動的障害物回避のための時間効率の良い反復学習プランニング
Time-Efficient Iterative Learning Planning for Safety-Critical Dynamic Obstacle Avoidance
反復学習プランニングに予測リスク制御バリア関数を統合し、動的環境での安全なナビゲーションを実現する手法を提案。
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著者: Zhiyi Chen, Shuli Lv, Chen Min, Yong Xu, Jian Sun, Quan Quan
分類: cs.RO
原文アブストラクト
Autonomous mobile robots require timeefficient planning and safety-critical dynamic obstacle avoidance under constrained onboard computation. While Iterative Learning Planning (ILP) offers lightweight and efficient traversal planning, it lacks explicit mechanisms for dynamic obstacle perception and avoidance. This article extends ILP to safety-critical navigation in dynamic environments by integrating an anticipatory risk-blended control barrier function (ARB-CBF). The extended ILP learns traversal-speed and steering-bias profiles via a fractionalpower update based on local obstacle risk, generating nominal control commands that ARB-CBF modifies at runtime for real-time safety guarantees. Algorithmic analysis demonstrates that the ILP replanning stage scales at O(kN) for k iterations and N waypoints, while ARB-CBF executes with linear complexity. Comprehensive simulations and real-world experiments validate the framework, demonstrating superior temporal efficiency and safety with lower computational overhead compared to optimizationbased baselines, making it highly suitable for resourceconstrained platforms.