Modularis: Modular Underwater Robot for Rapid Development and Validation of Autonomous Systems (2401.06243v1)
Abstract: Autonomous underwater robots typically require higher cost and time for demonstrations compared to other domains due to the complexity of the environment. Due to the limited capacity and payload flexibility, it is challenging to find off-the-shelf underwater robots that are affordable, customizable, and subject to environmental variability. Custom-built underwater robots may be necessary for specialized applications or missions, but the process can be more costly and time-consuming than purchasing an off-the-shelf autonomous underwater vehicle (AUV). To address these challenges, we propose a modular underwater robot, Modularis, that can serve as an open-source testbed system. Our proposed system expedites the testing of perception, planning, and control algorithms.
- A. Wilby and E. Lo, “Low-Cost, Open-Source Hovering Autonomous Underwater Vehicle (HAUV) for Marine Robotics Research based on the BlueROV2,” 2020 IEEE/OES Autonomous Underwater Vehicles Symposium (AUV), pp. 1–5, 09 2020. [Online]. Available: https://doi.org/10.1109/AUV50043.2020.9267913
- J. S. W. et al., “From market-ready ROVs to low-cost AUVs,” OCEANS 2021: San Diego – Porto, pp. 1–7, 09 2021. [Online]. Available: https://doi.org/10.23919/OCEANS44145.2021.9705798
- A. W. et al., “Frankenstein Goes Swimming: Software Architecture of a Modified BlueROV2 Heavy for Underwater Inspection and Maintenance ,” OCEANS 2022, Hampton Roads, pp. 1–5, 09 2022.
- C. Edge, S. Sakib Enan, M. Fulton, J. Hong, J. Mo, K. Barthelemy, H. Bashaw, B. Kallevig, C. Knutson, K. Orpen, and J. Sattar, “Design and experiments with loco auv: A low cost open-source autonomous underwater vehicle,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020, pp. 1761–1768.
- OpenRobotics. About gazebo. [Online]. Available: https://gazebosim.org/about
- C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” 2022.
- W. K.-Y. et al., “Implementation of paper - yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” 2022.