将预测运动不确定性与分布鲁棒风险感知控制相结合用于人群中的机器人安全导航
Integrating Predictive Motion Uncertainties with Distributionally Robust Risk-Aware Control for Safe Robot Navigation in Crowds
AI总结：
针对人群中机器人安全导航时人类运动预测不确定性难融入控制的问题，提出分布鲁棒机会约束MPC方法，用分布鲁棒条件风险价值近似实现实时计算，经真实行人数据验证可高效安全导航。
摘要：
在有人群的环境中确保安全导航对自主移动机器人至关重要。尽管机器学习的最新进展提供了预测拥挤区域中人类轨迹的有效方法，但由于人类运动的不确定性会导致模型预测结果不精确，如何将这些学习模型安全地整合到控制回路中仍不明确。在本研究中，我们应对这一挑战，提出了一种分布鲁棒机会约束模型预测控制（DRCC-MPC）方法，该方法：(i) 采用碰撞概率作为预先指定的、可解释的风险指标；(ii) 针对人类实际轨迹与其预测结果之间的偏差提供鲁棒性。我们以机会约束的形式考虑碰撞风险，为机器人安全性提供可解释的衡量标准。为实现机会约束的实时评估，我们采用分布鲁棒条件风险价值约束形式的机会约束保守近似。由此得到的公式既具备计算效率，又对分布外的人类运动具有鲁棒性。通过基于采样的优化技术的并行化，我们的方法可实时运行，在多项采用真实行人数据的案例研究中实现了成功且安全的导航。
英文摘要：
Ensuring safe navigation in human-populated environments is crucial for autonomous mobile robots. Although recent advances in machine learning offer promising methods to predict human trajectories in crowded areas, it remains unclear how one can safely incorporate these learned models into a control loop due to the uncertain nature of human motion, which can make predictions of these models imprecise. In this work, we address this challenge and introduce a distributionally robust chance-constrained model predictive control (DRCC-MPC) which: (i) adopts a probability of collision as a pre-specified, interpretable risk metric, and (ii) offers robustness against discrepancies between actual human trajectories and their predictions. We consider the risk of collision in the form of a chance constraint, providing an interpretable measure of robot safety. To enable real-time evaluation of chance constraints, we consider conservative approximations of chance constraints in the form of distributionally robust Conditional Value at Risk constraints. The resulting formulation offers computational efficiency as well as robustness with respect to out-of-distribution human motion. With the parallelization of a sampling-based optimization technique, our method operates in real-time, demonstrating successful and safe navigation in a number of case studies with real-world pedestrian data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
