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

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.

Authors

• Álvaro Serra-Gómez
• Bruno Brito
• Hai Zhu 0002
• Jen Jen Chung
• Javier Alonso-Mora

Keywords

• Robot kinematics
• Simulation
• Broadcasting
• Trajectory
• Collision avoidance
• Task analysis
• Robots
• Efficient Communication
• Multi-robot Collision Avoidance
• Time Step
• Path Planning
• Multi-agent Systems
• Trajectory Planning
• Collision Risk
• Model Predictive Control
• Transition Model
• Reward Function
• Policy Community
• Constrained Optimization Problem
• Future Trajectories
• Policy Learning
• Robot Motion
• Partial Observation
• Joint State
• Robot State
• Robot Dynamics
• Swarm Robotics
• Multi-agent Reinforcement Learning
• Velocity Of The Robot
• Nonlinear Model Predictive Control
• Micro Air Vehicles
• Constant Velocity Model
• Policy Gradient Method
• Stochastic Policy
• Observation Space
• State Space
• Set Of Robots