让机器人在人群中安全导航，自动适应预测误差变化。
CoCoNav: Conformal Control for Safe Robot Navigation in Crowds
- 用在线校准的置信区间动态调整轨迹误差范围。
- 仿真与四足实验中碰撞率低、任务成功率高、效率优于基线。
- 适合对安全性要求高的实时机器人导航场景。
人群中的安全高效机器人导航需在预测行人运动时应对不确定且可能变化的误差。现有反应式方法易产生振荡行为，而预测规划器常将预测视为精确或依赖受限误差模型。将保守不确定性集作为硬约束会导致模型预测控制（MPC）不可行。本文提出CoCoNav框架，结合在线置信校准与运行时认证规划。基于时间窗的置信比例-积分控制器动态调节轨迹误差边界，确保长期经验覆盖度；采用“松弛-验证”规划策略，先以软约束生成参考轨迹，再单独验证其及应急动作是否满足校准后的误差边界。仿真与四足机器人实验表明，CoCoNav在避免碰撞、任务成功和导航效率之间取得良好平衡，优于对比基线。
原文摘要 · Abstract (English)
Safe and efficient robot navigation in crowds requires anticipating pedestrian motion despite uncertain and potentially shifting prediction errors. Existing reactive methods can produce oscillatory behavior, while predictive planners often treat forecasts as exact or rely on restrictive error models. Incorporating conservative uncertainty sets as hard constraints can also render model predictive control (MPC) infeasible. We propose \textit{CoCoNav}, a crowd-navigation framework that combines online conformal calibration with runtime-certified planning. A horizon-specific conformal proportional--integral controller adapts trajectory-error bounds to regulate long-run empirical coverage, enabling the framework to respond to changing prediction errors. A \textit{relax-then-verify} planner preserves solver feasibility by generating nominal trajectories with soft-constrained MPC and separately certifying them, together with contingency maneuvers, against the calibrated bounds before execution. Simulations and quadruped experiments show that CoCoNav achieves a favorable balance among collision avoidance, task success, and navigation efficiency relative to the evaluated baselines.
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