一种基于特质的任务分配与调度的交叉方法
An Interleaved Approach to Trait-Based Task Allocation and Scheduling
摘要：
为了实现有效的异构多机器人团队，研究人员必须利用个体机器人的相对优势，协调它们的个体行为。具体来说，异构多机器人系统必须回答三个重要问题：\textit{who}（任务分配）、\textit{when}（调度）和\textit{how}（运动规划）.虽然每个问题的具体变体都是NP难的，但它们的相互依赖只会加剧共同解决这些问题所面临的挑战。在本文中，我们提出了一个新的框架，交错任务分配，调度和运动规划。本文提出了一种基于搜索的增量任务分配图搜索方法ITAGS（Incremental Task Allocation Graph Search）。与按顺序解决这三个问题的方法相比，ITAGS的交错方法可以有效地搜索分配，同时满足调度约束并考虑执行运动计划所需的时间。为了使有效的交错，我们开发了一个凸组合的两个搜索算法，优化的任务要求的满足，以及相关的时间表的完工时间。我们证明了ITAGS的有效性，使用详细的消融研究和比较两个国家的最先进的算法在模拟的紧急响应域。
英文摘要：
To realize effective heterogeneous multi-robot teams, researchers must leverage individual robots' relative strengths and coordinate their individual behaviors. Specifically, heterogeneous multi-robot systems must answer three important questions: \textit{who} (task allocation), \textit{when} (scheduling), and \textit{how} (motion planning). While specific variants of each of these problems are known to be NP-Hard, their interdependence only exacerbates the challenges involved in solving them together. In this paper, we present a novel framework that interleaves task allocation, scheduling, and motion planning. We introduce a search-based approach for trait-based time-extended task allocation named Incremental Task Allocation Graph Search (ITAGS). In contrast to approaches that solve the three problems in sequence, ITAGS's interleaved approach enables efficient search for allocations while simultaneously satisfying scheduling constraints and accounting for the time taken to execute motion plans. To enable effective interleaving, we develop a convex combination of two search heuristics that optimizes the satisfaction of task requirements as well as the makespan of the associated schedule. We demonstrate the efficacy of ITAGS using detailed ablation studies and comparisons against two state-of-the-art algorithms in a simulated emergency response domain.
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