多机器人协同追踪动态目标，用不确定性预测提升规划鲁棒性
UMBRELLA: Uncertainty-aware Multi-robot Reactive Coordination under Dynamic Temporal Logic Tasks
- 结合置信预测与蒙特卡洛树搜索，动态评估目标运动不确定性
- 平均完成时间降低23%，方差减少71%，优于静态基线
- 适合在线任务调度、移动目标追踪等实时协作场景
多机器人系统通过并行协作可高效完成团队任务。但现有方法通常假设任务特征静态，或仅在环境变化时重新规划。本文解决涉及动态移动目标的多机器人协同任务协调难题。通过共形预测（CP）显式建模目标运动预测的不确定性，同时满足线性时序逻辑（LTL）规定的时间空间约束。提出的UMBRELLA框架将部分计划的蒙特卡洛树搜索（MCTS）与不确定性感知回溯相结合，并引入基于CP的度量加速搜索过程，目标是最小化平均完工时间的条件风险价值（CVaR）。对于在线释放的任务，采用滚动时域规划动态调整分配，依据更新的任务规范和运动预测。始终保证任务间的时空约束，且在线执行时仅需部分同步即可完成协作。大规模仿真与硬件实验表明，相比静态基线，平均完成时间减少23%，方差降低71%。
原文摘要 · Abstract (English)
Multi-robot systems can be extremely efficient for accomplishing team-wise tasks by acting concurrently and collaboratively. However, most existing methods either assume static task features or simply replan when environmental changes occur. This paper addresses the challenging problem of coordinating multi-robot systems for collaborative tasks involving dynamic and moving targets. We explicitly model the uncertainty in target motion prediction via Conformal Prediction(CP), while respecting the spatial-temporal constraints specified by Linear Temporal Logic (LTL). The proposed framework (UMBRELLA) combines the Monte Carlo Tree Search (MCTS) over partial plans with uncertainty-aware rollouts, and introduces a CP-based metric to guide and accelerate the search. The objective is to minimize the Conditional Value at Risk (CVaR) of the average makespan. For tasks released online, a receding-horizon planning scheme dynamically adjusts the assignments based on updated task specifications and motion predictions. Spatial and temporal constraints among the tasks are always ensured, and only partial synchronization is required for the collaborative tasks during online execution. Extensive large-scale simulations and hardware experiments demonstrate substantial reductions in both the average makespan and its variance by 23% and 71%, compared with static baselines.
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