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.

翻译：摘要：多机器人系统通过并发协同行动能高效完成团队任务。然而，现有方法要么假设静态任务特征，要么在环境变化时简单重新规划。本文解决了一个挑战性问题：协调多机器人系统以完成涉及动态移动目标的协作任务。我们通过保形预测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指定的时空约束。提出的UMBRELLA框架将部分计划上的蒙特卡洛树搜索与不确定性感知展开相结合，并引入基于保形预测的指标来引导和加速搜索。优化目标是最小化平均完工时间的条件风险价值。针对在线发布的任务，采用滚动时域规划方案根据更新的任务规范和运动预测动态调整分配。任务间的时空约束始终得到保证，在线执行期间协作任务仅需部分同步。大规模仿真与硬件实验表明，与静态基准相比，平均完工时间及其方差分别降低23%和71%。