Path Planning of Multirotor UAV Based on the Improved Ant Colony Algorithm

Path planning is an important part of the unmanned aerial vehicle (UAV) to realize its autonomous capabilities. Aiming at the shortcomings of the traditional ant colony algorithm-based trajectory planning method, which has slow convergence speed and easy to fall into the local optimum, a path planni...

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Main Authors: Duo Qi, Zhihao Zhang, Qirui Zhang
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Journal of Robotics
Online Access:http://dx.doi.org/10.1155/2022/2168964
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author Duo Qi
Zhihao Zhang
Qirui Zhang
author_facet Duo Qi
Zhihao Zhang
Qirui Zhang
author_sort Duo Qi
collection DOAJ
description Path planning is an important part of the unmanned aerial vehicle (UAV) to realize its autonomous capabilities. Aiming at the shortcomings of the traditional ant colony algorithm-based trajectory planning method, which has slow convergence speed and easy to fall into the local optimum, a path planning method based on the improved ant colony algorithm is proposed. First, a dynamic adjusting factor is added into the heuristic function to improve the directivity of path selection and search speed. Then, the state transition strategy is improved to solve the problem of slow convergence in the initial stage and easy to fall into local optimum in the later stage. Finally, the path inflection point is smoothly optimized through the cubic B-spline curve. Simulation results show that the improved ant colony algorithm can quickly converge to the optimal path and well adapt to the flight requirements of multirotor UAV.
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institution Kabale University
issn 1687-9619
language English
publishDate 2022-01-01
publisher Wiley
record_format Article
series Journal of Robotics
spelling doaj-art-c6609b2813f445b788705c85d6bb3f802025-02-03T05:50:01ZengWileyJournal of Robotics1687-96192022-01-01202210.1155/2022/2168964Path Planning of Multirotor UAV Based on the Improved Ant Colony AlgorithmDuo Qi0Zhihao Zhang1Qirui Zhang2Aviation Swarm Technology and Operational Application LaboratoryAviation Swarm Technology and Operational Application LaboratoryUnit 93525 of PLAPath planning is an important part of the unmanned aerial vehicle (UAV) to realize its autonomous capabilities. Aiming at the shortcomings of the traditional ant colony algorithm-based trajectory planning method, which has slow convergence speed and easy to fall into the local optimum, a path planning method based on the improved ant colony algorithm is proposed. First, a dynamic adjusting factor is added into the heuristic function to improve the directivity of path selection and search speed. Then, the state transition strategy is improved to solve the problem of slow convergence in the initial stage and easy to fall into local optimum in the later stage. Finally, the path inflection point is smoothly optimized through the cubic B-spline curve. Simulation results show that the improved ant colony algorithm can quickly converge to the optimal path and well adapt to the flight requirements of multirotor UAV.http://dx.doi.org/10.1155/2022/2168964
spellingShingle Duo Qi
Zhihao Zhang
Qirui Zhang
Path Planning of Multirotor UAV Based on the Improved Ant Colony Algorithm
Journal of Robotics
title Path Planning of Multirotor UAV Based on the Improved Ant Colony Algorithm
title_full Path Planning of Multirotor UAV Based on the Improved Ant Colony Algorithm
title_fullStr Path Planning of Multirotor UAV Based on the Improved Ant Colony Algorithm
title_full_unstemmed Path Planning of Multirotor UAV Based on the Improved Ant Colony Algorithm
title_short Path Planning of Multirotor UAV Based on the Improved Ant Colony Algorithm
title_sort path planning of multirotor uav based on the improved ant colony algorithm
url http://dx.doi.org/10.1155/2022/2168964
work_keys_str_mv AT duoqi pathplanningofmultirotoruavbasedontheimprovedantcolonyalgorithm
AT zhihaozhang pathplanningofmultirotoruavbasedontheimprovedantcolonyalgorithm
AT qiruizhang pathplanningofmultirotoruavbasedontheimprovedantcolonyalgorithm