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arXiv · 2412.15369

Scalable and low-cost remote lab platforms: Teaching industrial robotics using open-source tools and understanding its social implications

Amit Kumar·Jaison Jose·Archit Jain·Siddharth Kulkarni·Kavi Arya
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Abstract

With recent advancements in industrial robots, educating students in new technologies and preparing them for the future is imperative. However, access to industrial robots for teaching poses challenges, such as the high cost of acquiring these robots, the safety of the operator and the robot, and complicated training material. This paper proposes two low-cost platforms built using open-source tools like Robot Operating System (ROS) and its latest version ROS 2 to help students learn and test algorithms on remotely connected industrial robots. Universal Robotics (UR5) arm and a custom mobile rover were deployed in different life-size testbeds, a greenhouse, and a warehouse to create an Autonomous Agricultural Harvester System (AAHS) and an Autonomous Warehouse Management System (AWMS). These platforms were deployed for a period of 7 months and were tested for their efficacy with 1,433 and 1,312 students, respectively. The hardware used in AAHS and AWMS was controlled remotely for 160 and 355 hours, respectively, by students over a period of 3 months.

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Amit Kumar, Jaison Jose, Archit Jain, Siddharth Kulkarni, Kavi Arya. 2024-12-19. Scalable and low-cost remote lab platforms: Teaching industrial robotics using open-source tools and understanding its social implications. https://doi.org/10.1007/978-981-96-3522-1_19

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