Resilient Task Allocation in Heterogeneous Multi-Robot Systems

TL;DR

This paper introduces a resilient task allocation framework for heterogeneous multi-robot systems that adapts to environmental disturbances, ensuring task resource requirements are met despite adverse conditions.

Contribution

It proposes an optimization-based method for dynamic robot reallocation that accounts for capability degradation under environmental perturbations.

Findings

Effective in maintaining task performance under adverse conditions

Demonstrated robustness through simulated multi-robot coverage and tracking tasks

Minimizes resource constraint violations during environmental disruptions

Abstract

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.