動的環境における安全なロボットナビゲーションのための動的対数ガウス過程制御バリア関数
Dynamic Log-Gaussian Process Control Barrier Function for Safe Robotic Navigation in Dynamic Environments
対数変換したガウス過程回帰で制御バリア関数をオンライン構築し、障害物の動きを先読みして動的環境でも安全にロボットを誘導する手法を提案した。
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著者: Xin Yin, Chenyang Liang, Yanning Guo, Jie Mei
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Control Barrier Functions (CBFs) have emerged as efficient tools to address the safe navigation problem for robot applications. However, synthesizing informative and obstacle motion-aware CBFs online using real-time sensor data remains challenging, particularly in unknown and dynamic scenarios. Motived by this challenge, this paper aims to propose a novel Gaussian Process-based formulation of CBF, termed the Dynamic Log Gaussian Process Control Barrier Function (DLGP-CBF), to enable real-time construction of CBF which are both spatially informative and responsive to obstacle motion. Firstly, the DLGP-CBF leverages a logarithmic transformation of GP regression to generate smooth and informative barrier values and gradients, even in sparse-data regions. Secondly, by explicitly modeling the DLGP-CBF as a function of obstacle positions, the derived safety constraint integrates predicted obstacle velocities, allowing the controller to proactively respond to dynamic obstacles' motion. Simulation results demonstrate significant improvements in obstacle avoidance performance, including increased safety margins, smoother trajectories, and enhanced responsiveness compared to baseline methods.
