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.

中文翻译：

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异构多机器人系统中的弹性任务分配

对于配备异构能力的多机器人系统，本文提出了一种机制，当异常环境条件（如天气事件或对抗性攻击）影响机器人在任务中的性能时，以弹性方式将机器人分配给任务。我们的主要目标是确保为每项任务分配必要的资源水平，以分配给任务的机器人的综合能力来衡量。通过跟踪外部扰动下的任务性能偏差，我们的框架量化了机器人能力（例如，视觉传感或空中机动性）受环境条件影响的程度。这使得基于优化的框架能够根据每个任务中最退化的能力灵活地将机器人重新分配给任务。面对资源限制和不利的环境条件，我们的算法最低限度地放宽了与某些任务相对应的资源约束，从而表现出优雅的性能下降。多机器人覆盖和目标跟踪场景中的模拟实验证明了所提出方法的有效性。