Fuzzy Logic Rules for Mapping Sensor Data to Robot Control
Fuzzy Logic and Systems
Fuzzy Logic and SystemsFuzzy logic and systems have become increasingly important in various fields, including engineering, artificial intelligence, and decision-making processes.This approach allows for the representation of imprecise and uncertain information, which is often encountered in real-world problems. Fuzzy logic provides a way to deal with ambiguity and vagueness, making it a valuable tool for modeling complex systems. However, like any other approach, fuzzy logic and systems come with their own set of challenges and limitations. One of the key advantages of fuzzy logicis its ability to capture and represent the uncertainty that is inherent in many real-world problems. Traditional logic relies on crisp, binary values (true or false), which may not accurately reflect the complexity of human reasoning and decision-making. Fuzzy logic, on the other hand, allows for the gradual transition between true and false, enabling a more nuanced and flexible approach to problem-solving. This makes it particularly useful in areas such as control systems, where precise mathematical models may not always be available or practical. Furthermore, fuzzy logic provides a way to incorporate human expertise and intuition into the decision-making process. By using linguistic variables and fuzzy sets, it is possible to encode and manipulate qualitative information in a systematic manner. This can be especially valuable in domains such as medical diagnosis, where the input data may be subjective and imprecise. By allowing for the representation of vague concepts such as "high," "low," or "medium," fuzzy logic enables the integration of human knowledge into automated systems. Despite its advantages, fuzzy logic and systems also face several challenges. One of the main criticismsis the lack of a solid theoretical foundation, which has led some researchers to question the validity and rigor of fuzzy logic as a formal system. While fuzzylogic has been successfully applied in various practical applications, there isstill ongoing debate about its philosophical and mathematical underpinnings. This has implications for the acceptance and adoption of fuzzy logic in certain academic and industrial circles. Another challenge is the computationalcomplexity associated with fuzzy logic systems. In many cases, the design and implementation of fuzzy logic controllers or decision-making systems can be computationally intensive, requiring significant resources in terms of memory andprocessing power. This can limit the practicality of fuzzy logic in real-time applications or embedded systems, where efficiency and speed are crucial. As a result, there is ongoing research into developing more efficient algorithms and hardware implementations for fuzzy logic. Furthermore, the interpretability of fuzzy logic systems can be a concern in certain contexts. While fuzzy logic allows for the representation of imprecise and qualitative information, it can also lead to complex and opaque models that are difficult to understand and interpret. Thisis particularly important in safety-critical applications, where the reasoning behind a decision must be transparent and comprehensible to human operators. Finding a balance between the expressive power of fuzzy logic and the need for interpretability is an ongoing challenge for researchers and practitioners. In conclusion, fuzzy logic and systems offer a valuable approach to dealing with uncertainty and imprecision in real-world problems. By allowing for the representation of gradual transitions and qualitative information, fuzzy logic enables a more flexible and human-centric approach to modeling and decision-making. However, the lack of a solid theoretical foundation, computational complexity, and concerns about interpretability are important challenges that need to be addressed. Despite these limitations, fuzzy logic continues to be a powerful tool in various domains, and ongoing research is focused on overcoming these challenges to further enhance its practicality and effectiveness.。
Fuzzy Logic and Systems
Fuzzy Logic and SystemsFuzzy logic is a mathematical concept that deals with reasoning that is approximate rather than fixed and exact. It is an extension of traditional Boolean logic and has found applications in various fields such as control systems,artificial intelligence, and decision-making processes. One of the key characteristics of fuzzy logic is its ability to handle vague and uncertain information, which makes it particularly useful in situations where precise measurements and clear boundaries are difficult to define. From a technical perspective, fuzzy logic operates on the principle of assigning degrees of truthto statements or propositions, rather than the conventional true or false values used in classical logic. This allows for a more nuanced and flexible approach to reasoning, where the boundaries between categories are not sharply defined. For example, in traditional logic, a statement such as "the temperature is hot" would be either true or false based on a specific threshold value. In contrast, fuzzy logic allows for a gradual transition between "hot" and "not hot," enabling a more realistic representation of the way humans perceive and interpret temperature. In the context of control systems, fuzzy logic has been widely used to model and control complex and non-linear systems. Unlike traditional control systems thatrely on precise mathematical models, fuzzy logic controllers can accommodate imprecise and incomplete information, making them well-suited for real-world applications where the environment is uncertain and dynamic. For instance, in automotive applications, fuzzy logic has been employed in anti-lock braking systems, engine control units, and automatic transmission systems to improve performance and safety under varying driving conditions. In the field ofartificial intelligence, fuzzy logic has been integrated into expert systems to emulate human decision-making processes. By incorporating fuzzy sets and fuzzy rules, these systems can handle uncertain and ambiguous information, making them more adaptable to real-world problems. This has led to advancements in areas such as medical diagnosis, financial forecasting, and natural language processing, where the ability to reason with uncertain and incomplete data is crucial. From a philosophical perspective, fuzzy logic challenges the traditional binary view of the world and acknowledges the inherent fuzziness and ambiguity in human cognition.It reflects the way humans think and communicate, where concepts are often vague and context-dependent. By embracing this inherent uncertainty, fuzzy logic offers a more human-centric approach to reasoning and problem-solving, bridging the gap between formal logic and human intuition. In conclusion, fuzzy logic represents a paradigm shift in the way we approach reasoning and decision-making. By embracing uncertainty and vagueness, it offers a more realistic and human-like approach to handling complex systems and uncertain information. Its applications in control systems, artificial intelligence, and decision support systems have demonstrated its effectiveness in dealing with real-world problems where precise measurements and clear boundaries are difficult to define. As we continue to grapple with the complexities of the world, fuzzy logic provides a valuable tool for navigating the inherent uncertainty and ambiguity that permeate our lives.。
关于fuzzy logic的简述(英文)
My Understanding about Fuzzy LogicWhen it comes to fuzzy logic, there are different kinds of definitions and understanding about this concept. However, in essence, I think，these definitions and understanding are similar. Because the fuzzy is based on the uncertainty of abstract thinking and concept, as well as the imprecise nature of things. As my understanding of fuzzy logic is superficial, so I have to use a relatively perfect definition to express my thought.In narrow sense: Fuzzy logic is a logical system, which is an extension of multi-valued logic.In a wider sense: Fuzzy logic (FL) is almost synonymous with the theory of fuzzy sets, a theory which relates to classes of objects with unsharp boundaries in which membership is a matter of degree.----- by Mahesh Todkar Fuzzy logic is not the unclear logic. Actually, it is founded on the fuzzy set, which was put forward by Pro. Zadeh in 1965. Then Zadeh developed fuzzy logic as a way of processing data. Instead of requiring a data element to be either a member or non-member of a set, he introduced the idea of partial set membership.Fuzzy logic is a method between the symbolic reasoning of traditional artificial intelligence and numerical computing theory of the conventional control. It does not rely on the model, it uses linguistic variables to represent the abstract variables and uses rules for fuzzy reasoning and processing. Moreover, it is also featured in its recognition of the intermediate transitional between true value ( True ) and false value ( False ).Hence, the most essential concept for fuzzy logic is the membership function, which defines how each point in the input space is mapped to a membership value between 0 and 1. The membership function is denoted by μ and also called as degree of membership or membership grade or degree of truth of proposal. There are many types of membership functions, like Piece-wise linear functions, Gaussian distribution function, Sigmoid curve and Singleton Membership Function etc.In addition, we should pay the major attention to the fuzzy inference, which is the process of formulating the mapping from a given input to an output using fuzzy logic.It involves Membership Functions (MF), Logical Operators and If-Then Rules. The MF is mentioned above, so an introduction about Logical Operators and If-Then Rules will be presented as followed.Fuzzy Logic Operators are used to write logic combinations between fuzzy notions.As for Zadeh operators, its definitions are :1)Intersection: μ(A AND B) = MIN(μ(A), μ(B))2)Union: μ(A OR B) = MAX(μ(A), μ(B))3)Negation: μ(NOT A) = 1 -μ(A)Fuzzy If-Then Rules are the statements used to formulate the conditional statements that comprise fuzzy logic. For example:if x is A then y is Bwhere,A &B – Linguistic values x – Element of Fuzzy set X y – Element of Fuzzy set YIn above example,Antecedent (or Premise)– if part of rule (i.e. x is A)Consequent (or Conclusion) – then part of rule (i.e. y is B)Here, interpreting if-then rule is a three–part process:1) Fuzzify input:Resolve all fuzzy statements in the antecedent to a degree of membership between 0 and 1.2) Apply fuzzy logic operator to multiple part antecedents:If there are multiple parts to the antecedent, apply fuzzy logic operators and resolve the antecedent to a single number between 0 and 1.3) Apply implication method:The output fuzzy sets for each rule are aggregated into a single output fuzzy set. Then the resulting output fuzzy set is defuzzified, or resolved to a single number.In general, from my perspective, compared with conventional binary logic, fuzzy logic is a breakthrough for the classification of things. To some degree, fuzzy logic makes the uncertainty and imprecision clearer. Though the membership functions vary from person to person, which indicates that fuzzy logic is subjective, its advantages are explicit. Just asMr. Hu Baoqing(from Wuhan University) notes that Benefits of Fuzzy Mathematics are:①The ability to model highly complex business problems②Improved cognitive modeling of expert system③The ability to model systems involving multiple experts④Reduced model complexity⑤Improved handling of uncertainty and possibilities……。
Fuzzy Logic and Systems
Fuzzy Logic and SystemsFuzzy logic and systems have become increasingly prevalent in various fields, offering a flexible and intuitive approach to handling uncertainty and imprecision. This versatile framework has found applications in diverse domains such as control systems, decision-making processes, artificial intelligence, and more. The essence of fuzzy logic lies in its ability to capture and process vague, ambiguous, or subjective information, which often eludes traditional binary logic systems. By allowing for degrees of truth and membership, fuzzy logic enables a more human-like reasoning and decision-making process, making it a valuable tool in addressing complex real-world problems. From a technical perspective, fuzzy logic operates on the principle of assigning degrees of truth to linguistic variables, thereby accommodating the imprecision inherent in natural language. This departure from the strict binary logic of true or false opens up new possibilities for modeling and control, particularly in systems where precise numerical values maybe elusive or impractical. By employing membership functions and fuzzy rules,fuzzy logic systems can effectively manage and process uncertain or incomplete information, making them well-suited for applications such as pattern recognition, predictive modeling, and optimization. In the realm of control systems, fuzzylogic offers a compelling alternative to traditional methods, particularly in scenarios where precise mathematical models are difficult to formulate. By leveraging linguistic variables and fuzzy rules, fuzzy control systems can exhibit robustness and adaptability in controlling complex, nonlinear processes. This has led to their widespread adoption in various industrial applications, including automotive systems, consumer electronics, and industrial automation. The abilityof fuzzy control systems to handle vague or fluctuating inputs makes them well-suited for real-time control tasks, where conventional control approaches may struggle to cope with uncertainties and variations. In the domain of decision support and expert systems, fuzzy logic provides a natural framework for capturing and processing human expertise and intuition. By representing knowledge in theform of fuzzy rules and membership functions, fuzzy expert systems can effectively model and emulate human decision-making processes. This makes them valuable tools for tasks such as diagnostic reasoning, risk assessment, and scenario analysis,where human judgment and domain knowledge play a crucial role. The ability of fuzzy expert systems to handle imprecise or incomplete information aligns well with the inherent uncertainties present in many real-world decision-making scenarios. Beyond technical applications, the impact of fuzzy logic extends to cognitive and behavioral domains, offering insights into human reasoning and decision-making processes. The parallels between fuzzy logic and human cognition have sparked interest in cognitive science and psychology, where researchers seek to understand and model the ways in which humans deal with uncertainty and imprecision. By exploring the connections between fuzzy logic and human reasoning, researchers hope to gain a deeper understanding of human cognition and potentially develop more effective computational models of human decision-making. In conclusion, fuzzy logic and systems offer a rich and versatile framework for addressing uncertainty and imprecision in diverse domains. From technical applications in control systems and decision support to insights into human cognition, fuzzy logic continues to inspire and inform interdisciplinary research and development. Its ability to capture and process vague, ambiguous, or subjective information makes it a valuable tool for modeling complex real-world problems and emulating human-like reasoning processes. As the boundaries of artificial intelligence and human cognition continue to converge, the principles of fuzzy logic are likely to remain a fertile ground for exploration and innovation.。
Fuzzy Logic and Neural Networks
Fuzzy Logic and Neural Networks Fuzzy logic and neural networks are two powerful tools in the field ofartificial intelligence that have revolutionized the way we approach complex problems. Fuzzy logic is a form of reasoning that deals with uncertainty and imprecision, allowing for more flexible decision-making in situations where traditional binary logic may fall short. On the other hand, neural networks are a type of machine learning algorithm inspired by the way the human brain works, capable of learning complex patterns and relationships in data. One of the key advantages of fuzzy logic is its ability to handle vague and ambiguous information, which is often present in real-world scenarios. For example, in a system that controls the temperature of a room, fuzzy logic can be used to adjust the temperature based on inputs such as "too hot" or "a little chilly," rather than precise numerical values. This flexibility makes fuzzy logic particularly usefulin applications where human judgment and intuition play a significant role.Neural networks, on the other hand, excel at tasks that involve patternrecognition and classification. By training a neural network on a large dataset,it can learn to recognize complex patterns in the data and make predictions or decisions based on those patterns. This makes neural networks well-suited fortasks such as image recognition, speech recognition, and natural language processing. When it comes to combining fuzzy logic and neural networks, researchers have found that the two approaches complement each other well. Fuzzy logic can be used to handle the uncertainty and imprecision in the inputs to a neural network, providing a more robust and flexible system overall. For example,in a medical diagnosis system, fuzzy logic can be used to interpret vague symptoms from a patient, which can then be fed into a neural network to make a moreaccurate diagnosis. Overall, the combination of fuzzy logic and neural networks has the potential to create more intelligent and adaptive systems that can handlea wide range of complex tasks. By leveraging the strengths of both approaches, researchers can develop AI systems that are better able to cope with the uncertainties and complexities of the real world. As we continue to push the boundaries of artificial intelligence, the synergy between fuzzy logic and neuralnetworks will undoubtedly play a crucial role in shaping the future of intelligent systems.。
模糊和精准二元作文
模糊和精准二元作文English:Fuzzy logic is a type of binary logic that allows for degrees of truth rather than simply true or false values. It is especially useful for handling applications that involve uncertainty and incomplete information. Fuzzy logic deals with vague or imprecise information by assigning a degree of membership to each element in a set. For example, in the statement "the weather is hot," traditional logic would only allow for it to be either true or false. However, in fuzzy logic, the statement can be assigned a degree of truth, such as , indicating a high likelihood of the weather being hot. This flexibility and ability to handle ambiguity make fuzzy logic suitable for a range of applications, including control systems, decision-making models, and pattern recognition tasks. On the other hand, precise logic is based on crisp, well-defined rules and values. It aims to establish a clear distinction between true and false, without any intermediary states. In precise logic, a statement is either true or false, with no room for uncertainty or partial truth. Precise logic is often used in applications that require exact information and precise decision-making, such as mathematics, scientific experiments, and computer programming. While fuzzy logic allows for the incorporation of uncertainty and imprecision, precise logic offers a more deterministic approach, focusing on precise and exact values. Both fuzzy and precise logic have their strengths and weaknesses, and the choice between them depends on the specific requirements of the application at hand.中文翻译：模糊逻辑是一种二元逻辑，它允许存在真实度或者真假度的程度，而不仅仅是简单的真或假。
Fuzzy Logic and Systems
Fuzzy Logic and SystemsFuzzy logic is a fascinating concept that has gained popularity in various fields, including artificial intelligence, control systems, and decision-making processes. It is a form of logic that allows for uncertainty and imprecision,unlike traditional Boolean logic, which deals with precise values of true or false. Fuzzy logic acknowledges the shades of gray that exist in the real world, where things are not always black and white. One of the key advantages of fuzzy logicis its ability to model human reasoning and decision-making processes more accurately. Humans often make decisions based on vague or ambiguous information, and fuzzy logic provides a framework for capturing this uncertainty. By usingfuzzy sets, which allow for membership values between 0 and 1, fuzzy logic can represent and reason with imprecise or incomplete information. In the field of control systems, fuzzy logic has been used to develop intelligent systems that can adapt to changing environments and make decisions in real-time. For example, fuzzy logic controllers have been implemented in various applications, such as automotive systems, consumer electronics, and industrial automation. These controllers can handle complex, nonlinear systems that are difficult to modelusing traditional control techniques. Moreover, fuzzy logic has also been applied in decision-making processes, where there is a need to evaluate multiple criteria or factors that are inherently subjective or qualitative. By using fuzzy logic, decision-makers can incorporate uncertainty and vagueness into their models, leading to more robust and flexible decision-making processes. This isparticularly useful in situations where there is a lack of precise data or where the decision-making criteria are subjective. Despite its many advantages, fuzzy logic also has its limitations and challenges. One of the main criticisms of fuzzy logic is its lack of formalism and mathematical rigor compared to traditionallogic systems. Some researchers argue that fuzzy logic is more of an art than a science, as it relies heavily on expert knowledge and intuition for defining fuzzy sets and rules. Additionally, the computational complexity of fuzzy logic systems can be a drawback, especially in real-time applications where fast response times are critical. Designing and tuning fuzzy logic controllers can be a time-consuming and labor-intensive process, requiring a deep understanding of the system dynamicsand the fuzzy logic principles. In conclusion, fuzzy logic is a powerful tool for modeling uncertainty and imprecision in various applications. Its ability to capture human reasoning and decision-making processes makes it a valuable approach for developing intelligent systems and decision support systems. While fuzzy logic has its limitations and challenges, ongoing research and advancements in the field continue to expand its capabilities and improve its practicality in real-world applications.。
Fuzzy Logic and Systems
Fuzzy Logic and Systems Fuzzy logic and systems are becoming increasingly important in today's world, as they provide a way to deal with uncertainty and imprecision in decision-making processes. Fuzzy logic is a mathematical framework that allows for reasoning with imprecise or vague information, while fuzzy systems are a type of artificial intelligence that use fuzzy logic to make decisions based on input data. In this essay, we will explore the concept of fuzzy logic and systems from multiple perspectives, including their history, applications, advantages, and limitations.Fuzzy logic was first introduced by Lotfi Zadeh in 1965 as a way to deal with the imprecision and uncertainty that are inherent in many real-world problems. Traditional logic is based on binary values (true or false), but in many situations, it is difficult to make a clear-cut distinction between what is true and what is false. Fuzzy logic allows for a more nuanced approach, where the degree of truth or falsity can be represented by a value between 0 and 1. This makes it possible to reason with imprecise or incomplete information, which is often the case in real-world decision-making.Fuzzy systems are a type of artificial intelligence that use fuzzy logic to make decisions based on input data. They are particularly useful in situations where traditional rule-based systems are insufficient, such as in complex control systems or decision-making processes. Fuzzy systems work by taking input data and mapping it to a set of fuzzy rules, which are then combined to produce an output. The output is a fuzzy value that represents the degree of confidence in the decision that was made.One of the key advantages of fuzzy logic and systems is their ability to deal with uncertainty and imprecision. This makes them particularly useful in situations where traditional methods are insufficient, such as in complex control systems or decision-making processes. Fuzzy systems are also able to learn from experience, which means that they can adapt to changing situations and improve their performance over time.Another advantage of fuzzy logic and systems is their ability to handle non-linear relationships between variables. Traditional linear models assume that the relationship between variables is constant, but in many real-world situations,this is not the case. Fuzzy systems are able to model non-linear relationships and make decisions based on this information, which can lead to more accurate and effective results.However, there are also some limitations to fuzzy logic and systems. One of the main limitations is their complexity, which can make them difficult to understand and implement. Fuzzy systems also require a large amount of data to be effective, which can be a challenge in situations where data is limited ordifficult to obtain. Additionally, fuzzy systems are not always able to provide clear explanations for their decisions, which can be a problem in situations where transparency and accountability are important.In conclusion, fuzzy logic and systems are an important tool for dealing with uncertainty and imprecision in decision-making processes. They have a number of advantages, including their ability to handle non-linear relationships between variables and their ability to learn from experience. However, they also have some limitations, including their complexity and the need for large amounts of data. Overall, fuzzy logic and systems are an important area of research in artificial intelligence and have the potential to revolutionize many fields, from engineering to finance to healthcare.。
fuzzy工具箱使用规则
Matlab模糊控制工具箱为模糊控制器的设计提供了一种非常便捷的途径，通过它我们不需要进行复杂的模糊化、模糊推理及反模糊化运算，只需要设定相应参数，就可以很快得到我们所需要的控制器，而且修改也非常方便。
下面将根据模糊控制器设计步骤，一步步利用Matlab工具箱设计模糊控制器。
首先我们在Matlab的命令窗口（command window）中输入fuzzy，回车就会出来这样一个窗口。
下面我们都是在这样一个窗口中进行模糊控制器的设计。
1．确定模糊控制器结构：即根据具体的系统确定输入、输出量。
这里我们可以选取标准的二维控制结构，即输入为误差e和误差变化ec，输出为控制量u。
注意这里的变量还都是精确量。
相应的模糊量为E，EC和U，我们可以选择增加输入(Add Variable)来实现双入单出控制结构。
2．输入输出变量的模糊化：即把输入输出的精确量转化为对应语言变量的模糊集合。
首先我们要确定描述输入输出变量语言值的模糊子集，如{NB，NM，NS，ZO，PS，PM，PB}，并设置输入输出变量的论域，例如我们可以设置误差E（此时为模糊量）、误差变化EC、控制量U的论域均为{-3，-2，-1，0，1，2，3}；然后我们为模糊语言变量选取相应的隶属度函数。
在模糊控制工具箱中，我们在Member Function Edit中即可完成这些步骤。
首先我们打开Member Function Edit窗口.然后分别对输入输出变量定义论域范围，添加隶属函数，以E为例，设置论域范围为[-3 3]，添加隶属函数的个数为7.然后根据设计要求分别对这些隶属函数进行修改，包括对应的语言变量，隶属函数类型。
3．模糊推理决策算法设计：即根据模糊控制规则进行模糊推理，并决策出模糊输出量。
首先要确定模糊规则，即专家经验。
对于我们这个二维控制结构以及相应的输入模糊集，我们可以制定49条模糊控制规则（一般来说，这些规则都是现成的，很多教科书上都有），如图。
Fuzzy Logic and Systems
Fuzzy Logic and SystemsFuzzy logic and systems have become increasingly important in various fields such as engineering, computer science, and artificial intelligence. The concept of fuzzy logic was first introduced by Lotfi Zadeh in the 1960s as a way to deal with the uncertainty and imprecision that often arises in real-world problems. Unlike traditional binary logic, which is based on crisp, true or false values, fuzzy logic allows for the representation of vague or ambiguous information using degrees of truth. This flexibility makes it a powerful tool for modeling and controlling complex systems. From an engineering perspective, fuzzy logic has been widely used in the design of control systems for various applications such as automotive, industrial, and consumer electronics. One of the key advantages of fuzzy logic control is its ability to handle nonlinear and complex systems that are difficult to model using traditional techniques. For example, in automotive applications, fuzzy logic has been used to improve the performance of automatic transmission systems, engine control units, and anti-lock braking systems. By incorporating human-like reasoning and decision-making processes, fuzzy logic controllers can adapt to changing operating conditions and provide robust performance across a wide range of operating scenarios. In the field of computer science, fuzzy logic has been applied to a wide range of problems such as pattern recognition, image processing, and natural language processing. Fuzzy logic-based systems have been used to develop intelligent algorithms for tasks such as speech recognition, handwriting recognition, and object detection in computer vision. By capturing the inherent uncertainty and imprecision in real-world data, fuzzy logic models can provide more accurate and reliable results compared to traditional binary logic approaches. This has led to significant advancements in the development of intelligent systems that can effectively deal with the complexities of real-world data and make decisions in uncertain environments. From a broader perspective, the adoption of fuzzy logic and systems has also had a significant impact on the field of artificial intelligence. Traditional AI techniques often struggle to deal with the inherent uncertainty and imprecision in real-world data, which can lead to suboptimal performance and unreliable results. By incorporating fuzzy logic principles into AI models, researchers have been able to develop morerobust and adaptive systems that can effectively handle uncertain and ambiguous information. This has opened up new possibilities for the development of AI systems that can operate in complex and dynamic environments, such as autonomous vehicles, smart infrastructure, and intelligent robotics. In conclusion, fuzzy logic and systems have become an integral part of modern engineering, computer science, and artificial intelligence. The flexibility and adaptability of fuzzy logic make it a powerful tool for modeling and controlling complex systems, and its ability to handle uncertainty and imprecision has led to significant advancements in various fields. As we continue to tackle increasingly complex and dynamic problems, the role of fuzzy logic and systems is likely to become even more prominent, shaping the development of intelligent systems that can effectively deal with the complexities of the real world.。
Fuzzy Logic
Fuzzy Logic and Systems
Fuzzy Logic and SystemsFuzzy logic is a fascinating area of study that has gained significant attention in various fields, including artificial intelligence, control systems, and decision-making processes. Unlike traditional binary logic, which deals with precise values of true or false, fuzzy logic allows for the representation of uncertainty and vagueness in a more human-like manner. This flexibility in handling imprecise information makes fuzzy logic a powerful tool for modeling complex systems that are inherently uncertain or ambiguous. One of the key advantages of fuzzy logic is its ability to capture the nuances and subtleties of human reasoning. In many real-world scenarios, decisions are rarely black and white; there is often a degree of uncertainty or ambiguity that needs to be taken into account. Fuzzy logic provides a framework for representing and reasoning with this type of fuzzy information, allowing for more nuanced and human-like decision-making processes. In the field of artificial intelligence, fuzzy logic has been used to develop expert systems that can mimic human decision-making processes. By incorporating fuzzy logic into AI algorithms, researchers have been able to create systems that can handle imprecise and uncertain information more effectively. This has led to significant advancements in areas such as image recognition, natural language processing, and autonomous control systems. Control systems are another area where fuzzy logic has found widespread application. Traditional control systems rely on precise mathematical models to make decisions about how to regulate a system's behavior. However, in many real-world scenarios, these models may be incomplete or inaccurate, leading to suboptimal performance. Fuzzy logic-based control systems offer a more robust alternative by allowing for the incorporation of imprecise information into the decision-making process. In the realm of decision-making, fuzzy logic has been used to develop decision support systems that can handle complex and uncertain information more effectively. By incorporating fuzzy logic into the decision-making process, these systems can provide more nuanced and context-aware recommendations, leading to better-informed decisions. This has applications in a wide range of fields, including business, healthcare, and finance, where decisions are often made in the face of uncertainty and ambiguity. Despite its many advantages, fuzzy logic is not without itschallenges. One of the main criticisms of fuzzy logic is its reliance on expert knowledge for defining fuzzy sets and rules. This can be a time-consuming and labor-intensive process, requiring domain experts to carefully craft the fuzzy logic system. Additionally, the interpretability of fuzzy logic systems can be a challenge, as the reasoning behind the system's decisions may not always be transparent to end-users. In conclusion, fuzzy logic is a powerful tool for modeling and reasoning with uncertain and imprecise information. Its ability to capture the nuances of human reasoning makes it a valuable asset in fields such as artificial intelligence, control systems, and decision-making. While there are challenges to overcome, the benefits of fuzzy logic in handling complex and uncertain information are undeniable. As research in this area continues to advance, we can expect to see even more innovative applications of fuzzy logic in the future.。
Fuzzy Logic and Systems
Fuzzy Logic and SystemsFuzzy logic, a fascinating and unconventional branch of computational intelligence, diverges from the traditional binary logic of 0s and 1s, embracing a more nuanced and human-like approach to reasoning. Unlike classical logic, which dictates that something is either true or false, fuzzy logic allows for degrees of truth, acknowledging the inherent ambiguity present in many real-world situations. This paradigm shift has revolutionized various fields, leading to more robust and adaptable systems. The fundamental concept underpinning fuzzy logic is the fuzzy set, a collection of elements with varying degrees of membership. Unlike crisp sets, where elements either belong or do not belong to the set, fuzzy sets permit partial membership, represented by a membership function ranging from 0 to 1. This function quantifies the degree to which an element belongs to the set, reflecting the inherent fuzziness or imprecision often encountered in human perception and decision-making. Consider, for instance, the concept of "tallness." In classical logic, an individual is either tall or not tall, based on a predetermined threshold. However, human perception of tallness is subjective and context-dependent. Fuzzy logic accommodates this ambiguity by defining "tallness" as a fuzzy set with a membership function that gradually increases as height increases. Thus, individuals are assigned degrees of "tallness" rather than being categorically classified. The flexibility and intuitiveness of fuzzy logic have made it an invaluable tool in a wide range of applications, from control systems to medical diagnosis. Fuzzy controllers, for example, excel in managing complex systems with nonlinear dynamics, where traditional control methods often falter. These controllers utilize fuzzy rules, expressed in linguistic terms, to mimic human expert knowledge and make decisions based on imprecise or incomplete information. In the medical domain, fuzzy logic has proven instrumental in developing diagnostic systems that can handle the inherent uncertainty associated with medical data. By incorporating fuzzy sets and rules, these systems can process ambiguous symptoms, interpret subjective patient descriptions, and generate probabilistic diagnoses, aiding clinicians in making informed decisions. Despite its numerous advantages, fuzzy logic is not without its critics. Some argue that its reliance on subjective membership functions introduces a degree ofarbitrariness into the system. However, proponents counter that this flexibility allows for the integration of human intuition and expertise, making fuzzy logic particularly well-suited for problems involving human perception and decision-making. As fuzzy logic continues to evolve and mature, its impact on various fields will undoubtedly expand, further blurring the lines between human and artificial intelligence.。
Fuzzy Logic and Systems
Fuzzy Logic and SystemsFuzzy logic and systems are a fascinating area of study that bridges the gap between traditional binary logic and the vagueness of human reasoning. This field has gained significant attention in recent years due to its ability to handle uncertainty and imprecision in a way that mimics human decision-making processes. Fuzzy logic allows for a more nuanced approach to problem-solving by assigning degrees of truth to statements rather than simply labeling them as true or false. This flexibility is particularly useful in situations where exact values are difficult to determine or where the boundaries between categories are blurred. One of the key advantages of fuzzy logic is its ability to handle complex systems with multiple variables and inputs. Traditional logic often struggles to cope with the intricacies of real-world problems, which may involve a multitude of factors that interact in non-linear ways. Fuzzy logic, on the other hand, excels in situations where the relationships between variables are not well-defined or where there is a high degree of uncertainty. By allowing for degrees of truth and membership functions, fuzzy logic can capture the nuances of these complex systems and provide more accurate and robust solutions. Another important aspect of fuzzy logic is its ability to model human reasoning and decision-making processes. Humans are inherently fuzzy creatures, prone to ambiguity, vagueness, and uncertainty in their thinking. Fuzzy logic mirrors this cognitive style by allowing for shades of gray in its reasoning, rather than the black-and-white distinctions of traditional logic. This makes fuzzy logic particularly well-suited for applications that involve human judgment or subjective criteria, such as expert systems, decision support systems, and artificial intelligence. Inaddition to its theoretical advantages, fuzzy logic has also found practical applications in a wide range of fields, including control systems, pattern recognition, image processing, and data analysis. Fuzzy logic controllers, for example, are widely used in industrial automation and robotics to handle complex control tasks that traditional controllers struggle with. Fuzzy systems have also been applied to medical diagnosis, financial forecasting, and natural language processing, demonstrating their versatility and effectiveness in real-world applications. Despite its many strengths, fuzzy logic is not without itslimitations and criticisms. Some researchers argue that fuzzy logic can be overly complex and difficult to interpret, particularly when dealing with systems with a large number of variables or fuzzy rules. Others question the validity of fuzzylogic as a formal system of reasoning, pointing to its reliance on heuristic methods and lack of a solid mathematical foundation. These criticisms highlightthe need for further research and development in the field of fuzzy logic to address these challenges and improve its applicability in a wider range of domains. In conclusion, fuzzy logic and systems offer a powerful and flexible approach to handling uncertainty and imprecision in decision-making processes. By allowing for degrees of truth and membership functions, fuzzy logic can model complex systems more accurately and capture the nuances of human reasoning. While there are valid criticisms of fuzzy logic, its practical applications and theoretical advantages make it a valuable tool for a wide range of fields. As research in this area continues to evolve, we can expect to see even more innovative applications offuzzy logic in the future, further solidifying its position as a key technologyfor handling uncertainty in a complex world.。
Fuzzy Logic and Systems
Fuzzy Logic and SystemsFuzzy logic is a fascinating field that challenges traditional binary thinking by introducing a more nuanced and flexible approach to decision-making. It represents a departure from the strict true/false dichotomy of classical logic, allowing for degrees of truth and uncertainty. This concept is inspired by how human beings often make decisions based on vague or incomplete information, justlike how we might describe something as "very hot" or "somewhat cold" instead of assigning clear-cut labels. When we apply fuzzy logic to systems or processes, we embrace a more realistic and human-like way of dealing with complexity. Instead of rigid rules, we can work with fuzzy sets, which accommodate the ambiguity and imprecision that are inherent in many real-world scenarios. This opens up new possibilities for modeling and controlling systems that defy simple categorization, such as climate control systems, automotive applications, and consumer electronics. One of the key benefits of fuzzy logic is its ability to capture the uncertaintyand variability present in many aspects of our lives. By allowing for the representation of vague concepts and fuzzy boundaries, it provides a more accurate reflection of the way we think and communicate. This is particularly valuable in fields such as artificial intelligence, where mimicking human reasoning anddecision-making processes can lead to more effective and user-friendly systems. From a practical standpoint, fuzzy logic has been successfully applied in a wide range of industries and applications. For example, in the automotive sector, fuzzy logic is used in anti-lock braking systems to optimize braking performance under varying road conditions. In consumer electronics, it can be found in devices like washing machines and air conditioners to automatically adjust settings based oninput variables such as load size or room temperature. Despite its many advantages, fuzzy logic is not without its challenges. Critics argue that the inherent subjectivity and lack of strict rules in fuzzy systems can lead to inconsistency and ambiguous results. However, proponents counter that thisflexibility is precisely what makes fuzzy logic so powerful in dealing with complex, uncertain, and nonlinear systems. Like any tool, its effectivenessdepends on how it is applied and tailored to specific contexts. In conclusion, fuzzy logic offers a unique and valuable perspective on decision-making and systemcontrol by embracing ambiguity and uncertainty. By recognizing and working with the inherent fuzziness of many real-world problems, we can develop more adaptive and responsive solutions that better reflect the complexities of the world around us. Through its practical applications and theoretical insights, fuzzy logic continues to push the boundaries of traditional logic and open up newpossibilities for understanding and interacting with the world.。
Fuzzy Logic and Systems
Fuzzy Logic and SystemsFuzzy logic and systems have become increasingly important in various fields such as engineering, artificial intelligence, and decision-making processes. However, despite its wide applications, there are still some challenges and limitations associated with fuzzy logic and systems that need to be addressed. One of the main problems with fuzzy logic is its complexity and the difficulty in interpreting the results. Fuzzy logic deals with vague and imprecise information, which can make it challenging to understand and interpret the results. This can be a significant barrier for its widespread adoption, as users may have difficulty trusting and understanding the output of fuzzy logic systems. Another issue with fuzzy logic is its computational complexity. Fuzzy logic systems often require a large amount of computational resources to process and analyze the input data. This can lead to longer processing times and higher costs, which may limit its practical applications in certain scenarios. Moreover, there is a lack of standardized methods for developing and evaluating fuzzy logic systems. This can lead to inconsistencies in the results and make it difficult to compare different fuzzy logic systems. Standardization and best practices for developing and evaluating fuzzy logic systems are essential to ensure their reliability and effectiveness. In addition, the integration of fuzzy logic with traditional logic systems can be challenging. Fuzzy logic operates on a different set of principles than traditional logic, which can make it difficult to integrate the two systems seamlessly. This can limit the potential applications of fuzzy logic in fieldsthat heavily rely on traditional logic systems. Furthermore, the lack of widespread understanding and expertise in fuzzy logic can be a significant barrier to its adoption. Many professionals may not have the necessary knowledge andskills to effectively implement and utilize fuzzy logic systems in their work. This can hinder the potential benefits that fuzzy logic can offer in various fields. Despite these challenges, there are ongoing efforts to address the limitations of fuzzy logic and systems. Researchers and practitioners are working on developing more user-friendly interfaces and visualization tools to help users better understand and interpret the results of fuzzy logic systems. Additionally, advancements in computational technology and algorithms are helping to improve theefficiency and performance of fuzzy logic systems, making them more practical for real-world applications. Standardization efforts are also underway to establish best practices for developing and evaluating fuzzy logic systems. By creating standardized methods and guidelines, the reliability and consistency of fuzzylogic systems can be improved, making them more trustworthy for users. Education and training programs are also being developed to increase the understanding and expertise in fuzzy logic. By providing professionals with the necessary knowledge and skills, the adoption and utilization of fuzzy logic systems can be enhanced, leading to more widespread applications and benefits. In conclusion, while there are challenges and limitations associated with fuzzy logic and systems, there are ongoing efforts to address these issues and improve the reliability andpracticality of fuzzy logic. With advancements in technology, standardization, and education, the potential of fuzzy logic in various fields can be fully realized, leading to more effective decision-making processes and innovative solutions.。
Fuzzy Logic and Systems
Fuzzy Logic and SystemsFuzzy logic is a type of logic that allows for the representation of imprecise or vague information. Unlike traditional binary logic, which operates on strict true or false values, fuzzy logic deals with degrees of truth. This makes it a valuable tool for modeling complex systems that may not have clear-cut boundaries or precise inputs. One of the key advantages of fuzzy logic is its ability to handle uncertainty. In many real-world scenarios, data is not always black and white. There may be shades of gray or areas of ambiguity that traditional logic cannot easily account for. Fuzzy logic allows for the incorporation of this uncertainty, making it a more flexible and adaptable approach to problem-solving. Another benefit of fuzzy logic is its simplicity. While traditional logic can be complex and difficult to apply in certain situations, fuzzy logic offers a more intuitive and natural way of reasoning. By using linguistic variables and fuzzy sets, fuzzy logic can capture the nuances and subtleties of human language and thought, making it easier to understand and work with. In addition to its flexibility and simplicity, fuzzy logic also has practical applications in a wide range of fields. From control systems and robotics to artificial intelligence and decision-making, fuzzy logic has proven to be a powerful tool for tackling complex problems. By allowing for the representation of uncertainty and imprecision, fuzzy logic can help improve the accuracy and efficiency of systems in various industries. Despite its many advantages, fuzzy logic is not without its limitations. One of the main criticisms of fuzzy logic is its subjective nature. Because fuzzy logic relies on human judgment and linguistic terms, there is a degree of subjectivity involved in the modeling process. This can lead to inconsistencies and inaccuracies if not properly managed. Another challenge of fuzzy logic is the difficulty of defining fuzzy sets and membership functions. While traditional logic relies on clear-cut definitions and boundaries, fuzzylogic requires a more nuanced approach to defining sets and functions. This can be a complex and time-consuming process, requiring careful consideration and expertise to ensure accurate results. Despite these limitations, fuzzy logic remains a valuable tool for addressing complex problems that traditional logic cannot easily solve. By embracing uncertainty and imprecision, fuzzy logic offersa more flexible and intuitive approach to modeling and reasoning. With its practical applications in various industries, fuzzy logic continues to play a crucial role in advancing technology and innovation.。
Fuzzy Logic and Systems
Fuzzy Logic and SystemsFuzzy Logic and Systems have emerged as a powerful tool in various fields, from engineering to artificial intelligence. This technology allows for the representation and manipulation of imprecise or vague data, providing a more flexible and human-like approach to decision-making. In this response, I will explore the concept of fuzzy logic and systems from multiple perspectives, highlighting its benefits, limitations, and potential applications. From an engineering perspective, fuzzy logic offers a unique way to handle complex systems that involve uncertainty and imprecision. Traditional logic relies on binary values (true or false), which can be limiting when dealing with real-world scenarios that involve degrees of truth or membership. Fuzzy logic allows for the representation of these degrees, enabling engineers to design systems that can handle imprecise inputs and produce more nuanced outputs. This has led to advancements in fields such as control systems, robotics, and image processing, where the ability to handle uncertainty is crucial. From a cognitive science perspective, fuzzy logic provides a framework that aligns with the way humansthink and make decisions. Human reasoning is often based on vague or imprecise information, and fuzzy logic provides a mathematical model to capture this inherent fuzziness. By incorporating fuzzy logic into cognitive models, researchers can better understand and simulate human decision-making processes. This has implications for fields such as psychology, economics, and decision sciences, where human behavior and decision-making are of interest. From an artificial intelligence perspective, fuzzy logic has been instrumental in developing intelligent systems that can mimic human reasoning. Fuzzy logic allows machines to reason with imprecise or uncertain information, enabling them to handle real-world data that is often ambiguous. This has led to advancements in areas such as expert systems, natural language processing, and machine learning. By incorporating fuzzy logic into AI algorithms, researchers can improve the performance and adaptability of intelligent systems, making them more robust and capable of handling complex and uncertain environments. However, it is important to acknowledge the limitations of fuzzy logic and systems. One limitation is the challenge of defining membership functions and fuzzy rules that accurately capturethe underlying system dynamics. Designing an effective fuzzy system requires domain expertise and careful consideration of the problem at hand. Additionally, fuzzy logic can be computationally expensive, especially when dealing with large-scale systems or real-time applications. This can limit its applicability incertain contexts where efficiency is a critical factor. Despite these limitations, fuzzy logic and systems have found numerous applications in various fields. In engineering, fuzzy control systems have been successfully applied to complex processes such as temperature control, traffic management, and industrial automation. In finance, fuzzy logic has been used to model and predict stockmarket behavior, credit risk, and investment decisions. In healthcare, fuzzy systems have been employed for medical diagnosis, patient monitoring, and drug dosage control. These examples highlight the versatility and potential of fuzzy logic in solving real-world problems. In conclusion, fuzzy logic and systemsoffer a flexible and human-like approach to decision-making, allowing for the representation and manipulation of imprecise or vague data. From an engineering perspective, fuzzy logic enables the design of systems that can handle uncertainty and produce nuanced outputs. From a cognitive science perspective, fuzzy logic aligns with human reasoning and decision-making processes. From an artificial intelligence perspective, fuzzy logic enables intelligent systems to reason with imprecise or uncertain information. While there are limitations to consider, fuzzy logic has found applications in various fields and continues to be a valuable tool for solving complex problems.。
Fuzzy Logic and Systems
Fuzzy Logic and SystemsFuzzy logic is a fascinating concept that has revolutionized the field of artificial intelligence and systems. It is a form of reasoning that deals with uncertainty and imprecision, allowing for a more human-like approach to decision-making. In traditional logic, statements are either true or false, but in fuzzy logic, statements can be partially true or partially false, allowing for a more nuanced understanding of the world. One of the key advantages of fuzzy logic is its ability to handle vague and ambiguous information. In real-world scenarios, not all information is clear-cut and precise. Fuzzy logic allows for the representation of this uncertainty, making it a valuable tool in situations where traditional logic falls short. For example, in self-driving cars, fuzzy logic can be used to make decisions based on imprecise sensor data, allowing the car to navigate safely in complex environments. Another important aspect of fuzzy logic is its ability to mimic human decision-making processes. Humans often make decisions based on incomplete information and subjective criteria. Fuzzy logic allows machines to emulate this human-like reasoning, making them more adaptable and versatile in a variety of situations. This can lead to more intuitive anduser-friendly systems that better meet the needs of their users. However, fuzzy logic is not without its challenges. One of the main criticisms of fuzzy logic is its lack of formalism and rigor compared to traditional logic. Because fuzzy logic deals with degrees of truth rather than binary values, it can be more difficult to analyze and verify the correctness of fuzzy systems. This can make it challenging to ensure the reliability and safety of fuzzy logic-based systems, especially in critical applications. Despite these challenges, fuzzy logic continues to be a valuable tool in a wide range of applications. From consumer electronics to industrial automation, fuzzy logic has proven to be an effective way to model and control complex systems. Its ability to handle uncertainty and imprecision makes it well-suited for real-world problems where traditional logic falls short. As technology continues to advance, fuzzy logic is likely to play an increasingly important role in shaping the future of artificial intelligence and systems. In conclusion, fuzzy logic is a powerful and versatile tool that has revolutionized the field of artificial intelligence and systems. Its ability to handleuncertainty and imprecision, mimic human decision-making processes, and adapt to a variety of situations makes it a valuable asset in a wide range of applications. While it may pose challenges in terms of formalism and rigor, the benefits of fuzzy logic far outweigh the drawbacks. As technology continues to evolve, fuzzy logic is likely to play a key role in shaping the future of intelligent systems.。
Fuzzy Logic and Systems
Fuzzy Logic and SystemsFuzzy logic, a concept that deviates from the conventional binary logic oftrue or false, embraces the shades of grey in between. It mirrors human reasoning, which often navigates through ambiguities and uncertainties, unlike the rigid confines of traditional computer logic. This approach finds its strength intackling complex systems where precise measurements are elusive, offering a robust framework for dealing with real-world problems. The core principle of fuzzy logic lies in its acceptance of partial truths. Instead of a strict categorization of 0 or 1, it allows for degrees of truth, represented by values ranging from 0 to 1. This nuanced approach is particularly beneficial in handling imprecise concepts, such as "tall" or "hot," which lack clear-cut boundaries in everyday language. By assigning degrees of membership to these linguistic terms, fuzzy logic enables machines to understand and process information in a more human-like manner. The applications of fuzzy logic are vast and impactful, spanning across various fields. In control systems, it excels in managing complex processes like temperature regulation and traffic flow. By accounting for fluctuations and uncertainties inherent in these systems, fuzzy logic provides smoother and more efficientcontrol mechanisms. The automotive industry leverages this technology foroptimized braking systems and cruise control, enhancing safety and driving experience. Furthermore, fuzzy logic plays a significant role in artificial intelligence, particularly in expert systems. These systems aim to emulate human expertise in specific domains, employing fuzzy rules to make decisions based on incomplete or ambiguous data. Medical diagnosis, financial analysis, and natural language processing are just a few areas where fuzzy logic empowers systems to tackle complex problems with remarkable accuracy. Despite its advantages, fuzzy logic also faces certain limitations. One challenge lies in determiningappropriate membership functions, the mathematical representations that define the degree of truth for fuzzy sets. These functions are often subjective and depend on the specific problem at hand, requiring careful consideration and fine-tuning. Moreover, the computational complexity of fuzzy logic systems can be demanding, especially for large-scale applications, necessitating efficient algorithms and hardware optimization. In conclusion, fuzzy logic offers a powerful approach tobridging the gap between human reasoning and computational logic. By embracing imprecision and uncertainty, it empowers systems to handle real-world complexities with greater flexibility and efficiency. Its applications in control systems, artificial intelligence, and beyond continue to expand, promising innovative solutions to challenges that traditional logic struggles to address. While challenges remain in its implementation, fuzzy logic remains a valuable tool in the quest for intelligent systems that mirror the intricacies of human thought.。
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Figure 1. A mobile robot gripper system for experiments
Since the proximity sensors as well as the controller outputs Speed and Steer are imprecise, it does not make sense to develop complicated, exact algorithms to use the sensor data for world modelling and to control the robot motion with a high resolution. If the control of a mobile robot is compared with the driving behaviour of a human, it can be well understood that fuzzy logic rules emulating the human decision-making process with \IF-THEN" rules can be applied in the design of such a robot controller.
Our previous work ZWK96] presents a fuzzy control solution towards such an integration for robot motion control. Beyond the classical control algorithms, like PID control and potential eld BLL92], \intelligent computing methods", like neural networks and fuzzy logic, are increasingly applied in sensory systems and robot control. Fuzzy control approach is gradually becoming an important approach for sensor-based control of robots. Applications range from the purely reactive fuzzy controller, e.g. PW93], to the mixture of \behaviours" like single-goal directness and reactive collision-avoidance, e.g. Ish95] and Rus95]. In this paper, we present the development of modular fuzzy rule bases for realising several typical subtasks, such as \path tracking", \local collision avoidance", \contour tracking", \situation evaluation", etc. The concepts of mapping the sensor space to the control output space with fuzzy logic rules are illustrated in section 2. The formulation and implementation of the fuzzy rule bases are presented in details in section 3. Section 4 demonstrates brie y the realisation of collision-free movement from start to goal as well as an exploration task. This section also discusses the problem of controller optimisation. The last section summarises the advantages of using fuzzy logic rules for robot control.
1 Introduction
The conventional robot control architecture employs the so called SMPA (Sensing-Modelling-Planning-Action) strategy, which is based on the classical symbolism of AI. Recently, problems are found out with such a control architecture: 1). Algorithms for modelling and planning can be highly complex; 2). The time delay from perception to action is usually long due to the computational distance between them; 3). A system based on such an architecture is not fault tolerant. Therefore, a lot of recent work on robot control aims at nding e cient sensor-based solutions to shorten the distance between perception and action. The behaviour-based approaches Se95] use the parallel instead of hierarchical control structure and try to implement the so-called embodied, situated behaviours without building complete world models and anning any actions and motions. Obviously, both control architectures have advantages and disadvantages, so it becomes an important topic how to integrate them.
2 Robot, Sensors and their Connection
2.1 A Mobile Robot System for Experiments
The concept presented in this paper has been implemented for the real mobile gripper system Khepera. The mobile platform of Khepera is of circular shape with a diameter of 52mm. Additional modules can be mounted on the top of Khepera, e.g. a gripper module, see Fig. 1. The environment is currently observed by eight infra-red (IR) sensors (six at the front and two at the rear), while a vision module is now being tested. The sensibility of the IR sensors varies for di erent ob-
jects and is limited to 5cm. The directly controlled values are the velocities of the robot's left and right wheel, which are denoted as v and v respectively. In order to test robot independent control programs, we have derived a computation table with which the robot's forward speed (Speed) and steering angle (Steer) can be translated to v and v .
l r l r
They are local. Normally, only part of the environment, which is directly involved in the current robot motion, is perceived by the sensor system. Each sensor measurement represents one aspect of the object's features. No time-costly sensor fusion is performed (sensor data fusion is then transformed to task fusion). They are task-oriented. Modelling and interpretation of the sensor data depend on the control tasks. Only the control-relevant data are selected, pre-processed and represented.