A new particle swarm optimisation variant-based experimental verification of an industrial robot trajectory planning

Abstract

A new variant of particle swarm optimisation (PSO), constriction coefficient neighbourhood varying inertia weight varying acceleration coefficients particle swarm optimisation (CNVIWVAC PSO) and a conventional differential evolution (DE) algorithms are proposed in this work to do optimal time - mechanical energy trajectory planning for an industrial robotic manipulator (MTAB ARISTO 6XT). Three pick-and-place operations are considered. Minimisation of travelling time of robot end effector and mechanical energy of the actuators are considered as objective functions. This is to ensure fast execution of the desired operation in a minimum possible spending of mechanical energy. All kinematic and dynamic constraints such as position, velocity, acceleration, jerk and torque bounds are considered to ensure smooth as well as practical trajectory. Two stationary obstacles are considered in the path robot manipulator. A comparative analysis of proposed algorithms (DE and CNVIWVAC PSO) with a point-to-point (PTP) algorithm (own system of the MTAB ARISTO 6XT robot) has been carried out by means of experimental tests. The proposed algorithms have been evaluated and experimentally validated. The results proved that the proposed algorithms are better than the existing system (PTP) of robot.