Trajectory Planning of a Six-DOF Robot Based on a Hybrid Optimization Algorithm
Xing Jin, Kang Junfeng, Jingjing Zhang, Xiang Yang · 2016
This work adopts a standard Denavit-Hartenberg method to model a PUMA 560 spot welding robot as the object of study. The forward and inverse kinematics solutions are then analyzed. To address the shortcomings of the ant colony algorithm, factors from the particle swarm optimization and the genetic algorithm are introduced into this algorithm. Subsequently, the resulting hybrid algorithm and the ant colony algorithm are used to conduct trajectory planning in the shortest path. Experimental data and simulation result show that the hybrid algorithm is significantly better in terms of initial solution speed and optimal solution quality than the ant colony algorithm. The feasibility and effectiveness of the hybrid algorithm in the trajectory planning of a robot are thus verified.