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

A Bioinspired Control Strategy Ensures Maneuverability and Adaptability for Dynamic Environments in an Underactuated Robotic Fish

Bioinspired underwater robots can move efficiently, with agility, even in complex aquatic areas, reducing marine ecosystem disturbance during exploration and inspection. These robots can improve animal farming conditions and preserve wildlife. This study proposes a muscle-like control for an underactuated robot in carangiform swimming mode. The artifact exploits a single DC motor with a non-blocking transmission system to convert the motor’s oscillatory motion into the fishtail’s oscillation. The transmission system combines a magnetic coupling and a wire-driven mechanism. The control strategy was inspired by central pattern generators (CPGs) to control the torque exerted on the fishtail. It integrates proprioceptive sensory feedback to investigate the adaptability to different contexts. A parametrized control law relates the reference target to the fishtail’s angular position. Several tests were carried out to validate the control strategy. The proprioceptive feedback revealed that the controller can adapt to different environments and tail structure changes. The control lawparameters variation accesses the robotic fish’s multi-modal swimming. Our solution can vary the swimming speed of 0.08 body lengths per second (BL/s), and change the steering direction and performance by an angular speed and turning curvature radius of 0.08 rad/s and 0.25 m, respectively. Performance can be improved with design changes, while still maintaining the developed control strategy. This approach ensures the robot’s maneuverability despite its underactuated structure. Energy consumption was evaluated under the robotic platform’s control and design. Our bioinspired control system offers an effective, reliable, and sustainable solution for exploring and monitoring aquatic environments, while minimizing human risks and preserving the ecosystem. Additionally, it creates new and innovative opportunities for interacting with marine species. Our findings demonstrate the potential of bioinspired technologies to advance the field of marine science and conservation

How to Achieve Maneuverability and Adaptability in an Underactuated Robotic Fish by using a Bio-inspired Control Approach

Biomimetic robotics can help support underwater exploration and monitoring while minimizing ecosystem distur-bance. It also has potential applications in sustainable aqua-farming management, biodiversity preservation, and animal-robot interaction studies. This study proposes a bio-inspired control strategy for an underactuated robotic fish, which utilizes a single DC motor to drive a mechanism that converts the motor's oscillating motion into an oscillatory motion of the robotic fishtail through a magnetic coupling and a wire-driven system. The proposed control strategy for the robotic fish is based on central pattern generators (CPGs) and incorporates proprioceptive sensory feedback. The torque exerted on the fishtail is adjusted based on its position, allowing for increased or decreased body speed and steering with different angular speeds and radii of curvature despite the underactuated design. The robotic fish can vary the swimming speed of 0.08 body lengths per second (BL/s) with a related change in the tail-beating frequency up to 2.3 Hz, and it can vary the steering angular speed in the range of 0.08 rad/s with a relative change in the curvature radius of 0.25 m. The controller can adapt to changes in tail structure, weight, or the surrounding environment based on the proprioceptive feedback. Design changes to the modular design can improve speed and steering performances, maintaining the control strategy developed

Development of an Autonomous Fish-Inspired Robotic Platform for Aquaculture Inspection and Management

Aquaculture applications are increasingly utilizing precision techniques such as computer vision technologies to perform a variety of inspection tasks. This work presents the development of three activities essential for the creation of a biomimetic robotic platform with onboard intelligence and autonomous task execution capabilities. The proposed robot is inspired by carangiform movement and achieves various trajectories through a magnetic actuation system with a single motor for propulsion. Fluid dynamics studies can improve the performance of the proposed propulsion system, thus ensuring greater energy efficiency. Thanks to its modular and scalable structure, the platform can integrate different components such as a vision system. The investigated vision-based model shows promising results for deployment in marine environments and can be adapted to detect various marine species. This fish-inspired robot platform has potential applications in the sustainable inspection and management of aquaculture facilities

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