提出多层规划框架，高效生成满足动力学与时空约束的复杂路径。
Multi-layer Motion Planning with Kinodynamic and Spatio-Temporal Constraints
- 分三层：先定空间序列，再生成几何先导路径，最后优化动力学路径。
- 在阿克曼车模型上效率显著优于已有方法，可生成交叉等复杂动作。
- 适合需要高精度路径规划的自动驾驶、机器人导航场景。
我们提出一种新型多层规划方法，用于计算同时满足动力学和时空约束的路径。该三阶段框架首先根据空间约束建立可能的路径序列，并据此计算几何先导路径；该路径引导一个渐近最优的采样型动力学规划器，通过最小化 STL-鲁棒性代价，联合满足时空与动力学约束。实验中，我们采用速度控制的阿克曼车模型验证该方法，结果表明相比先前方法具有显著效率提升；此外，本方法可生成如交叉穿越等复杂路径动作，这是此前方法未能实现的。
原文摘要 · Abstract (English)
We propose a novel, multi-layered planning approach for computing paths that satisfy both kinodynamic and spatiotemporal constraints. Our three-part framework first establishes potential sequences to meet spatial constraints, using them to calculate a geometric lead path. This path then guides an asymptotically optimal sampling-based kinodynamic planner, which minimizes an STL-robustness cost to jointly satisfy spatiotemporal and kinodynamic constraints. In our experiments, we test our method with a velocity-controlled Ackerman-car model and demonstrate significant efficiency gains compared to prior art. Additionally, our method is able to generate complex path maneuvers, such as crossovers, something that previous methods had not demonstrated.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。