Challenges in using NVIDIA Jetson Nano board for an Autonomous Underwater Vehicle

The development of autonomous underwater vehicles presents unique challenges, particularly in integrating and operating various sensors and control mechanisms. This paper focuses on the challenges of using the NVIDIA Jetson Nano platform to implement an autonomous underwater vehicle for pisciculture. There are many reported papers on AUVs using the NVIDIA Jetson Nano platform. However, these papers do not disclose the challenges generally faced by the new builders of AUVs. This paper presents the initial setup of the Jetson Nano, including installing essential software packages and configuring the Python environment for AUV communication. The details of physical connections of various components with the Jetson Nano board and testing procedures of various sensors like pH, turbidity, pressure, and dissolved oxygen. Also, the connection and control of T200 thrusters and a camera. Challenges encountered during the initialization and setup processes and solutions derived from extensive troubleshooting are discussed. Our findings provide a comprehensive guideline for future developers working with an NVIDIA Jetson Nano board. The successful integration and testing of the components on the NVIDIA Jetson Nano demonstrate its viability for controlling and managing the autonomous underwater vehicle effectively.