摘要
合理高效的路径规划是移动机器人进行探索任务的前提,针对粒子群算法存在的收敛速度慢、效率低等问题,通过对粒子群算法中的权重系数和学习因子进行优化,进一步提高粒子群算法的收敛精度和搜索能力,并把粒子群算法、改进后的粒子群算法分别应用到移动机器人路径规划中,在相同环境下,改进粒子群算法所规划路径的距离最优值和距离平均值均比粒子群算法有了明显减小,并且,改进粒子群算法搜索到最优路径的迭代次数也比粒子群算法有了一定的减少。仿真结果表明,改进粒子群算法能使机器人高效移动到目标位置,验证了改进粒子群算法的可行性和有效性。
Reasonable and efficient path planning is the prerequisite for mobile robots to carry out exploratory tasks,in view of the particle swarm optimization algorithm has slow convergence speed,low efficiency problem,By optimizing the weight coefficient and learning factor in particle swarm optimization,the convergence accuracy and searching ability of particle swarm optimization are further improved,the improved particle swarm optimization,basic particle swarm optimization are applied to the path planning o f mobile robot respectively.the distance optimum value and the distance average value of the path planned by the improved particle swarm optimization algorithm are significantly smaller than the standard particle swarm optimization algorithm,in the same environment,Moreover,the number of iterations for the improved particle swarm optimization algorithm to find the optimal path is less than that of the standard particle swarm optimization algorithm.The simulation results show that the improved particle swarm optimization algorithm can make the robot move to the target position efficiently,which verifies the feasibility and efficiency of the improved particle swarm optimization algorithm.
作者
孙冬
赵开新
SUN Dong;ZHAO Kaixin(School of Computer Science and Technology,Henan Institute of Technology,Xinxiang 453003,China)
出处
《河南工学院学报》
CAS
2020年第6期19-23,共5页
Journal of Henan Institute of Technology
基金
河南省科技攻关项目(202102210153)
河南省高等学校教学名师工作室项目(教高〔2019〕618号)。
关键词
粒子群优化算法
学习因子
权重系数
路径规划
机器人
particle swarm optimization algorithm
learning factor
weight coefficient
path planning
robot
