Industrial Robotic Setups: Tools and Technologies for Tracking and Analysis in Industrial Processes
Source abstract
Abstract
Since the development of industrial robots, they have been used to enhance efficiency and reduce the need for manual labor. Industrial robots have become a universal tool across all economic sectors, with the integration of software that is extremely important for the effective operation of machines and processes. Robotic action accuracy is currently experiencing rapid development in all robot-involving activities. Currently, a significant breakthrough has been observed in modifying algorithms and controlling robot actions, as well as in monitoring and planning software and hardware compatibility to prevent errors in real-time. The integration of the Internet of Things, machine learning, and other advanced techniques has enhanced the intelligent features of industrial robots. As industrial automation advances, there is an increasing demand for precise control in a variety of robotic arm applications. It is essential to refine current solutions to address the challenges posed by the high connectivity, complex computations, and various scenarios involved. This review examines the application of vision-based models, particularly YOLO (You Only Look Once) variants, in object detection within industrial robotic environments, as well as other machine learning models for tasks such as classification and localization. Finally, this review summarizes the results presented in selected publications, compares represented methods, identifies challenges in prospective object-tracking technologies, and suggests future research directions.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robot Manipulation and Learning · Fault Detection and Control Systems · Advanced Control Systems Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechatronics, Robotics and Digital Manufacturing, Vilnius Gediminas Technical University, LT-10105 Vilnius, Lithuania
Funding
National Taiwan University of Science and Technology
References (100+)
Show source-provided references
1. Balancing assembly lines with industrial and collaborative robots: Current trends and future research directions10.1016/j.cie.2024.110254 ↗
2. IoT Integration for Machine Learning System using Big Data Processing
3. Mazhar, A., Tanveer, A., Izhan, M., and Khan, M.Z.T. (2023). Robust Control Approaches and Trajectory Planning Strategies for Industrial Robotic Manipulators in the Era of Industry 4.0: A Comprehensive Review. Eng. Proc., 56.10.3390/ASEC2023-15330 ↗
4. Makulavičius, M., Petkevičius, S., Rožėnė, J., Dzedzickis, A., and Bučinskas, V. (2023). Industrial Robots in Mechanical Machining: Perspectives and Limitations. Robotics, 12.10.3390/robotics12060160 ↗
5. An analysis and reliability-based optimization design method of trajectory accuracy for industrial robots considering parametric uncertainties10.1016/j.ress.2024.110626 ↗
6. In-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning10.1016/j.rcim.2019.01.006 ↗
7. A Path Correction Method Based on Global and Local Matching for Robotic Autonomous Systems
8. Hull, B., and John, V. (1988). Non-Destructive Testing, Springer Inc.10.1007/978-1-349-85982-5 ↗
9. A survey on inspecting structures using robotic systems10.1177/1729881416663664 ↗
10. Mineo, C., Montinaro, N., Fustaino, M., Pantano, A., and Cerniglia, D. (2022). Fine Alignment of Thermographic Images for Robotic Inspection of Parts with Complex Geometries. Sensors, 22.10.3390/s22166267 ↗
11. Cuevas, E., López, M., and García, M. (2012, January 13–15). Ultrasonic Techniques and Industrial Robots: Natural Evolution of Inspection Systems. Proceedings of the 4th International Symposium on NDT in Aerospace, Berlin, Germany.
12. Index-based triangulation method for efficient generation of large three-dimensional ultrasonic C-scans
13. Internet of things for smart factories in industry 4.0, a review10.1016/j.iotcps.2023.04.006 ↗
14. A Survey of Robotic Harvesting Systems and Enabling Technologies10.1007/s10846-022-01793-z ↗
15. Rahmati, M. (J. Intell. Manuf. Spec. Equip., 2025). Dynamic role-adaptive collaborative robots for sustainable smart manufacturing: An AI-driven approach, J. Intell. Manuf. Spec. Equip., ahead-of-print.10.1108/JIMSE-01-2025-0001 ↗
16. Enhanced Interactivity in VR-based Telerobotics: An Eye-tracking Investigation of Human Performance and Workload10.1016/j.ijhcs.2023.103079 ↗
17. Writing a Scientific Review Article: Comprehensive Insights for Beginners10.1155/2024/7822269 ↗
18. Computer Vision-Assisted Object Detection and Handling Framework for Robotic Arm Design Using YOLOV510.14201/adcaij.31586 ↗
19. Zhao, H., Tang, Z., Li, Z., Dong, Y., Si, Y., Lu, M., and Panoutsos, G. (2024, January 25–27). Real-Time Object Detection and Robotic Manipulation for Agriculture Using a YOLO-Based Learning Approach. Proceedings of the IEEE International Conference on Industrial Technology, Bristol, UK.10.1109/ICIT58233.2024.10540740 ↗
20. Wang, Y., Zhou, Y., Wei, L., and Li, R. (2023). Design of a Four-Axis Robot Arm System Based on Machine Vision. Appl. Sci., 13.10.3390/app13158836 ↗
21. Zhong, T., Gan, Y., Han, Z., Gao, H., and Li, A. (2023, January 17–19). A Lightweight Object Detection Network for Industrial Robot Based YOLOv5. Proceedings of the Proceedings-2023 China Automation Congress, CAC 2023, Chongqing, China.10.1109/CAC59555.2023.10449979 ↗
22. Kondratev, S., Pikalov, V., Belokopytov, R., Muravyev, A., and Boikov, A. (2023, January 8–10). Designing an Advanced Control System for ABB IRB 140 Robotic Manipulator: Integrating Machine Learning and Computer Vision for Enhanced Object Manipulation. Proceedings of the 2023 5th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA), Lipetsk, Russia.10.1109/SUMMA60232.2023.10349427 ↗
23. Li, L., Cherouat, A., Snoussi, H., Wang, T., Lou, Y., and Wu, Y. (, January 6–7). Vision-Based Deep learning for Robot Grasping Application in Industry 4.0. Proceedings of the Technological Systems, Sustainability and Safety (TS3), Paris, France.
24. Addressing challenges in industrial pick and place: A deep learning-based 6 Degrees-of-Freedom pose estimation solution10.1016/j.compind.2024.104130 ↗
25. Augmented Autoencoders: Implicit 3D Orientation Learning for 6D Object Detection10.1007/s11263-019-01243-8 ↗
26. STRAW-YOLO: A detection method for strawberry fruits targets and key points10.1016/j.compag.2024.109853 ↗
27. Kang, S., Hu, Z., Liu, L., Zhang, K., and Cao, Z. (2025). Object Detection YOLO Algorithms and Their Industrial Applications: Overview and Comparative Analysis. Electronics, 14.10.3390/electronics14061104 ↗
28. YOLO-Based Miner Detection Using Thermal Images in Underground Mines
29. Detection and classification of Shiitake mushroom fruiting bodies based on Mamba YOLO
30. YOLO-VDS: Accurate detection of strawberry developmental stages for embedded agricultural robots10.1088/2631-8695/adb00f ↗
31. YOLO with feature enhancement and its application in intelligent assembly10.1016/j.robot.2024.104844 ↗
32. Optimizing object detection for autonomous robots: A comparative analysis of YOLO models10.1016/j.measurement.2025.118676 ↗
33. Testing human-hand segmentation on in-distribution and out-of-distribution data in human–robot interactions using a deep ensemble model10.1016/j.mechatronics.2025.103365 ↗
34. Sapkota, R., and Karkee, M. (2025). Object Detection with Multimodal Large Vision-Language Models: An In-depth Review. TechRxiv.10.2139/ssrn.5233953 ↗
35. An algorithm for multi-directional text detection in natural scenes10.1016/j.dsp.2025.105482 ↗
36. Robot End-Effector Mounted Camera Pose Optimization in Object Detection-Based Tasks
37. Using optical sensors for industrial robot-human interactions in a Gazebo environment10.5954/ICAROB.2023.OS6-8 ↗
38. Minimum distance calculation using skeletal tracking for safe human-robot interaction10.1016/j.rcim.2021.102253 ↗
39. Human-Robot Interaction and Collaboration (HRI-C) Utilizing Top-View RGB-D Camera System
40. Development of a vision based pose estimation system for robotic machining and improving its accuracy using LSTM neural networks and sparse regression10.1016/j.rcim.2021.102262 ↗
41. Towards Industry 4.0: Color-Based Object Sorting Using a Robot Arm and Real-Time Object
42. Lao, D., Quan, Y., Wang, F., and Liu, Y. (2023). Error Modeling and Parameter Calibration Method for Industrial Robots Based on 6-DOF Position and Orientation. Appl. Sci., 13.10.3390/app131910901 ↗
43. A prediction and compensation method of robot tracking error considering pose-dependent load decomposition10.1016/j.rcim.2022.102476 ↗
44. Recent developments in computer vision and artificial intelligence aided intelligent robotic welding applications10.1007/s00170-023-11456-4 ↗
45. High precision and efficiency robotic milling of complex parts: Challenges, approaches and trends10.1016/j.cja.2020.12.030 ↗
46. Bilal, M.T., Tyapin, I., and Choux, M.M.H. (2022, January 17–20). Enhancing Object Localization Accuracy by using Multiple Camera Viewpoints for Disassembly Systems. Proceedings of the IECON Proceedings (Industrial Electronics Conference) 2022, Brussels, Belgium.10.1109/IECON49645.2022.9968575 ↗
47. Toward Vision-Based High Sampling Interaction Force Estimation with Master Position and Orientation for Teleoperation10.1109/LRA.2021.3094848 ↗
48. Srinivasamurthy, C., Sivavenkatesh, R., and Gunasundari, R. (2023, January 7–8). Six-Axis Robotic Arm Integration with Computer Vision for Autonomous Object Detection using TensorFlow. Proceedings of the 2023 2nd International Conference on Advances in Computational Intelligence and Communication, ICACIC 2023, Puducherry, India.10.1109/ICACIC59454.2023.10434959 ↗
49. Deshpande, T.R., and Sapkal, S.U. (2021, January 18–20). Development of Vision Enabled Articulated Robotic Arm with Grasping Strategies for Simple Objects. Proceedings of the 2021 IEEE Bombay Section Signature Conference, IBSSC 2021, Gwalior, India.10.1109/IBSSC53889.2021.9673332 ↗
50. Gu, X. (2024, January 26–28). Design of delay compensation system for visual tracking control of industrial robots. Proceedings of the Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), Xi’an, China.10.1117/12.3030639 ↗
51. Integration of Robotic Vision and Automatic Tool Changer Based on Sequential Motion Primitive for Performing Assembly Tasks
52. Visual perception for the 3D recognition of geometric pieces in robotic manipulation10.1007/s00170-015-7708-8 ↗
53. Nair, D., Pakdaman, A., and Plöger, P.G. (2020). Performance Evaluation of Low-Cost Machine Vision Cameras for Image-Based Grasp Verification. arXiv.
54. ROS-Industrial based robotic cell for Industry 4.0: Eye-in-hand stereo camera and visual servoing for flexible, fast, and accurate picking and hooking in the production line10.1016/j.rcim.2022.102453 ↗
55. Robust Neural Dynamics Method for Redundant Robot Manipulator Control With Physical Constraints10.1109/TII.2023.3249768 ↗
56. Lai, Z., Xiong, R., Wu, H., and Guan, Y. (2018, January 12–15). Integration of Visual Information and Robot Offline Programming System for Improving Automatic Deburring Process. Proceedings of the 2018 IEEE International Conference on Robotics and Biomimetics, ROBIO 2018, Kuala Lumpur, Malaysia.10.1109/ROBIO.2018.8665148 ↗
57. Zhao, X., Zhang, Y., Wang, H., Liu, Y., Zhang, B., and Hu, S. (2022). Research on Trajectory Recognition and Control Technology of Real-Time Tracking Welding. Sensors, 22.10.2139/ssrn.4159894 ↗
58. Wu, X., Tian, R., Lei, Y., Gao, H., and Fang, Y. (2024). Real-Time Space Trajectory Judgment for Industrial Robots in Welding Tasks. Machines, 12.10.3390/machines12060360 ↗
59. Probabilistic Deep Q Network for real-time path planning in censorious robotic procedures using force sensors10.1007/s11554-021-01122-x ↗
60. Artificial Neural Network Based Kinematics: Case Study on Robotic Surgery10.1007/978-3-030-20131-9_182 ↗
61. Hybrid Force-Impedance Control for Fast End-Effector Motions10.1109/LRA.2023.3270036 ↗
62. Fekik, A., Azar, A.T., Hamida, M.L., Denoun, H., Kais, D., Saidi, S.M., Bousbaine, A., Kasim, I., Kamal, N.A., and Al Mhdawi, A.K. (2023, January 10–12). Sliding Mode Control of the PUMA 560 Robot. Proceedings of the 2023 International Conference on Control, Automation and Diagnosis, ICCAD 2023, Rome, Italy.10.1109/ICCAD57653.2023.10152456 ↗
63. Gao, R., Zhang, W., Wang, G., and Wang, X. (2023). Experimental Research on Motion Analysis Model and Trajectory Planning of GLT Palletizing Robot. Buildings, 13.10.3390/buildings13040966 ↗
64. Research on planning and optimization of trajectory for underwater vision welding robot10.1016/j.array.2022.100253 ↗
65. Wang, J., Wen, K., Lei, T., Xiao, Y., and Pan, Y. (2023). Automatic Aluminum Alloy Surface Grinding Trajectory Planning of Industrial Robot Based on Weld Seam Recognition and Positioning. Actuators, 12.10.3390/act12040170 ↗
66. Robotic path planning for non-destructive testing–A custom MATLAB toolbox approach10.1016/j.rcim.2015.05.003 ↗
67. A Path Planning Method of Robotic Belt Grinding for Workpieces with Complex Surfaces10.1109/TMECH.2020.2974925 ↗
68. A Path Planning Method for Robotic Belt Surface Grinding10.1016/S1000-9361(11)60060-5 ↗
69. Cheng, C., Lv, X., Zhang, J., and Zhang, M. (2021, January 24–26). Robot Arm Path Planning Based on Improved RRT Algorithm. Proceedings of the 2021 3rd International Symposium on Robotics and Intelligent Manufacturing Technology, ISRIMT 2021, Changzhou, China.10.1109/ISRIMT53730.2021.9596670 ↗
70. A novel BIM and vision-based robotic welding trajectory planning method for complex intersection curves10.1016/j.measurement.2025.117587 ↗
71. Positioning error compensation of an industrial robot using neural networks and experimental study10.1016/j.cja.2021.03.027 ↗
72. Iterative Learning Control for Robotic Path Following With Trial-Varying Motion Profiles10.1109/TMECH.2022.3164101 ↗
73. A vision-based robotic system for precision pollination of apples10.1016/j.compag.2025.110158 ↗
74. Juříček, M., Parák, R., and Kůdela, J. (2023). Evolutionary Computation Techniques for Path Planning Problems in Industrial Robotics: A State-of-the-Art Review. Computation, 11.10.3390/computation11120245 ↗
75. Advance on Agricultural Robot Hand–Eye Coordination for Agronomic Task: A Review10.1016/j.eng.2025.01.022 ↗
76. An adaptive framework for trajectory following in changing-contact robot manipulation tasks10.1016/j.robot.2024.104785 ↗
77. A data driven recurrent neural network approach for reproduction of variable visuo-haptic force feedback in surgical tool insertion10.1016/j.eswa.2023.122221 ↗
78. Kruzic, S., Music, J., Kamnik, R., and Papic, V. (October, January 28). Estimating robot manipulator end-effector forces using deep learning. Proceedings of the 2020 43rd International Convention on Information, Communication and Electronic Technology, MIPRO 2020-Proceedings, Opatija, Croatia.10.23919/MIPRO48935.2020.9245129 ↗
79. Roveda, L., Riva, D., Bucca, G., and Piga, D. (2021, January 12–14). External joint torques estimation for a position-controlled manipulator employing an extended kalman filter. Proceedings of the 2021 18th International Conference on Ubiquitous Robots, UR 2021, Gangneung, Republic of Korea.10.1109/UR52253.2021.9494674 ↗
80. Motion Planning and Adaptive Neural Tracking Control of an Uncertain Two-Link Rigid-Flexible Manipulator With Vibration Amplitude Constraint10.1109/TNNLS.2021.3054611 ↗
81. Trajectory Tracking of Variable Centroid Objects Based on Fusion of Vision and Force Perception10.1109/TCYB.2023.3240502 ↗
82. Force-Based Algorithm for Motion Planning of Large Agent10.1109/TCYB.2020.2994122 ↗
83. Orientation Tracking Incorporated Multicriteria Control for Redundant Manipulators With Dynamic Neural Network10.1109/TIE.2023.3273253 ↗
84. H2R Bridge: Transferring vision-language models to few-shot intention meta-perception in human robot collaboration10.1016/j.jmsy.2025.03.016 ↗
85. Error compensation of industrial robot based on deep belief network and error similarity10.1016/j.rcim.2021.102220 ↗
86. Zhang, J., and Yan, W. (2023, January 15–17). 6-DOF UR3 Robot Manipulation Based on Deep Learning. Proceedings of the 16th International Conference on Advanced Computer Theory and Engineering, ICACTE 2023, Hefei, China.10.1109/ICACTE59887.2023.10335347 ↗
87. Robot error compensation based on incremental extreme learning machines and an improved sparrow search algorithm10.1007/s00170-023-10957-6 ↗
88. Wrütz, T., Group, V., and Biesenbach, R. (2016, January 21–23). Robot Offline Programming Tool (RoBO-2L) for Model-Based Design with MATLAB. Proceedings of the 2nd International Conference on Engineering Science and Innovative Technology, ESIT 2016, Phuket, Thailand.
89. Golz, J., Wruetz, T., Eickmann, D., and Biesenbach, R. (2016, January 15–17). RoBO-2L, a Matlab interface for extended offline programming of KUKA industrial robots. Proceedings of the 2016 11th France-Japan and 9th Europe-Asia Congress on Mechatronics, MECATRONICS 2016/17th International Conference on Research and Education in Mechatronics, REM 2016, Compiègne, France.10.1109/MECATRONICS.2016.7547117 ↗
90. Al-Mahasneh, A.J., Falkenhain, J., Mousa, M., Biesenbach, R., Al-Mahasneh, A., and Baniyounis, M. (2024, January 22–25). Development of ANFIS Controller for Trajectory Tracking Control Using ROBO2L MATLAB Toolbox for KUKA Industrial Robot via RSI. Proceedings of the 2024 21st International Multi-Conference on Systems, Signals & Devices (SSD), Erbil, Iraq.
91. Trajectory Optimization for a 6 DOF Robotic Arm Based on Reachability Time10.33166/AETiC.2024.01.003 ↗
92. Dynamic analysis, simulation, and control of a 6-DOF IRB-120 robot manipulator using sliding mode control and boundary layer method10.1007/s11771-018-3909-2 ↗
93. Robot control parameters auto-tuning in trajectory tracking applications10.1016/j.conengprac.2020.104488 ↗
94. Contact Force Estimation for Robot Manipulator Using Semiparametric Model and Disturbance Kalman Filter10.1109/TIE.2017.2748056 ↗
95. Wei, Y., Li, W., Yang, Y., Yu, X., and Guo, L. (2022, January 26–28). Decoupling Observer for Contact Force Estimation of Robot Manipulators Based on Enhanced Gaussian Process Model. Proceedings of the 2022 8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022, Chengdu, China.10.1109/CCIS57298.2022.10016359 ↗
96. Integrating CNN and RANSAC for improved object recognition in industrial robotics10.1016/j.sasc.2025.200240 ↗
97. BANSAI: Towards Bridging the AI Adoption Gap in Industrial Robotics with Neurosymbolic Programming10.1016/j.procir.2024.10.125 ↗
98. Industrial serial robot calibration considering geometric and deformation errors10.1016/j.rcim.2022.102328 ↗
99. Righettini, P., Strada, R., and Cortinovis, F. (2023). Neural Network Mapping of Industrial Robots’ Task Times for Real-Time Process Optimization. Robotics, 12.10.3390/robotics12050143 ↗
100. A novel material removal rate model based on single grain force for robotic belt grinding10.1016/j.jmapro.2021.05.029 ↗