用模仿学习生成高效飞行路径，实时避障且内存极低。
Neural-Primitive: An Efficient End-to-end Local Planner with Primitive-based Imitation Learning for Autonomous Flight
- 基于轨迹基元的离线数据构建与神经网络端到端映射
- 推理速度低于1毫秒，内存占用不足1.5MiB，实测无碰撞
- 无需微调即可直接部署到真实无人机，泛化能力强
在未知复杂环境中实现自主飞行受限于机载轨迹生成的计算-质量-内存三重困境。本文提出一种基于模仿学习的高效端到端局部规划器。设计轻量级离线基元数据采集框架，在非凸环境中生成安全且高质量的轨迹基元；采用紧凑神经网络将感知输入直接映射为蕴含高阶动力学信息的多项式系数。所学策略可实时生成平滑、经验上无碰撞且动力学可行的轨迹，无需后端求解。在标准台式机上计算时间低于1毫秒，飞行中平均耗时3.68毫秒，内存占用低于1.5MiB。大量仿真基准测试表明其在规划延迟与目标抵达质量方面均具优势。零样本部署的现实实验进一步验证了该方法出色的模拟到现实迁移能力。
原文摘要 · Abstract (English)
Autonomous flight in unknown cluttered environments is hindered by the computation-quality-memory trilemma of onboard trajectory generation. In this paper, we propose an efficient end-to-end local planner via imitation learning. A lightweight offline-primitive-based dataset collection framework is designed to produce safe and high-quality trajectory primitives in non-convex environments. A compact neural network directly maps sensory inputs to polynomial coefficients that inherently encode higher-order dynamical information. The learned policy generates smooth, empirically collision-free and dynamically feasible trajectories in real time without back-end solving. It achieves ultra-fast computation (below 1ms on a standard desktop and average 3.68ms during onboard flight), while maintaining low onboard memory requirements (less than 1.5MiB). Extensive simulation benchmarks demonstrate superiority in both planning latency and target-reaching progress quality. Zero-shot deployment in real-world experiments further validates the robust sim-to-real transfer capability of the proposed method.
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