Soft Actor-Critic-based Control Barrier Adaptation for Robust Autonomous Navigation in Unknown Environments
This work addresses robust navigation for autonomous robots in unknown environments, presenting an incremental improvement by combining existing methods.
The paper tackles the problem of motion planning failures in autonomous navigation by dynamically adapting safety constraints to avoid deadlocks and collisions, resulting in a framework that enables robots to reach goals safely without compromising performance in simulations and physical experiments.
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.