能力が未知・変動する異種ロボットチームのための適応的タスク割り当て
Adaptive Task Allocation for Heterogeneous Multi-Robot Teams with Evolving and Unknown Robot Capabilities
各ロボットのタスク遂行能力が事前に不明だったり時間とともに変化する状況で、実行中の進捗から能力をオンライン推定しつつタスクを再割り当てする枠組みを提案した。
著者: Yousef Emam, Siddharth Mayya, Gennaro Notomista, Addison Bohannon, Magnus Egerstedt
分類: cs.RO, cs.SY, eess.SY, math.OC
原文アブストラクト
For multi-robot teams with heterogeneous capabilities, typical task allocation methods assign tasks to robots based on the suitability of the robots to perform certain tasks as well as the requirements of the task itself. However, in real-world deployments of robot teams, the suitability of a robot might be unknown prior to deployment, or might vary due to changing environmental conditions. This paper presents an adaptive task allocation and task execution framework which allows individual robots to prioritize among tasks while explicitly taking into account their efficacy at performing the tasks---the parameters of which might be unknown before deployment and/or might vary over time. Such a \emph{specialization} parameter---encoding the effectiveness of a given robot towards a task---is updated on-the-fly, allowing our algorithm to reassign tasks among robots with the aim of executing them. The developed framework requires no explicit model of the changing environment or of the unknown robot capabilities---it only takes into account the progress made by the robots at completing the tasks. Simulations and experiments demonstrate the efficacy of the proposed approach during variations in environmental conditions and when robot capabilities are unknown before deployment.
