@article{nguyen_ore_castro-bolinaga_hall_young_2024, title={TOWARDS AUTONOMOUS, OPTIMAL WATER SAMPLING WITH AERIAL AND SURFACE VEHICLES FOR RAPID WATER QUALITY ASSESSMENT}, volume={67}, ISSN={["2769-3287"]}, DOI={10.13031/ja.15796}, abstractNote={Highlights A practical workflow for optimizing sampling tours for a team of surface and aerial vehicles was developed. Proposed workflow considers unique sensing capabilities of surface vehicles when assigning sampling locations. Likely optimal tours can be found in less than 30 s for practical water quality sampling requirements. Abstract. Most current marine aquaculture operations are located in coastal estuarine areas within one mile of the shoreline, and water quality in these production areas can quickly become unfavorable due to hydrodynamic processes and excessive runoff. The deployment of autonomous, robotic systems can improve the speed and spatiotemporal resolution of water sampling and sensing in mariculture production areas to assess water quality in the context of food safety. Specifically, teams of both aerial and surface vehicles can be deployed simultaneously to capitalize on the benefits of each system; however, a method to optimally design a feasible sampling tour for each robot is needed to maximize sample capacity and ensure efficient water sampling missions. This research brief presents the problem formulation and a solution method to determine optimal tours for a team of aquatic surface and aerial vehicles while considering different vehicle sampling capacities and endurance constraints. This method was implemented to design sampling missions of 15, 20, and 30 samples in both a 0.25 km2 and 3.9 km2 site, using sampling capacity and endurance constraints corresponding to real-world robots used for water sampling in mariculture environments. Results indicate that this optimization problem can be solved in near-real time in the field and yields feasible sampling tours for surface and aerial vehicles under different constraints. This work is a practical step towards developing teams of collaborative robots to persistently monitor adverse mariculture growing conditions so producers can implement data-driven, timely management strategies. Keywords: Mariculture, Robotics, Traveling salesperson problem, Vehicle routing.}, number={1}, journal={JOURNAL OF THE ASABE}, author={Nguyen, Anh and Ore, John -Paul and Castro-Bolinaga, Celso and Hall, Steven G. and Young, Sierra}, year={2024}, pages={91–98} }
 @article{nguyen_holt_knauer_abner_lobaton_young_2023, title={Towards rapid weight assessment of finishing pigs using a handheld, mobile RGB-D camera}, volume={226}, ISSN={["1537-5129"]}, url={https://doi.org/10.1016/j.biosystemseng.2023.01.005}, DOI={10.1016/j.biosystemseng.2023.01.005}, abstractNote={Pig weight measurement is essential for monitoring performance, welfare, and production value. Weight measurement using a scale provides the most accurate results; however, it is time consuming and may increase animal stress. Subjective visual evaluations, even when conducted by an experienced caretaker, lack consistency and accuracy. Optical sensing systems provide alternative methods for estimating pig weight, but studies examining these systems only focus on images taken from stationary cameras. This study fills a gap in existing technology through examining a handheld, portable RGB-D imaging system for estimating pig weight. An Intel RealSense camera collected RGB-D data from finishing pigs at various market weights. 3D point clouds were computed for each pig, and latent features from a 3D generative model were used to predict pig weights using three regression models (SVR, MLP and AdaBoost). These models were compared to two baseline models: median prediction and linear regression using body dimension measurements as predictor variables. Using 10-fold cross validation mean absolute error (MAE) and root-mean-square error (RMSE), all three latent feature models performed better than the median prediction model (MAE = 12.3 kg, RMSE = 16.0 kg) but did not outperform linear regression between weight and girth measurements (MAE = 4.06 kg, RMSE = 4.94 kg). Of the models under consideration, SVR performed best (MAE = 9.25 kg, RMSE = 12.3 kg, mean absolute percentage error = 7.54%) when tested on unseen data. This research is an important step towards developing rapid pig body weight estimation methods from a handheld, portable imaging system by leveraging deep learning feature outputs and depth imaging technology.}, journal={BIOSYSTEMS ENGINEERING}, author={Nguyen, Anh H. and Holt, Jonathan P. and Knauer, Mark T. and Abner, Victoria A. and Lobaton, Edgar J. and Young, Sierra N.}, year={2023}, month={Feb}, pages={155–168} }