Autoplay of the YouTube playlist for all videos in this chapter.  This description box will not be updated with information about each video as the videos advance.

This video introduces the general motion planning problem , several variants, and properties of different motion planners.

This video introduces the notions of C-space (configuration space) obstacles, connected components of the free configuration space, and collision detection.

This video introduces graph representations of free C-space, including undirected and directed graphs, weighted and unweighted graphs, and trees.

This video describes A* graph search, one of the most popular and efficient methods for finding optimal paths in a graph.

This video introduces roadmap methods for complete path planning: if a path exists, then a roadmap method is guaranteed to find one. Such methods tend to be applied to only simple, low-dimensional problems, however. One example, given in the video, is path planning for a planar polygon translating among polygonal obstacles.

This video introduces grid methods for path planning, where the free C-space is represented by a regular grid that can be searched using standard graph search methods (e.g., A*). To increase efficiency, multi-resolution grids can also be employed.

This video introduces the popular sampling-based probabilistic roadmap (PRM) approach to motion planning.

This video introduces the popular sampling-based rapidly-exploring random trees (RRT) approach to motion planning.

This video introduces the virtual potential field method for reactive motion planning, where obstacles are at a high potential and the goal is at the minimum potential. The negative of the gradient of the potential is a force that pushes the robot away from obstacles and toward the goal.

This video is a brief introduction to the broad field of optimization-based approaches for robot motion planning.