A Survey on Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms: A Guide for Creating a New Testbed [Survey]
Connected and automated vehicles (CAVs) and robot swarms (RSs) hold transformative potential for enhancing safety, efficiency, and sustainability in the transportation and manufacturing sectors. Extensive testing and validation of these technologies are crucial for their deployment in the real world. While simulations are essential for initial testing, they often have limitations in capturing the complex dynamics of real-world interactions. This limitation underscores the importance of small-scale testbeds. These testbeds provide a realistic, cost-effective, and controlled environment for testing and validating algorithms, acting as an essential intermediary between simulation and full-scale experiments. This work serves to facilitate researchers’ efforts in identifying existing small-scale testbeds suitable for their experiments and provide insights for those who want to build their own. In addition, it delivers a comprehensive survey of the current landscape of these testbeds. We derive 62 characteristics of testbeds based on the well-known <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sense–plan–act</i> paradigm and offer an online table comparing 23 small-scale testbeds based on these characteristics. The online table is hosted on our designated public webpage, <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://bassamlab.github.io/testbeds-survey</uri>, and we invite testbed creators and developers to contribute to it. We closely examine nine testbeds in this article, demonstrating how the derived characteristics can be used to present testbeds. Furthermore, we discuss three ongoing challenges concerning small-scale testbeds that we identified, i.e., small-scale to full-scale transition, sustainability, and power and resource management.
Authors
- Heeseung Bang (ORCID: https://orcid.org/0000-0003-4331-1952)
- Maximilian Kloock (ORCID: https://orcid.org/0000-0002-3470-6429)
- Sean Wilson (ORCID: https://orcid.org/0000-0002-6282-4772)
- Liam Paull (ORCID: https://orcid.org/0000-0003-2492-6660)
- Sangeet Sankaramangalam Ulhas (ORCID: https://orcid.org/0009-0006-1292-8959)
- Johannes Betz (ORCID: https://orcid.org/0000-0001-9197-2849)
- Viet-Anh Le (ORCID: https://orcid.org/0000-0002-9829-7150)
- Patrick Scheffe (ORCID: https://orcid.org/0000-0002-2707-198X)
- Amanda Prorok (ORCID: https://orcid.org/0000-0001-7313-5983)
- Spring Berman (ORCID: https://orcid.org/0000-0001-9239-0509)
- Bassam Alrifaee (ORCID: https://orcid.org/0000-0002-5982-021X)
- Armin Mokhtarian (ORCID: https://orcid.org/0000-0002-5345-4538)
- Simon Schäfer (ORCID: https://orcid.org/0000-0002-6482-2383)
- Jianye Xu (ORCID: https://orcid.org/0009-0001-0150-2147)
Institutions
- Georgia Institute of Technology (US)
- University of Cambridge (GB)
- Universität der Bundeswehr München (DE)
- Arizona State University (US)
- Technical University of Munich (DE)
- University of Delaware (US)
- RWTH Aachen University (DE)
Publication Details
- Journal
- IEEE Robotics & Automation Magazine
- Published
- 2024-12-30
- DOI
- https://doi.org/10.1109/mra.2024.3505772
- Citations
- 9
- Primary Topic
- Robotic Path Planning Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 2.24