基于改进麻雀搜索算法的移动机器人路径规划

Mobile robot path planning based on improved SSA

麻雀搜索算法(SSA)是移动机器人路径规划中的一种高效方法,但存在易陷入局部最优解,种群多样性较差的问题。针对以上问题,首先,提出一种Tent混沌映射增加种群多样性,使粒子脱离局部极值;其次,引入方向因子来减少尖峰拐点;最后,使用三次样条插值来平滑路径,使路径更平滑。仿真实验结果表明:在不同的静态环境中,通过对比A*算法、蚁群算法和传统的SSA,改进后的SSA(ISSA)极大限地增加了初始种群的有效性,有效缩短了路径长度、减少了转折点次数,得到更平滑的路径,证明了算法的可行性。

Sparrow search algorithm(SSA)is an efficient method in path planning of mobile robots,but it is easy to fall into a local optimal solution and the population diversity is poor.To solve the above problems,firstly,a Tent chaotic mapping is proposed to increase the diversity of the population and make the particles deviate from the local extreme value.Secondly,a directional factor is introduced to reduce the peak inflection point.Finally,cubic spline interpolation is used to smooth the path and make the path smoother.The simulation experiment results show that in different static environments,by comparing the Aalgorithm,the ant colony algorithm and the traditional SSA,the improved SSA(ISSA)greatly increases the effectiveness of the initial population,effectively shortens the path length and reduces number of turning points,and a smoother path is obtained,which proves the feasibility of the algorithm.