民主多机器人探索:计算粒子群优化全局最优参数的新方法

2015 World Symposium on Computer Networks and Information Security (WSCNIS) Pub Date : 2015-09-01 DOI:10.1109/WSCNIS.2015.7368299

O. Moslah, Yassine Hachaichi, Younes Lahbib, Raed Kouki, A. Mami

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引用次数: 2

摘要

在多机器人探索作业中，每个机器人在探索了自己当前的位置后，必须不断地决定下一步要移动的地方。本文将粒子群算法(Particle Swarm Optimization, PSO)扩展到机器人中，称为机器人粒子群算法(robotic Particle Swarm Optimization, RPSO)，一种计算机器人新位置的方法。为了使该方法更好地适用于集体勘探问题，并使勘探面积最大化，我们使用了一种新的方法来计算pso的全局最优参数。在模拟环境下的实验结果表明，本文提出的计算pso全局最优参数的新方法增加了探测面积。

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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Democratic multi-robot exploration: New method to compute Particle Swarm Optimizations' global best parameter

In multi-robot exploration operation, each robot has to continuously decide which place to move next, after exploring their current location. In this paper we use the extended version of Particle Swarm Optimization (PSO) to robotic application, which is referred to as Robotic Particle Swarm Optimization (RPSO), a technique to compute robots' new location. To better adapt this technique to the collective exploration problem, and maximize the exploring area, we used a new method for computing PSOs' global best parameter. Experiment results obtained in a simulated environment show that our new method of computing PSOs' global best parameter increase the explored area.

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2015 World Symposium on Computer Networks and Information Security (WSCNIS)

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