Compared to a single robot, multi-robot systems (MRS) offer several advantages in complex multi-task scenarios. The overall efficiency of MRS relies heavily on an efficient task allocation and scheduling process. Multi-robot task allocation (MRTA) is often formulated as a multiple traveling salesman problem, which is NP-hard and typically addressed offline. This paper specifically addresses the online allocation problem in multi-manipulator systems within multi-task scenarios. The tasks are initially pre-allocated to alleviate the computational burden of online allocation. Subsequently, considering collision constraints, we search for the current feasible set of manipulators and employ greedy algorithms to achieve local optima as the online allocation result within this set. Our method can handle the online addition of new, unknown tasks to the task list. Moreover, we demonstrate the feasibility of our approach through simulations and on a realistic platform, where multiple manipulators are tasked with polishing the white body of automobile parts. The results demonstrate that our method is effective and efficient for online allocation and scheduling scenarios.

中文翻译：

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考虑碰撞约束和未知任务的多机械臂系统在线任务分配与调度


与单个机器人相比，多机器人系统（MRS）在复杂的多任务场景中具有多种优势。 MRS的整体效率在很大程度上依赖于高效的任务分配和调度过程。多机器人任务分配（MRTA）通常被表述为多个旅行商问题，这是 NP 难题，通常是离线解决的。本文专门解决了多任务场景下多机械臂系统的在线分配问题。任务最初是预先分配的，以减轻在线分配的计算负担。随后，考虑碰撞约束，我们搜索当前可行的操纵器集合，并采用贪心算法实现局部最优作为该集合内的在线分配结果。我们的方法可以处理在线添加新的、未知的任务到任务列表中。此外，我们通过模拟和在现实平台上证明了我们的方法的可行性，其中多个机械手负责抛光汽车零件的白色车身。结果表明，我们的方法对于在线分配和调度场景是有效且高效的。