Collision Avoidance Among Dense Heterogeneous Agents Using Deep Reinforcement Learning

Abstract

Navigating in a complex congested social environment without collision is a crucial and challenging task. Recent studies have demonstrated the considerable success of Deep Reinforcement Learning (DRL) in multi-agent collision avoidance. However, the assumption of these studies that agents are homogeneous circles deviates from reality, leading to performance deterioration in congested scenarios. The current work extends the DRL-based approaches to develop a collision avoidance method for congested scenarios wherein the heterogeneity of agents can no longer be disregarded. Considering shape heterogeneity, we use the Orientated Bounding Capsule (OBC) to model the agents and transform the interactive state space of Robot-Obstacle agent pair. For speed heterogeneity, we design a velocity-related collision risk function to shape the behavior of the robot. Experimental results demonstrate that our proposed method outperforms state-of-the-art DRL-based approaches in terms of success rate and safety. It also exhibits desired collision avoidance behavior.