Data-Driven Adaptive Task Allocation for Heterogeneous Multi-Robot Teams Using Robust Control Barrier Functions

TL;DR

This paper presents a framework that uses Gaussian processes and robust control barrier functions to adaptively allocate tasks in multi-robot teams, ensuring robustness against environmental disturbances and improving task execution accuracy.

Contribution

It introduces a novel approach combining Gaussian processes, differential inclusions, and control barrier functions for robust, adaptive multi-robot task allocation.

Findings

Effective disturbance learning with Gaussian processes

Robust task execution demonstrated on real robot teams

Improved allocation accuracy under environmental uncertainties

Abstract

Multi-robot task allocation is a ubiquitous problem in robotics due to its applicability in a variety of scenarios. Adaptive task-allocation algorithms account for unknown disturbances and unpredicted phenomena in the environment where robots are deployed to execute tasks. However, this adaptivity typically comes at the cost of requiring precise knowledge of robot models in order to evaluate the allocation effectiveness and to adjust the task assignment online. As such, environmental disturbances can significantly degrade the accuracy of the models which in turn negatively affects the quality of the task allocation. In this paper, we leverage Gaussian processes, differential inclusions, and robust control barrier functions to learn environmental disturbances in order to guarantee robust task execution. We show the implementation and the effectiveness of the proposed framework on a real multi-robot system.