摘要
针对粒子群优化(PSO)算法存在的优化精度低以及早熟的缺点,提出一种改进的PSO算法用于机器人路径规划.根据梯度下降法中变量沿负梯度方向变化的原则,提出了改进的粒子速度更新模型.为了提高粒子的搜寻效率及精度,增加了自适应粒子位置更新系数.引入ε贪心策略设计了改进的粒子群优化算法.在部分优化测试函数上的多次试验结果表明,所提算法较其他算法模型搜索精度至少提高2倍,收敛速度也有大幅度的提升.将所提算法和改进的DC-HPSO(动态聚类混合粒子群优化)算法应用于静态障碍物下的路径规划仿真和实际试验,结果表明所提模型具有高精度、高效率、高成功率的优点.
An improved PSO(particle swarm optimization) algorithm is proposed for robot path planning, aiming at the shortcomings of PSO algorithm, such as the low optimization accuracy and the premature. Firstly, an improved model of particle velocity update is proposed according to the principle that the variables change in the direction of negative gradient in gradient descent method. Then, an adaptive particle position update coefficient is added in order to improve the efficiency and the accuracy of particle search. Finally, an improved PSO algorithm is designed by introducing ε-greedy strategy. The experimental results on some optimized test functions show that the search accuracy of the proposed algorithm is at least twice that of other algorithms, and the convergence speed is also greatly improved. The proposed algorithm and the improved DC-HPSO(dynamic clustering hybrid PSO) algorithm are applied to the simulations and the actual experiments of static path planning. Results show that the proposed model has the advantages of high accuracy, high efficiency and high success rate.
作者
康玉祥
姜春英
秦运海
叶长龙
KANG Yuxiang;JIANG Chunying;QIN Yunhai;YE Changlong(School of Mechatronics Engineering,Shenyang Aerospace University,Shenyang 110136,China;Suzhou Automobile Research Institute of Tsinghua University,Suzhou 215000,China)
出处
《机器人》
EI
CSCD
北大核心
2020年第1期71-78,共8页
Robot
基金
沈阳市科技计划(F16-216-6-00)。
关键词
路径规划
粒子群优化
移动机器人
梯度下降算法
自适应
path planning
particle swarm optimization
mobile robot
gradient descent algorithms
self-adaption
