Multi-objective crowd-aware robot navigation system using deep reinforcement learning

Navigating efficiently and safely through human crowds is essential for mobile robots in diverse applications such as delivery services, home assistance, healthcare, and manufacturing. However, traditional navigation methods are adversely affected by the high randomness of human movements, seriously hindering robot navigation in crowd environments. To tackle these problems, this paper proposes a deep reinforcement learning-based multi-objective crowd-aware robot navigation system called Multi-Objective Dual-Selection Reinforcement Learning (MODSRL). To deal with multiple objectives, including safety, time efficiency, collision avoidance, and path smoothness during navigation, a set of reward functions is used in MODSRL. To address the challenge of hesitation at the beginning when navigating in a crowd environment, a Dual-Selection Attention Module is developed, which enables the robot to make efficient decisions while reducing hesitation. Experimental results demonstrate that the proposed MODSRL outperforms existing approaches in terms of five different metrics. In particular, the average success rate of the proposed MODSRL outperforms ERVO, CADRL, LSTM-RL, and OM-SARL algorithms by 17.1%, 26.4%, 16.6%, and 9.2%, respectively, sufficing to show its robustness in complex crowd environments.