We propose an approach to find low-makespan solutions to multi-robot multi-task planning problems in environments where robots block each other from completing tasks simultaneously. We introduce a formulation of the problem that allows for an approach based on greedy descent with random restarts for generation of the task assignment and task sequence. We then use a multi-agent path planner to evaluate the makespan of a given assignment and sequence. The planner decomposes the problem into multiple simple subproblems that only contain a single robots and a single task, and can thus be solved quickly to produce a solution for a fixed task sequence. The solutions to the subproblems are then combined to form a valid solution to the original problem. We showcase the approach on robotic stippling and robotic bin picking with up to 4 robot arms. The makespan of the solutions found by our algorithm are up to 30% lower compared to a greedy approach.

翻译：我们提出了一种方法，用于在机器人因任务同时执行而相互阻塞的环境中，为多机器人多任务规划问题寻找低完工期解决方案。我们对问题进行了形式化建模，从而允许采用一种基于随机重启的贪心下降策略来生成任务分配与任务序列。随后，我们使用多智能体路径规划器评估给定分配与序列的完工期。该规划器将原问题分解为多个仅包含单个机器人与单个任务的简单子问题，因此能够快速求解，为固定任务序列生成解决方案。子问题的解被组合后形成原问题的可行解。我们在最多包含4个机器人手臂的机器人点画与机器人料箱拾取任务中展示了该方法。与贪心方法相比，我们的算法找到的解的完工期最多降低了30%。