# ENDURUNS: An Integrated and Flexible Approach for Seabed Survey Through Autonomous Mobile Vehicles
## 3 System Functionalities
### Data Sensing, Storage and Processing  
The most relevant measuring instruments hosted by the AUV are a Multi-Beam Echo-Sounder (MBES) [22, 61] and the imaging device for acquiring visual data of the seafloor. The MBES used onboard the AUV is a Norbit WBMS with a 400 KHz bandwidth and an average power consumption of 40 W [69]. The imaging device is based on the technology described in the European patent [70] and it is specifically conceived for image acquisition and processing [71, 72]. The imaging device is conceived for having a low power consumption (i.e., 0.12 W per image) and its image acquisition module is designed to perform an optimal control of the CMOS image sensor with the aim of saving up storage space and energy. The device is equipped with a Field Program Gate Array (FPGA) integrated circuit, hosting a firmware-level image analysis algorithm capable to discard not relevant image regions. The data acquired by AUV instrumentation will be stored onboard. Due to the power and bandwidth constraints, large amount of data cannot be easily processed and stored on board, or transferred to USV units. For this reason, the AUV is designed to host a low power consumption CPU capable to manage the hosted sensors and execute simple data compression algorithms [48, 73, 74, 75]. The chosen board is the latest available from the Raspberry family (i.e., Raspberry Pi 4 Model B, released in June 2019) [76]. It uses a 1.5 GHz 64-bit quad-core Arm Cortex-A72 CPU with 4 GB DDR4 RAM,  
Figure 4: An example where the hybrid AUV alternate gliding navigation mode (**a**,**c**,**e**,**g**) without data acquisition (dashed lines) and thruster navigation mode (**b**,**d**,**f**) with data acquisition (continuous lines). Red stars represent Cold Water Corals (CWC) bushes present in the pilot area (see Figure 6). and an improved GPU for image/video management. Benchmarks show significant increases in speed, memory, and connectivity compared to the prior-generations, being comparable to an entry-level x86 PC systems. Power consumption is typically inside the 4-8 W range, depending on the activated units. Raspberry family has native support for all IO operations therefore the development of software interfaces is very simplified by existing packages available under Linux-based OS. This is important because AUV must be closely interfaced with the MBES, whose data transfer rate is very high (>1 MB/s, several GB/h) and so an efficient data management, cleaning and compression software tool has to be implemented onboard. Since the USV is also designed to host an onboard computer and disposes of a larger power supply, more expensive computations will be performed on the USV. Algorithms for MBES data analysis can return a simplified bathymetric representation [77] as well as a preliminary characterization of the terrain attributes useful for habitat-mapping tasks [59]. Algorithms for image analysis can detect end classify relevant organisms contained in the images collected by the imaging device [10; 72; 78; 79]. To support these algorithms, USV hosts an embedded computer (i.e., NVIDIA Jetson TX2) equipped with a GPU (massive parallel unit) [80], ensuring high computing power with low energy consumption (about 7.5 W). More precisely, Jetson TX2 is an embedded system-on-module (SoM) with a quad-core ARM Cortex-A57, 8 GB DDR4 RAM and an integrated GPU with 256 CUDA cores. It supports a wide set of highly optimized image and data processing algorithms, even those based on Deep Neural Networks (DNNs), both with native libraries or with open-source frameworks, e.g., OpenCV [81], allowing the use of many C++/Python functions over the CUDA layer. GPU-capabilities are essential for processing onboard the full resolution sonar images provided by the MBES after AUV data transfer and before transmission, because of bandwidth constraints. Image textures are classified at the full scale and automatically tagged according to possible interesting features.