Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. Written for computer scientists and engineers with interests in artificial intelligence, robotics, or control theory, this is the only book on this topic that tightly integrates a vast body of literature from several fields into a coherent source for teaching and reference in a wide variety of applications. Difficult mathematical material is explained through hundreds of examples and illustrations.

The book includes topics, such as: path planning, avoiding obstacles, following the path, go-to-goal control, localization, and visual-based motion control. The theoretical concepts are illustrated with a developed control architecture with soft computing and artificial intelligence methods. The proposed vision-based motion control strategy involves three stages. The first stage consists of the overhead camera calibration and the configuration of the working environment. The second stage consists of a path planning strategy using several traditional path planning algorithms and proposed planning algorithm. The third stage consists of the path tracking process using previously developed Gauss and Decision Tree control approaches and the proposed Type-1 and Type-2 controllers. Two kinematic structures are utilized to acquire the input values of controllers. These are Triangle Shape-Based Controller Design, which was previously developed and Distance-Based Triangle Structure that is used for the first time in conducted experiments. Four different control algorithms, Type-1 fuzzy logic, Type-2 Fuzzy Logic, Decision Tree Control, and Gaussian Control have been used in overall system design. The developed system includes several modules that simplify characterizing the motion control of the robot and ensure that it maintains a safe distance without colliding with any obstacles on the way to the target. The topics of the book are extremely relevant in many areas of research, as well as in education in courses in computer science, electrical and mechanical engineering and in mathematics at the graduate and undergraduate levels.

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning, but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the 'configuration spaces' of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. This text and reference is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

A state-of-the-art survey that reports on the progress made in selected areas of this important and growing field, aiding the analysis of existing networks and the design of new and more efficient algorithms for solving various problems on these networks.

This book presents extensive research on two main problems in robotics: the path planning problem and the multi-robot task allocation problem. It is the first book to provide a comprehensive solution for using these techniques in large-scale environments containing randomly scattered obstacles. The research conducted resulted in tangible results both in theory and in practice. For path planning, new algorithms for large-scale problems are devised and implemented and integrated into the Robot Operating System (ROS). The book also discusses the parallelism advantage of cloud computing techniques to solve the path planning problem, and, for multi-robot task allocation, it addresses the task assignment problem and the multiple traveling salesman problem for mobile robots applications. In addition, four new algorithms have been devised to investigate the cooperation issues with extensive simulations and comparative performance evaluation. The algorithms are implemented and simulated in MATLAB and Webots.

The present book addresses various power system planning issues for professionals as well as senior level and postgraduate students. Its emphasis is on long-term issues, although much of the ideas may be used for short and mid-term cases, with some modifications. Back-up materials are provided in twelve appendices of the book. The readers can use the numerous examples presented within the chapters and problems at the end of the chapters, to make sure that the materials are adequately followed up. Based on what Matlab provides as a powerful package for students and professional, some of the examples and the problems are solved in using M-files especially developed and attached for this purpose. This adds a unique feature to the book for in-depth understanding of the materials, sometimes, difficult to apprehend mathematically. Chapter 1 provides an introduction to Power System Planning (PSP) issues and basic principles. As most of PSP problems are modeled as optimization problems, optimization techniques are covered in some details in Chapter 2. Moreover, PSP decision makings are based on both technical and economic considerations, so economic principles are briefly reviewed in Chapter 3. As a basic requirement of PSP studies, the load has to be known. Therefore, load forecasting is presented in Chapter 4. Single bus Generation Expansion Planning (GEP) problem is described in Chapter 5. This study is performed using WASP-IV, developed by International Atomic Energy Agency. The study ignores the grid structure. A Multi-bus GEP problem is discussed in Chapter 6 in which the transmission effects are, somehow, accounted for. The results of single bus GEP is used as an input to this problem. SEP problem is fully presented in Chapter 7. Chapter 8 devotes to Network Expansion Planning (NEP) problem, in which the network is planned. The results of NEP, somehow, fixes the network structure. Some practical considerations and improvements such as multi-voltage cases are discussed in Chapter 9. As NEP study is typically based on some simplifying assumptions and Direct Current Load Flow (DCLF) analysis, detailed Reactive Power Planning (RPP) study is finally presented in Chapter 10, to guarantee acceptable ACLF performance during normal as well as contingency conditions. This, somehow, concludes the basic PSP problem. The changing environments due to power system restructuring dictate some uncertainties on PSP issues. It is shown in Chapter 11 that how these uncertainties can be accounted for. Although is intended to be a text book, PSP is a research oriented topic, too. That is why Chapter 12 is devoted to research trends in PSP. The chapters conclude with a comprehensive example in Chapter 13, showing the step-by-step solution of a practical case.

The dominant theme of this book is to introduce the different path planning methods and present some of the most appropriate ones for robotic routing; methods that are capable of running on a variety of robots and are resistant to disturbances; being real-time, being autonomous, and the ability to identify high risk areas and risk management are the other features that will be mentioned in the introduction of the methods. The introduction of the profound significance of the robots and delineation of the navigation and routing theme is provided in the first chapter of the book. The second chapter is concerned with the subject of routing in unknown environments. In the first part of this chapter, the family of bug algorithms including are described. In the following, several conventional methods are submitted. The last part of this chapter is dedicated to the introduction of two recently developed routing methods. In Chapter 3, routing is reviewed in the known environment in which the robot either utilizes the created maps by extraneous sources or makes use of the sensor in order to prepare the maps from the local environment. The robot path planning relying on the robot vision sensors and applicable computing hardware are concentrated in the fourth chapter. The first part of this chapter deals with routing methods supported mapping capabilities. The second part manages the routing dependent on vision sensor typically known as the best sensor within the routing subject. The movement of two-dimensional robots with two or three degrees of freedom is analyzed within the third part of this chapter. In Chapter 5, the performance of a few of the foremost important routing methods initiating from the second to fourth chapters is conferred regarding the implementation in various environments. The first part of this chapter is engaged in the implementation of the algorithms Bug1, Bug2, and Distbug on the pioneering robot. In the second part, a theoretical technique is planned to boost the robot's performance in line with obstacle collision avoidance. This method, underlying the tangential escape, seeks to proceed the robot through various obstacles with curved corners. In the third and fourth parts of this chapter, path planning in different environments is preceded in the absence and the presence of danger space. Accordingly, four approaches, named artificial fuzzy potential field, linguistic technique, Markov decision making processes, and fuzzy Markov decision making have been proposed in two following parts and enforced on the Nao humanoid robot.