An adaptive-learning algorithm to solve the inverse kinematics problem of a 6 D.O.F serial robot manipulator |
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Affiliation: | 1. Human and Robot Interaction Laboratory, School of Electrical and Computer Engineering, University of Tehran, North Kargar Street, Iran;2. Faculty of New Sciences and Technologies, University of Tehran, Iran;3. School of Electrical and Computer Engineering, University of Tehran, North Kargar Street, Iran;1. School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510006, PR China;2. Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China |
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Abstract: | An adoptive learning strategy using an artificial neural network ANN has been proposed here to control the motion of a 6 D.O.F manipulator robot and to overcome the inverse kinematics problem, which are mainly singularities and uncertainties in arm configurations. In this approach a network have been trained to learn a desired set of joint angles positions from a given set of end effector positions, experimental results has shown an excellent mapping over the working area of the robot, to validate the ability of the designed network to make prediction and well generalization for any set of data, a new training using different data set has been performed using the same network, experimental results has shown a good generalization for the new data sets.The proposed control technique does not require any prior knowledge of the kinematics model of the system being controlled, the basic idea of this concept is the use of the ANN to learn the characteristics of the robot system rather than to specify explicit robot system model. Any modification in the physical set-up of the robot such as the addition of a new tool would only require training for a new path without the need for any major system software modification, which is a significant advantage of using neural network technology. |
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