Path planning optimization for mechatronic systems with the use of genetic algorithm and ant colony

Abstract

Path planning optimization of mechatronic systems is a very important field of research that has been growing rapidly in recent years. Coordinate measuring machines (CMM), robotic arms, CNC machines, are often using a big amount of points to control the path planning. Within these efforts, some encouraging results are presented in this work on the optimization of path planning. By integrating ant colony techniques into genetic algorithm, path optimization can be reached up to 50% instead of the simple genetic algorithm. At the same time, the calculation of the optimal solution that makes this technique even more efficient is accelerated. From measurements made in a simulation model, reductions in overall trajectories were recorded up to 40% in cases where the number of points exceeded 500 while the optimal path planning time was reduced by up to 20%. In real-system implementation these values are slightly reduced due to the real-time execution of the movements. This integration makes the proposed technique particularly attractive in cases where are path planning between a large number of points and the calculation time is required to be small.