凿岩钻臂逆解的自适应粒子群算法  

Adaptive particle swarm optimization for rock drilling arm inverse kinematics

作　　者：高静 赵宏强[1,2] 周煊亦 Gao Jing;Zhao Hongqiang;Zhou Xuanyi(Mechanical and Electrical Engineering,Central South University,Changsha 410083,China;Sunward Intelligent Equipment Co.,Ltd.,Changsha 410100,China)

机构地区：[1]中南大学机电工程学院,长沙410083 [2]山河智能装备有限公司,长沙410100

出　　处：《电子测量技术》2018年第16期1-6,共6页Electronic Measurement Technology

基　　金：国家自然科学基金(51375499);湖南省重大科技专项(2011XK4001)项目资助

摘　　要：针对凿岩钻臂逆解中存在奇异解或多解性,且一般方法难以直接求解的问题,采用粒子群优化算法（PSO）对凿岩钻臂逆运动学进行求解。通过分析凿岩钻臂正运动模型,对末端位姿矩阵进行欧拉角转换,并建立目标函数。根据粒子的每一次迭代确定种群粒子位置状态的成功率,作为反馈参数动态调整惯性权值系数来改进粒子群算法中惯性权值函数,以避免传统粒子群算法易陷入局部最优和＂早熟＂。通过仿真数据对比,自适应权值优化（AIWPSO）提高了算法的全局搜索能力和求解精度,有效解决凿岩钻臂逆解的求解精度低和速度慢的问题。In order to solve the singularity or multi-solution problems existing in the inverse solution of rock drill arm, the problems are difficult to solve through general method, this paper proposes an idea that apply the particle swarm optimization algorithm （PSO） to research the inverse solution of rock drill arm. By analyzing the forward kinematics model of rock drilling arm, the Euler angle of the end pose matrix is simplified and the objective function is established. The inertia weight function of particle swarm optimization algorithm is improved to overcome the problem of traditional particle swarm optimization, which is easy to fall into local optimum and slow convergence speed. The success rate of particle state is determined according to the iteration of the particle as the feedback parameter to dynamically adjust the inertia weight coefficient. The comparison of simulation data, the adaptive inertia weight particle swarm optimization （AIWPSO） improves the global search ability and accuracy of the algorithm, effectively solving the problems of low precision and slow inverse solution speed of rock drilling arm.

关 键 词：运动学逆解 粒子群优化 凿岩钻臂 自适应权值优化 

分 类 号：TP242.3[自动化与计算机技术—检测技术与自动化装置]