This book shows how to plan trajectories (i.e. time-dependent paths) for autonomous robots using a dynamic model within the A* framework.

Drawing from optimal control's model predictive control framework, the book develops a paradigm called Sampling Based Model Predictive Optimization (SBMPO), which generates graph trees through input sampling of a dynamic model, enabling A*-type algorithms to find optimal trajectories. The book covers various robotic platforms and tasks, including manipulators lifting heavy loads, mobile robots navigating steep hills, energy-efficient skid-steered movements, thermally informed space exploration planning, and climbing robots in obstacle-rich environments. It also explores methods for updating dynamic models for robust operation and provides sample code for applying SBMPO to additional problems.

This resource is aimed at researchers, engineers, and advanced students in motion planning and control for robotic and autonomous systems.