A drl based distributed formation control scheme with stream based collision avoidance

TL;DR

This paper introduces a deep reinforcement learning-based distributed control scheme for autonomous vehicle formation and collision avoidance, utilizing local sensors and a stream-based obstacle avoidance method to improve scalability and performance.

Contribution

It presents a novel DRL-based distributed formation control approach that integrates stream-based obstacle avoidance and local sensor data for scalable multi-agent coordination.

Findings

Outperforms existing algorithms in formation maintenance

Reduces collision rates effectively

Achieves scalable distributed control

Abstract

Formation and collision avoidance abilities are essential for multi-agent systems. Conventional methods usually require a central controller and global information to achieve collaboration, which is impractical in an unknown environment. In this paper, we propose a deep reinforcement learning (DRL) based distributed formation control scheme for autonomous vehicles. A modified stream-based obstacle avoidance method is applied to smoothen the optimal trajectory, and onboard sensors such as Lidar and antenna arrays are used to obtain local relative distance and angle information. The proposed scheme obtains a scalable distributed control policy which jointly optimizes formation tracking error and average collision rate with local observations. Simulation results demonstrate that our method outperforms two other state-of-the-art algorithms on maintaining formation and collision avoidance.