An Introduction to Genetic Algorithms for Scientists and Engineers
An Introduction to Genetic Algorithms for Scientists and Engineers
Robotics and Computer-Integrated Manufacturing
Obstacle avoidance for kinematically redundant manipulators using a dual neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Iterative strategies for obstacle avoidance of a redundant manipulator
WSEAS Transactions on Mathematics
Iterative genetic algorithm based strategy for obstacles avoidance of a redundant manipulator
AMERICAN-MATH'10 Proceedings of the 2010 American conference on Applied mathematics
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Redundancy of a serial manipulator means that more joints than necessary are available in order to achieve a specified task of the manipulator end-effector. Manipulators are thus allowed to achieve complex tasks by taking into account additional constraints. This paper presents a genetic algorithm based strategy for redundancy resolution with two performance criteria accomplishment, while the end-effector achieves a number of prescribed configurations. The additional constraints added to the main end-effector task are obstacle avoidance and minimization of the sum of joint displacements. The strategy variables are the manipulator base location and the joint configuration adequate to every prescribed end-effector configuration. The strategy is realized using Matlab program with genetic algorithm tool and the illustrative simulations are obtained for a planar redundant manipulator with four degrees of freedom that achieve five different end-effector configurations, while the two performance criteria are accomplished.