Robotics is an ever-expanding field and intelligent planning continues to play a major role. Given that the intention of mobile robots is to carry out tasks independent from human aid, robot intelligence is needed to make and plan out decisions based on various sensors. Planning is the fundamental activity that implements this intelligence into the mobile robots to complete such tasks. Understanding problems, challenges, and solutions to path planning and how it fits in is important to the realm of robotics. Intelligent Planning for Mobile Robotics: Algorithmic Approaches presents content coverage on the basics of artificial intelligence, search problems, and soft computing approaches. This collection of research provides insight on both robotics and basic algorithms and could serve as a reference book for courses related to robotics, special topics in AI, planning, applied soft computing, applied AI, and applied evolutionary computing. It is an ideal choice for research students, scholars, and professors alike.

Soft Computing today is a very vast field whose extent is beyond measure. The boundaries of this magnificent field are spreading at an enormous rate making it possible to build computationally intelligent systems that can do virtually anything, even after considering the hostile practical limitations. Soft Computing, mainly comprising of Artificial Neural Networks, Evolutionary Computation, and Fuzzy Logic may itself be insufficient to cater to the needs of various kinds of complex problems. In such a scenario, we need to carry out amalgamation of same or different computing approaches, along with heuristics, to make fabulous systems for problem solving. There is further an attempt to make these computing systems as adaptable as possible, where the value of any parameter is set and continuously modified by the system itself. This book first presents the basic computing techniques, draws special attention towards their advantages and disadvantages, and then motivates their fusion, in a manner to maximize the advantages and minimize the disadvantages. Conceptualization is a key element of the book, where emphasis is on visualizing the dynamics going inside the technique of use, and hence noting the shortcomings. A detailed description of different varieties of hybrid and adaptive computing systems is given, paying special attention towards conceptualization and motivation. Different evolutionary techniques are discussed that hold potential for generation of fairly complex systems. The complete book is supported by the application of these techniques to biometrics. This not only enables better understanding of the techniques with the added application base, it also opens new dimensions of possibilities how multiple biometric modalities can be fused together to make effective and scalable systems.

Soft Computing today is a very vast field whose extent is beyond measure. The boundaries of this magnificent field are spreading at an enormous rate making it possible to build computationally intelligent systems that can do virtually anything, even after considering the hostile practical limitations. Soft Computing, mainly comprising of Artificial Neural Networks, Evolutionary Computation, and Fuzzy Logic may itself be insufficient to cater to the needs of various kinds of complex problems. In such a scenario, we need to carry out amalgamation of same or different computing approaches, along with heuristics, to make fabulous systems for problem solving. There is further an attempt to make these computing systems as adaptable as possible, where the value of any parameter is set and continuously modified by the system itself. This book first presents the basic computing techniques, draws special attention towards their advantages and disadvantages, and then motivates their fusion, in a manner to maximize the advantages and minimize the disadvantages. Conceptualization is a key element of the book, where emphasis is on visualizing the dynamics going inside the technique of use, and hence noting the shortcomings. A detailed description of different varieties of hybrid and adaptive computing systems is given, paying special attention towards conceptualization and motivation. Different evolutionary techniques are discussed that hold potential for generation of fairly complex systems. The complete book is supported by the application of these techniques to biometrics. This not only enables better understanding of the techniques with the added application base, it also opens new dimensions of possibilities how multiple biometric modalities can be fused together to make effective and scalable systems.

Rapid advancements in the application of soft computing tools and techniques have proven valuable in the development of highly scalable systems and resulted in brilliant applications, including those in biometric identification, interactive voice response systems, and data mining. Although many resources on the subject adequately cover the theoretic concepts, few provide clear insight into practical application. Filling this need, Real Life Applications of Soft Computing explains such applications, including the underlying technology and its implementation. While these systems initially seem complex, the authors clearly demonstrate how they can be modeled, designed, and implemented. Written in a manner that makes it accessible to novices, the book begins by covering the theoretical foundations of soft computing. It supplies a concise explanation of various models, principles, algorithms, tools, and techniques, including artificial neural networks, fuzzy systems, evolutionary algorithms, and hybrid algorithms. Supplying in-depth exposure to real life systems, the text provides: Multi-dimensional coverage supported by references, figures, and tables Warnings about common pitfalls in the implementation process, as well as detailed examinations of possible solutions A timely account of developments in various areas of application Solved examples and exercises in each chapter Detailing a wide range of contemporary applications, the text includes coverage of those in biometric systems, including physiological and behavioral biometrics. It also examines applications in legal threat assessment, robotic path planning, and navigation control. The authors consider fusion methods in biometrics and bioinformatics and also provide effective disease identification techniques. Co

Autonomous Mobile Robots: Planning, Navigation, and Simulation presents detailed coverage of the domain of robotics in motion planning and associated topics in navigation. This book covers numerous base planning methods from diverse schools of learning, including deliberative planning methods, reactive planning methods, task planning methods, fusion of different methods, and cognitive architectures. It is a good resource for doing initial project work in robotics, providing an overview, methods and simulation software in one resource. For more advanced readers, it presents a variety of planning algorithms to choose from, presenting the tradeoffs between the algorithms to ascertain a good choice. Finally, the book presents fusion mechanisms to design hybrid algorithms.

On-Road Intelligent Vehicles: Motion Planning for Intelligent Transportation Systems deals with the technology of autonomous vehicles, with a special focus on the navigation and planning aspects, presenting the information in three parts. Part One deals with the use of different sensors to perceive the environment, thereafter mapping the multi-domain senses to make a map of the operational scenario, including topics such as proximity sensors which give distances to obstacles, vision cameras, and computer vision techniques that may be used to pre-process the image, extract relevant features, and use classification techniques like neural networks and support vector machines for the identification of roads, lanes, vehicles, obstacles, traffic lights, signs, and pedestrians. With a detailed insight into the technology behind the vehicle, Part Two of the book focuses on the problem of motion planning. Numerous planning techniques are discussed and adapted to work for multi-vehicle traffic scenarios, including the use of sampling based approaches comprised of Genetic Algorithm and Rapidly-exploring Random Trees and Graph search based approaches, including a hierarchical decomposition of the algorithm and heuristic selection of nodes for limited exploration, Reactive Planning based approaches, including Fuzzy based planning, Potential Field based planning, and Elastic Strip and logic based planning. Part Three of the book covers the macroscopic concepts related to Intelligent Transportation Systems with a discussion of various topics and concepts related to transportation systems, including a description of traffic flow, the basic theory behind transportation systems, and generation of shock waves.