Modern Robotics - Motion Planning

Learn motion planning fundamentals for robotics through this comprehensive 50-minute lecture from Northwestern University covering essential algorithms and methodologies. Explore the overview of motion planning principles and understand configuration space obstacles that robots must navigate around. Master graph theory applications including trees and graph search algorithms crucial for pathfinding. Examine complete path planners that guarantee solutions when they exist, and discover grid-based methods for systematic motion planning approaches. Dive deep into sampling-based methods for motion planning across two detailed segments, understanding how probabilistic approaches can efficiently solve complex planning problems. Study virtual potential fields as an elegant approach to robot navigation, where attractive and repulsive forces guide robot movement. Conclude with nonlinear optimization techniques that refine and optimize planned robot trajectories for improved performance and efficiency.