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Author: search.filters.author.Jaisumroum, Nattapon

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    Decentralized control of cooperative robotics system using neural networks for the purpose of an object balancing flat plate tasks
    (2020-02-01)
    Jaisumroum, Nattapon
    ;
    Chotiprayanakul, Pholchai
    ;
    Sriseubsai, Wipoo
    In this article, the manipulation to handle the object on a plate using neural networks, design of 1-DOF robot arm under cooperative control, will be explained. The robot's system specifies the object position and velocity, an assignment oriented components for cooperative control. The novelty of this experiment is that under the decentralized control, the robot estimates the position and speed of the object for control end-effector of robot arm using the camera to track position and speed of the object according to training and assigned collaborative tasks which differ from other experiments that use sensors. The experiment includes three robot manipulators which were capable balancing the objects on flat plate with dataset to training and control servo motor assigned to the corresponding position and the end-effector in decentralized to control robotics. Overall, neural network method can be a training scheme using a cooperative robotics in a decentralized control.
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    Decentralized control with neural network of cooperative robot manipulator for object balancing task on flat plate
    (2017-12-13)
    Jaisumroum, Nattapon
    ;
    Chotiprayanakul, Pholchai
    ;
    Limnararat, Sunpasit
    In this paper, a decentralized framework for kinematic control of cooperative manipulators systems is developed. The motion of the robot system is specified at the object position, by adopting a task-oriented formulation for cooperative tasks. In the controller of robot computes, the end-effector motion of robots in a decentralized on the camera position frame of the knowledge of the assigned cooperative task. The motion of manipulator is reference computed by object and its neighbors. The joint motion of robots is reference corresponding from the end-effector. This study approach decentralized control of collaborative manipulators, tested in the simulation on MATLAB® software composed by neural network method. A neural network is used to approximate a decentralized control law designed by the back-propagation technique. The motion for each joint is controlled independently using local angular position and velocity measurements. Finally, the experiment shows the feasibility of the proposed control scheme using a robotics.
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    A conceptual framework of decentralized learning neural network control approach for multi-robot cooperation in an object balancing task
    (2016-12-27)
    Jaisumroum, Nattapon
    ;
    Chotiprayanakul, Pholchai
    ;
    Limnararat, Sunpasit
    This paper presents a conceptual framework of a neural network control approach for robot manipulator cooperative, which is based on decentralized learning. Back propagation neural network is used for learning procedure to adapt and adjust the neuron-controller's parameters which depend on the approximated error. Dynamic model of two cooperating 3-DOF robot manipulators are defined and implement with neural network control. Visual feedback enables two robots to correct and calibrate their movement to compensate their object balancing task whereas both robots hold a flat plate balancing a round object on it. This conceptual framework of the decentralized learning procedure will be verified by a simulation and experiments in near future.
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    Self-tuning control with neural network for robot manipulator
    (2016-01-24)
    Jaisumroum, Nattapon
    ;
    Chotiprayanakul, Pholchai
    ;
    Limnararat, Sunpasit
    This paper presents an approach of the self-tuning control with neural network for robot manipulator in an object balancing task. A 3DOF robot arm (Novint Falcon 3D haptic) is used to hold a flat plate balancing a cylindrical object put on. Since a neural network algorithm were presented earlier in [1], [2] in order to learn and control the posture of the robot, we now employ the visual feedback into neural network to enable the robot arm learn to move its end effector. A webcam is used to determine position of a cylinder object rolling on a flat plate that the robot is holing. The images are processed to the object position by neural network. The output of the neural network is the height of the robot's end-effector that the robot has to lift the plate. The neural network must learn and self-calibrate by some repeating trail movements until the virtual feedback enable the robot arm adopts recalibrating parameters to stabilize the rolling task. The results of experiments show the learning procedure of the neural network is succeed in self-tuning control.