Robotics: control, sensing, vision, and intelligence
Robotics: control, sensing, vision, and intelligence
Dynamics and motion control of a two-link robot manipulator with a passive joint
IROS '95 Proceedings of the International Conference on Intelligent Robots and Systems-Volume 2 - Volume 2
Experimental study of an underactuated manipulator
IROS '95 Proceedings of the International Conference on Intelligent Robots and Systems-Volume 2 - Volume 2
A Solution of Inverse Kinematics of Robot Arm Using Network Inversion
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
Advances in Engineering Software
Advances in Engineering Software
Reliability-based approach to the inverse kinematics solution of robots using Elman's networks
Engineering Applications of Artificial Intelligence
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This paper is devoted to solve the positioning control problem of underactuated robot manipulator. Artificial Neural Networks Inversion technique was used where a network represents the forward dynamics of the system trained to learn the position of the passive joint over the working space of a 2R underactuated robot. The obtained weights from the learning process were fixed, and the network was inverted to represent the inverse dynamics of the system and then used in the estimation phase to estimate the position of the passive joint for a new set of data the network was not previously trained for. Data used in this research are recorded experimentally from sensors fixed on the robot joints in order to overcome whichever uncertainties presence in the real world such as ill-defined linkage parameters, links flexibility, and backlashes in gear trains. Results were verified experimentally to show the success of the proposed control strategy.