基于Soft Actor-Critic的控制屏障自适应方法用于未知环境中的鲁棒自主导航
Soft Actor-Critic-based Control Barrier Adaptation for Robust Autonomous Navigation in Unknown Environments
AI总结：
本文提出基于Soft Actor-Critic的策略在运行时自适应调整控制屏障函数参数，以解决自主导航中安全约束过保守或过宽松导致的死锁与碰撞问题，并通过仿真与物理实验验证其安全且非保守的导航效果。
摘要：
自主导航过程中，运动规划失败通常发生在安全约束过于保守导致死锁，或过于宽松导致碰撞的情况下。为了提高鲁棒性，机器人必须动态调整其安全约束，以确保在平衡安全性与性能指标的同时到达目标。为此，我们提出了一种基于Soft Actor-Critic（SAC）的策略，用于在运行时调整控制屏障函数（CBF）约束参数，从而保证安全且不过于保守的运动。所提出的方法面向通用的高层运动规划器、低层控制器和目标系统模型设计，并且仅在仿真中进行训练。通过大量仿真和物理实验，我们证明了该框架能够有效调整CBF约束，使机器人能够在不牺牲安全性的前提下到达最终目标。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
