基于粗糙集约简与狮群优化算法的机器人路径规划研究

Robot Path Planning Based on Rough Set Reduction and Lion Swarm Optimization

为了解决机器人路径规划冗余数据多、寻优质量差和收敛速度慢等问题,提出一种粗糙集约简技术与狮群优化算法相结合的寻找机器人最优路径算法。首先,采用栅格法对机器人工作环境建模;其次,用粗糙集知识约简和核对初始决策表进行简化,获得最小化决策表,用于训练初始狮子种群;最后,用改进的狮群算法寻找最优路径。实验结果表明,所提算法路径寻优质量高、收敛速度快,且具有较高的寻优稳定性。

In order to solve the problems of redundant data,poor path searching quality and slow convergence speed in robot path planning,an optimal path algorithm based on rough set reduction technology and lion swarm optimization was proposed.Firstly,the grid method was adopted to model the working environment of the robot.Secondly,rough set reduction and core were used to simplify the initial decision table into a minimization decision table which was used to train the initial lion population.Finally,the improved lion swarm algorithm was used to find the optimal path.Experimental results showed that the proposed algorithm had the advantages of high path searching quality,fast convergence speed and considerable optimization stability.