Aquaculture, especially fish farming, plays a vital role in ensuring food security in the United States and worldwide. However, for fish farming to be sustainable and economically viable, drastic improvements to the current labor-intensive and resource-inefficient operations are required. The hybrid aerial/underwater robotic system (HAUCS) aims to bring fundamental innovations to how pond-based farms operate. HAUCS is an end-to-end framework that consists of three principal subsystems: 1) a team of collaborative aero-amphibious robotic sensing platforms integrated with water quality sensors; 2) a land-based home station that can provide automated charging and sensor cleaning; and 3) a backend processing center that includes a machine-learning-based water quality prediction model and farm control center. HAUCS will be capable of collaborative monitoring and decision-making on farms of varying scales. The HAUCS platform, payload, and prediction model are discussed. The initial deployment of the HAUCS framework at a land-based aquaculture fish farm is presented.

Over the last several decades, developments in underwater laser line scan (LLS) serial imaging sensors have resulted in significant improvements in turbid water imaging performance. In the last few years, there has been renewed interest in distributed, truly multistatic time-varying intensity (TVI) (i.e., multiple transmitter nonsynchronous LLS) sensor configurations. In addition to being capable of high-quality image acquisition through tens of beam attenuation lengths, while simultaneously establishing a non-line-of-sight free-space communications link, these system architectures also have the potential to provide a more synoptic image coverage of larger regions of seabed and the flexibility to simultaneously examine a target from different perspectives. A related issue worth investigation is how to utilize these capabilities to improve rendering of the underwater scenes. In this regard, light field rendering (LFR)-a type of image-based rendering (IBR) technique-offers several advantages. Compared to other IBR techniques, LFR can provide signal-to-noise ratio (SNR) improvements and the ability to image through obscuring objects in front of the target. On the other hand, multistatic nonsynchronous LLS can be readily configured to acquire image sequences needed to generate LFR. This paper investigates the application of LFR to images taken from a distributed bistatic nonsynchronous LLS imager using both line-ofsight and non-line-of-sight imaging geometries to create multiperspective rendering of an unknown underwater scene. The issues related to effectively applying this technique to underwater LLS imagery are analyzed and an image postprocessing flow to address these issues is proposed. The results from a series of experiments at the Harbor Branch Oceanographic Institute at the Florida Atlantic University (HBOI-FAU, Fort Pierce, FL, USA) optical imaging test tank demonstrated the capability of using bistatic/multistatic nonsynchronous LLS system to generated LFR and, therefore, verify the proposed image processing flow. The benefits of LFR to underwater imaging in challenging environments were further demonstrated via imaging against a variety of obstacles such as mesh screens, bubbles, and water at different turbidity. Image quality metrics based on mutual information and texture features were used in the analysis of the experimental results.

An ultra-stable optical reference cavity with re-entrant fused silica mirrors and a ULE (Ultra Low Expansion) spacer structure is designed for the ultranarrow clock laser for an Al⁺ optical standard. The designed cavity has a low thermal noise limit of 1 × 10^-16 and a flexible zero crossing temperature of the effective coefficient of thermal expansion (CTE). The CTE zero crossing temperature difference between a composite cavity and a pure ULE cavity can be tuned from −10°C to 23°C, which enables operation of the designed reference cavity near room temperature without worrying about the CTE zero crossing temperature of the ULE spacer. This design can also be applied to cavities with different lengths. Vibration immunity of the cavity is also achieved through structure optimization.

Robust underwater imaging sensors are essential in applications such as identifying and classifying marine animals in turbid waters, typically found around coastal monitoring sites. The Unobtrusive Multi-static Serial LiDAR Imager (UMSLI) has been developed to capture images taken in degraded underwater environments and provides superior range and image contrast over conventional optical cameras. In contrast to traditional underwater LiDAR systems that employ blue-green lasers, UMLSI will not harm the vision of marine animals, which is an important aspect in such settings. As with any other underwater LiDAR sensors, improving the quality of images taken in highly turbid water is essential to the UMSLI system. This paper proposes a novel machine learning image enhancement technique based on a Generative Adversarial Network (GAN) framework. One main contribution of the method is the incorporation of a correntropy-based perceptual loss. This technique has shown to be effective in enhancing ground-based images and has been adapted for LiDAR image enhancement. Given the limitation of existing underwater LiDAR data, a method for simulating degraded data for training is also presented. This LiDAR technique was validated using LiDAR data captured by the UMSLI system within Florida Atlantic University’s Harbor Branch Oceanographic Institute (FAU-HBOI) optical test facility and at the Marine and Coastal Research Laboratory at the Pacific Northwest National Laboratory (MCRL-PNNL). This image enhancement technique can be readily extended to other underwater LiDAR systems.

Marine food chains are highly stressed by aggressive fishing practices and environmental damage. Aquaculture has increasingly become a source of seafood which spares the deleterious impact on wild fisheries. However, continually monitoring water quality to successfully grow and harvest fish is labor intensive. The Hybrid Aerial Underwater Robotic System (HAUCS) is an Internet of Things (IoT) framework for aquaculture farms to relieve the farm operators of one of the most labor-intensive and time-consuming farm operations: water quality monitoring. To this end, HAUCS employs a swarm of unmanned aerial vehicles (UAVs) or drones integrated with underwater measurement devices to collect the in situ water quality data from aquaculture ponds. A critical aspect in HAUCS is to develop an effective path planning algorithm to be able to sample all the ponds on the farm with minimal resources (i.e., the number of UAVs and the power consumption of each UAV). Three methods of path planning for the UAVs are tested, a Graph Attention Model (GAM), the Google Linear Optimization Package (GLOP) and our proposed solution, the HAUCS Path Planning Algorithm (HPP). The designs of these path planning algorithms are discussed, and a simulator is developed to evaluate these methods’ performance. The algorithms are also experimentally validated at Southern Illinois University’s Aquaculture Research Center to demonstrate the feasibility of HAUCS. Based on the simulations and experimental studies, HPP is particularly suited for large farms, while GLOP or GAM is more suited to small or medium-sized farms.