Relative Velocity-Based Reward Model for Socially-Aware Navigation with Deep Reinforcement Learning

Abstract

Mobile robots are increasingly deployed in shared environments where they must learn to navigate alongside humans. Deep Reinforcement Learning (DRL) techniques have shown promise in developing navigation policies that account for interactions within crowds, fostering socially acceptable movement. However, these techniques often depend heavily on collision avoidance rewards to ensure safe navigation. In this study, we introduce a novel reward component based on relative velocity for collision avoidance, which integrates both the robot's and humans' kinematics within personal distance constraints. We conducted a thorough evaluation comparing this new reward model against a conventional one in simulated environments using advanced DRL methods. Our findings indicate that the proposed reward model improves the robots' ability to avoid collisions and navigate towards their goals while being socially acceptable.