MeCO, or the Medium Cost Open-source autonomous underwater vehicle (AUV), is a versatile autonomous vehicle designed to support research and development in underwater human-robot interaction (UHRI) and marine robotics in general. An inexpensive platform to build compared to similarly-capable AUVs, the MeCO design and software are released under open-source licenses, making it a cost effective, extensible, and open platform. It is equipped with UHRI-focused systems, such as front and side facing displays, light-based communication devices, a transducer for acoustic interaction, and stereo vision, in addition to typical AUV sensing and actuation components. Additionally, MeCO is capable of real-time deep learning inference using the latest edge computing devices, while maintaining low-latency, closed-loop control through high-performance microcontrollers. MeCO is designed from the ground up for modularity in internal electronics, external payloads, and software architecture, exploiting open-source robotics and containerarization tools. We demo strate the diverse capabilities of MeCO through simulated, closed-water, and open-water experiments. All resources necessary to build and run MeCO, including software and hardware design, have been publicly made available on GitHub.
The MeCO AUV was created from the support provided by the US National Science Foundation Grant IIS-2220956 (NRI: Enhancing Autonomous Underwater Robot Perception for Aquatic Species Management), led by Principal Investigator Junaed Sattar.
Current Student Investigators: David Widhalm, Demetrious Kutzke, Sakshi Singh, Rishi Mukherjee, Grant Schwidder
Past Student Investigators: Cory Ohnsted, Corey Knutson, Ying-Kun Wu
MeCO paper pre-print in arXiv.