状態不確かさ下での安全ナビゲーション：ロバスト制御バリア関数のためのオンライン適応
Safe Navigation under State Uncertainty: Online Adaptation for Robust Control Barrier Functions
状態推定誤差がある環境で、ロバスト制御バリア関数の保守性をオンラインで適応的に低減し、複数障害物間を走行する車両の安全なナビゲーションを実現する手法を提案した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
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著者: Ersin Das, Rahal Nanayakkara, Xiao Tan, Ryan M. Bena, Joel W. Burdick, Paulo Tabuada, Aaron D. Ames
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
Measurements and state estimates are often imperfect in control practice, posing challenges for safety-critical applications, where safety guarantees rely on accurate state information. In the presence of estimation errors, several prior robust control barrier function (R-CBF) formulations have imposed strict conditions on the input. These methods can be overly conservative and can introduce issues such as infeasibility, high control effort, etc. This work proposes a systematic method to improve R-CBFs, and demonstrates its advantages on a tracked vehicle that navigates among multiple obstacles. A primary contribution is a new optimization-based online parameter adaptation scheme that reduces the conservativeness of existing R-CBFs. In order to reduce the complexity of the parameter optimization, we merge several safety constraints into one unified numerical CBF via Poisson's equation. We further address the dual relative degree issue that typically causes difficulty in vehicle tracking. Experimental trials demonstrate the overall performance improvement of our approach over existing formulations.
