Preface

Motion planning is an interesting and inherent area in most robotics research and development. It ranges from a local motion control of a single actuator to a complex mobile machine compounded of sophisticated capabilities, such as path generation, trajectory tracking, global autonomous navigation, wide-space coverage for inspection and exploration, and local or global pathfinding in unknown environments. This book has organized a selection of recent advances in chapters divided by three general perspectives: a) aerial path planning and tracking, b) artificial intelligence (AI)-based optimization for planning, and c) wheeled robots planning and control. The chapters present specific planning works, essentially on estimation, prediction, optimization, observation, and control in dynamic scenarios.

The first section introduces concepts and methods for unmanned aerial path planning and tracking that are critical for prospective new complex paradigms that contribute to the continued resilience of airspace coverage and applications performed by unmanned aerial vehicles.

The second section presents AI approaches, heuristic and meta-heuristic optimization solutions for planning. Heuristic methods are problem-solving algorithms that emulate human thinking. Traditional graphical maps used for path planning when combined with heuristic approaches perform faster than some deterministic solutions, providing feasible paths. Moreover, optimization algorithms inspired by biological entities show fast convergence and efficiency in estimating locomotion for dynamic robotic agents. Bioinspired methods might perhaps solve multi-objective optimization problems, multi-robot path planning, formation control, and self-organization of distributed systems.

The third section treats one of the most traditional types of robotic platforms, the wheeled robots, which rely on a wide range of navigation control techniques. Wheeled vehicles depend on path planning and tracking to reach certain autonomy when facing the environmental dynamics to which they are subjected to. Local planning performance is critical due to multiple unpredictable obstacles in order to attain safe and suitable pathways for self-driving vehicles. A robotic platform depends on its kinematic mobility constraints to considerably perform path following. Longitudinal and lateral controls are required to efficiently track complex polynomial routes. Some mechanical structure designs depend on active and passive motions (e.g., limb bar linkage), where numerous analytical solutions are dominated by algebraic and derivative methods to model dynamic local planning considering obstacles, goals, and routes, simultaneously.

Finally, I would like to acknowledge the author service manager Ms. Sara Debeuc for her editorial support to complete this book. Additionally, I would like to thank the anonymous external reviewers for providing valuable comments to improve the chapters' technical quality.

> **Edgar A. Martínez García** Instituto de Ingeniería y Tecnología, Universidad Autónoma de Ciudad Juárez, Juárez, Mexico

Section 1
