Abstract
Time-optimal trajectories drive quadrotors to their dynamic limits, but computing such trajectories involves solving non-convex problems via iterative nonlinear optimization, making them prohibitively costly for real-time applications. In this work, we investigate learning-based models that imitate a model-based time-optimal trajectory planner to accelerate trajectory generation. Given a dataset of collision-free geometric paths, we show that modeling architectures can effectively learn the patterns underlying time-optimal trajectories. We introduce a quantitative framework to analyze local analytic properties of the learned models, and link them to the Backward Reachable Tube of the geometric tracking controller. To enhance robustness, we propose a data augmentation scheme that applies random perturbations to the input paths. Compared to classical planners, our method achieves substantial speedups, and we validate its real-time feasibility on a hardware quadrotor platform. Experiments demonstrate that the learned models generalize to previously unseen path lengths. The code for our approach can be found here: this https URL
Abstract (translated)
时间最优轨迹可使四旋翼飞行器达到其动态极限，但计算此类轨迹涉及通过迭代非线性优化解决非凸问题，这使得实时应用的成本过高。在这项工作中，我们研究了基于学习的模型，这些模型模仿基于模型的时间最优轨迹规划器来加速轨迹生成过程。给定一组无碰撞几何路径的数据集，我们展示了建模架构可以有效地学习时间最优轨迹背后的模式。我们引入了一个定量框架来分析所学模型的局部解析属性，并将其与几何跟踪控制器的可逆到达管相联系。为了增强鲁棒性，我们提出了一种数据增强方案，该方案对输入路径施加随机扰动。 相比于传统规划器，我们的方法实现了显著的速度提升，并通过硬件四旋翼平台验证了其实时可行性。实验表明，学习模型可以推广到之前未见过的路径长度。我们方法的代码可以从这里找到：[此URL](https://this-url-here.com)
URL
https://arxiv.org/abs/2506.13915
