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首页> 外文期刊>WSEAS Transactions on Circuits and Systems >Population Density Particle Swarm Optimized Improved Multi-robot Cooperative Localization Algorithm
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Population Density Particle Swarm Optimized Improved Multi-robot Cooperative Localization Algorithm

机译：人口密度粒子群优化改进多机器人协同定位算法

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摘要

In light of the accuracy of particle swarm optimization-particle filter (PSO-PF) inadequate for multi-robot cooperative positioning, the paper presents population density particle swarm optimization-particle filter (PDPSO-PF), which draws cooperative co-evolutionary algorithm in ecology into particle swarm optimization. By taking full account of the competitive relationship between the environment and particle swarm, through dynamic adjustment of particle swarm densities based on Lotka-Volterra competition equations, PDPSO-PF improves particle diversities, speeds up the evolution of the algorithm and enhances the effectiveness of prediction for multi-robot positioning. Studies show that PDPSO-PF improves both the convergence speed and accuracy, thus is suitable for multi-robot cooperative positioning.
机译：针对粒子群优化粒子滤波（PSO-PF）不能满足多机器人协同定位的要求，提出了种群密度粒子群优化粒子滤波（PDPSO-PF），提出了协同进化算法。生态学融入粒子群优化。通过充分考虑环境与粒子群之间的竞争关系，通过基于Lotka-Volterra竞争方程对粒子群密度进行动态调整，PDPSO-PF改善了粒子多样性，加快了算法的发展并提高了预测的有效性用于多机器人定位。研究表明，PDPSO-PF既提高了收敛速度，又提高了精度，因此适用于多机器人协同定位。

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