Productivity and quality enrichment through multi criteria trajectory optimisation of an industrial robot

To foster the fourth industrial revolution, a lot of efforts are being created to maximising the production by optimising the present manufacturing processes. One in every of such try is done to optimise activities of robot manipulators in industries. In this work, two new variants of intelligent algorithms, specifically, canonical particle swarm optimisation with mutation operator (CMPSO) and self accommodative differential evolution (SADE) are proposed to optimise the trajectory of a robot manipulator (MTAB ARISTO 6XT) to reduce production cost. Optimal trajectory is generated from the information of internal structure of robots by considering robot mechanics and dynamics. A multi criteria cost function represents the production price. The algorithms produce cost effective and collision-free trajectories by considering knot points on the trajectory. A productivity and economic study was carried out. The economic benefits yielded by CMPSO and SADE are good. The results proved the goodness of the proposed algorithms.