Potential Odor Intensity Grid Based UAV Path Planning Algorithm with Particle Swarm Optimization Approach

Yang Liu 1 Xuejun Zhang 2 Xiangmin Guan 3 Daniel Delahaye 4, 5
1 Beihang University
2 Shandong Jiaotong University [Jinan]
3 CAUC - Civil Aviation University of China
4 IMT - Institut de Mathématiques de Toulouse UMR5219
5 MAIAA - ENAC - Laboratoire de Mathématiques Appliquées, Informatique et Automatique pour l'Aérien
Abstract : This paper proposes a potential odor intensity grid based optimization approach for unmanned aerial vehicle (UAV) path planning with particle swarm optimization (PSO) technique. Odor intensity is created to color the area in the searching space with highest probability where candidate particles may locate. A potential grid construction operator is designed for standard PSO based on different levels of odor intensity. The potential grid construction operator generates two potential location grids with highest odor intensity. Then the middle point will be seen as the final position in current particle dimension. The global optimum solution will be solved as the average. In addition, solution boundaries of searching space in each particle dimension are restricted based on properties of threats in the flying field to avoid prematurity. Objective function is redesigned by taking minimum direction angle to destination into account and a sampling method is introduced. A paired samples -test is made and an index called straight line rate (SLR) is used to evaluate the length of planned path. Experiments are made with other three heuristic evolutionary algorithms. The results demonstrate that the proposed method is capable of generating higher quality paths efficiently for UAV than any other tested optimization techniques.
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Journal articles
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https://hal-enac.archives-ouvertes.fr/hal-01366808
Contributor : Laurence Porte <>
Submitted on : Thursday, September 15, 2016 - 1:54:20 PM
Last modification on : Friday, January 10, 2020 - 9:08:59 PM
Long-term archiving on: Friday, December 16, 2016 - 3:26:48 PM

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ENAC | MAIAA | MAIAA-OPTIM | CNRS | INSMI | IMT | UNIV-TLSE2 | OPTIM | UNIV-TLSE3 | UT1-CAPITOLE | RPAS

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Yang Liu, Xuejun Zhang, Xiangmin Guan, Daniel Delahaye. Potential Odor Intensity Grid Based UAV Path Planning Algorithm with Particle Swarm Optimization Approach. Mathematical Problems in Engineering, Hindawi Publishing Corporation, 2016, 2016, ⟨10.1155/2016/7802798⟩. ⟨hal-01366808⟩

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