Neural-Primitive：一种用于自主飞行的基于原语模仿学习的高效端到端局部规划器
Neural-Primitive: An Efficient End-to-end Local Planner with Primitive-based Imitation Learning for Autonomous Flight
浏览论文内容
中文总结 AI 辅助
针对自主飞行机载轨迹生成的计算-质量-内存困境，提出基于模仿学习的Neural-Primitive规划器，通过轻量级原语框架与紧凑神经网络实现高效规划，仿真与实飞验证其性能优势与迁移能力。
中文摘要 AI 辅助
未知杂乱环境中的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，设计了轻量级的离线原语数据集采集框架，用于在非凸环境中生成安全且高质量的轨迹原语；采用紧凑神经网络直接将感知输入映射为多项式系数，该系数固有地编码了高阶动力学信息。所学习的策略无需后端求解即可实时生成平滑、经验证无碰撞且动力学可行的轨迹，计算速度极快（标准桌面端耗时低于1ms，机载飞行时平均耗时3.68ms），同时机载内存需求低（小于1.5MiB）。大量仿真基准测试表明其在规划延迟和目标到达进展质量上均具优势，真实世界实验的零样本部署进一步验证了该方法强大的仿真到真实迁移能力。
英文摘要
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.
发表机构
- Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院 Cyber-系统与控制研究所）
- Huzhou Institute, Zhejiang University（浙江大学湖州研究院）
- Institute of Systems Engineering, China Academy of Engineering Physics（中国工程物理研究院系统工程研究所）
- School of Automation, Hangzhou Dianzi University（杭州电子科技大学自动化学院）
- Department of Automation, North China Electric Power University (Baoding)（华北电力大学（保定）自动化系）
- Differential Robotics Technology Company（差分机器人技术公司）
机构由 AI 辅助整理，请以论文原文为准。
