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Programmable Remote Laboratory for Mobile Robots

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Cross Reality and Data Science in Engineering (REV 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1231))

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Abstract

As mobile robots become more popular, more remote laboratories (RL) using mobile robots are being developed for online education. However, in most current RL for mobile robots, the control which users have over the robot is limited by the pre-programmed controlled algorithms. This limitation creates a gap in the learning experience of the students. This paper proposes an implementation of a fully programmable remote laboratory for mobile robots into a web application. In the RL user-interface, students are able to implement and execute their C++ source code and see the results in real-time through a live feed webcam. The AJAX technology is used to transfer data between the web application and web server, making it possible to execute the code. This application is designed to provide students with interactive tools and a contextual learning scene, making this research of significant relevance to online engineering education by providing students with direct programming experience on remote laboratory website.

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Acknowledgement

This work was supported in part by RENEW which is a technology partner to POWDER. RENEW/POWDER is part of the National Science Foundation’s PAWR program and is funded in part by NSF award CNS-1827940 and the PAWR Industry Consortium.

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Authors and Affiliations

  1. Texas Southern University, Texas, USA

    Rafael Franco-Vera, Xuemin Chen & Wei Wayne Li

Authors
  1. Rafael Franco-Vera
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  2. Xuemin Chen
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  3. Wei Wayne Li
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Corresponding author

Correspondence to Rafael Franco-Vera .

Editor information

Editors and Affiliations

  1. Carinthia University of Applied Sciences, Villach, Austria

    Michael E. Auer

  2. College of Engineering, University of Georgia, Athens, GA, USA

    Dominik May

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Franco-Vera, R., Chen, X., Li, W.W. (2021). Programmable Remote Laboratory for Mobile Robots. In: Auer, M., May, D. (eds) Cross Reality and Data Science in Engineering. REV 2020. Advances in Intelligent Systems and Computing, vol 1231. Springer, Cham. https://doi.org/10.1007/978-3-030-52575-0_6

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