Crossover elite opposition-based particle swarm optimization algorithm for positioning control of rock drilling robotic drilling arm
HUANG Kai-qi
CHEN Rong-hua
DING Wen-si
Abstract：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 is presented and the algorithm flow is given in this paper. The kinematics model of drilling arm is established, and the inverse kinematics is solved by using the CEOPSO algorithm. The crossover operator is introduced into EOPSO. The adaptive inertia weight and the crossover probability parameter control technologies are adopted. On the basis of maintaining the information exchange between the individual and the optimal solution, the global searching ability of the algorithm and the positioning 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 and EOPSO, and its iterative process is stable. The positioning and control performance of rock drilling robotic drilling arm can be improved effectively.
Keywords：rock drilling robotdrilling armpositioning controlparticle swarm optimization (PSO)elite opposition-based learningcrossover operatorinverse kinematics
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 303-311 )