Hierarchically depicting vehicle trajectory with stability in complex environments

The rapid development of autonomous robots has resulted in marked societal and economic benefits. However, enabling robots to navigate complex environments with human-like agility remains a formidable challenge. Unlike robots, humans excel at pathfinding because of their superior spatial awareness and their ability to leverage experience. Inspired by these observations, we designed a neural network to simulate the intuitive pathfinding abilities of humans, integrating global environmental information and previous experiences to identify feasible pathways. Experiments demonstrated that, unlike traditional algorithms whose efficiency deteriorates in complex settings, the proposed method maintains stable computational performance. To further enhance motion quality, we introduce a numerically stable spatiotemporal trajectory optimizer with a unique bilayer polynomial trajectory representation in flat space. This optimization leverages differential flatness to enhance efficiency and fundamentally eliminates singularities in the original problem, thereby robustly converging to continuous and feasible motion even in complex maneuvering scenarios. Our hierarchical motion planner, validated through large-scale maze experiments, combines front-end path planning with back-end trajectory refinement, achieving robust and efficient navigation. We anticipate that our planner will advance stable navigation for robots in complex environments, thereby propelling the progress of robotic autonomy.