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Inverse kinematics of robot manipulator is to determine the joint variables for a given Cartesian position and orientation of an end effector. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Although artificial neural network (ANN) can be gainfully used to yield the desired results but the gradient descent learning algorithm does not have ability to search for global optimum and it gives slow convergence rate. This paper proposes structured ANN with hybridization of Gravitational Search Algorithm to solve inverse kinematics of 6R PUMA robot manipulator. The ANN model used is multi-layered perceptron neural network (MLPNN) with back-propagation (BP) algorithm which is compared with hybrid multi layered perceptron gravitational search algorithm (MLPGSA). An attempt has been made to find the best ANN configuration for the problem. It has been observed that MLPGSA gives faster convergence rate and improves the problem of trapping in local minima. It is found that MLPGSA gives better result and minimum error as compared to MLPBP.

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In industrial environment product assembly constitutes a major part in manufacturing. Introduction of automation in manufacturing activities has positively influenced the business in many counts. With the advent of new technologies manufacturing houses are willing to adopt new technologies and strategies to make their products more reliable and competitive. The present work deals with the development of a multiple sensor integrated robot end-effector which can be gainfully used for product assembly in industries. The sensors prescribed for the purpose help the assembly robot in identifying the correct part, navigation the robot arm and inspecting the assembly for its correctness. Experiments on these aspects have been conducted successfully and the relevant results are presented to explain the effectiveness and usefulness of the sensor integrated endeffector.