Path planning is usually solved by addressing either the (high-level) route planning problem (waypoint sequencing to achieve the final goal) or the (low-level) path planning problem (trajectory prediction between two waypoints avoiding collisions). However, real-world problems usually require simultaneous solutions to the route and path planning subproblems with a holistic and efficient approach. In this paper, we introduce NaviFormer, a deep reinforcement learning model based on a Transformer architecture that solves the global navigation problem by predicting both high-level routes and low-level trajectories. To evaluate NaviFormer, several experiments have been conducted, including comparisons with other algorithms. Results show competitive accuracy from NaviFormer since it can understand the constraints and difficulties of each subproblem and act consequently to improve performance. Moreover, its superior computation speed proves its suitability for real-time missions.

翻译：路径规划通常通过解决（高层级）路径规划问题（为实现最终目标进行航点排序）或（低层级）路径规划问题（在两个航点之间预测无碰撞轨迹）来处理。然而，实际问题通常需要以整体且高效的方式同时解决路径与轨迹规划子问题。本文提出NaviFormer，一种基于Transformer架构的深度强化学习模型，通过同时预测高层级路径和低层级轨迹来解决全局导航问题。为评估NaviFormer，我们开展了多项实验，包括与其他算法的对比。结果表明，NaviFormer能够理解各子问题的约束与难点，并据此采取行动以提升性能，因而具有竞争性的精度。此外，其卓越的计算速度证明其适用于实时任务。