ROBONEX – The AI Powered Pick & Place Robot

Sakshi SavleStudent, Dept. of AI & ML, Smt. Indira Gandhi College of Engineering, Ghansoli, New Mumbai, Maharashtra, IndiaTanisha MauryaStudent, Dept. of AI & ML, Smt. Indira Gandhi College of Engineering, Ghansoli, New Mumbai, Maharashtra, IndiaSakshi GoraveStudent, Dept. of AI & ML, Smt. Indira Gandhi College of Engineering, Ghansoli, New Mumbai, Maharashtra, IndiaProf. Manisha HatkarProfessor, Dept. of AI & ML, Smt. Indira Gandhi College of Engineering, Ghansoli, New Mumbai, Maharashtra, India

Vol 10 No 4 (2026): Volume 10, Issue 4, April 2026 | Pages: 100-104

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 11-04-2026

doi Logo doi.org/10.47001/IRJIET/2026.104013

Abstract

This paper presents Robonex – The AI Powered Pick & Place Robot developed through the integration of embedded systems and computer vision. The proposed robot is designed to detect the specified object, grasp it using four degree of freedom gripper, and autonomously navigate to place it at defined goal position. The robot is built on a four wheel drive mobile base having path following and obstacle avoidance capabilities to enable reliable navigation. The system has been physically implemented and subjected to continuous testing to evaluate its operational feasibility. This paper outlines the overall system architecture, including the embedded hardware and vision components, and discusses the successful execution of autonomous pick and place operations by the developed system.

Keywords

Pick and place operations, computer vision, embedded systems, robotic gripper (4-DOF), autonomous navigation


Citation of this Article

Sakshi Savle, Tanisha Maurya, Sakshi Gorave, & Prof. Manisha Hatkar. (2026). ROBONEX – The AI Powered Pick & Place Robot. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(4), 100-104. Article DOI https://doi.org/10.47001/IRJIET/2026.104013

References
  1. Dr. E. D. Francis, P. B. R. Shankar, G. Deepak, and P. Aknadh, “Robotic Arm For Manufacturing With Artificial Intelligence,” International Journal of Creative Research Thoughts (IJCRT), vol. 13, no. 4, pp. h497–h505, Apr. 2025.
  2. P. Rajesh, N. Kumar, and K. Ganesh, “Design and Analysis of Robotic Arm for Efficient Pick and Place Operations,” International Journal of Creative Research Thoughts (IJCRT), vol. 12, no. 5, pp. n530–n536, May 2024.
  3. S. Zhang, Y. Liu, H. Wang, and L. Chen, “Design and Implementation of an AI-Based Robotic Arm for Strawberry Harvesting,” Agriculture, vol. 14, no. 11, pp. 1–18, Nov. 2024.
  4. R. S. Pawar, A. B. Patil, and S. R. Deshmukh, “Pick and Place Robotic Arm: A Review Paper,” International Journal of Scientific Research in Engineering and Management (IJSREM), vol. 07, no. 06, pp. 1–5, Jun. 2023.
  5. P. Rajesh, N. Kumar, and K. Ganesh, “Robotic Arm for Pick and Place Application,” International Journal of Creative Research Thoughts (IJCRT), vol. 13, no. 4, pp. h497–h505, Apr. 2025.
  6. O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” Proc. Int. Conf. Med. Image Comput. Comput. Assist. Interv. (MICCAI), pp. 234–241, 2015.
  7. D. Silver et al., “Mastering the Game of Go with Deep Neural Networks and Tree Search,” Nature, vol. 529, no. 7587, pp. 484–489, 2016.
  8. S. Thrun, W. Burgard, and D. Fox, “A Real-Time Algorithm for Mobile Robot Mapping With Applications to Multi-Robot and 3D Mapping,” Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 321–328, 2000.
  9. G. Grisetti, C. Stachniss, and W. Burgard, “Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters,” IEEE Trans. Robot., vol. 23, no. 1, pp. 34–46, 2007.
  10. B. Siciliano and O. Khatib, Eds., Springer Handbook of Robotics, 2nd ed. Berlin, Germany: Springer, 2016.
  11. R. C. Arkin, Behavior-Based Robotics, Cambridge, MA, USA: MIT Press, 1998.
  12. M. Quigley et al., “ROS: An Open-Source Robot Operating System,” Proc. IEEE Int. Conf. Robot. Autom. Workshop, pp. 1–6, 2009.
  13. A.Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Adv. Neural Inf. Process. Syst. (NeurIPS), vol. 25, pp. 1097–1105, 2012.
  14. T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., pp. 785–794, 2016.
  15. H. Durrant-Whyte and T. Bailey, “Simultaneous Localization and Mapping: Part I,” IEEE Robot. Autom. Mag., vol. 13, no. 2, pp. 99–110, 2006.
  16. H. Durrant-Whyte and T. Bailey, “Simultaneous Localization and Mapping: Part II,” IEEE Robot. Autom. Mag., vol. 13, no. 3, pp. 108–117, 2006.
  17. S. S. R. Das, S. K. Sahoo, and B. K. Mishra, “IoTBased Smart Surveillance and Object Detection System Using Deep Learning,” IEEE Access, vol. 9, pp. 123456–123468, 2021.
  18. M. A. Fischler and R. C. Bolles, “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography,” Commun. ACM, vol. 24, no. 6, pp. 381–395, 1981.
  19. C. Wang, A. Bochkovskiy, and H. Y. M. Liao, “Scaled-YOLOv4: Scaling Cross Stage Partial Network,” Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops, pp. 130–139, 2021.