用强化学习动态调整安全约束，让机器人在未知环境里又快又安全地导航。
Soft Actor-Critic-based Control Barrier Adaptation for Robust Autonomous Navigation in Unknown Environments
- 基于SAC算法在线调节控制屏障函数参数，实现安全与效率的平衡
- 仿真与实测均证明可成功到达目标且零碰撞
- 无需真实环境训练，适用于多种机器人系统
自主导航中运动规划失败常因安全约束过严导致死锁，或过松引发碰撞。为提升鲁棒性，机器人需动态调整安全约束，在保证抵达目标的同时平衡安全性与性能。本文提出一种基于软演员-评论家（SAC）的策略，实时自适应调整控制屏障函数（CBF）的约束参数，确保安全且非保守的运动。该方法适用于通用高层运动规划器、低层控制器及目标系统模型，仅在仿真中训练。通过大量仿真和物理实验验证，所提框架能有效自适应CBF约束，使机器人在不牺牲安全的前提下成功抵达终点。
原文摘要 · Abstract (English)
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.
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