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Motion planning for agile fixed-wing UAVs in complex low-altitude environments

Published online by Cambridge University Press:  19 May 2025

Fei Zou Open the ORCID record for Fei Zou [Opens in a new window] ,
Jie Li  and
Yifeng Niu
Fei Zou
Affiliation:
College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
Jie Li
Affiliation:
College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
Yifeng Niu*
Affiliation:
College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
*
Corresponding author: Yifeng Niu; Email: niuyifeng@nudt.edu.cn
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Abstract

The autonomous safe flight of fixed-wing unmanned aerial vehicles (UAVs in complex low-altitude environments presents significant challenges and holds practical application value. This paper proposes a motion planning method for agile fixed-wing UAVs to address safety issues in navigating narrow corridors within such environments. In the path planning phase, we introduce the Improved Batch Informed Trees (IBIT*) to enhance both the solving speed and quality of BIT*. The IBIT* incorporates strategies such as using Rapidly Exploring Random Tree (RRT)-Connect for initial pathfinding, informed sparse sampling, and re-selecting parent nodes. During the trajectory planning phase, we first decouple the roll angle of the UAV from its three-dimensional position based on the agility of fixed-wing UAVs; subsequently, we address constraints related to smoothness and mission time by leveraging the characteristics of the Minimum Control Effort; finally, we design a differentiable penalty function to satisfy the dynamic performance constraints of the UAV. The effectiveness and superiority of the proposed motion planning method are demonstrated through numerical simulations and physical flight experiments.

Keywords

motion planningagile fixed-wing UAVscomplex low-altitude environmentspath planningtrajectory planning
Type
Research Article
Information
Robotica , First View , pp. 1 - 20
Copyright
© College of Intelligence Science and Technology, National University of Defense Technology, China, 2025. Published by Cambridge University Press

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