With Whom to Communicate: Learning Efficient Communication for Multi-Robot Collision Avoidance

TL;DR

This paper introduces an adaptive communication strategy for multi-robot collision avoidance, enabling robots to decide when and with whom to communicate, reducing unnecessary data exchange while maintaining safety in dynamic environments.

Contribution

It proposes a novel learning-based method for robots to selectively communicate based on collision risk, improving efficiency over traditional constant broadcasting approaches.

Findings

Reduced communication load compared to baseline methods.

Maintained collision avoidance performance with less data exchange.

Effective in simulation with quadrotor teams.

Abstract

Decentralized multi-robot systems typically perform coordinated motion planning by constantly broadcasting their intentions as a means to cope with the lack of a central system coordinating the efforts of all robots. Especially in complex dynamic environments, the coordination boost allowed by communication is critical to avoid collisions between cooperating robots. However, the risk of collision between a pair of robots fluctuates through their motion and communication is not always needed. Additionally, constant communication makes much of the still valuable information shared in previous time steps redundant. This paper presents an efficient communication method that solves the problem of "when" and with "whom" to communicate in multi-robot collision avoidance scenarios. In this approach, every robot learns to reason about other robots' states and considers the risk of future collisions before asking for the trajectory plans of other robots. We evaluate and verify the proposed communication strategy in simulation with four quadrotors and compare it with three baseline strategies: non-communicating, broadcasting and a distance-based method broadcasting information with quadrotors within a predefined distance.