Lessons from the 2023 IEEE Autonomous Drone Chase Challenge
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Abstract
The IEEE Drone Chase Challenge was held in 2022 and 2023 to foster development in Unmanned Aerial Systems and to provide a venue for collegiate students developing integrated UAS solutions in which to compete. The challenge is comprised of two stages: an online simulator-based stage and a physical in-person final. The development of each competitor’s unique solutions and difficulties faced by each finalist team are described herein. Improvements for other future competitions are suggested based on the experiences of the competitors and hosts from the 2023 IEEE Drone Chase Challenge. First, software integration and documentation must be complete and easy to follow for competitors, allowing them to focus on solution development, rather than troubleshooting errors. Second, scoring metrics must be designed to test for robustness to mitigate the effect of luck and other external conditions on the evaluation of a solution. Despite the current limitations realized during the competition, competitors, hosts, and the research community benefit from developing soft and technical skills through competition participation.
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References
Alyamkin, S., Ardi, M., Berg, A. C., Brighton, A., Chen, B., Chen, Y., ... & Zhuo, S. (2019). Low-power computer vision: Status, challenges, and opportunities. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 9(2), 411-421. https://doi.org /10.1109/JETCAS.2019.2911899
Federal Aviation Administration. (n.d.). FAA Aerospace Forecast Fiscal Years 2023-2043. https://www.faa.gov/sites/faa.gov/files/2023-Unmanned%20Aircraft%20Systems%2 0and%20Advance%20Air%20Mobility_0.pdf
Hart, P. E., Nilsson, N. J., & Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE transactions on Systems Science and Cybernetics, 4(2), 100-107. https://doi.org/10.1109/TSSC.1968.300136
McEnroe, P., Wang, S., & Liyanage, M. (2022). A survey on the convergence of edge computing and AI for UAVs: Opportunities and challenges. IEEE Internet of Things Journal, 9(17), 15435-15459. https://doi.org/10.1109/JIOT.2022.3176400
Wang, B., Xie, J., Li, S., Wan, Y., Gu, Y., Fu, S., & Lu, K. (2020). Computing in the air: An open airborne computing platform. IET Communications, 14(15), 2410-2419. https://doi.org/10.1049/iet-com.2019.0515
Yasin, J. N., Mohamed, S. A., Haghbayan, M. H., Heikkonen, J., Tenhunen, H., & Plosila, J. (2020). Unmanned aerial vehicles (uavs): Collision avoidance systems and approaches. IEEE access, 8, 105139-105155. https://doi.org/10.1109/ACCESS.2020. 3000064