Neural-Primitive enables efficient autonomous flight planning

By PulseAugur Editorial · [1 sources] · 2026-08-24 04:00

Researchers have developed Neural-Primitive, an efficient end-to-end local planner for autonomous flight that utilizes imitation learning. This system generates safe and high-quality trajectory primitives through a lightweight dataset collection framework. A compact neural network maps sensory inputs to polynomial coefficients, enabling real-time generation of smooth, collision-free, and dynamically feasible trajectories with minimal computation and memory usage. AI

IMPACT This method could significantly reduce computation and memory requirements for onboard trajectory generation in autonomous systems.

RANK_REASON The cluster contains a research paper detailing a new method for autonomous flight planning. [lever_c_demoted from research: ic=1 ai=1.0]

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Neural-Primitive enables efficient autonomous flight planning

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arXiv cs.AI TIER_1 English(EN) · Zhitao Liu, Guangtong Xu, Zihan Wang, Jialiang Hou, Chao Xu, Fei Gao · 2026-08-24 04:00

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

arXiv:2608.20948v1 Announce Type: cross 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…

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

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