Obstacle avoidance control of redundant robots using variants of particle swarm optimization

Authors:
Goh Shyh Chyan;S. G. Ponnambalam
Affiliations:
School of Engineering, Monash University, Sunway Campus, 46150 Bandar Sunway, Malaysia;School of Engineering, Monash University, Sunway Campus, 46150 Bandar Sunway, Malaysia
Venue:
Robotics and Computer-Integrated Manufacturing
Year:
2012

Citing 2
Cited 1

A Research on Particle Swarm Optimization and Its Application in Robot Manipulators

PACIIA '08 Proceedings of the 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application - Volume 02
Obstacle avoidance for kinematically redundant manipulators using a dual neural network

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Optimal robust sliding mode tracking control of a biped robot based on ingenious multi-objective PSO

Neurocomputing

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Abstract

Four variants of Particle Swarm Optimization (PSO) are proposed to solve the obstacle avoidance control problem of redundant robots. The study involved simulating the performance of a 5 degree-of-freedom (DOF) robot manipulator in an environment with static obstacle. The robot manipulator is required to move from one position to a desired goal position with minimum error while avoiding collision with obstacles in the workspace. The four variants of PSO are namely PSO-W, PSO-C, qPSO-W and qPSO-C where the latter two algorithms are hybrid version of the first two. The hybrid PSO is created by incorporating quadratic approximation operator (QA) alongside velocity update routine in updating particles' position. The computational results reveal that PSO-W yields better performance in terms of faster convergence and accuracy.