Current state-of-the-art crowd navigation approaches are mainly deep reinforcement learning (DRL)-based. However, DRL-based methods suffer from the issues of generalization and scalability. To overcome these challenges, we propose a method that includes a Collision Probability (CP) in the observation space to give the robot a sense of the level of danger of the moving crowd to help the robot navigate safely through crowds with unseen behaviors. We studied the effects of changing the number of moving obstacles to pay attention during navigation. During training, we generated local waypoints to increase the reward density and improve the learning efficiency of the system. Our approach was developed using deep reinforcement learning (DRL) and trained using the Gazebo simulator in a non-cooperative crowd environment with obstacles moving at randomized speeds and directions. We then evaluated our model on four different crowd-behavior scenarios. The results show that our method achieved a 100% success rate in all test settings. We compared our approach with a current state-of-the-art DRL-based approach, and our approach has performed significantly better, especially in terms of social safety. Importantly, our method can navigate in different crowd behaviors and requires no fine-tuning after being trained once. We further demonstrated the crowd navigation capability of our model in real-world tests.

翻译：当前最先进的人群导航方法主要基于深度强化学习（DRL）。然而，基于DRL的方法存在泛化性和可扩展性问题。为克服这些挑战，我们提出了一种方法，在观测空间中引入碰撞概率（CP），使机器人能够感知移动人群的危险程度，从而帮助其在面对未知行为的人群时安全导航。我们研究了在导航过程中调整需关注的移动障碍物数量的影响。在训练过程中，我们生成了局部路径点以增加奖励密度并提高系统的学习效率。该方法基于深度强化学习（DRL）开发，并在Gazebo模拟器中以非协作人群环境（障碍物以随机速度和方向移动）进行训练。随后，我们在四种不同人群行为场景下评估了模型。结果表明，我们的方法在所有测试设置中均实现了100%的成功率。我们将该方法与当前最先进的基于DRL的方法进行对比，结果显示我们的方法表现显著更优，尤其在社交安全性方面。重要的是，该方法能够适应不同人群行为，且仅需一次训练后无需微调。我们进一步通过实际测试验证了模型的群体导航能力。