Risk-Aware Deep Reinforcement Learning for Robot Crowd Navigation

Abstract

Ensuring safe and efficient navigation in crowded environments is a critical goal for assistive robots. Recent studies have emphasized the potential of deep reinforcement learning techniques to enhance robots’ navigation capabilities in the presence of crowds. However, current deep reinforcement learning methods often face the challenge of robots freezing as crowd density increases. To address this issue, a novel risk-aware deep reinforcement learning approach is proposed in this paper. The proposed method integrates a risk function to assess the probability of collision between the robot and pedestrians, enabling the robot to proactively prioritize pedestrians with a higher risk of collision. Furthermore, the model dynamically adjusts the fusion strategy of learning-based and risk-aware-based features, thereby improving the robustness of robot navigation. Evaluations were conducted to determine the effectiveness of the proposed method in both low- and high-crowd density settings. The results exhibited remarkable navigation success rates of 98.0% and 93.2% in environments with 10 and 20 pedestrians, respectively. These findings emphasize the robust performance of the proposed method in successfully navigating through crowded spaces. Additionally, the approach achieves navigation times comparable to those of state-of-the-art methods, confirming its efficiency in accomplishing navigation tasks. The generalization capability of the method was also rigorously assessed by subjecting it to testing in crowd environments exceeding the training density. Notably, the proposed method attains an impressive navigation success rate of 90.0% in 25-person environments, surpassing the performance of existing approaches and establishing itself as a state-of-the-art solution. This result highlights the versatility and effectiveness of the proposed method in adapting to various crowd densities and further reinforces its applicability in real-world scenarios.