Chotiprayanakul, Pholchai

Preferred name

Alternative Name

Chotiprayanakul, P.

Main Affiliation

School of Engineering

pholchai.ch@kmitl.ac.th

Now showing 1 - 6 of 6

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Multiple-performance optimization for human-robot collaboration: A translation motion in a tele-sandblasting maintenance system
(2016-01-01)
Jientrakul, Ranon
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Yuangyai, Chumpol
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Chotiprayanakul, Pholchai
;
Limnararat, Sunpasit
Human-robot collaboration is a vital part of teleoperation systems. Determining the design parameters satisfying the optimal human performance is very important in the design of a teleoperation system for a specific task. Specifically, to thoroughly represent human performance for a task, multiple performance measures in a teleoperation system are evaluated. The experiment was also conducted on a mixed-reality tele-sandblasting system which is setup to study 2D-motion translation in tele-sandblasting task. Then, identifying the design setting to satisfy the optimal measures are difficult due to conflicts among them. Therefore, this paper presents a multipleperformance optimization using a desirability function. The results provide the range of setting condition promoting the optimal human performance measures. © IEOM Society International.
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Variation minimization in tele-sandblasting system: The effect of human-arm movement error
(2018-08-14)
Jientrakul, Ranon
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Attavanish, Pornsak
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Chotiprayanakul, Pholchai
;
Limnararat, Sunpasit
;
Yuangyai, Chumpol
In tele-sandblasting task, human arm movement is a critical source of producing variation in position of sandblasting nozzle resulting in high operating cost and low productivity. Each operator behaves differently leading to unpredictable movements. Skilled operators are able to reduce the variation; however, developing skills requires a training period. In this paper, we proposed a new approach which is the use of a novel operator's arm movement pattern incorporated with a Kalman filter to reduce the effect of human-arm movement error. A virtual tele-sandblasting system is used to validate our approach. The experimental results verify that our proposed approach is able to significantly reduce the effect of human arm movement error. The approach helps operators to perform the task more comfortably and takes short training time.
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Self-tuning control with neural network for robot manipulator
(2016-01-24)
Jaisumroum, Nattapon
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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.
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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
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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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Investigating multiple human performance measures in teleoperation task: A translation task in a tele-sandblasting maintenance system
(2018-05-01)
Jientrakul, Ranon
;
Yuangyai, Chumpol
;
Cheng, Chen Yang
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Chotiprayanakul, Pholchai
;
Limnararat, Sunpasit
In sandblasting tasks for complex steel structure maintenance, teleoperation is required to keep humans away from occupational risk and hazard. On the other hand, teleoperation typically degrades system-human performances, resulting in poor product quality and must be designed such that the performances remain as high as possible. However, designing the teleoperation system regarding to a single performance measure may lead to an improper design. In this article, we propose two novel loss-function-based human-performance measures to incorporate with a widely used performance measure, movement time, to thoroughly represent performance: unfinished surface and damaged surface. We aim to investigate the effects of two main design parameters, viewing distance and path width. The results show that only path width is significant for overall performances. Furthermore, the effect of gender is significant such that men outperform women in cleaning the surface. Finally, the optimal setting conditions are suggested to achieve their optimal performances.

Chotiprayanakul, Pholchai

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