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

Authors: S. Mahalakshmi; A. Arokiasamy

Addresses: Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam, 611 002, Tamil Nadu, India ' Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam, 611 002, Tamil Nadu, India

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.

Keywords: industrial robot; MTAB ARISTO 6XT robot; pick and place operation; minimum time-energy trajectory planning; DE; CNVIWVAC PSO.

DOI: 10.1504/IJOR.2021.116250

International Journal of Operational Research, 2021 Vol.41 No.3, pp.399 - 422

Received: 10 Dec 2018
Accepted: 20 Dec 2018
Published online: 15 Jul 2021 *

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