让机器人在人群中实时调整行为优先级，更安全高效地导航。
Learning Dynamic Weight Adjustment for Spatial-Temporal Trajectory Planning in Crowd Navigation
- 用神经网络动态预测运动规划中各目标的权重。
- 仿真和实测均显示安全性优于固定权重与现有方法。
- 适合需要在人群里自主避障的移动机器人应用。
密集人群中的机器人导航因人类行为复杂、环境动态且障碍物多而极具挑战。本文提出一种基于神经网络的动态权重调整方案，用于优化型运动规划器中目标权重的自适应选择。采用时空轨迹规划方法，融合多重目标以平衡安全、效率与目标达成。设计了网络结构、观测编码方式及奖励函数，通过强化学习训练策略网络，使机器人能根据环境与行人信息实时调整行为。仿真结果表明，相比固定权重规划器及当前最先进的学习方法，本方法显著提升安全性，并验证了所学策略可根据实际场景自适应调整权重。该方法在一条300米长的拥挤走廊中，通过自主配送机器人任务得到验证，证明了其可行性。
原文摘要 · Abstract (English)
Robot navigation in dense human crowds poses a significant challenge due to the complexity of human behavior in dynamic and obstacle-rich environments. In this work, we propose a dynamic weight adjustment scheme using a neural network to predict the optimal weights of objectives in an optimization-based motion planner. We adopt a spatial-temporal trajectory planner and incorporate diverse objectives to achieve a balance among safety, efficiency, and goal achievement in complex and dynamic environments. We design the network structure, observation encoding, and reward function to effectively train the policy network using reinforcement learning, allowing the robot to adapt its behavior in real time based on environmental and pedestrian information. Simulation results show improved safety compared to the fixed-weight planner and the state-of-the-art learning-based methods, and verify the ability of the learned policy to adaptively adjust the weights based on the observed situations. The approach's feasibility is demonstrated in a navigation task using an autonomous delivery robot across a crowded corridor over a 300 m distance.
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