Path Planning for Complex Environments Using Multi-Agent RRTs

Course Project, Mentored by Dr. Vaibhav Srivastava, Dept. of ECE, Michigan State University

• Derived from the CL-RRT, I’ll implement a merit-based token system to dynamically update the planning order based on a measure of each agent’s incentive to replan, yielding a greater reduction of the global cost
• The most straightforward approach to decentralized, multi agent path planning is to allow all agents to continuously plan their own paths subject to constraints imposed by the other agents’ paths