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Two-Tier AI Surveillance: Enhancing Weapon Detection Through Edge and Cloud Collaboration
(2026-01-01)
Sun, Sanchai
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Songsuwankit, Kanoknuch
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Thamrongrongveerachart, Chawapon
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Yanyong, Sarucha
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Sri-on, Jiramate
This research presents a novel two-tier AI-based surveillance system designed for real-time weapon detection to enhance security and prevent potential robberies. The system leverages the computational capabilities of both edge and cloud resources, integrating a YOLOv5s model on a Jetson Nano for initial detection and a YOLOv8l model on a server equipped with an Nvidia A100 for refined analysis. The initial detection performed by the Jetson Nano rapidly identifies potential threats and forwards compressed images to the server, optimizing bandwidth usage and transmission speed. Upon receipt, the server applies image enhancement techniques to restore and upscale the images from 640 pixels back to 1280 pixels before further verification. Utilizing Python Django, the server processes the enhanced images with a more sophisticated model to ensure high accuracy in detection. The integration of edge computing optimizes the system’s performance by enabling real-time processing and reducing latency. This hybrid approach enhances the efficiency and scalability of the surveillance system, ensuring robust and timely detection. Upon confirming the presence of a weapon, the system sends immediate alerts via LINE Notify to both users and local law enforcement, thereby enabling prompt responses to potential security threats. Additionally, the Jetson Nano hosts a Flask application, allowing users to download previously recorded videos for further review and evidence collection. The proposed solution combines edge computing, cloud processing, and image optimization techniques to provide an efficient, scalable, and high-performance solution for real-time weapon detection which enhancing accuracy and reliability in real-world surveillance.
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AI-Based Optimization Framework for Scheduling Autonomous Rail-Guided Vehicles in Warehouse Systems
(2026-01-01)
Keawchai, Rattanaphapra
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Yanyong, Sarucha
Scheduling tasks for autonomous Rail-Guided Vehicle (RGV) systems presents a complex optimization challenge that critically influences warehouse automation performance. This research develops an AI-based RGV scheduling framework that allows configuration of robot parameters such as maximum velocity, acceleration, deceleration, and track dimensions, accounting for velocity constraints imposed by curved tracks. The study includes five computational intelligence algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), hybrid GAPSO, and hybrid SUPER-SAPSO. The framework integrates path planning, layered collision penalty models, and multi-RGV task assignment under a physics-based travel time model while minimizing RGV idle time and addressing workload imbalance. Experimental results demonstrate comparative analyses of the efficiency and convergence speed of the various computational intelligence algorithms in optimizing overall warehouse efficiency.
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Experimental realization of PSO-based hybrid adaptive sliding mode control for force impedance control systems
(2025-06-01)
Yanyong, Sarucha
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Kaitwanidvilai, Somyot
This paper presents a practical solution for an adaptive impedance force controller with online learning capabilities, designed to mitigate the effects of inaccuracies in system identification models. The proposed hybrid algorithm addresses the challenges associated with online learning in real-world machines. Additionally, the system demonstrates the ability to adapt to environmental changes, maintaining high-quality performance despite variations. A sliding surface guarantees system stability, while Particle Swarm Optimization (PSO) optimizes impedance parameters, reducing the risk of local minima. The hybrid algorithm also reduces overshoot and undershoot, resulting in faster system responses. Simulation and experimental results demonstrate that the proposed technique outperforms conventional force control systems in terms of learning ability and overall performance.
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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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Localization for Outdoor Mobile Robot Using LiDAR and RTK-GNSS/INS
(2024-01-01)
Thepsit, Thitipong
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Konghuayrob, Poom
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Saenthon, Anakkapon
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Yanyong, Sarucha
Two types of sensors, light detection and ranging (LiDAR) and real-time kinematic of global navigation satellite system with inertial navigation system (RTK-GNSS/INS), are used for the localization of outdoor mobile robots. However, using LiDAR and RTK-GNSS/INS independently was found to be insufficient for achieving precise positioning. Therefore, a sensor fusion approach based on an adaptive-network-based fuzzy inference system (ANFIS) was implemented to enhance reliability. In this research, data from both sensors were collected to create a dataset for training with ANFIS. The findings indicated that the model derived from the fusion of these two sensors provided results that were much closer to the actual values obtained using each sensor independently. The result demonstrated the effectiveness of the ANFIS-based fusion method in terms of improving the accuracy and reliability of the positioning system for outdoor mobile robots.
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Sensor Fusion of Light Detection and Ranging and iBeacon to Enhance Accuracy of Autonomous Mobile Robot in Hard Disk Drive Clean Room Production Line
(2023-01-01)
Yanyong, Sarucha
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Parichatprecha, Rattapoohm
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Chaisiri, Punyavee
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Kaitwanidvilai, Somyot
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Konghuayrob, Poom
In this paper, the adaptive Monte Carlo localization (AMCL) error in terms of similar data detected by light detection and ranging (LiDAR) in different locations is investigated. This localization causes a robot to move to the incorrect location temporarily. We propose the fusion of landmark-based localization using an iBeacon device combined with the AMCL algorithm. This technique can solve the probabilistic localization problem of the conventional techniques applied in mobile robots by fusing the timed elastic band (TEB) and scan-matching algorithms, which reduces the error from 7 cm to less than 3 cm. The proposed technique is implemented on a clean-room-type mobile robot with 100 kg payload certificated by the SOP39 standard.
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Energy Prediction of Cleanroom-type Differential Drive Mobile Robot Based on Recurrent Neural Network
(2023-01-01)
Yanyong, Sarucha
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Konghuayrob, Poom
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Chaisiri, Punyavee
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Kaitwanidvilai, Somyot
The battery charger time is a major issue for mobile robots. The study of the power usage of each component is important for optimizing the overall power consumption. Additionally, knowing the total energy consumption before commanding a robot to execute a task is essential for effective queue management and determining which robots are ready to execute tasks or move to the charging station. In this paper, we propose an energy modeling system consisting of an energy sensing technique, logging, and a recurrent neural network prediction model. The model is configured to recognize the dynamic system of the drive unit with the support of the robot operating system. The proposed model has a prediction error of only 3.58%. The simulation and experimental results demonstrate the effectiveness of the proposed system.
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A Novel Robust Adaptive Control for PMDC Servo Motor Incorporating Recursive Least Square and Particle Swarm Optimization
(2023-01-01)
Yanyong, Sarucha
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Kaitwanidvilai, Somyot
Position control is a crucial control system used extensively in various industrial applications. In many of these applications, system parameters, such as the mass of an object can change unpredictably. Non-adaptive position control proves inadequate in successfully managing these fluctuations. Thus, an online adaptive control system plays a vital role in maintaining high performance despite changing parameters. This paper proposes a novel online adaptive fixed-structure robust controller for a PMDC (Permanent Magnet Direct Current) servo motor. This controller employs Recursive Least-square and Particle Swarm Optimization techniques to form the adaptive system. Simulation results demonstrate the effectiveness of our proposed technique and its potential application.
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Adaptive Force Control Using a Standard Deviation-based Hybrid Approach
(2023-01-01)
Songthai, Maethinee
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Yanyong, Sarucha
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Kaitwanidvilai, Somyot
A good tracking response is essential for a force control system to achieve high performance in object handling. One of the significant challenges of the force control system is that the plant dynamics are directly dependent on the environment and the mechanical properties of the object. It's well-known that the mechanical properties of an object in manufacturing vary based on the type of product, gripper tools, and environment; hence, a non-adaptive controller may not be efficiently adopted. To address this issue, this paper proposes impedance control with hybrid adaptive algorithms to ensure the actuator system tracks the desired force command accurately. Particle Swarm Optimization (PSO) is adopted to adapt the impedance control parameters, thereby applying the capabilities of the learning system. A hybrid adaptive force control with a fitness function of the standard deviation and summation of error is proposed. The tracking response of the conventional adaptive controller was examined and compared with the proposed controller. The simulation results demonstrate the effectiveness of the proposed system.
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Autonomous Modular Harvesting System for Vertical Aeroponic Farming
(2023-01-01)
Asnapetch, Papinwich
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Ruangrit, Chanet
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Tongsri, Nabhatara
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Chuenyoo, Napat
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Poonthongpan, Peeradon
As the global population grows exponentially, food consumption rises at a drastic rate. However, the limited availability of agricultural lands and inadequate traditional farming techniques have led to an emergence of alternatives such as hydroponic and aeroponic farming. Nonetheless, these systems still have limits, especially in terms of their constrained expansion when reliant on machinery, and the restricted vertical reach for human labor-driven harvesting. Therefore, we proposed an autonomous modular harvesting system for vertical farming of green oak lettuce. The structure is designed as an expandable cartesian gantry. With the initial base area of 40 x 40 cm, the system could be expanded to 120 cm in length and 240 cm in height, potentially hosting up to 96 lettuces. The system is also integrated with advanced computer vision to detect the presence of a plant and evaluate its readiness, primarily through color detection. Additionally, a specialized mechanism is installed to harvest the ripe lettuce. The system shows a notably high level of accuracy and impressive success rate in both detecting and harvesting.

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