robot. The use of specially designed flexible ankle joint allows fast detection and prediction of robot sideway instability. Placing an additional one degree-offreedom rotary joint with built-in angle detection sensor at the robot ankle allows the robot’s body to tilt freely in any sideway direction and detect the tendency of imbalance that may potentially occur. Based on this essential sensor’s information, the controller will quickly adjust position of the counterbalance mass located at the robot waist in order to restore the sideway balance of the robot. The advantage of using counterbalance mass and rotary joint at the ankle is to allow the walking subsystem and sideway balancing subsystem of the robot to be decoupled from each other and work inindependently controlled modes. It is different from the traditional approach when the robot’s posture is corrected to satisfy both conditions at once, smooth forward walking and continuous sideway (sagittal) stability. Details of the proposed method are presented as follows. In section 2 the locomotion mechanism, ankle structure, sensing technique and balancing strategy are introduced. Section 3 discusses the mathematical model of the system. In section 4 experiment method is discussed and the viability of the proposed system is proven by the experimental result. Finally, the conclusions are described in section 5.2. Mechanical Structure of Biped Robot2.1. Robot locomotion mechanismThe biped robot is designed to realize two dimensional walking with minimum number of actuations. The locomotion system of the robot consists of four actuators, two for the hip joints and two for the knee joints. The ankle joint is not actuated by any actuators but instead it utilizes a series of parallelogram mechanism to passively control the ankle joint in order to maintain the position of the foot. The usage of parallelogram mechanism provides benefits by reducing the number of actuators needed which results in the simplification of the mechanism design and reduction of the overall robot’s weight. Fig 1(a) shows the stick diagram of the leg in different configuration. The orientation of link a is always parallel to the hip due to the constraint applied by link 1 and link 2. The orientation of link b which represents the foot is always parallel to link a due to the constraint applied by link 3 and link 4. Therefore, the foot is always kept parallel by the parallelogram mechanism regardless of any configuration of the leg. Fig 1(b), (c) show the physical implementation of the parallel leg in different postures. The prototype of the biped robot is mainly constructed using hollow sections of extruded aluminium due to its lightness and strength. The overall height of the biped robot is 0.9 m with the total weight of 7 kg. The length for both thigh and shank are 0.3 m and the spacing between two legs is 0.15 m. For the actuation, each joint is equipped with Robotis Dynamixel RX-64 Smart Actuator, which combines gear transmission, controller, driver and network function in a single package. The output of the hip motor is connected directly to the hip joint and the output of the knee motor is transmitted to the knee joint via a four bar linkage. The purpose of placing the actuators on the hip is to reduce the weight of the leg which will minimize the dynamics forces created by the leg movement. The other advantage of this structural arrangement is that the angular count at each joint is always referenced to the fixed vertical axis of the stationary world coordinate frame regardless of the leg postures.(a) (b) (c)Fig. 1. (a) Stick diagram of parallelogram leg; (b), (c) Robot standing with different leg configurations2.2. Flexible ankle joint to utilize stability measurementIn order to achieve a stable walk on a biped robot, the ability to accurately detect any possible instability is quite crucial. This paper introduces a new approach of sensing the instability by introducing an additional degree of freedom in sideway direction next to the ankle joint. Fig 2(a) shows the structure of that degree of freedom where the free rotary joint on the frontal plane is placed at the ankle between the foot and ankle joint. It will let the unconstrained robot body standing on one leg to tilt (angle ) freely in sideway (sagittal) direction for any possible disturbance in that direction. By installing a rotary sensor on the free joint the controller will be able to detect instantly any instability and immediately react to restore the balance.(a)(b)Fig. 2 (a) Schematic picture of the flexible ankle structure; (b) Physical implementation offlexible ankle2.3. Split balancing mass for faster system responseThe walking cycle of bipedal robot consists of single support phase and double support phase which are executed sequentially and repeatedly. In single support phase, the robot is standing on one leg while another leg is transferred forward. During this phase, the robot body will be tilted sideways due to the unbalanced torque created by the weight of the lifted leg and the dynamic forces generated due to the leg movement. In order to maintain stability of the robot, a set of counterbalance masses are located at a specific position to compensate the unbalanced mass of the lifted leg and other possible disturbance. Fig 3 shows the simplified 3-masses model of bipedal robot: mL represents the lumped mass of the hanging leg, mB1 represents the major balancing mass and mB2 represents the minor balancing mass.Major balancing mass is mainly used to compensate the weight of the lifted leg. This mass is positioned at a precalculated location in order to balance the torque created by the mass of the lifted leg mL. The minor balancing mass mB2 is continuously repositioned based on the information gathered from the sensor located at the additional ankle joint. This mass works as a counterbalance to maintain the robot to be always vertical regardless of any external sideway disturbance.The use of two separate counterbalance masses provides several advantages such as:• Faster response time can be achieved by only moving small inerti a counterbalancing mass instead of moving a larger one,• Energy efficiency can be improved by reducing load of the motor that drives a smaller inertia counterbalancing mass.Fig. 3 Simplified model of the bipedal robot3. System modeling and controlFrom the diagram on Fig 3, the dynamic equation using Newton’s second law about point O gives:ΣT O=IθT dis1+m L g(d cosθ+r sinθ)−m B2g(αcosθ+r sinθ)−m B1g(d s cosθ+sinθ)+m B2α̈r−cθ−kθ=θ(m L(r2+d2)+m B2(r2+a2)+m B1(d s2+r2))（1）Since the major balancing mass m B1is mainly use to compensate for the torque created by the weight of the hanging leg m L, the major balancing mass can be positioned at the pre-calculated location on the opposite side of the hanging leg. The required position of the major balancing mass d s can be calculated based on the equilibrium of torque at point O as follows:ΣT O=0m L d−m B1d s=0（2）m L d=m B1d sd s=m L m B1dSubstituting d s from Eq.(2) into L.H.S of Eq.(1) gives:T dis1+m L g(d cosθ+r sinθ)−m B2g(αcosθ+r sinθ)−m B1g(m Lm B1d cosθ+r sinθ)m B2α̈r−cθ−kθ=θ(m L(r2+d2)+m B2(r2+a2)+m B1(d s2+r2)) T dis1+g cosθ(m L d−m L d−m B2a)+gr sinθ+m B2α̈r−m B2a2θ=θ(m L(r2+d2)+m B2r2+m B1(d s2+r2))+cθ+kθ（3）Rearranging the differential equation from Eq.(3) gives:T dis1−m B2g∙a cosθ+(m L+m B2+m B1)g∙r sinθ+m B2α̈r−m B2a2θ=θ(m L(r2+ d2)+m B2r2+m B1(d s2+r2))+cθ+kθ (4) The differential equation in Eq.(4) has two distinct parts. The right hand side only presents parts of the ordinary differential equation with constant time invariant coefficients and the left hand side presents all remaining parts of the equation. Therefore, the left hand side includes:● Non-linear functions of the main dependent argument θ, namely sinθ，cosθ●Additional but independent from the main argument θparameter α̈●The parameters that comprises both a and θarguments, namely m B2a2θFig 4 shows the simplified control block diagram of the balancing system. The PID controller constantly monitors the tilt angle (θ) from the sensor of the additional ankle joint and compares the reading with that of the desired angle. If any tilt is detected, the controller will actuate the balancing mass to the opposite direction in order to regain the balance.Fig. 4 Simplified block diagram of the stability control system4. Experimental resultsThe experiment is carried out to verify the effectiveness of the proposed mechanical structure and control strategies in maintaining the robot’s balance in single support phase. During the experiment, the robot is standing on one leg with another leg lifted up and floating. The external disturbance is applied by making a push on the edge of the robot’s hip which will cause the robot to tilt sideways (Fig 5(a)). The intensity of the pushing force is measured by a force sensor mounted on the hip (Fig 5(b)).Fig 6 shows the measurement of the tilt angle θfrom the additional ankle joint, balancing mass position a and the disturbance force when the external disturbance is applied. It is apparent from the figure that once the disturbance is applied the sensor detects change in tilt angle and the controller immediately reacts by moving the mass to the opposite direction of the tilt in order to regain the balance. Fig 7 shows the measurement when an excessive disturbance force is applied approximately at the 37th seconds, the tilt angle changes abruptly and the balancing mass is not able to recover the balance. The saturated angle measurement at the end of the plots indicates that the robot is falling. It is due to the fact that the value of minor mass mB2 and the allowable range ofits motion a are limited in this design. The overall resistance to the externally generated force can be increased by either increasing mB2 or a.(a) (b)Fig. 5 (a) Hip plane of the robot; (b) Force sensor attachment to measure applieddisturbance forceFig. 6. System response to disturbance (balance maintained)Fig. 7. System response to disturbance (excessive force applied)5. ConclusionThis paper presents stability control method for bipedal walking robot which includes the leg design with additional (redundant) degree of freedom at the ankle joint and split balancing mass. The proposed method enables the sensing, control and balancing of bipedal robot to be implemented in a simple yet cost effective manner. The effectiveness of the design method is proven by the experimental results. The implementation of this method also allows the walk controlling algorithms to be decoupled from the stability control algorithms to increase the system response time.References[1] Vukobratovic M., Juricic D., 1969. Contribution to the synthesis of biped gait, IEEE Transaction on Biomedical Engineering 16, p.1-6.[2] Erbatur K., Kurt O., 2006. “Humanoid Walking Robot Control with Natural ZMP References,” IEEE Industrial Electronics - Proceedings of the 32nd Annual Conference, p. 4100-4106.[3] Lim H.-ok, Setiawan S. A., Takanishi A., 2001. “Balance and impedance control for biped humanoid robot locomotion,” Intelligent Robots and Systems - Proceedings of the 2001 IEEE/RSJ International Conference, p. 494-499.[4] Liu L., Zhao M., Lin D., Wang J., Chen K., 2003. “Gait designing of biped robot according to human walking based on six-axis force sensors,”Computational Intelligence in Robotics and Automation - Proceedings of the 2003 IEEE International Symposium, p.360–365.[5] Sardain P., Bessonnet G., 2004. Zero moment point-measurements from a human walker wearing robot feet as shoes, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 34, p. 638–648.[6] Loffler K., Gienger M., Pfeiffer F., Ulbrich H., 2004. Sensors and control concept of a biped robot, IEEE Transactions on Industrial Electronics 51, p. 972-980.[7] Takanishi A., Kato I., 1991. “A biped walking robot having a ZMP measurement system using universal force-moment sensors,” Intelligent Robots and Systems - Proceedings of the 1991 IEEE/RSJ International Workshop, p. 1568-1573.[8] Kagami S., Takahashi Y., Nishiwaki K., Mochimaru M., Mizoguchi H., “High-speed matrix pressure sensor for humanoid robot by using thin force sensing resistance rubber sheet,” Sensors - Proceedings of the 2004 IEEE Conference, p. 1534–1537.[9] Kalamdani A., Messom C., Siegel M., 2007. Robots with sensitive feet, IEEE Instrumentation and Measurement Magazine 10, p. 46–53.[10] Caux S., Mateo E., Zapata R.,1998. “Balance of biped robots: special double inverted pendulum” Systems, Man and Cybernetics - IEEE International Conference, p. 3961-3969.[11] Braunl T., 2006. Embedded Robotics, Springer, Germany.译文资料：机器人1、介绍现在，机械和机器人的应用帮助人类执行他们的任务已经变得越来越广泛。
Kinematic Calibration Robot Alignment
Kinematic Calibration Robot Alignment Kinematic calibration and robot alignment are crucial processes in the field of robotics. These processes ensure that the robot's movements are accurate, precise, and aligned with its intended trajectory. However, achieving the perfect calibration and alignment can be a challenging task, as it requires a deep understanding of the robot's kinematics, as well as advanced technical skills. In this response, we will explore the importance of kinematic calibration and robot alignment, the challenges associated with these processes, and the various perspectives that need to be considered to achieve optimal results.First and foremost, it is essential to understand the significance of kinematic calibration and robot alignment. These processes are vital for ensuring the overall performance and accuracy of the robot. Without proper calibration and alignment, the robot's movements may be inaccurate, leading to errors in its tasks and potentially causing safety hazards. For instance, in industrial settings, misaligned robots can result in defective products, production delays, and even accidents. Therefore, investing time and effort into kinematic calibration and robot alignment is crucial for maximizing the efficiency and safety of robotic systems.One of the primary challenges in kinematic calibration and robot alignment is the complexity of the robot's kinematic structure. Robots come in various configurations, such as articulated arms, delta robots, and SCARA robots, each with its unique kinematic properties. Understanding and modeling these kinematic structures accurately is essential for successful calibration and alignment. Additionally, factors such as joint backlash, flexibility, and thermal expansion further complicate the calibration process. Thus, robotic engineers and technicians must possess a deep understanding of kinematics and advanced mathematical skills to effectively calibrate and align the robot.Another challenge in kinematic calibration and robot alignment is the need for advanced technical equipment and tools. High-precision sensors, such as laser trackers and coordinate measuring machines, are often required to measure the robot's actual positions and orientations accurately. Furthermore, specialized software for kinematic modeling and calibration is essential for performing the necessary calculations and adjustments. The costof acquiring and maintaining such equipment and tools can be a barrier for smaller organizations or institutions, limiting their ability to achieve optimal robot calibration and alignment.Moreover, the dynamic nature of robotic systems poses a significant challenge in kinematic calibration and robot alignment. Robots are often required to perform tasks in varying environments and conditions, leading to changes in their kinematic behavior over time. Factors such as wear and tear, temperature variations, and payload changes can affect the robot's kinematic parameters, necessitating periodic recalibration and realignment. Therefore, implementing robust and adaptive calibration techniques that can account for these dynamic changes is essential for maintaining the robot's accuracy and performance.From a human perspective, the challenges of kinematic calibration and robot alignment can be emotionally taxing for robotic engineers and technicians. The meticulous and repetitive nature of calibration tasks can be mentally exhausting, requiring a high level of concentration and attention to detail. Frustration may arise when facing technical issues or discrepancies in the calibration results, especially when the root cause is not immediately apparent. Additionally, the pressure to ensure the robot's safety and performance adds to the emotional burden, as any miscalibration or misalignment can have serious consequences.In conclusion, kinematic calibration and robot alignment are critical processes that require a comprehensive understanding of the robot's kinematics, advanced technical skills, and the use of high-precision equipment. The challenges associated with these processes, including the complexity of kinematic structures, the need for advanced technical tools, and the dynamic nature of robotic systems, necessitate a multi-faceted approach to achieve optimal results. From a human perspective, the emotional and mental toll of performing these tasks should not be overlooked. Despite the challenges, the rewards of achieving precise and accurate robot calibration and alignment are significant, contributing to improved efficiency, safety, and overall performance of robotic systems.。
关于智能机器人的一些资料
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欠驱动机器人 外文文献
One of the most sophisticated forms of legged motion is that of biped locomotion. From a dynamic systems point of view, human locomotion stands out among other forms of biped locomotion chiefly due to the fact that during a significant part of the human walking cycle the moving body is not in the static equilibrium. At the INRIA lab of Grenoble, France, we have started working on the development of an anthropomorphic biped walker. The envisioned prototype will have elementary adaptation capability on an unforeseen uneven terrain. The purpose of the project is not limited to the realization of a complex machine, the construction and control of which nevertheless pose formidable engineering challenge. We also intend to initiate a synergy between robotics and human gait study. Human locomotion, despite being well studied and enjoying a rich database, is not well understood and a robotic simulcrum potentially can be very useful. In order to gain a better understanding of the inherently non-linear dynamics of a full-fledged walking machine we have found it instructive to first explore the behavior of a particularly simple walker model.
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