The Development Of A Robust Algorithm For Uav Path Planning In 3d Environment
Significant research has been conducted on Unmanned Aerial Vehicle (UAV) path planning using evolutionary algorithms, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), and Biogeographic-Based Optimization (BBO). However, the performance of most of these...
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| Format: | Thesis |
| Language: | English |
| Published: |
2016
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| Online Access: | http://eprints.usm.my/46908/1/The%20Development%20Of%20A%20Robust%20Algorithm%20For%20Uav%20Path%20Planning%20In%203d%20Environment.pdf |
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| Summary: | Significant research has been conducted on Unmanned Aerial Vehicle (UAV) path planning using evolutionary algorithms, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), and Biogeographic-Based Optimization (BBO). However, the performance of most of these algorithms tend to decline in terms of function and computational cost when dealing with robust systems. Thus, a new algorithm known as infection evolution (IE) was developed in this study. IE simplifies calculation and maximizes the efficiency of generating an improved path plan in a 3D environment. Nine terrain maps were used as case studies, and 100 simulations were carried out for each case to determine the average performance of the proposed algorithm. All simulations were performed using MATLAB with visualization of UAV path planning. The performance of the IE algorithm was compared with that of PSO, GA, DE, and BBO at their respective optimized settings. IE attained a 92% probability rate of achieving a short path length in 100 case studies. With regard to computational cost, IE attained a 97% probability rate of achieving a faster processing speed in comparison with tested algorithms. Therefore, the IE algorithm exhibits significant potential for UAV path planning optimization |
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