解决动态环境下多机器人长期任务分配难题，支持充电与任务拆分。
Heterogeneous Multi-robot Task Allocation for Long-Endurance Missions in Dynamic Scenarios
- 构建可充电、可拆分任务的异构机器人协同框架
- 提出混合整数线性规划模型并设计高效启发式算法
- 支持在线重规划，适用于无人机巡检等真实场景
本文针对异构机器人团队在动态环境中执行长期任务的多机器人任务分配（MRTA）问题，提出一种新框架。由于机器人（尤其是空中机器人）电池有限，该框架允许机器人进行充电，并支持任务的分割或接力完成。同时处理需多机器人协同执行的任务。我们理论分析了这一新型问题，并将其最优建模为混合整数线性规划（MILP）。进一步设计了一种启发式算法以求解近似解，并集成到可应对突发情况的在线任务规划与执行架构中，实现计划的实时修复或重计算。实验验证了该问题在无人机巡检等实际场景中的重要性；在小规模场景中，我们的启发式算法性能优于其他变体，并接近精确解；同时证明了在线重规划框架的有效性。
原文摘要 · Abstract (English)
We present a framework for Multi-Robot Task Allocation (MRTA) in heterogeneous teams performing long-endurance missions in dynamic scenarios. Given the limited battery of robots, especially for aerial vehicles, we allow for robot recharges and the possibility of fragmenting and/or relaying certain tasks. We also address tasks that must be performed by a coalition of robots in a coordinated manner. Given these features, we introduce a new class of heterogeneous MRTA problems which we analyze theoretically and optimally formulate as a Mixed-Integer Linear Program. We then contribute a heuristic algorithm to compute approximate solutions and integrate it into a mission planning and execution architecture capable of reacting to unexpected events by repairing or recomputing plans online. Our experimental results show the relevance of our newly formulated problem in a realistic use case for inspection with aerial robots. We assess the performance of our heuristic solver in comparison with other variants and with exact optimal solutions in small-scale scenarios. In addition, we evaluate the ability of our replanning framework to repair plans online.
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