Underactuated Robotic Fish Control: Maneuverability and Adaptability Through Proprioceptive Feedback

Bioinspired robotics is a promising technology for minimizing environmental disruption during underwater inspection, exploration, and monitoring. In this research, we propose a control strategy for an underactuated robotic fish that mimics the oscillatory movement of a real fish’s tail using only one DC motor. Our control strategy is bioinspired to Central Pattern Generators (CPGs) and integrates proprioceptive sensory feedback. Specifically, we introduced the angular position of the tail as an input control variable to integrate a feedback into CPG circuits. This makes the controller adaptive to changes in the tail structure, weight, or the environment in which the robotic fish swims, allowing it to change its swimming speed and steering performance. Our robotic fish can swim at a speed between 0.18 and 0.26 body lengths per second (BL/s), with a tail beating frequency between 1.7 and 2.3 Hz. It can also vary its steering angular speed in the range of 0.08 rad/s, with a relative change in the curvature radius of 0.25 m. With modifications to the modular design, we can further improve the speed and steering performance while maintaining the developed control strategy. This research highlights the potential of bioinspired robotics to address pressing environmental challenges while improving solutions efficiency, reliability and reducing development costs

Automated image-based analysis unveils acute effects due to sub-lethal pesticide doses exposure

Pesticides are still abused in modern agriculture. The effects of their exposure to even sub-lethal doses can be detrimental to ecosystem stability and human health. This work aims to validate the use of machine learning techniques for recognizing motor abnormalities and to assess any effect post-exposure to a minimal dosage of these substances on a model organism, gaining insights into potential risks for human health. The test subject was the Mediterranean fruit fly, Ceratitis capitata (Wiedemann) (Diptera: Tephritidae), exposed to food contaminated with the LC 30 of Carlina acaulis essential oil. A deep learning approach enabled the pose estimation within an arena. Statistical analysis highlighted the most significant features between treated and untreated groups. Based on this analysis, two learning-based algorithms, Random Forest (RF) and XGBoost were employed. The results were compared through different metrics. RF algorithm generated a model capable of distinguishing treated subjects with an area under the receiver operating characteristic curve of 0.75 and an accuracy of 0.71. Through an image-based analysis, this study revealed acute effects due to minimal pesticide doses. So, even small amounts of these biocides drifted far from distribution areas may negatively affect the environment and humans