Robotic Path Planning and Task Execution: Algorithms and Behavior Trees

Programming robots to navigate environments and perform complex, sequence-based tasks requires a solid understanding of path planning and decision-making logic. This text-only course guides you from foundational definitions of robotic state spaces to implementing core navigation algorithms and structuring complex robot behaviors. Through written explanations and clear code walk-throughs, you will learn how to translate physical coordinates into actionable robot instructions. What you'll learn: - Understand core concepts of grid-based environments, configuration spaces, and robot kinematic constraints. - Implement search-based path planning algorithms including Breadth-First Search, Dijkstra, and A*. - Explore sample-based planning techniques such as Rapidly-exploring Random Trees (RRT) for high-dimensional spaces. - Design and structure robot decision-making processes using modular Behavior Trees. - Apply sequencing logic to coordinate mobile manipulation tasks step-by-step. - Integrate modern best practices for path smoothing and handling dynamic obstacles in real-time environments. You will start with foundational terminology and navigation mathematics before reading through structured algorithm walk-throughs and building robust behavior-sequencing logic. This course is designed for aspiring roboticists, software engineers, and students new to robotics planning; no advanced hardware or prior robotics experience is required. Start reading today to master the core algorithms that drive autonomous robot navigation and task execution.