An Investigation of Novel Control Strategy for An AMR Mapping and Inventory Management Using Lidar Sensor

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 5

Year of Publication : 2024

Authors : Thi-Mai-Phuong Dao, Thu-Ha Nguyen, Duy-Thuan Vu, Thi-Duyen Bui, Duc-Hiep Nguyen, Ngoc-Khoat Nguyen

10.14445/23488379/IJEEE-V11I5P123

How to Cite?

Thi-Mai-Phuong Dao, Thu-Ha Nguyen, Duy-Thuan Vu, Thi-Duyen Bui, Duc-Hiep Nguyen, Ngoc-Khoat Nguyen, "An Investigation of Novel Control Strategy for An AMR Mapping and Inventory Management Using Lidar Sensor," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 5, pp. 261-267, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I5P123

Abstract:

Traditional material handling methods in industrial environments often involve manually operated carts and forklifts. These methods are labor-intensive, inherently hazardous, and characterized by repetitive tasks.  Autonomous Mobile Robots (AMRs) have emerged as a promising solution to address these limitations, offering the potential for significant efficiency improvements in factories and warehouses. This paper presents a research study on an AMR system designed for material handling applications. The robot is built on the Raspberry Pi 3B+ embedded computing platform and utilises the LM298 power amplifier module. These hardware components are integrated to form a robust control system. The Raspberry Pi serves as the Central Processing Unit (CPU), receiving sensor data from the A1M8 Lidar sensor. The processed data is then transmitted to the Arduino Mega 2560 microcontroller, which controls the LM298 driver circuit. The Lidar sensor plays a critical role in constructing a map of the surrounding environment and providing essential data to the Raspberry Pi. The LM298 driver circuit effectively controls the motors, enabling the robot’s movement. The Raspberry Pi’s Broadcom BCM2837B0 processor, a quadcore A53 (ARMv8) 64-bit SoC operating at 1.4 GHz, ensures efficient data processing and control capabilities. Experimental results verify the applicability of the control system proposed in this study in achieving reliable and efficient material handling operations.

Keywords:

AMR, AMR vehicle, ROS, Mapping, Lidar sensor.

References:

[1] Gerald Cook, and Feitian Zhang, Mobile Robots: Navigation, Control and Sensing, Surface Robots and AUVs, IEEE Press, 2020.
[Google Scholar] [Publisher Link]
[2] Francisco Rubio, Francisco Valero, and Carlos Llopis-Albert, “A Review of Mobile Robots: Concepts, Methods, Theoretical Framework, and Applications,” International Journal of Advanced Robotic Systems, vol. 16, no. 2, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Manuel Cardona, Allan Palma, and Josue Manzanares, “COVID-19 Pandemic Impact on Mobile Robotics Market,” 2020 IEEE Andescon, Ecuador, pp. 1-4, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Rplidar A1: Low Cost 360 Degree Laser Range Scanner, Introduction and Datasheet, Shanghai Slamtec. Co. Ltd., 2016. [Online]. Available: https://www.generationrobots.com/media/rplidar-a1m8-360-degree-laser-scanner-development-kit-datasheet-1.pdf
[5] Lentin Joseph, and Jonathan Cacace, Mastering ROS for Robotics Programming: Design, Build, and Simulate Complex Robots using the Robot Operating System, 2nd ed., Packt Publishing Ltd., 2018.
[Google Scholar] [Publisher Link]
[6] Giuseppe Fragapane et al., “Planning and Control of Autonomous Mobile Robots for Intralogistics: Literature Review and Research Agenda”, European Journal of Operational Research, vol. 294, no. 2, 2021, pp. 405-426.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Shuzhi Sam Ge, and Frank L. Lewis, Autonomous Mobile Robots: Sensing, Control, Decision Making, and Applications, CRC Press, 2006.
[Google Scholar] [Publisher Link]
[8] Wikipedia, Automated Guided Vehicle. [Online]: Available: https://en.wikipedia.org/wiki/Automated_guided_vehicle
[9] Roland Siegwart, Illah R. Nourbakhsh, and Davide Scaramuzza, Introduction to Autonomous Mobile Robots, The MIT Press, 2nd ed., 2011.
[Google Scholar] [Publisher Link]
[10] ROS.org, Costmap_2D. [Online]. Available: http://wiki.ros.org/costmap_2d
[11] Kaveh Azadeh, Rene De Koster, and Debjit Roy, “Robotized and Automated Warehouse Systems: Review and Recent Developments,” Transportation Science, vol. 53, no. 4, pp. 917-1212, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Takeshi Shimmura, Ryosuke Ichikari, and Takashi Okuma, “Human-Robot Hybrid Service System Introduction for Enhancing Labor and Robot Productivity,” Advances in Production Management Systems, Towards Smart and Digital Manufacturing, pp. 661-669, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Samyeul Noh, Jiyoung Park, and Junhee Park, “Autonomous Mobile Robot Navigation in Indoor Environments: Mapping, Localization, and Planning,” 2020 International Conference on Information and Communication Technology Convergence (ICTC), Korea (South), pp. 908-913, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[14] N. Dinesh Kumar et al., “Mapping and Navigation of Autonomous Robots with Lidar for Indoor Applications”, Mapping and Navigation of Autonomous Robot with LiDAR for Indoor Applications, pp. 1-11, 2023.  
[15] Oliveira Junior, and Alexandre de, “Combining Particle Filter and Fiducial Markers in a Slam-based Approach to Indoor Localization of Mobile Robots,” Master Thesis, IPB Digital Library, 2022.
[Google Scholar] [Publisher Link]
[16] Morgan Quigley, Brian Gerkey, and William D. Smart, Programming Robots with ROS: A Practical Introduction to the Robot Operating System, O’Reilly Media Inc., USA, 2015.
[Google Scholar] [Publisher Link]
[17] ROS.org, Move_base. [Online]. Available: http://wiki.ros.org/move%20_base
[18] Dang Thai Son et al., “The Practice of Mapping-based Navigation System for Indoor Robot with RPLidar and Raspberry Pi,” International Conference on System Science and Engineering (ICSSE), Vietnam, pp. 279-282, 2021.
[CrossRef] [Google Scholar] [Publisher Link]