Data-Driven Adaptive Task Allocation for Heterogeneous Multi-Robot Teams Using Robust Control Barrier Functions
This addresses the challenge of maintaining task allocation quality in robotics despite unknown disturbances, though it appears incremental as it builds on existing robust control methods.
The paper tackles the problem of adaptive task allocation for heterogeneous multi-robot teams under environmental disturbances by learning these disturbances using Gaussian processes, differential inclusions, and robust control barrier functions, resulting in guaranteed robust task execution demonstrated on a real multi-robot system.
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.