CGD：面向无人机轨迹规划的约束引导扩散策略
CGD: Constraint-Guided Diffusion Policies for UAV Trajectory Planning
AI总结：
针对传统优化规划器计算成本高且模仿学习策略难以处理动态可行性与约束变化的问题，本文提出约束引导扩散方法，将轨迹规划分解为无碰撞路径寻找与动态可行时间参数化，实现快速且可泛化的无人机轨迹生成。
摘要：
传统的基于优化的规划器虽然有效，但计算成本高，导致轨迹生成缓慢。一种成功的降低计算时间的策略是利用模仿学习（IL）从这些规划器（被视为专家演示器）中开发快速的神经网络（NN）策略。尽管所得的NN策略能够快速生成与专家轨迹相似的轨迹，但（1）其输出并未显式考虑动态可行性，（2）这些策略无法适应与训练时所用约束不同的约束变化。为克服这些局限，我们提出了约束引导扩散（CGD），这是一种新颖的基于IL的轨迹规划方法。CGD利用混合学习/在线优化方案，将扩散策略与一个替代的高效优化问题相结合，从而能够生成无碰撞、动态可行的轨迹。CGD的核心思想包括将专家所解决的原始困难优化问题划分为两个更易处理的子问题：（a）高效地寻找无碰撞路径，以及（b）为这些路径确定动态可行的时间参数化以获得轨迹。与传统的神经网络架构相比，我们通过数值评估证明了在训练期间从未遇到过的新约束场景下，性能和动态可行性方面的显著提升。
英文摘要：
Traditional optimization-based planners, while effective, suffer from high computational costs, resulting in slow trajectory generation. A successful strategy to reduce computation time involves using Imitation Learning (IL) to develop fast neural network (NN) policies from those planners, which are treated as expert demonstrators. Although the resulting NN policies are effective at quickly generating trajectories similar to those from the expert, (1) their output does not explicitly account for dynamic feasibility, and (2) the policies do not accommodate changes in the constraints different from those used during training. To overcome these limitations, we propose Constraint-Guided Diffusion (CGD), a novel IL-based approach to trajectory planning. CGD leverages a hybrid learning/online optimization scheme that combines diffusion policies with a surrogate efficient optimization problem, enabling the generation of collision-free, dynamically feasible trajectories. The key ideas of CGD include dividing the original challenging optimization problem solved by the expert into two more manageable sub-problems: (a) efficiently finding collision-free paths, and (b) determining a dynamically-feasible time-parametrization for those paths to obtain a trajectory. Compared to conventional neural network architectures, we demonstrate through numerical evaluations significant improvements in performance and dynamic feasibility under scenarios with new constraints never encountered during training.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
