データ駆動型ロバストバリア関数による安全な長期運用
Data-Driven Robust Barrier Functions for Safe, Long-Term Operation
外乱を凸包の和としてモデル化し、制御バリア関数を用いて外乱を受ける制御アフィン系の制約充足をモジュール式に実現する制御器合成手法を提案し、ロボット群で検証した。
著者: Yousef Emam, Paul Glotfelter, Sean Wilson, Gennaro Notomista, Magnus Egerstedt
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Applications that require multi-robot systems to operate independently for extended periods of time in unknown or unstructured environments face a broad set of challenges, such as hardware degradation, changing weather patterns, or unfamiliar terrain. To operate effectively under these changing conditions, algorithms developed for long-term autonomy applications require a stronger focus on robustness. Consequently, this work considers the ability to satisfy the operation-critical constraints of a disturbed system in a modular fashion, which means compatibility with different system objectives and disturbance representations. Toward this end, this paper introduces a controller-synthesis approach to constraint satisfaction for disturbed control-affine dynamical systems by utilizing Control Barrier Functions (CBFs). The aforementioned framework is constructed by modelling the disturbance as a union of convex hulls and leveraging previous work on CBFs for differential inclusions. This method of disturbance modeling grants compatibility with different disturbance-estimation methods. For example, this work demonstrates how a disturbance learned via a Gaussian process may be utilized in the proposed framework. These estimated disturbances are incorporated into the proposed controller-synthesis framework which is then tested on a fleet of robots in different scenarios.
