Multirotor autopilots often depend on open-loop control without the feedback of propeller speeds, although they are a critical factor in determining motion characteristics. This paper proposes a system that leverages actual propeller speeds as direct feedback to the autopilot to improve the state estimation and dynamics of the multirotor. Software-in-the-Loop (SITL) and Hardware-in-the-Loop (HITL) simulations with real data, in different scenarios, are conducted to demonstrate the impact of combining propeller speeds with typical drone sensors. The results show that the drone becomes more stable with lower trajectory errors. Further, a noticeable reduction in the vehicle position median error while following a trajectory is shown, and a considerable increase in the flying duration time before crashing in case of a motor fault. These results highlight the potential of adding propeller speed feedback to increase the autopilot's controllability which enhances drone performance in sensitive applications.
Monitoring of aquatic habitats for water quality and biodiversity requires regular sampling, often in off-shore locations and underwater. Such sampling is commonly performed manually from research vessels, or if autonomous, is constrained to permanent installations. Consequentially, high frequency ecological monitoring, such as for harmful algal blooms, are limited to few sites and/or temporally infrequent. Here, we demonstrate the use of MEDUSA, an Unmanned Aerial-Aquatic Vehicle which is capable of performing underwater sampling and inspection at up to 10 m depth, and is composed of a multirotor platform, a tether management unit and a tethered micro Underwater Vehicle. The system is validated in the task of vertical profiling of Chlorophyll-a levels in freshwater systems by means of a custom solid sample filtering mechanism. This mechanism can collect up to two independent samples per mission by pumping water through a pair of glass-fibre GF/F filters. Chlorophyll levels measured from the solid deposits on the filters are consistent and on par with traditional sampling methods, highlighting the potential of using UAAVs to sample aquatic locations at high frequency and high spatial resolution.
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