Chotiprayanakul, Pholchai

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Chotiprayanakul, P.

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School of Engineering

pholchai.ch@kmitl.ac.th

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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
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Chotiprayanakul, Pholchai
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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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Human-robot-environment interaction interface for robotic grit-blasting of complex steel bridges
(2012-11-01)
Chotiprayanakul, P.
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Liu, D. K.
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Dissanayake, G.
This paper presents a human-robot-environment interaction (HREI) interface using haptic feedback for a grit-blasting robot operating in close proximity to a complex steel bridge structure. The productivity requirements dictate the need for efficient algorithms for mapping, exploration, and collision-free motion planning. While a large portion of the grit-blasting operation can be automated, a tele-operation is essential to deal with some difficult to access sections such as edges, complex corners, and surfaces which can only be approached through hole. A 3-dimensional virtual force field (3D-VF <sup>2</sup>) method is developed for capturing the relationship between the robot and its environment. A novel haptic force generation method and a workspace mapping algorithm allow intuitive interaction between the operator and the robot through haptic feedback. The strategies presented are verified in extensive simulations and experiments conducted on a steel bridge with a prototype grit-blasting robot. © 2012 Elsevier B.V.
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A design of HDPE flexible spline of harmonics gear
(2018-08-14)
Chotiprayanakul, Pholchai
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Khamsri, Nattakul
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Kumjaroen, Neeracha
In a design of a small robot, selecting a servo-motor is the most important method in designing process. Common miniature servo motors for small robots normally use conventional gear train set or planetary gear set. Gear train and planetary gear give very low gear ratio whereas its weight is too heavy. On the other hand, Harmonic gear system, which is developed from strain wave drive gearing, gives better the highest gear ratio per weight than those two gear systems. In this paper, a plastic flexible spline gear is presented in order to replace a thin metal spline. The plastic flexible spline gear is designed under gear physical requirement and servo-motor properties. The spline thicknesses are varied in a range of 1 to 4 millimetre and simulations on strength, fatigue, and torque requirement are made to verify the designs.
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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
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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
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Limnararat, Sunpasit
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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
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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
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Chotiprayanakul, Pholchai
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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
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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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Effect of view distance and movement scale on haptic-based teleoperation of industrial robots in complex environments
(2011-01-01)
Chotiprayanakul, Pholchai
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Liu, Dikai
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Paul, Gavin
This paper presents the study on the effect of view distance and movement scale on performance of haptic based teleoperation of a sandblasting robot in complex steel bridge maintenance environments. The operational performance, measured by the Index of Performance (IP), is defined based on the speed and the control accuracy of the manipulator. View distance (i.e. the distance between a display space and an object movement space) and movement scale between hand movement and manipulator movement, which are normally selected empirically, have significant effect the performance. In this paper, an experimental approach is used for determining view distance and movement scale. The sandblasting robotic system is used as an example industrial application in the experiments. Results of the experiments show a range of the view distance and the movement scale that can improve the performance of haptic-based teleoperation of industrial robots in complex environments.
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Preliminary Study of Epoxy Coating Process on Basketry Inlay with 3D Printed Model
(2019-10-25)
Chotiprayanakul, Pholchai
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Noda, Atsushi
In Japan and South-East Asia Countries, Basketry is a heritage handcraft made with natural material such as bamboo strip and wheat straw. Shape and size of basketry products are depended on the basketry pattern and now FDM (Fused Deposition Modelling) 3D printer is used to a print the basketry pattern. This paper will present a bit further in epoxy coating on basketry products to make it more rigid and better shining appearance either while the coated basketry still have the 3D printing model inside as a structure or it will be taken off after coating epoxy. There are a lot of makers who try to use the 3D printed model for resin casting mold but many are fail to get the casted out of the 3D printed mold. Thus we do this preliminary study to investigate the adhesive strength of the casting epoxy resin holding on the 3D FDM printed model. An experiment was designed to find the effect of the curing time and the bamboo fiber adding for resin reinforce. In experiment, a flat dog-bone shape specimen for tensile testing is redesigned by separating into 2 identical parts and they are printed with ABS plastic filament. We glue epoxy resin on 12.5 X 10 mm. contact area in middle of the specimen. We left them cure and dry for 6, 12, and 24 hours before tensile testing then repeat the experiment again with bamboo strip added in between the contact area. The result of the experiment shows that bamboo strip reinforce and curing time affect the adhesive strength.

Chotiprayanakul, Pholchai

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