先読みで局所解を回避するロボットナビゲーション：運動予測と安全制御の融合
Proactive Local-Minima-Free Robot Navigation: Blending Motion Prediction with Safe Control
障害物の将来運動をニューラルネットで予測し、その予測からガウス過程でバリア関数をオンライン学習して、局所解のない安全制御（MCBF）に組み込むことで、混雑した動的環境での移動ロボットの安全で効率的なナビゲーションを実現した。
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著者: Yifan Xue, Ze Zhang, Knut Åkesson, Nadia Figueroa
分類: cs.RO
原文アブストラクト
This work addresses the challenge of safe and efficient mobile robot navigation in complex dynamic environments with concave moving obstacles. Reactive safe controllers like Control Barrier Functions (CBFs) design obstacle avoidance strategies based only on the current states of the obstacles, risking future collisions. To alleviate this problem, we use Gaussian processes to learn barrier functions online from multimodal motion predictions of obstacles generated by neural networks trained with energy-based learning. The learned barrier functions are then fed into quadratic programs using modulated CBFs (MCBFs), a local-minimum-free version of CBFs, to achieve safe and efficient navigation. The proposed framework makes two key contributions. First, it develops a prediction-to-barrier function online learning pipeline. Second, it introduces an autonomous parameter tuning algorithm that adapts MCBFs to deforming, prediction-based barrier functions. The framework is evaluated in both simulations and real-world experiments, consistently outperforming baselines and demonstrating superior safety and efficiency in crowded dynamic environments.
