Konghuayrob, Poom

Preferred name

Konghuayrob, Poom

Main Affiliation

School of Engineering

poom.ko@kmitl.ac.th

Now showing 1 - 10 of 34

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Autonomous Rescue Hexapod Robot with AI Human Detection and Tracking
(2023-01-01)
Saengsint, Chissanupong
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Juntacheevakul, Kidtipod
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Chalermkitpaisan, Kirawut
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Suthapintu, Napak
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Tomaneenilrat, Pavares
The occurrence of catastrophic caused tons of thousands of lives. To enhance the efficacy of search and rescue operations in challenging to access region, we have introduced an autonomous hexapod robot equipped with a partial human detection and tracking system that is capable to identify fragments of the human body. The chosen hexagonal arrangement of six legs enables the robot to navigate through rough terrains. This configuration provides stable mobility, with an average acceleration of 0.8 m/s2 and a peak acceleration of 3.1 m/s2 during walking, making the camera of the robot remains steady for human identification. Due to the employed DeeplabV3+ architecture for the training process, partial human bodily structure detection has achieved 94% accuracy. Moreover, the robot can track the detected individual by maintaining 1m distance.
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The Cattle Rut Behavior Detection Base on AI Deep Neural Network
(2022-01-01)
Phaechaiyaphum, Phatsarut
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Konghuayrob, Poom
This paper proposes an application model of camera object detection to observe the cattle rut through its pose. This research is based on deep neural network method along with transfer learning technique. This paper employs four different deep convolutional neural network architectures 1) SSD Mobile-Net V2 2) SSD ResNet 101 V1 FPN 3) YOLOv5s and 4) Mask R-CNN. All the network mentioned above are pre-trained on COCO dataset. To observe the cattle rut behavior, the images of cattle behavior were taken in natural open environment, the data obtained are split into training and testing datasets. In this research cattle behaviors are categorized mainly in two classes, (1) normal behavior and (2) the cattle rut behavior. The result obtained after training the models shows that the YOLOv5s model obtained the highest mean average precision which is 95.5% and least training time. Thus, this paper proposes that YOLOv5s model can be applied to detect the cattle behavior for precise artificial breeding in order to increase the cattle population in farm to serve higher consumption demand in the near future.
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Decentralised and Centralised Fixed Structure H∞ Robust Loop Shaping for the MIMO Microsurgical Manipulation Based on PSOGSA
(2026-01-01)
Kaitwanidvilai, Somyot
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Konghuayrob, Poom
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Aoyama, Hisayuki
This paper presents decentralised and centralised fixed-structure H∞ robust control methods optimised by the Particle Swarm Optimisation and Gravitational Search Algorithm (PSOGSA) for a coupled multi-input multi-output (MIMO) microsurgical manipulator. The design framework explicitly considers uncertainties and disturbance constraints. Conventional H∞ loop-shaping controllers are typically of high order, complex, and difficult to implement in practice. To address this limitation, Proportional-Integral-Derivative (PID)-structured decentralised and centralised H∞ controllers are proposed, providing compact structures while retaining robustness. The novelty of this work lies in embedding H∞ robustness criteria into practical PID-based frameworks, bridging the gap between theoretical robust design and experimental implementation in microsurgical applications. The proposed controllers are evaluated against a reduced-order H∞ controller derived from Hankel norm approximation and a Ziegler–Nichols tuned PID controller, using both simulation and experimental studies. Results demonstrate that the proposed controllers achieve improved stability margins (0.449–0.521 compared with 0.436 for the reduced-order design), maintain low root-mean-square errors (≈0.067–0.089) and remain robust under voltage disturbances where conventional PID control fails. These findings confirm the contribution of a practical and efficient robust control strategy for enhancing the precision and reliability of microsurgical manipulators.
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Remote sensing to minimize energy consumption of six-axis robot arm using particle swarm optimization and artificial neural network to control changes in real time
(2020-01-01)
Kaitwanidvilai, Somyot
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Chanarungruengkij, Veerasak
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Konghuayrob, Poom
We propose a new method for the analysis and design of a robotic system that minimizes the energy consumption of a six-axis robot arm by controlling the velocity and acceleration of each arm of the robot to achieve the specified trajectory of the robot determined from a lean manufacturing method. A dynamic model of the PUMA 560 robot has been simulated on MATLAB, while the Robotics Toolbox and particle swarm optimization (PSO) are utilized to search for optimal paths and the optimal velocity and acceleration of the robot arms. The optimal velocity and acceleration are described as those giving minimum overall energy consumption constrained by a specified cycle time of the entire robotic system. Typically, the picking and placing of materials are carried out by humans, causing a variation in production rate, whereas our system using a robot arm ensures a stable production rate. Moreover, the optimal results obtained from PSO are adopted to train an artificial neural network (ANN) to extend the design system from discrete optimal values to a continuous and near-optimal value. In other words, the ANN is used to obtain an approximate optimal value between those obtained from PSO to make the system applicable to a real-world system. As shown by the simulation results, this method reduces the energy consumption of 12.3% from the initial energy and reduces the time for optimization by 99.8% compared with that for the PSO technique.
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Optimization of 3D SLAM and Localization for Quadruped Robot Dogs Using Autoencoder-based Techniques
(2025-01-01)
Thamrongveerachart, Chawapon
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Thepsit, Thitipong
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Ningnoi, Paphop
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Konghuayrob, Poom
This paper presents an optimized 3D SLAM and localization framework for quadruped robot dogs using a custom autoencoder-based point cloud compression technique. Our approach combines denoising, voxelization, self-attention, and PointNet modules to reduce the size of the point cloud by more than 80% while removing outliers and maintaining structural clarity. We use a 3D laser-scan matcher for localization, which combines onboard odometry from the Unitree Go2 quadruped robot with LiDAR odometry from a Livox Mid-360 sensor. Our approach maintains accurate pose estimation and considerably reduces processing time by working directly on compressed point clouds. Comparative tests show that the system is appropriate for real-time applications in unstructured contexts due to its enhanced map clarity and decreased localization delay. The proposed framework offers an effective and computationally efficient solution for deploying 3D SLAM in agile legged robots operating in dynamic or hazardous settings.
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Hybrid Deep Learning and FAST-BRISK 3D Object Detection Technique for Bin-picking Application
(2024-01-01)
Taweesoontorn, Thanakrit
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Yanyong, Sarucha
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Konghuayrob, Poom
In the field of industrial robotics, robotic arms have been significantly integrated, driven by their precise functionality and operational efficiency. We here propose a hybrid method for bin-picking tasks using a collaborative robot, or cobot combining the You Only Look Once version 5 (YOLOv5) convolutional neural network (CNN) model for object detection and pose estimation with traditional feature detection based on the features from accelerated segment test (FAST) technique, feature description using binary robust invariant scalable keypoints (BRISK) algorithms, and matching algorithms. By integrating these algorithms and utilizing a low-cost depth sensor camera for capturing depth and RGB images, the system enhances real-time object detection and pose estimation speed, facilitating accurate object manipulation by the robotic arm. Furthermore, the proposed method is implemented within the robot operating system (ROS) framework to provide a seamless platform for robotic control and integration. We compared our results with those of other methodologies, highlighting the superior object detection accuracy and processing speed of our hybrid approach. This integration of robotic arm, camera, and AI technology contributes to the development of industrial robotics, opening up new possibilities for automating challenging tasks and improving overall operational efficiency.
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Particle swarm optimization based specified order robust-gap H∞ loop shaping controller design
(2017-01-01)
Konghuayrob, Poom
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Kaitwanividvilai, Somyot
The demand of data storage capacity in hard disk drive is expected to increase significantly which the areal density will achieve 10 Tbit/in2 in the near future. An increase of high areal density leads to the reduction of distance per data tracks. Due to the narrow track pitch, hard disk drive system is easily sensitive to the disturbance and noise. This is the benchmark problem for controlling the high precision servo mechanism. The alternative robust loop shaping based v-gap metric is proposed to synthesize the optimal controller for stabilizing a voice coil motor in hard disk drive servo system. Additionally, the designed loop shaping is evaluated by the Riccati procedure with regard to the system robustness. This paper applies the potential particle swarm optimization (PSO) to minimize the close loop gap between the loop shaping of the plant with weighting function and the plant with proposed controller. Moreover, the structure of proposed controller can be specified as the 2nd order controller which is uncomplicated to implement in the actual application. The simulation illustrates similar results in terms of the performance tracking and disturbance rejection of proposed controller against the H<inf>∞</inf> loop shaping. Furthermore, the system stability index called stability margin with 0.434 emphasizes the robustness of the proposed controller.
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Specified order - H∞ loop shaping control for hard disk drive servo using pso
(2016-08-01)
Konghuayrob, Poom
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Kaitwanidvilai, Somyot
The demand of data storage density has been increasing continuously, but the data access time and track pitch need to be decreased. The high precision servo control system is required to achieve the predicted goal that will be crossing 10 TB/in2 area, I density before 2020. Due to the fact that the precision of read/write head is sensitive to disturbance and noise, it is the challenging problem for the practical control. To overcome this problem, this paper proposes a new servo controller designed, specified order - H<inf>∞</inf> loop shaping (SOHLS) which adapts the particle swarm optimization (PSO) for searching the optimal controller parameters. The proposed technique can solve the problem of high order that was caused from the conventional H<inf>∞</inf> loop shaping (HLS), in addition, it can guarantee the robustness of the servo system,. Based on simulation results, the performances of the proposed SOHLS approach are investigated in comparison with that of the conventional H<inf>∞</inf> robust loop shaping, fixed-structure H<inf>∞</inf> control based on nonsmooth algorithm including reduced order controller by Hangkel norm model reduction technique. The proper repeatable runout (RRO) and non-repea table runout (NRRO) were applied, to the system to verify the robustness of all designed, controllers. The results demonstrate the advantages of the proposed SOHLS which gains better performance and more robustness than the other conventional controllers.
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The Closed Loop Controller for Mini Demo Biomass Power Plant Fuel-Feeding Arm based Pneumatic System
(2021-04-01)
Konghuayrob, Poom
This paper shows the establishment of a mini demo Biomass power plant fuel-feeding arm with controller, motor and pneumatics system to study how to control arm for picking and placing of fuel operation properly. The arm mechanical elements, control driver circuit and electrical components were designed, built and selected follows the real Biomass power plant fuel-feeding arm. Low level PID controller, proportional pneumatics valve and motor with encoder are utilized for defining the correct close loop position of mini demo arm. Moreover, this system is designed to plug with the Robot operating system (ROS) the easy robotics motion control system which use to calculate the desired position before sending to the robot arm close loop system. The results show the effectiveness of the established mini demo Biomass arm can archive the correct position even controlled by nonlinear air flow system.
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Anomaly Flying Height Prediction Based on Clustering Techniques in Hard Disk Drive Manufacturing
(2025-01-01)
Kanjanapruthipong, Worawit
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Konghuayrob, Poom
In this research, we present a method for predicting anomaly flying height (FH) profiles in hard disk drive (HDD) manufacturing by analyzing FH data at the FH1 stage. Anomalies at FH1 can lead to calibration issues at FH2, disrupting the production process. We propose an AI-based approach using unsupervised clustering techniques to group FH profiles of the read/write head. We evaluated four clustering algorithms, KMeans, MiniBatchKMeans, Birch, and BisectingKMeans, along with the Elbow method to determine the optimal number of clusters. By identifying anomalous FH profiles early at FH1, the method enables proactive intervention, reducing calibration process time and improving production efficiency. Our model achieved an accuracy of 0.939 without relying on manual feature selection (e.g., pressure and temperature), which is often difficult to capture using traditional linear or rule-based models owing to the nonlinear nature of FH profiles. These results demonstrate the practical potential of clustering techniques in enhancing HDD manufacturing processes.

Konghuayrob, Poom

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