论文标题
异质多机器人系统中的弹性任务分配
Resilient Task Allocation in Heterogeneous Multi-Robot Systems
论文作者
论文摘要
对于配备了异质功能的多机器人系统，本文提出了一种机制，可以在天气事件或对抗性攻击等异常环境条件（如天气事件或对抗性攻击）上会影响任务中机器人的性能时，以弹性的方式将机器人分配给任务。我们的主要目标是确保为每个任务分配必要的资源级别，该资源衡量，以分配给任务的机器人的汇总功能。通过跟踪外部扰动下的任务性能偏差，我们的框架可以量化机器人功能（例如，视觉传感或空中移动性）受环境条件影响的程度。这使一个基于优化的框架能够根据每个任务中最降低的功能灵活地将机器人弹性地重新分配给任务。面对资源局限性和不利的环境条件，我们的算法最少放松与某些任务相对应的资源约束，从而表现出优美的性能下降。在多机器人覆盖范围和目标跟踪方案中进行的模拟实验证明了该方法的功效。
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.
