凿岩机器人钻臂定位控制交叉精英反向粒子群算法
发布时间：2018-11-04 08:26
【摘要】：在利用粒子群优化算法(particle swarm optimization,PSO)进行凿岩机器人钻臂定位过程中,存在收敛速度慢和易于陷入局部最优解等问题.为此,提出一种交叉精英反向粒子群优化算法(crossover elite opposition-based particle swarm optimization,CEOPSO)并给出算法的流程.建立凿岩机器人钻臂运动学模型并对其逆向运动学进行求解.将交叉算子引入EOPSO中,采用自适应惯性权重和交叉概率参数控制技术,在维护粒子个体与最优解之间信息交换的基础上,增加粒子个体之间的信息交换,提高算法的全局搜索能力和钻臂定位效率.仿真结果表明,CEOPSO的平均位置误差和平均姿态误差均小于PSO和EOPSO算法,且迭代过程平稳,可以有效提高凿岩机器人钻臂的定位控制性能.
[Abstract]:In the process of drilling robot arm localization using particle swarm optimization (particle swarm optimization,PSO), there are some problems such as slow convergence speed and easy to fall into local optimal solution. Therefore, a cross-elitist reverse particle swarm optimization (crossover elite opposition-based particle swarm optimization,CEOPSO) algorithm is proposed and the flow of the algorithm is given. The kinematics model of drill arm of rock drilling robot is established and its inverse kinematics is solved. The crossover operator is introduced into EOPSO, and adaptive inertial weight and crossover probability parameter control techniques are adopted to increase the information exchange between particle individuals and optimal solutions on the basis of maintaining information exchange between particle individuals and optimal solutions. The algorithm improves the global searching ability and the efficiency of drilling arm location. The simulation results show that the average position error and the average attitude error of CEOPSO are smaller than those of PSO and EOPSO algorithms, and the iterative process is stable, which can effectively improve the positioning control performance of drilling arm of drilling robot.
【作者单位】： 江西理工大学机电工程学院;华南理工大学机械与汽车工程学院;
【基金】：国家自然科学基金项目(11272122) 广东省部产学研重大项目(2012A090300011) 江西省科技厅对外合作重点项目(20123BBE50085)资助~~
【分类号】：TP18;TP242
本文编号：2309280
[Abstract]:In the process of drilling robot arm localization using particle swarm optimization (particle swarm optimization,PSO), there are some problems such as slow convergence speed and easy to fall into local optimal solution. Therefore, a cross-elitist reverse particle swarm optimization (crossover elite opposition-based particle swarm optimization,CEOPSO) algorithm is proposed and the flow of the algorithm is given. The kinematics model of drill arm of rock drilling robot is established and its inverse kinematics is solved. The crossover operator is introduced into EOPSO, and adaptive inertial weight and crossover probability parameter control techniques are adopted to increase the information exchange between particle individuals and optimal solutions on the basis of maintaining information exchange between particle individuals and optimal solutions. The algorithm improves the global searching ability and the efficiency of drilling arm location. The simulation results show that the average position error and the average attitude error of CEOPSO are smaller than those of PSO and EOPSO algorithms, and the iterative process is stable, which can effectively improve the positioning control performance of drilling arm of drilling robot.
【作者单位】： 江西理工大学机电工程学院;华南理工大学机械与汽车工程学院;
【基金】：国家自然科学基金项目(11272122) 广东省部产学研重大项目(2012A090300011) 江西省科技厅对外合作重点项目(20123BBE50085)资助~~
【分类号】：TP18;TP242
【相似文献】
相关期刊论文 前10条
1 孙浦生;我国首台“隧道凿岩机器人”问世[J];石油工程建设;2001年02期
2 ;863计划成果“隧道凿岩机器人”问世[J];矿业快报;2001年01期
3 谢习华,何清华,郭勇;隧道凿岩机器人钻臂移位跟踪与控制系统[J];凿岩机械气动工具;2000年02期
4 曾桂英,郭勇,何清华;凿岩机器人机械手定位系统的精度分析[J];机电工程;2002年02期
5 李力争,何清华;凿岩机器人钻臂平行联动机构的机理建模[J];机电工程;2001年02期
6 何清华,曾桂英;凿岩机器人的车体定位[J];中南工业大学学报(自然科学版);1999年03期
7 谢习华,何清华,周亮;隧道凿岩机器人的车体定位方法[J];同济大学学报(自然科学版);2001年09期
8 朱建新,贺湘宇,何清华;凿岩机器人壁碰问题[J];中南工业大学学报(自然科学版);2000年04期
9 ;隧道凿岩机器人[J];传感器世界;2001年04期
10 周宏兵,龚艳玲,邹湘伏,赵宏强;隧道凿岩机器人钻臂各液压缸驱动力计算[J];凿岩机械气动工具;2004年01期
相关重要报纸文章 前1条
1 记者 冯济武;隧道凿岩机器人在长沙研制成功[N];中国机电日报;2000年
，本文编号：2309280
本文链接：https://www.wllwen.com/kejilunwen/zidonghuakongzhilunwen/2309280.html
