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.

翻译：在自主导航过程中，当安全约束过于保守（导致死锁）或过于宽松（导致碰撞）时，常会发生运动规划失败。为提高鲁棒性，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抵达目标。为此，我们提出一种基于Soft Actor-Critic（SAC）的策略，用于在运行时自适应调整控制屏障函数（CBF）约束参数，从而确保运动既安全又不保守。所提方法面向通用的高层运动规划器、底层控制器及目标系统模型设计，且仅在仿真环境中进行训练。通过大量仿真与物理实验，我们证明该框架能有效调整CBF约束，使机器人在不牺牲安全性的前提下抵达最终目标。