## Book Chapter: Chapter 10

Motion Planning

#### Chapter 10 Autoplay

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.

#### 10.1. Overview of Motion Planning

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

#### 10.2.1. C-Space Obstacles

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

#### 10.2.3. Graphs and Trees

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

#### 10.2.4. Graph Search

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

#### 10.3. Complete Path Planners

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.

#### 10.4. Grid Methods for Motion Planning

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.

#### 10.5. Sampling Methods for Motion Planning (Part 1 of 2)

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

#### 10.5. Sampling Methods for Motion Planning (Part 2 of 2)

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

#### 10.6. Virtual Potential Fields

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.

#### 10.7. Nonlinear Optimization

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