发表机构
The University of Manchester; Italian Institute of Technology; Genisom AI; University of Warwick; Newcastle University（曼彻斯特大学; 意大利技术研究院; Genisom AI; 华威大学; 纽卡斯尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出CoCoNav框架，结合在线保形校准与“先松弛再验证”规划器，在人群环境机器人导航中实现了避碰、任务成功与效率的良好平衡。
AI 中文摘要
人群环境中安全高效的机器人导航需要预测行人运动，同时应对不确定且可能变化的预测误差。现有反应式方法易产生振荡行为，而预测式规划器常将预测结果视为精确值或依赖限制性误差模型；将保守不确定性集作为硬约束还会导致模型预测控制（MPC）不可行。本文提出人群导航框架CoCoNav，其结合在线保形校准与运行时可验证规划。一种针对特定时间范围的保形比例-积分控制器可调整轨迹误差边界以调节长期经验覆盖率，使框架能应对变化的预测误差。一种“先松弛再验证”规划器通过带软约束的MPC生成标称轨迹，并在执行前连同应急机动动作一起对照校准后的边界单独验证，从而保持求解器可行性。仿真与四足机器人实验表明，与所评估的基线方法相比，CoCoNav在避碰、任务成功率和导航效率之间实现了良好平衡。
英文摘要
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.
