Localizing multiple odor sources in dynamic environment using ranged subgroup PSO with flow of wind based on open dynamic engine library

W. Jatmiko, W. Pambuko, P. Mursanto, A. Muis, B. Kusumoputro, K. Sekiyama, T. Fukuda
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引用次数: 16

Abstract

A new algorithm based on Modified Particle Swarm Optimization (MPSO) which follows a local gradient of the chemical concentration within a plume and follow direction of the wind velocity is investigated. Moreover, the niche or parallel search characteristic is adopted on MPSO to solve the multi-peak and multi-source problem. When using parallel MPSO, subgroup of robot is introduced then each subgroup can locate the odor source. Unfortunately, there is a possibility that more that one subgroup locates one odor sources. This is inefficient because other subgroups locate other source, then we proposed a ranged subgroup method for coping for that problem, then the searching performance will increase. Then ODE (Open Dynamics Engine) library is used for physical modeling of the robot like friction, balancing moment and others. Finally the statistical analysis shows that the new approach is technically sounds.
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基于开放动态引擎库的带风向的范围子群粒子群优化算法在动态环境中对多气味源进行定位
研究了一种基于改进粒子群优化算法(MPSO)的羽流化学物质浓度局部梯度和风速方向的新算法。此外,该算法还利用小生境或并行搜索特性来解决多峰多源问题。采用并行粒子群算法时,引入机器人的子群,每个子群对气味源进行定位。不幸的是,有可能不止一个小组定位一个气味来源。由于其他子组定位其他源的效率低下,因此我们提出了一种范围子组方法来解决这个问题,从而提高了搜索性能。然后使用ODE (Open Dynamics Engine)库对机器人进行摩擦、平衡力矩等物理建模。统计分析表明,该方法在技术上是可行的。
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