Research on Adaptive Planning of Three-Dimensional Trajectory for Uncrewed Aerial Vehicle Inspection Based on Nonlinear Weibull Algorithm

Traditional methods for solving the path planning problem of inspection UAVs in complex environments often suffer from issues such as convergence to locally optimal paths and poor global search accuracy. To address these shortcomings, this study proposes a Non-linear Weibull Flight Reptile Search Al...

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Main Authors: Zhuang Liu, Ning Yang, Jiaxing Fu, Huanqing Cai, Xuebei Wei
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
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Online Access:https://ieeexplore.ieee.org/document/11084797/
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author Zhuang Liu
Ning Yang
Jiaxing Fu
Huanqing Cai
Xuebei Wei
author_facet Zhuang Liu
Ning Yang
Jiaxing Fu
Huanqing Cai
Xuebei Wei
author_sort Zhuang Liu
collection DOAJ
description Traditional methods for solving the path planning problem of inspection UAVs in complex environments often suffer from issues such as convergence to locally optimal paths and poor global search accuracy. To address these shortcomings, this study proposes a Non-linear Weibull Flight Reptile Search Algorithm (NWFRSA) for adaptive path planning of inspection UAVs. By introducing an improved Iterative Chaotic Map with Infinite Collapses (ICMIC) initialization strategy, the diversity and quality of initial path solutions are enhanced. An S-shaped characteristic function-based nonlinear evolution factor is employed to balance global exploration and local exploitation of the optimal path, while a Weibull flight operator mutation strategy is designed to enable the algorithm to escape from locally optimal paths, enrich the search space, and improve convergence accuracy. Meanwhile, this study constructs a path planning model for inspection UAVs and designs a weighted objective function considering total path length, flight altitude, flight turning angle, and threat model, thereby transforming path planning in three-dimensional space into a constrained multi-objective optimization problem. The results show that under flight environments with varying complexity of obstacle and threat region distribution, compared with the Particle Swarm Optimization algorithm (PSO), Butterfly Optimization Algorithm (BOA), and Reptile Search Algorithm (RSA), NWFRSA can effectively reduce the path cost (by 4.53%–34.47%), contributing to improved.
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institution Kabale University
issn 2169-3536
language English
publishDate 2025-01-01
publisher IEEE
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spelling doaj-art-93d456f5abfc4e7da37eaf01fe490ca92025-08-20T03:58:41ZengIEEEIEEE Access2169-35362025-01-011312981412982710.1109/ACCESS.2025.359047711084797Research on Adaptive Planning of Three-Dimensional Trajectory for Uncrewed Aerial Vehicle Inspection Based on Nonlinear Weibull AlgorithmZhuang Liu0https://orcid.org/0009-0000-0464-6894Ning Yang1Jiaxing Fu2Huanqing Cai3Xuebei Wei4China Electric Power Research Institute (CEPRI) Company Ltd., Wuhan, Hubei, ChinaChina Electric Power Research Institute (CEPRI) Company Ltd., Wuhan, Hubei, ChinaState Grid Inner Mongolia Eastern Power Company Ltd., Hohhot, ChinaChina Electric Power Research Institute (CEPRI) Company Ltd., Wuhan, Hubei, ChinaMicro Technology Company Ltd., Nanjing, Jiangsu, ChinaTraditional methods for solving the path planning problem of inspection UAVs in complex environments often suffer from issues such as convergence to locally optimal paths and poor global search accuracy. To address these shortcomings, this study proposes a Non-linear Weibull Flight Reptile Search Algorithm (NWFRSA) for adaptive path planning of inspection UAVs. By introducing an improved Iterative Chaotic Map with Infinite Collapses (ICMIC) initialization strategy, the diversity and quality of initial path solutions are enhanced. An S-shaped characteristic function-based nonlinear evolution factor is employed to balance global exploration and local exploitation of the optimal path, while a Weibull flight operator mutation strategy is designed to enable the algorithm to escape from locally optimal paths, enrich the search space, and improve convergence accuracy. Meanwhile, this study constructs a path planning model for inspection UAVs and designs a weighted objective function considering total path length, flight altitude, flight turning angle, and threat model, thereby transforming path planning in three-dimensional space into a constrained multi-objective optimization problem. The results show that under flight environments with varying complexity of obstacle and threat region distribution, compared with the Particle Swarm Optimization algorithm (PSO), Butterfly Optimization Algorithm (BOA), and Reptile Search Algorithm (RSA), NWFRSA can effectively reduce the path cost (by 4.53%–34.47%), contributing to improved.https://ieeexplore.ieee.org/document/11084797/Inspection UAVpath planningreptile search algorithmchaotic mappingWeibull flight operator
spellingShingle Zhuang Liu
Ning Yang
Jiaxing Fu
Huanqing Cai
Xuebei Wei
Research on Adaptive Planning of Three-Dimensional Trajectory for Uncrewed Aerial Vehicle Inspection Based on Nonlinear Weibull Algorithm
IEEE Access
Inspection UAV
path planning
reptile search algorithm
chaotic mapping
Weibull flight operator
title Research on Adaptive Planning of Three-Dimensional Trajectory for Uncrewed Aerial Vehicle Inspection Based on Nonlinear Weibull Algorithm
title_full Research on Adaptive Planning of Three-Dimensional Trajectory for Uncrewed Aerial Vehicle Inspection Based on Nonlinear Weibull Algorithm
title_fullStr Research on Adaptive Planning of Three-Dimensional Trajectory for Uncrewed Aerial Vehicle Inspection Based on Nonlinear Weibull Algorithm
title_full_unstemmed Research on Adaptive Planning of Three-Dimensional Trajectory for Uncrewed Aerial Vehicle Inspection Based on Nonlinear Weibull Algorithm
title_short Research on Adaptive Planning of Three-Dimensional Trajectory for Uncrewed Aerial Vehicle Inspection Based on Nonlinear Weibull Algorithm
title_sort research on adaptive planning of three dimensional trajectory for uncrewed aerial vehicle inspection based on nonlinear weibull algorithm
topic Inspection UAV
path planning
reptile search algorithm
chaotic mapping
Weibull flight operator
url https://ieeexplore.ieee.org/document/11084797/
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