基于离子运动-人工蜂群算法的移动机器人路径规划

Path planning of mobile robots based on ion motion-artificial bee colony algorithm

针对移动机器人在仓储环境下的路径规划问题,提出了一种基于离子运动的人工蜂群(IM-ABC)算法用于路径规划。该方法为提高传统的人工蜂群(ABC)算法在路径规划中的收敛速度和搜索能力,采用一种模拟离子运动规律来更新蜂群的策略。首先,在算法前期利用离子运动算法中的阴阳离子交叉搜索来更新引领蜂和跟随蜂,从而引导种群进化方向,极大提高种群开发能力;其次,在算法后期为了避免前期过早收敛导致局部最优,引领蜂采用随机搜索,跟随蜂则利用反向轮盘赌来选择蜜源,以扩大种群多样性;最后,在全局更新机制中提出自适应性花香浓度以改善抽样方式,进而得到改进后的IM-ABC算法。标准测试函数测试与仿真实验结果表明,IM-ABC算法不仅能快速收敛,且和传统ABC算法相比迭代次数减少了58.3%,寻优性能提升了12.6%,表现出较高的规划效率。

Aiming at the path planning of mobile robots in storage environment,a path planning method based on Ion Motion-Artificial Bee Colony(IM-ABC)algorithm was proposed.In order to improve the convergence speed and searching ability of the traditional Artificial Bee Colony(ABC)algorithm in path planning,a strategy of simulating ion motion was used to update the swarm in this method.Firstly,at the early stage of the algorithm,the anion-cation cross search in ion motion algorithm was used to update the leading bees and following bees,so as to guide the direction of population evolution and greatly improve the development ability of population.Secondly,at the late stage of the algorithm,in order to avoid the local optimum caused by premature convergence in the early stage,random search was adopted by the leading bees and reverse roulette was used by the following bees to select honey sources and expand population diversity.Finally,an adaptive floral fragrance concentration was proposed in the global update mechanism to improve the sampling method,and then the IM-ABC algorithm was obtained.Benchmark function test and simulation experiment results show that the IM-ABC algorithm can not only rapidly converge,but also reduce the number of iterations by 58.3%and improve the optimization performance by 12.6%compared to the traditional ABC algorithm,indicating the high planning efficiency of IM-ABC algorithm.