Introduction to Robot Motion Planning: Path and Trajectory Generation

Navigating a robot safely through a complex environment requires robust motion planning algorithms that translate high-level goals into precise physical actions. This text-based course guides you through the core mathematical principles and algorithmic foundations of robotic path planning. By reading through detailed explanations, step-by-step conceptual walkthroughs, and clean pseudocode examples, you will transition from understanding basic geometric representations to implementing modern collision-avoidance strategies. You will gain the confidence to analyze robotic workspaces, configure search-based planners, and design smooth, executable trajectories for various mobile and articulated systems. What you'll learn: - Understand the fundamentals of configuration space, obstacle representation, and degree-of-freedom constraints. - Apply grid-based search algorithms such as A* and Dijkstra to find shortest paths in discretized environments. - Implement sampling-based planning methods including Probabilistic Roadmaps (PRM) and Rapidly-exploring Random Trees (RRT). - Formulate continuous mathematical models for smooth trajectory generation and polynomial path interpolation. - Configure potential fields and basic obstacle-avoidance techniques for real-time local planning. - Analyze the foundational kinematics of robotic arms and wheeled mobile robots to ensure physical feasibility. The course begins with essential terminology, coordinate transformations, and configuration space theory before progressing to global search algorithms, sampling methods, and dynamic trajectory generation. This course is designed for beginners, aspiring robotics engineers, and software developers with no prior background in robotics, starting with foundational concepts and building upward step-by-step. Start reading today to master the core algorithms that allow robots to navigate their world autonomously.