异质多机器人任务分配用于动态场景中的长续航任务
Heterogeneous Multi-robot Task Allocation for Long-Endurance Missions in Dynamic Scenarios
AI总结：
本文提出了一种异质多机器人任务分配框架，通过混合整数线性规划和启发式算法解决动态场景中长续航任务的协调分配问题，并验证了其在空中机器人检查任务中的有效性。
摘要：
我们提出了一种用于异质团队在动态场景中执行长续航任务的多机器人任务分配（MRTA）框架。鉴于机器人的电池有限，尤其是对于空中车辆，我们允许机器人充电，并且允许将某些任务拆分和/或中继。我们还处理必须由一组机器人协调执行的任务。鉴于这些特性，我们引入了一种新的异质MRTA问题，我们将其理论分析并最优地形式化为混合整数线性规划问题。然后，我们贡献了一个启发式算法来计算近似解，并将其集成到一个能够通过在线修复或重新计算计划来应对意外事件的使命规划和执行架构中。我们的实验结果表明，我们新提出的任务在现实情况下使用空中机器人进行检查的应用相关性。我们评估了我们的启发式求解器在小规模场景中与其他变体和精确最优解的性能。此外，我们评估了我们的重新规划框架在线上修复计划的能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
