Optimal trajectory planning of complicated robotic timber joints based on particle swarm optimization and an adaptive genetic algorithm

Abstract

In this paper, a methodology for path distance and time synthetic optimal trajectory planning is described in order to improve the work efficiency of a robotic chainsaw when dealing with cutting complex timber joints. To demonstrate this approach one specific complicated timber joint is used as an example. The trajectory is interpolated in the joint space by using a quantic polynomial function which enables the trajectory to be constrained in the kinematic limits of velocity, acceleration, and jerk. The particle swarm optimization (PSO) is applied to optimize the path of all cutting surfaces of the timber joint in operating space to achieve the shortest path. Based on the optimal path, an adaptive genetic algorithm (AGA) is used to optimize the time interval of interpolation points of every joint to realize the time-optimal trajectory. The results of the simulation show that the PSO method shortens the distance of the trajectory and that the AGA algorithm reduces time intervals and helps to obtain smooth trajectories, validating the effectiveness and practicability of the two proposed methodology on path and time optimization for 6-DOF robots when used in cutting tasks.