Virtual Traffic Lights for Multi-Robot Navigation: Decentralized Planning with Centralized Conflict Resolution
By: Sagar Gupta , Thanh Vinh Nguyen , Thieu Long Phan and more
Potential Business Impact:
Robots work together safely, avoiding crashes.
We present a hybrid multi-robot coordination framework that combines decentralized path planning with centralized conflict resolution. In our approach, each robot autonomously plans its path and shares this information with a centralized node. The centralized system detects potential conflicts and allows only one of the conflicting robots to proceed at a time, instructing others to stop outside the conflicting area to avoid deadlocks. Unlike traditional centralized planning methods, our system does not dictate robot paths but instead provides stop commands, functioning as a virtual traffic light. In simulation experiments with multiple robots, our approach increased the success rate of robots reaching their goals while reducing deadlocks. Furthermore, we successfully validated the system in real-world experiments with two quadruped robots and separately with wheeled Duckiebots.
Similar Papers
Decentralized Multi-Robot Relative Navigation in Unknown, Structurally Constrained Environments under Limited Communication
Robotics
Robots find their way without getting stuck.
Multi-Objective Reinforcement Learning for Large-Scale Mixed Traffic Control
Multiagent Systems
Makes traffic lights fairer and safer for all cars.
Scalable Multi-Agent Path Finding using Collision-Aware Dynamic Alert Mask and a Hybrid Execution Strategy
Multiagent Systems
Robots find paths without bumping into each other.