Resilient Task Allocation in Heterogeneous Multi-Robot Systems
This addresses the problem of maintaining task performance in multi-robot systems for applications like surveillance or disaster response, but it is incremental as it builds on existing optimization-based allocation methods.
The paper tackles the problem of resilient task allocation in heterogeneous multi-robot systems under anomalous environmental conditions like weather events or adversarial attacks, resulting in a framework that enables flexible reallocation and graceful performance degradation with minimal constraint relaxation in simulated coverage and target tracking scenarios.
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.