Journal of Applied Science and Engineering

Published by Tamkang University Press

ESCI jase impact factor scopus logo open access rate of Scopus journal

SIR-Inspired Risk-Field-Guided Ant Colony Optimization for Static Fixed-Altitude UAV Path Planning

 2027

 Volume 36

Songru Yang, Ruicheng Zhang, Qianda Luo*, and Yuehua Zhao

College of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, China

Received: July 31, 2026
Accepted: September 14, 2026
Publication Date: October 09, 2026

上傳圖片

Length–risk trade-off for the cross-algorithm comparison. Points closer to the lower-left region indicate shorter and safer paths

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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Collision-free routing is not necessarily safe for unmanned aerial vehicles operating near spatially distributed hazards, because a geometrically short path may accumulate excessive exposure. This paper studies static fixed-altitude UAV path planning in known environments with non-blocking soft risks. An SIR-inspired risk-field-guided ant colony optimization framework, termed SIR-ACO, is proposed. A deterministic influence propagation surrogate based on susceptible-infected-removed states transforms sparse risk sources into a continuous horizontal risk field; the field is then coupled with ACO through path evaluation, transition guidance, and pheromone regulation. Experiments in three representative environments with 30 independent runs show that SIR-ACO reduces mean risk exposure by 64.2%, 74.1%, and 68.9% relative to basic ACO, and additional 40×40 random-map tests show the same exposure-reduction trend against stochastic ACO baselines. Incremental component analysis identifies SIR-inspired risk-field evaluation as the primary source of exposure reduction, while mass-matched distance-decay and risk-weight-sweep comparisons show that the proposed representation changes the spatial safety-efficiency trade-off beyond merely adding a continuous distance penalty. Comparisons with A*, Risk-A*, and RRT* are included as deterministic and stochastic references. The results support SIR-ACO as a safety-enhanced ACO framework for static global planning, while also showing that it is not a universal replacement for deterministic graph search when a fixed risk cost is directly available.

Keywords: Fixed-altitude UAV; Path planning; Ant colony optimization; Risk field; SIR model; Swarm intelligence

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