基于改进二阶粒子群算法的机器人全局路径规划

Juan Li, Jinyu Su
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引用次数: 0

摘要

利用粒子群算法实现机器人的全局路径规划时,经常出现过早收敛和陷入局部最优的问题。因此,采用改进的二阶粒子群算法改进其早收敛性并分析其稳定性。建立了惯性权重与学习因子之间的logistic函数关系。采用线性递减的惯性权值和自适应学习因子,使惯性权值随学习因子变化而改变全局或局部飞行能力,提高跳出局部最优的能力。通过仿真比较,证明本文提出的改进二阶粒子群算法在全局路径规划中具有跳出局部最优的能力,同时避免了过早收敛,还能提高算法的收敛速度,验证了算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robot Global Path Planning Based on Improved Second-Order PSO Algorithm
When using particle swarm optimization (PSO) to realize the global path planning of the robot, premature convergence and falling into the local optimum often occur. Therefore, an improved second-order PSO is used to improve premature convergence and analyze its stability. The logistic function relationship is established between the inertia weight and the learning factors. Using linearly decreasing inertia weight and adaptive learning factors, making the inertia weight change together with the learning factors to change the global or local flight ability to improve the ability to jump out of the local optimum. Through simulation comparison, it is proved that the improved second-order PSO proposed in this paper has the ability to jump out of the local optimum in the global path planning, and at the same time avoids premature convergence, and can also improve the convergence speed of the algorithm, which verifies the effectiveness of the algorithm.
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