制御バリア関数による障害物回避のための安全性学習
Learning Safety for Obstacle Avoidance via Control Barrier Functions
複雑な形状のロボットでも障害物回避を可能にするため、残差ニューラルネットワークでクリアランスを予測し、制御バリア関数と組み合わせて安全な軌道を生成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？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
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著者: Shuo Liu, Zhe Huang, Calin A. Belta
分類: cs.RO
原文アブストラクト
Obstacle avoidance is central to safe navigation, especially for robots with arbitrary and nonconvex geometries operating in cluttered environments. Existing Control Barrier Function (CBF) approaches often rely on analytic clearance computations, which are infeasible for complex geometries, or on polytopic approximations, which become intractable when robot configurations are unknown. To address these limitations, this paper trains a residual neural network on a large dataset of robot-obstacle configurations to enable fast and tractable clearance prediction, even at unseen configurations. The predicted clearance defines the radius of a Local Safety Ball (LSB), which ensures continuous-time collision-free navigation. The LSB boundary is encoded as a Discrete-Time High-Order CBF (DHOCBF), whose constraints are incorporated into a nonlinear optimization framework. To improve feasibility, a novel relaxation technique is applied. The resulting framework ensure that the robot's rigid-body motion between consecutive time steps remains collision-free, effectively bridging discrete-time control and continuous-time safety. We show that the proposed method handles arbitrary, including nonconvex, robot geometries and generates collision-free, dynamically feasible trajectories in cluttered environments. Experiments demonstrate millisecond-level solve times and high prediction accuracy, highlighting both safety and efficiency beyond existing CBF-based methods.
