用Transformer架构统一解决导航中的路线与轨迹规划问题。
NaviFormer: A Deep Reinforcement Learning Transformer-like Model to Holistically Solve the Navigation Problem
- 基于Transformer的强化学习模型，同时处理高阶路线与低阶轨迹规划。
- 在多场景测试中表现准确，能理解任务约束并动态调整策略。
- 计算速度快，适合实时导航应用，尤其适用于复杂环境。
路径规划通常分为高层路线规划（确定到达目标的路径点序列）和低层轨迹规划（两点间避障轨迹生成）。然而，真实场景往往需要同时高效解决这两个子问题。本文提出NaviFormer，一种基于Transformer架构的深度强化学习模型，可联合预测全局路径与局部轨迹，实现对导航问题的统一求解。通过多项实验对比验证，NaviFormer在准确性上表现优异，能有效识别各子问题的约束与难点，并据此优化行为。此外，其卓越的计算速度表明该模型具备部署于实时导航任务的潜力。
原文摘要 · Abstract (English)
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.
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