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.

翻译：确保在人类密集环境中安全导航对于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在拥挤区域预测人类轨迹的有效方法，但由于人类运动的不确定性（可能导致这些模型的预测不精确），如何安全地将这些学习模型整合到控制回路中仍不明确。本研究针对这一挑战，提出了一种分布式鲁棒机会约束模型预测控制（DRCC-MPC），该方法：(i) 采用碰撞概率作为预设的、可解释的风险指标；(ii) 针对实际人类轨迹与预测轨迹之间的差异提供鲁棒性。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度量。为实现机会约束的实时评估，我们采用分布式鲁棒条件风险价值约束对机会约束进行保守近似。所提出的公式在计算效率与对分布外人类运动的鲁棒性方面均具优势。通过并行化采样优化技术，该方法可实现实时运行，并在多个基于真实行人数据的案例研究中展示了成功且安全的导航性能。