ALVO: Adaptive Learning with Velocity Obstacles for UGV Navigation in Dynamic Scenes

Abstract

Autonomous navigation of unmanned ground vehicles (UGVs) in dynamic scenes is a challenging task that requires them to avoid obstacles and move toward the goal simultaneously. This paper proposes ALVO, an adaptive learning policy that leverages velocity obstacles for UGV navigation. ALVO employs an adaptive gating-based mechanism for reactive obstacle avoidance, which enables the UGV to either slow down or proactively navigate around obstacles based on the relative importance of the environmental state and the goal. A reward function based on velocity obstacles is also designed to guide the UGV to navigate toward the goal while avoiding obstacles. Extensive experiments demonstrate that ALVO outperforms the competing approaches in various dynamic environments. We also implemented our method on a real UGV and showed that it performed well in real-world scenarios.