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Safe Robot Navigation Among Moving and Steady Obstacles is the first book to focus on reactive navigation algorithms in unknown dynamic environments with moving and steady obstacles. The first three chapters provide introduction and background on sliding mode control theory, sensor models, and vehicle kinematics. Chapter 4 deals with the problem of optimal navigation in the presence of obstacles. Chapter 5 discusses the problem of reactively navigating. In Chapter 6, border patrolling algorithms are applied to a more general problem of reactively navigating. A method for guidance of a Dubins-like mobile robot is presented in Chapter 7. Chapter 8 introduces and studies a simple biologically-inspired strategy for navigation a Dubins-car. Chapter 9 deals with a hard scenario where the environment of operation is cluttered with obstacles that may undergo arbitrary motions, including rotations and deformations. Chapter 10 presents a novel reactive algorithm for collision free navigation of a nonholonomic robot in unknown complex dynamic environments with moving obstacles. Chapter 11 introduces and examines a novel purely reactive algorithm to navigate a planar mobile robot in densely cluttered environments with unpredictably moving and deforming obstacles. Chapter 12 considers a multiple robot scenario. For the Control and Automation Engineer, this book offers accessible and precise development of important mathematical models and results. All the presented results have mathematically rigorous proofs. On the other hand, the Engineer in Industry can benefit by the experiments with real robots such as Pioneer robots, autonomous wheelchairs and autonomous mobile hospital.
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.
"Planning can be used in a variety of applications. In this paper we will discuss those planning techniques that Apply to the task of robotic path planning - Here a planner is used to generate "paths" which a robot can follow to maneuver from some point A to another point B, while at the same time avoiding all obstacles. All approaches discussed in this paper are based on viewing the robot as a sphere. By assuming this, the need to consider the robot's orientation as it moves along a proposed path is eliminated. Another requirement is that not only must a successful path be found, but this path should also be the shortest path through the space. Since "finding the shortest path between two points that avoids a collection of poly-hedral obstacles in three dimensions is already computationally intractable" and 3-D robotic vision may not be available, the discussion in this paper will be restricted to a 2D plane (this infers that the robot's terrain is a flat hard surface). Object recognition will also not be considered, only the ability to determine that there is some object present (whether it 's a table, chair or T.V. doesn't matter). Its length and width must be known or determined. The height of the object is not important as the robot will go around the object and not under or over it (can only obtain height information from a 3D plane). To simplify the overall problem domain we assume that obstacles are not in motion (IE, the objects are not in constant motion; objects can be moved to new stationary locations and new paths around them searched for). The discussion will also restrict the degrees of freedom of the robot to 2. This is again done to reduce the complexity of the domain. As more degrees of freedom are considered, the path planning problem becomes increasingly complex. Finally, we will assume the robot's velocity remains constant (again to reduce the complexity of the domain)."--Abstract.