Application of Deep Learning in the Deployment of an Industrial SCARA Machine for Real-Time Object Detection

Kapusi, Tibor Péter; Erdei, Timotei István; Husi, Géza; Hajdú, András

doi:10.3390/robotics11040069

Cited by 13 publications

(5 citation statements)

References 33 publications

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“…Şimşek et al [26] further emphasized the role of deep learning in solving various industrial challenges, focusing on the ability of machines to communicate and make decisions. Kapusi et al [27] provided a practical example of this, demonstrating the application of deep learning in real-time object detection for an industrial SCARA robot. These studies collectively highlight the potential of deep learning in applications with small datasets within the context of Industry 4.0.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Developing a YOLOv3-Based Neural Network to Enable Digital Cameras to Detect Features on the Reflective Surface of Objects in Robotic Applications: A Low-Computational Approach with a New Public Dataset

Bajrami,

Palpaceli

2024

Preprint

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This study explores the integration of Artificial Intelligence (AI) and collaborative robotics in industrial surface finishing, focusing on feature detection on object reflective surfaces using neural networks in computationally constrained environments. Central to this investigation is the utilization of cameras considered as sensors that can capture detailed images of reflective surfaces. These images are then analyzed by a custom neural network, demonstrating the feasibility of feature detection with non-powerful computers. The public dataset for recognizing light reflection defects, available in the SPADD repository, supports this approach. Employing open-source tools like Darknet YOLOv3 and Yolo\_mark, this method proves particularly advantageous for small and medium-sized enterprises (SMEs) due to its low cost and maintenance requirements. This research highlights the challenges of AI and collaborative robotics in industrial applications, especially in adapting to varying conditions and the need for continuous training with new data coming from several sensors. Looking ahead, plans are in place to integrate this system with cobot-assisted polishing tasks, using cameras to enhance manufacturing efficiency and quality. This work contributes to the broader understanding of practical applications of AI in industrial automation, surface finishing processes, and the effective use of sensor technology.

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Section: Dataset Descriptionmentioning

confidence: 99%

Developing a YOLOv3-Based Neural Network to Enable Digital Cameras to Detect Features on the Reflective Surface of Objects in Robotic Applications: A Low-Computational Approach with a New Public Dataset

Bajrami,

Palpaceli

2024

Preprint

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“…In the field of optimization, numerous methods have been developed and applied to various engineering problems [17][18][19][20][21][22][23][24][25][26][27]. Widely used optimization algorithms and techniques are fuzzy logic [28,29], adaptive neuro-fuzzy inference systems [30,31], the Taguchi method [32,33], the grey system theory [26,34,35], teaching-learning-based optimization [36,37], genetic algorithms [38], particle swarm optimization [39], tabu search [40], and simulated annealing [13][14][15][16].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing Parameters for an Electrical Car Employing Vehicle Dynamics Simulation Program

2023

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At the Faculty of Engineering of the University of Debrecen we have dealt with the design and construction of electric prototype race cars for more than a decade. With a focus on more conscious design and racing we developed a vehicle dynamics simulation program, which can be used to generate the dynamics functions of the cars from their technical characteristics and data. In this publication, we deal with the optimization of the technical parameters of the above-mentioned cars for various competition tasks using our simulation program. This is a completely new field of application of the used optimization methods. This concept and idea can effectively help student teams all over the world to prepare for various domestic and international competitions. One of the applied methods is a graphic procedure, the other one is the widely used “adaptive simulated annealing” (ASA). After a brief description of the simulation program, the applied optimization methods and developed MATLAB codes for them are described. Finally, to demonstrate the effectiveness of the methods, we optimize the parameters of a prototype race car for different competition tasks and present the obtained results.

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“…The data of the joint positions obtained with the kinematic control were saved in matrices called "math_scara" and "math_ur10" respectively, to perform a real simulation in the CoppeliaSim environment [46], [47].…”

Section: Pick and Place And Palletizing Cell Simulationmentioning

confidence: 99%

Mechatronics Design and Robotic Simulation of Serial Manipulators to Perform Automation Tasks in the Avocado Industry

Paredes,

Palomares,

Alva

et al. 2023

IJACSA

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Peru is considered one of the principal agroindustrial avocado exporters worldwide. At the beginning of 2022, the volume exported was 8.3% higher than in 2021, so the design and simulation of a pick and place and palletizing cell for agro-exporting companies in the Region of La Libertad was proposed. A methodology was followed that presented a flow diagram of the design of the cell, considering the size of the avocado and the dimensions of the box-type packaging. The forward and inverse kinematics for the Scara T6 and UR10 robots were developed in Matlab according to the Denavit-Hartenberg algorithm, and 3D CAD, dynamic modeling, and trajectory calculation were performed in Solidworks using a "planner" algorithm developed in Matlab, which takes into account the start and end points, maximum speeds, and travel time of each robot. Then, in CoppeliaSim, the working environment of the cell and the robots with their respective configurations are created. Finally, the simulation of trajectories is performed, describing the expected movement, getting the time of the finished task was calculated, where the Scara T6 robot had a working time of 1.18 s and the UR10 of 2.32 s. For 2023 -2025, its implementation is proposed in the Camposol Company located in the district of Chao -La Libertad, considering the dynamic control of the system.

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Application of Deep Learning in the Deployment of an Industrial SCARA Machine for Real-Time Object Detection

Cited by 13 publications

References 33 publications

Developing a YOLOv3-Based Neural Network to Enable Digital Cameras to Detect Features on the Reflective Surface of Objects in Robotic Applications: A Low-Computational Approach with a New Public Dataset

Developing a YOLOv3-Based Neural Network to Enable Digital Cameras to Detect Features on the Reflective Surface of Objects in Robotic Applications: A Low-Computational Approach with a New Public Dataset

Optimizing Parameters for an Electrical Car Employing Vehicle Dynamics Simulation Program

Mechatronics Design and Robotic Simulation of Serial Manipulators to Perform Automation Tasks in the Avocado Industry

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