skip to main content

Mobile Robotic-Arm Development for A Small-Scale Inter-Room Logistic Delivery using 2D LIDAR-guided Navigation

Mobile Robotic-Arm Development for a Small-Scale Inter-Room Logistic Delivery using 2D LIDAR-guided Navigation

*Hadha Afrisal scopus  -  Department of Electrical Engineering, Faculty of Engineering, Universitas Diponegoro, Jl. Prof. Soedarto SH, Tembalang, Semarang, Indonesia, 50275, Indonesia
Ghanis Kauchya Nugraha  -  Department of Electrical Engineering, Faculty of Engineering, Universitas Diponegoro, Indonesia
Aan Aria Nanda  -  Department of Electrical Engineering, Faculty of Engineering, Universitas Diponegoro, Indonesia
Ahmad Didik Setiyadi  -  Department of Electrical Engineering, Faculty of Engineering, Universitas Diponegoro, Indonesia
Olimjon Toirov  -  Department of Electrical Machine, Tashkent State Technical University, Tashkent City, Uzbekistan 100095, Uzbekistan
Rifky Ismail  -  Department of Mechanical Engineering, Faculty of Engineering, Universitas Diponegoro, Indonesia
Wahyul Amien Syafei  -  Department of Electrical Engineering, Faculty of Engineering, Universitas Diponegoro, Indonesia
Munawar Agus Riyadi  -  Department of Electrical Engineering, Faculty of Engineering, Universitas Diponegoro, Indonesia
Iwan Setiawan  -  Department of Electrical Engineering, Faculty of Engineering, Universitas Diponegoro, Indonesia
Open Access Copyright (c) 2022 TEKNIK

Citation Format:
Abstract
This research aims to develop a low-cost mobile robotic arm for an indoor delivery system. Current pandemic and possible future threat of communicable disease become challenging scenarios in developing an unmanned logistic delivery system with a minimum human involvement, especially for inter-room items delivery inside a highly regulated building such as in hospital, clinic, pharmaceutical, foods and beverages industries. In this paper, a prototype of mobile robotic arm is designed to achieve an autonomous level of navigation utilizing a 2D LIDAR with a guided remote monitoring and control of object selections for loading/unloading process. The mobile robotic arm is an integrated robotics system of mobile robot with an attached robotic arm in its body. The base of the mobile robotic arm utilizes a differential-drive wheels configuration equipped with wheels odometry system. The robotic arm is configured of a 4-DoF SCARA-like structure. The 2-dimensional environment map is generated using LIDAR sensor utilizing Hector SLAM method prior to navigation. The autonomous navigation is performed using a 2D LIDAR-based technique by employing an A* algorithm for path planning and tracking mechanism. Experimental works were conducted in a small building environment consisting of some rooms and narrow corridors. The result of experiments show that the prototype of mobile robotic arm can safely and effectively navigate through the testing environment, subsequently to load and unload objects from one room to another room without colliding to objects and obstacles.
Fulltext View|Download
Keywords: Mobile Robotic Arm; Inter-room Delivery; Mapping and Navigation; Pick and Place

Article Metrics:

  1. Avgousti, S., Christoforou, E. G., Panayides, A. S., Voskarides, S., Novales, C., Nouaille, L., Vieyres, P. (2016). Medical telerobotic systems: current status and future trends. Biomedical engineering online, 15, 96
  2. Cheng, Y., & Wang, G. Y. (2018). Mobile robot navigation based on lidar. 2018 Chinese Control And Decision Conference (CCDC), (pp. 1243–1246)
  3. Chitrakar, B., Zhang, M., & Bhandari, B. (2021). Improvement strategies of food supply chain through novel food processing technologies during COVID-19 pandemic. Food Control, 125, 108010
  4. Corke, P. (2017). Robotics, vision and control: fundamental algorithms in MATLAB® second, completely revised (Vol. 118). Springer
  5. Deng, H., Xiong, J., & Xia, Z. (2017). Mobile manipulation task simulation using ROS with Moveit. 2017 IEEE International Conference on Real-time Computing and Robotics (RCAR), (pp. 612–616)
  6. Evans, C. R., Medina, M. G., & Dwyer, A. M. (2018). Telemedicine and telerobotics: from science fiction to reality. Updates in surgery, 70, 357-362
  7. Fang, Y., Hu, J., Liu, W., Shao, Q., Qi, J., & Peng, Y. (2019). Smooth and time-optimal S-curve trajectory planning for automated robots and machines. Mechanism and Machine Theory, 137, 127–153
  8. Gul, F., Rahiman, W., & Nazli Alhady, S. S. (2019). A comprehensive study for robot navigation techniques. Cogent Engineering, 6, 1632046
  9. Gupta, A., Bhargava, P., Agrawal, S., Deshmukh, A., & Kadam, B. (2018). Comparative Study of Different Approaches to Inverse Kinematics. International Conference on Advances in Computing and Data Sciences, (pp. 556–563)
  10. Haidegger, T., Virk, G. S., Herman, C., Bostelman, R., Galambos, P., Györök, G., & Rudas, I. J. (2020). Industrial and medical cyber-physical systems: Tackling user requirements and challenges in robotics. In Recent Advances in Intelligent Engineering (pp. 253-277). Springer
  11. Harik, E. H., Korsaeth, A., & others. (2018). Combining hector slam and artificial potential field for autonomous navigation inside a greenhouse. Robotics, 7, 22
  12. Kolhatkar, C., & Wagle, K. (2021). Review of SLAM Algorithms for Indoor Mobile Robot with LIDAR and RGB-D Camera Technology. Innovations in Electrical and Electronic Engineering, 397–409
  13. Kwon, H., An, S., Lee, H.-Y., Cha, W. C., Kim, S., Cho, M., & Kong, H.-J. (2022). Review of Smart Hospital Services in Real Healthcare Environments. Healthcare Informatics Research, 28, 3–15
  14. Le, A. V., Prabakaran, V., Sivanantham, V., & Mohan, R. E. (2018). Modified a-star algorithm for efficient coverage path planning in tetris inspired self-reconfigurable robot with integrated laser sensor. Sensors, 18, 2585
  15. Magid, E., Zakiev, A., Tsoy, T., Lavrenov, R., & Rizvanov, A. (2021). Automating pandemic mitigation. Advanced Robotics, 35, 572–589
  16. Niemeyer, G., Preusche, C., Stramigioli, S., & Lee, D. (2016). Telerobotics. In Springer handbook of robotics (pp. 1085-1108). Springer
  17. Norzam, W. A., Hawari, H. F., & Kamarudin, K. (2019). Analysis of mobile robot indoor mapping using GMapping based SLAM with different parameter. IOP Conference Series: Materials Science and Engineering, 705, p. 012037
  18. Olalekan, A. F., Sagor, J. A., Hasan, M. H., & Oluwatobi, A. S. (2021). Comparison of Two SLAM Algorithms Provided by ROS (Robot Operating System). 2021 2nd International Conference for Emerging Technology (INCET), (pp. 1–5)
  19. Pandey, A., Pandey, S., & Parhi, D. R. (2017). Mobile robot navigation and obstacle avoidance techniques: A review. Int Rob Auto J, 2, 00022
  20. Shen, Y., Guo, D., Long, F., Mateos, L. A., Ding, H., Xiu, Z., . . . others. (2020). Robots under COVID-19 pandemic: A comprehensive survey. IEEE Access, 9, 1590–1615
  21. Sierra Marı́n, S. D., Gomez-Vargas, D., Céspedes, N., Múnera, M., Roberti, F., Barria, P., . . . Cifuentes, C. A. (2021). Expectations and Perceptions of Healthcare Professionals for Robot Deployment in Hospital Environments During the COVID-19 Pandemic. Frontiers in Robotics and AI, 8, 102
  22. Tavakoli, M., Carriere, J., & Torabi, A. (2020). Robotics, Smart Wearable Technologies, and Autonomous Intelligent Systems for Healthcare During the COVID-19 Pandemic: An Analysis of the State of the Art and Future Vision. Advanced Intelligent Systems, 2000071
  23. Yamamoto, T., Terada, K., Ochiai, A., Saito, F., Asahara, Y., & Murase, K. (2018). Development of the research platform of a domestic mobile manipulator utilized for international competition and field test. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (pp. 7675–7682)
  24. Yu, H.-F., & XIANG, W. E. (2017). Application of A-Star Algorithm in Robot for Catering Service Based on Laser Radar Navigation. DEStech Transactions on Computer Science and Engineering
  25. Zhang, X., Fang, Z., Lu, Z., Xiao, J., Cheng, X., & Zhang, X. (2021). 3D Reconstruction of Weak Feature Indoor Scenes Based on Hector SLAM and Floorplan Generation. 2021 IEEE 7th International Conference on Virtual Reality (ICVR), (pp. 117–126)
  26. Zhen, S., Zhao, Z., Liu, X., Chen, F., Zhao, H., & Chen, Y.-H. (2020). A Novel Practical Robust Control Inheriting PID for SCARA Robot. IEEE Access

Last update:

No citation recorded.

Last update: 2024-11-20 17:46:20

No citation recorded.