Multi-agent pursuit ensures safety with smart communication control
Robust Multi-Agent Pursuit with Preemptive Communication via Reachability-Based Control Barrier Functions
Multiagent SystemsRobotics
Summary
Robots working together often need to share their positions to avoid bumping into each other, but in real life, they can’t always communicate perfectly. The authors came up with a way for each robot to guess where others might be based on the last message received and decide when to share its own position again to keep everyone safe. This method reduces unnecessary communication while still preventing collisions. They tested it in chasing games to show it works well even when messages aren’t continuous.
What this means in practice
- •For robotics teams: Coordinate multiple robots safely when communication is unreliable by reducing unnecessary state transmissions while avoiding collisions.
- •For autonomous vehicle operators: Improve coordination and safety among self-driving vehicles exchanging position data intermittently to prevent crashes in dynamic environments.
Authors
Madison Book, Justin Bradley
Abstract
Safe multi-agent coordination typically assumes what real-world, deployed, resource-constrained robots cannot provide: continuous, perfect state sharing. Robust control strategies must be complemented with robust communication strategies to deliver resilient deployments. In this work, we propose a unified control/communication strategy that provides safe control to guarantee inter-agent safety under intermittent communication and demonstrate its effectiveness in a pursuit-evasion scenario. Each agent computes the reachable set of neighboring agents based on the last state information received, then solves for a safe control input based on control barrier functions to ensure agents avoid collision. We prove that inter-agent collision avoidance is guaranteed provided communication staleness remains bounded. As the reachable set inflates between state transmissions, each agent reasons about when to preemptively transmit its state information to neighboring agents based on novel metrics characterizing uncertainty risk and kinematic risk. We implement and simulate a variety of pursuit-evasion scenarios and show that preemptive communication reduces messages by 77.5% compared to periodic and event-triggered baselines with zero collisions.