ガウス過程とロバスト制御バリア関数による異種マルチロボットのデータ駆動型適応タスク割当
Data-Driven Adaptive Task Allocation for Heterogeneous Multi-Robot Teams Using Robust Control Barrier Functions
環境外乱をガウス過程で学習し、ロバスト制御バリア関数と組み合わせることで、モデル誤差に強い適応的タスク割当を実現し、実機マルチロボットで有効性を示した論文。
著者: Yousef Emam, Gennaro Notomista, Paul Glotfelter, Magnus Egerstedt
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
