共有環境における人間の動きを考慮したマルチロボットタスク割り当て
Efficient Human-Aware Task Allocation for Multi-Robot Systems in Shared Environments
人間の移動パターンを捉える動きのマップ(MoD)を活用し、人間との干渉による遅延を考慮したマルチロボットのタスク割り当て手法を提案。従来手法より最大26%ミッション完了時間を短縮した。
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著者: Maryam Kazemi Eskeri, Ville Kyrki, Dominik Baumann, Tomasz Piotr Kucner
分類: cs.RO
原文アブストラクト
Multi-robot systems are increasingly deployed in applications, such as intralogistics or autonomous delivery, where multiple robots collaborate to complete tasks efficiently. One of the key factors enabling their efficient cooperation is Multi-Robot Task Allocation (MRTA). Algorithms solving this problem optimize task distribution among robots to minimize the overall execution time. In shared environments, apart from the relative distance between the robots and the tasks, the execution time is also significantly impacted by the delay caused by navigating around moving people. However, most existing MRTA approaches are dynamics-agnostic, relying on static maps and neglecting human motion patterns, leading to inefficiencies and delays. In this paper, we introduce \acrfull{method name}. This method leverages Maps of Dynamics (MoDs), spatio-temporal queryable models designed to capture historical human movement patterns, to estimate the impact of humans on the task execution time during deployment. \acrshort{method name} utilizes a stochastic cost function that includes MoDs. Experimental results show that integrating MoDs enhances task allocation performance, resulting in reduced mission completion times by up to $26\%$ compared to the dynamics-agnostic method and up to $19\%$ compared to the baseline. This work underscores the importance of considering human dynamics in MRTA within shared environments and presents an efficient framework for deploying multi-robot systems in environments populated by humans.
