Overview
There has been a recent explosion of interest in autonomous robots, ranging from Google and Uber’s self-driving
cars to Amazon’s delivery drones. Planning the motions of these robots so they satisfy physical constraints and do
not collide with obstacles is a challenge. This course is an introduction to algorithmic techniques for robot motion
planning. Topics will include configuration space representations, roadmap methods, cell decomposition methods,
collision detection, sampling-based path planning, nonholonomic motion planning, and multiple robot coordination.
We will motivate these techniques by applications of motion planning to mobile robots and robot manipulators,
assembly and manipulation planning, computer aided design, and computer games.
Objectives
The primary objectives of the course are to introduce students to fundamental mathematical concepts and algorithmic approaches for robot motion planning. Through written homeworks and programming assignments, students will learn to implement basic motion planners for autonomous robots and will be exposed to open-source motion planning software.

Eligibility:

• You are excited about developing motion planners for robotics and automation.
• You are a senior undergraduate student, postgraduate student or faculty member in
  engineering and computer science.
• You are engineers and researchers from industry, government organizations, and R&D
  laboratories working on robotics

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