RRT*-Guided Dual-Layer PPO Robust Control and Planning for Autonomous Bicycles in Rugged and Constrained Terrain

This paper addresses the problem that autonomous bicycles struggle to achieve precise obstacle avoidance and robust dynamic balance at the same time in rugged terrain and narrow constrained spaces. A hybrid hierarchical control architecture that integrates Rapidly-exploring Random Trees (RRT*) with a dual-layer Proximal Policy Optimization (PPO) scheme is proposed, referred to hereafter as the RRT*-2LPPO architecture. In the global planning layer, RRT* combined with cubic B-spline smoothing generates C2-continuous reference trajectories. In the local control layer, decision-making and execution are hierarchically coupled through a dual-layer cooperation scheme. The upper-layer PPO network incorporates LiDAR data and uses the vehicle body pitch angle to enhance rugged terrain perception for heading planning, while the lower-layer PPO network coordinates the momentum wheel and the steering mechanism to maintain vehicle stability. Validation across various challenging scenarios demonstrates that the proposed framework achieves excellent robustness and consistently attains the highest navigation success rates, significantly outperforming traditional control methods and non-hierarchical architectures.