Integrated Vision-Kinematics Control System for Autonomous Robotic Manipulation using CNN and IoRT
DOI:
https://doi.org/10.25139/ijair.v8i1.11347Keywords:
Autonomous Robotic Manipulation, Vision-Kinematics Integration, Yolov5 Object Detection, Inverse Kinematics, Internet Of Robotic Things, Real-Time Control, Pick-And-Place Robotics, Embedded AI Systems, Low-Cost RoboticsAbstract
This paper presents a novel integrated vision-kinematics control system for autonomous robotic manipulation, leveraging deep learning and the Internet of Robotic Things (IoRT). Unlike previous works that focus on isolated modules, our framework uniquely combines real-time YOLOv5-based object detection deployed on a Raspberry Pi with geometric inverse kinematics implemented on an ESP32 controller for a 6-DOF robotic arm. Under controlled conditions (fixed camera height of 40 cm, uniform workspace illumination, and high-contrast colour-coded objects), the detection module achieved a 100% detection rate on the test set (300 images containing 900 object instances) with a mean average precision ([email protected]) of 0.93 across three object classes. The system demonstrates precise end-effector positioning with an average error of 4.3 mm in open-loop control mode, a significant achievement given the absence of joint feedback sensors. All components are seamlessly integrated via an MQTT-based IoRT communication layer, ensuring reliable, low-latency data exchange for remote monitoring and control. Comprehensive experimental validation shows an 80% success rate in autonomous pick-and-place operations, with stable performance under multi-object scenarios and communication reliability exceeding 99%. This work bridges the gap between high-cost industrial systems and low-cost research platforms by demonstrating that intelligent system integration can compensate for hardware limitations, offering a scalable, modular, and cost-effective framework for intelligent robotic manipulation in Industry 4.0 applications, particularly suitable for educational robotics and light industrial automation.
References
P. K. Mathavan Jeyabalan, A. Nehrujee, S. Elias, M. Magesh Kumar, S. Sujatha, and S. Balasubramanian, “Design and Characterisation of a Self-Aligning End-Effector Robot for Single-Joint Arm Movement Rehabilitation,” Robotics, vol. 12, no. 6, 2023, doi: 10.3390/robotics12060149.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 779–788, 2016, doi: 10.1109/CVPR.2016.91.
M. A. Muktadir, S. Yi, and A. M. Elliott, “Design of robot grippers for binder jet products handling,” Sci. Rep., vol. 14, no. 1, pp. 1–14, 2024, doi: 10.1038/s41598-024-56385-8.
J. Becedas, I. Payo, and V. Feliu, “Two-flexible-fingers gripper force feedback control system for its application as end effector on a 6-DOF manipulator,” IEEE Trans. Robot., vol. 27, no. 3, pp. 599–615, 2011, doi: 10.1109/TRO.2011.2132850.
Y. Zhang and L. Zhang, “Research on Education Robot Control System Based on ESP32,” J. Educ. Educ. Res., vol. 7, no. 2, pp. 299–302, 2024, doi: 10.54097/3x86qp78.
H. Z. Khaleel and A. J. Humaidi, “Towards accuracy improvement in solution of inverse kinematic problem in redundant robot: A comparative analysis,” Int. Rev. Appl. Sci. Eng., vol. 15, no. 2, pp. 242–251, 2024, doi: 10.1556/1848.2023.00722.
M. Blatnický, J. Dižo, J. Gerlici, M. Sága, T. Lack, and E. Kuba, “Design of a robotic manipulator for handling products of automotive industry,” Int. J. Adv. Robot. Syst., vol. 17, no. 1, pp. 1–11, 2020, doi: 10.1177/1729881420906290.
A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” 2020, [Online]. Available: http://arxiv.org/abs/2004.10934
C. Y. Wang, A. Bochkovskiy, and H. Y. M. Liao, “Scaled-yolov4: Scaling cross stage partial network,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 13024–13033, 2021, doi: 10.1109/CVPR46437.2021.01283.
V. Kumar, Q. Wang, W. Minghua, S. Rizwan, S. M. Shaikh, and X. Liu, “Computer vision based object grasping 6DoF robotic arm using picamera,” Proc. - 2018 4th Int. Conf. Control. Autom. Robot. ICCAR 2018, no. March, pp. 111–115, 2018, doi: 10.1109/ICCAR.2018.8384653.
Y. Bai, M. Luo, and F. Pang, “An algorithm for solving robot inverse kinematics based on foa optimised bp neural network,” Appl. Sci., vol. 11, no. 15, 2021, doi: 10.3390/app11157129.
P. P. Ray, “Internet of Robotic Things: Concept, Technologies, and Challenges,” IEEE Access, vol. 4, pp. 9489–9500, 2016, doi: 10.1109/ACCESS.2017.2647747.
N. Naik, “Choice of effective messaging protocols for IoT systems: MQTT, CoAP, AMQP and HTTP,” 2017 IEEE Int. Symp. Syst. Eng. ISSE 2017 - Proc., 2017, doi: 10.1109/SysEng.2017.8088251.
J. Wan, S. Tang, H. Yan, D. Li, S. Wang, and A. V. Vasilakos, “Cloud robotics: Current status and open issues,” IEEE Access, vol. 4, pp. 2797–2807, 2016, doi: 10.1109/ACCESS.2016.2574979.
D. Suprianto, G. S. Adi, R. Agustina, N. Hidayati, and A. M. Imammuddin, “Hybrid Multi-Servo Motor Controller Within an IoT-Enabled Smart Mechatronics Framework,” vol. 10, no. 2, pp. 121–128, 2025, doi: 10.25139/inform.v10i2.10100.
M. U. Atique, “Inverse Kinematics Solution for a 3DOF Robotic Structure using Denavit-Hartenberg Convention,” pp. 2–6.
Q. Bi, Z. Liu, M. Wang, and M. Lai, “An automatic camera calibration method based on checkerboard,” pp. 209–226, 2017, doi: 10.3166/TS.34.209-226.
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with International Journal of Artificial Intelligence & Robotics (IJAIR) agree to the following terms:
-
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
-
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
-
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.




