Path Planning for Complex Environments Using Multi-Agent RRTs
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