異種マルチロボットシステムにおける耐障害性のあるタスク割り当て
Resilient Task Allocation in Heterogeneous Multi-Robot Systems
天候や攻撃でロボットの能力が劣化しても、各タスクに必要な能力を確保できるよう、劣化した能力に基づいてロボットを柔軟に再割り当てする最適化手法を提案した論文。
著者: Siddharth Mayya, Diego S. D'antonio, David Saldaña, Vijay Kumar
分類: cs.MA, cs.RO
原文アブストラクト
For a multi-robot system equipped with heterogeneous capabilities, this paper presents a mechanism to allocate robots to tasks in a resilient manner when anomalous environmental conditions such as weather events or adversarial attacks affect the performance of robots within the tasks. Our primary objective is to ensure that each task is assigned the requisite level of resources, measured as the aggregated capabilities of the robots allocated to the task. By keeping track of task performance deviations under external perturbations, our framework quantifies the extent to which robot capabilities (e.g., visual sensing or aerial mobility) are affected by environmental conditions. This enables an optimization-based framework to flexibly reallocate robots to tasks based on the most degraded capabilities within each task. In the face of resource limitations and adverse environmental conditions, our algorithm minimally relaxes the resource constraints corresponding to some tasks, thus exhibiting a graceful degradation of performance. Simulated experiments in a multi-robot coverage and target tracking scenario demonstrate the efficacy of the proposed approach.
