Research on Particle Swarm Optimization algorithm incorporating Entropy Weight Method for UAV gas plume tracking subtask

Qing-Xue Zeng, Lei Cheng, Jiaqi Zhong
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Abstract

Unlike ground robot that performs Gas Source Localization task (GSL) in a two-dimensional plane, UAV can be applied to various three-dimensional space scenarios due to their flexibility. However, due to the interference of wind speed and wind direction, the gas diffusion distribution is uneven, resulting in different confusion degrees in the gas distribution. Based on this, this paper proposes a Particle Swarm optimization algorithm (PSO) incorporating Entropy Weight method (EWM) for UAV gas plume tracking strategy, which referred to as EWM-PSO algorithm below, and the Gaussian gas plume model in three-dimensional space is used to simulate the interference between wind speed and wind direction. As for this algorithm, EWM is used to calculate the entropy value and comprehensive score at each position, PSO uses the comprehensive score as its fitness value, which will guide the UAV move towards the position with high concentration value, i.e., the position of the gas plume source. After setting up two groups of comparative experiments, the results verify that this algorithm has high universality and accuracy.
结合熵权法的粒子群算法在无人机气羽跟踪子任务中的研究
与地面机器人在二维平面上执行气源定位任务(GSL)不同,无人机由于其灵活性可以应用于各种三维空间场景。然而,由于风速和风向的干扰,气体扩散分布不均匀,导致气体分布混乱程度不同。基于此,本文提出了一种结合熵权法(EWM)的无人机气体羽流跟踪策略粒子群优化算法(PSO),以下简称EWM-PSO算法,并利用三维空间中的高斯气体羽流模型模拟风速与风向的干扰。该算法使用EWM计算每个位置的熵值和综合得分,粒子群算法以综合得分作为适应度值,引导无人机向浓度值较高的位置移动,即气体羽流源的位置。建立了两组对比实验,结果验证了该算法具有较高的通用性和准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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