凿岩机器人钻臂定位控制交叉精英反向粒子群算法

Crossover elite opposition-based particle swarm optimization algorithm for positioning control of rock drilling robotic drilling arm

中文关键词  凿岩机器人  钻臂  定位控制  粒子群优化  精英反向学习  交叉算子  运动学逆解

英文关键词  rock drilling robot  drilling arm  positioning control  particle swarm optimization (PSO)  elite oppositionbased learning  crossover operator  inverse kinematics

中文摘要

      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 particleswarm 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.

英文摘要

      In the positioning process of rock drilling robotic drilling arm using particle swarm optimization (PSO)algorithm, there are some problems, such as low convergence speed, tending to be trapped in local optimal solution, etc..In order to solve these problems, a crossover elite opposition-based particle swarm optimization (CEOPSO) algorithm ispresented and the algorithm flow is given in this paper. The kinematics model of drilling arm is established, and the inversekinematics is solved by using the CEOPSO algorithm. The crossover operator is introduced into EOPSO. The adaptiveinertia weight and the crossover probability parameter control technologies are adopted. On the basis of maintaining theinformation exchange between the individual and the optimal solution, the global searching ability of the algorithm and thepositioning efficiency of drilling arm are improved by increasing the information exchange between the individual particles.Simulation results show that the average position error and mean posture error of CEOPSO are less than those of PSO andEOPSO, and its iterative process is stable. The positioning and control performance of rock drilling robotic drilling armcan be improved effectively.