未知環境におけるロバスト自律ナビゲーションのためのSoft Actor-Criticに基づく制御バリア適応
Soft Actor-Critic-based Control Barrier Adaptation for Robust Autonomous Navigation in Unknown Environments
強化学習(SAC)を用いて制御バリア関数の制約パラメータを実行時に適応させ、安全性を保ちつつ保守的になりすぎない自律ナビゲーションを実現する手法を提案した。
著者: Nicholas Mohammad, Nicola Bezzo
分類: cs.RO, cs.LG
原文アブストラクト
Motion planning failures during autonomous navigation often occur when safety constraints are either too conservative, leading to deadlocks, or too liberal, resulting in collisions. To improve robustness, a robot must dynamically adapt its safety constraints to ensure it reaches its goal while balancing safety and performance measures. To this end, we propose a Soft Actor-Critic (SAC)-based policy for adapting Control Barrier Function (CBF) constraint parameters at runtime, ensuring safe yet non-conservative motion. The proposed approach is designed for a general high-level motion planner, low-level controller, and target system model, and is trained in simulation only. Through extensive simulations and physical experiments, we demonstrate that our framework effectively adapts CBF constraints, enabling the robot to reach its final goal without compromising safety.
