Crowd navigation has received increasing attention from researchers over the last few decades, resulting in the emergence of numerous approaches aimed at addressing this problem to date. Our proposed approach couples agent motion prediction and planning to avoid the freezing robot problem while simultaneously capturing multi-agent social interactions by utilizing a state-of-the-art trajectory prediction model i.e., social long short-term memory model (Social-LSTM). Leveraging the output of Social-LSTM for the prediction of future trajectories of pedestrians at each time-step given the robot's possible actions, our framework computes the optimal control action using Model Predictive Control (MPC) for the robot to navigate among pedestrians. We demonstrate the effectiveness of our proposed approach in multiple scenarios of simulated crowd navigation and compare it against several state-of-the-art reinforcement learning-based methods.

翻译：近年来，人群导航问题受到研究者日益关注，迄今已涌现出多种解决方案。我们提出的方法将智能体运动预测与规划相结合，通过采用当前最先进的轨迹预测模型——社交长短期记忆模型（Social-LSTM），在避免机器人冻结问题的同时捕捉多智能体社交交互。该框架利用Social-LSTM在每个时间步基于机器人可能动作对行人未来轨迹的预测输出，采用模型预测控制（MPC）计算机器人的最优控制动作，实现其在行人中的自主导航。我们在多场景模拟人群导航中验证了所提方法的有效性，并与多种基于强化学习的先进方法进行了对比。