Autonomous vehicle navigation represents a global view of the wide field of ground mobile robotics. This view is ensured by the notable and broad background of the author, especially in the context of multirobot systems.

Chapter 1 reviews the history of ground unmanned vehicles. Additionally, it highlights the motion control paradigms in this field as well as the challenges associated with hierarchical or multicontroller architectures. Local or reactive navigation approaches are generally applied to cluttered environments where having a detailed map of the environment is not possible. This approach is analyzed in detail in the second chapter. Simulations and experimental results demonstrate the feature of these controllers by means of a single robot in a cluttered and static environment. Chapter 3 reviews some basic motion control strategies for various robot kinematics. Special mention is provided for the hybrid control architectures. Simulation results with more advanced hybrid control approaches are introduced in chapter 4. Until now, the control problem was based on following a predetermined reference trajectory. In chapter 5, we change the control goal by following a set of targets or waypoints. This new paradigm allows more flexibility of the robot’s movement, resulting in an ideal candidate for architectures focused on obstacle avoidance and optimal closed-loop control. This is the contribution of this chapter, that is, the analysis of various strategies to generate efficient routes between waypoints and how to steer the robot between them. State-of-the-art approaches are discussed (for example, expanding trees and rapidly exploring random trees (RRT)). Another challenge from a control standpoint is the control of a group of robots, namely cooperative multirobot strategies. Interesting problems arise when more than one robot is operating in the same environment, among others: cooperative exploration under uncertainty, multirobot navigation in formation, and collisions. The previous waypoint-based control approaches are enlarged here to cope with those new features. Simulations and physical experiments are given as well. The book concludes with a lessons learned chapter where the author remarks on the recommendations for multicontroller architectures, navigation through waypoints, obstacle avoidance, and cooperative multirobot systems. Two appendices close the book, detailing the simulation and experimental platforms used. Some key definitions related to the stability theory are stated as well.

This book can be used both for introductory courses to mobile robotics, especially the first chapters, and for more advanced courses and research. The contributed algorithms are demonstrated via simulations and physical experiments, which adds another plus to the book.