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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 7802798, 16 pages
http://dx.doi.org/10.1155/2016/7802798
Research Article

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

1School of Electronic & Information Engineering, Beihang University, Beijing, China
2School of Information Science & Electric Engineering, Shandong Jiaotong University, Jinan, China
3School of General Aviation, Civil Aviation Management Institute of China, Beijing, China
4MAIAA Laboratory, Ecole Nationale de l’Aviation Civile, Toulouse, France

Received 18 March 2016; Accepted 21 August 2016

Academic Editor: Mauro Pontani

Copyright © 2016 Yang Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.