Neural-Primitive: An Efficient End-to-end Local Planner with Primitive-based Imitation Learning for Autonomous Flight

2026-08-21

AI Summary

本文提出一种基于模仿学习的高效端到端局部规划器，解决了自主飞行中轨迹生成的计算-质量-内存三难问题，通过轻量级数据集收集框架和紧凑神经网络实现实时安全飞行。

Abstract

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.

Problem

• autonomous flight
• cluttered environments
• computation-quality-memory trilemma
• onboard trajectory generation

Innovation

• imitation learning
• primitive-based dataset
• compact neural network
• real-time trajectory generation
• low memory requirement