让机器人在人群中导航时兼顾安全、效率与社交礼仪。
Socially Aware Robot Crowd Navigation via Online Uncertainty-Driven Risk Adaptation
- 用神经网络学习风险，实时评估路径候选的安全性。
- 在复杂人群环境中成功率更高，社交行为更自然。
- 适合需要人机共处的智能服务场景，如商场导览。
在人类与机器人共享的拥挤环境中进行导航仍具挑战性，要求机器人既高效又尊重人类运动习惯。现有方法多侧重安全或效率，忽视社交意识。本文提出基于学习的风险模型预测控制（LR-MPC），一种数据驱动的导航算法，平衡效率、安全与社交意识。LR-MPC包含两个阶段：离线风险学习阶段，使用启发式MPC基线（HR-MPC）生成的风险数据训练概率集成神经网络（PENN）；在线自适应推理阶段，通过多路径快速随机树（Multi-RRT）规划器生成局部航点，并由PENN评估各航点风险，结合认知不确定性与随机不确定性进行预测过滤，最终选取最安全航点作为MPC输入实现实时导航。大量实验表明，相比基线方法，LR-MPC在成功率与社交意识上均有提升，使机器人能在复杂人群环境中高度适应且干扰极低。
原文摘要 · Abstract (English)
Navigation in human-robot shared crowded environments remains challenging, as robots are expected to move efficiently while respecting human motion conventions. However, many existing approaches emphasize safety or efficiency while overlooking social awareness. This article proposes Learning-Risk Model Predictive Control (LR-MPC), a data-driven navigation algorithm that balances efficiency, safety, and social awareness. LR-MPC consists of two phases: an offline risk learning phase, where a Probabilistic Ensemble Neural Network (PENN) is trained using risk data from a heuristic MPC-based baseline (HR-MPC), and an online adaptive inference phase, where local waypoints are sampled and globally guided by a Multi-RRT planner. Each candidate waypoint is evaluated for risk by PENN, and predictions are filtered using epistemic and aleatoric uncertainty to ensure robust decision-making. The safest waypoint is selected as the MPC input for real-time navigation. Extensive experiments demonstrate that LR-MPC outperforms baseline methods in success rate and social awareness, enabling robots to navigate complex crowds with high adaptability and low disruption. A website about this work is available at https://sites.google.com/view/lr-mpc.
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