Deficient domestic wastewater management, industrial waste, and floating debris are some leading factors that contribute to inland water pollution. The surplus of minerals and nutrients in overly contaminated zones can lead to the invasion of different invasive weeds. Lemnoideae, commonly known as duckweed, is a family of floating plants that has no leaves or stems and forms dense colonies with a fast growth rate. If not controlled, duckweed establishes a green layer on the surface and depletes fish and other organisms of oxygen and sunlight. In the last decades, duckweed blooms have become an expensive plague to remove in Lake Titicaca, causing significant biodiversity and economic losses. To address this issue, we propose the development of an unmanned surface vehicle (USV) to continuously monitor and collect duckweed. This device includes a steering angle estimation system (SAE) that combines a duckweed detector, a steering angle classifier, and a momentum-inspired module to determine continuously the orientation of the USV when it is inspecting a zone. We train and test our deep learning-based models with two novel datasets: first, a dataset for end-to-end steering angle classification collected synthetically using a bespoke simulator; and second, a dataset for duckweed detection. An experimental evaluation of the USV and SAE system is provided in virtual and outdoor environments.

This study introduces a robust model-based framework designed for the verification and validation (V&V) of Attitude Determination and Control Systems (ADCSs) in nanosatellites, focusing on magnetic actuation while still being applicable to larger spacecraft platforms. By employing Model-in-the-Loop (MIL), Software-in-the-Loop (SIL), Processor-in-the-Loop (PIL), and Hardware-in-the-Loop (HIL) methodologies, this framework enables a thorough and systematic approach to testing and validation. The framework facilitates the assessment of long-term maneuvers, addressing challenges such as initial small-attitude errors and restricted 3D movements. Two specific maneuvers are evaluated: detumbling and nadir pointing, utilizing quaternions and a comprehensive suite of sensors, including six sun sensors, a three-axis magnetometer, a three-axis gyroscope, GPS, and three magnetorquers. The methodologies—MIL, SIL, PIL, and HIL—integrate the behaviors of digital sensors, analog signals, and astrodynamic perturbations. Based on an optimized SIL environment, Monte Carlo simulations were performed to optimize control gains for nadir pointing, achieving a mean pointing accuracy of 11.69° (MIL) and 18.22° (PIL), and an angular velocity norm of 0.0022 rad/s for detumbling. The HIL environment demonstrated a mean pointing accuracy of 9.96° and an angular velocity norm of 0.0024 rad/s. This comprehensive framework significantly advances the design and verification processes for nanosatellite ADCSs, enhancing the reliability and performance of nanosatellite missions.