基于改进蛇优化算法的轮式机器人路径规划

Wheeled robot path planning based on improved snake optimizer

为解决轮式机器人路径规划中效率低、寻优速度慢等问题,提出一种改进的蛇优化算法(improved snake optimizer,ISO)。在初始阶段引入正弦混沌映射扩大算法寻优空间,提升解的质量。同时设计了一种双向搜索策略,在最佳和最差个体引导的两个方向上逼近全局最优值,使收敛速度更快。并在算法中增加改进的进化种群动力机制,替换质量较差的个体从而提高种群质量。另外利用精英对立学习策略来提高算法的局部开发性能。仿真结果表明,ISO算法在轮式机器人路径规划过程中,相比其它对比算法各项指标更优,寻优效率更高,可以有效帮助轮式机器人完成规划任务。

To solve the issues of inadequate productivity and sluggish optimization velocity in wheeled robot path planning,an improved snake optimizer(ISO)was proposed.In the initial stage,sine chaotic mapping expansion algorithm is introduced to optimize the space and improve the quality of the solution.A bidirectional search strategy is devised to approximate the global optimal value simultaneously in the two directions led by the best and the worst individual,which makes the convergence speed faster.The improved evolutionary population dynamic mechanism is added to the algorithm to replace the poor quality individuals so as to improve the population quality.In addition,utilizing the elite opposition-based learning strategy is used to improve the local development ability of the algorithm.The simulation results show that the ISO algorithm performs better in various indicators and has higher optimization efficiency compared to other comparative algorithms in wheeled robot path planning process and can effectively help the wheeled robot to complete the planning task.