Gioiello, Flavia (Università Politecnica delle Marche), Moliterno, Beatrice (Università politecnica delle marche), Bartolucci, Veronica (Università Politecnica delle Marche), Di Nardo, Francesco (Università Politecnica delle Marche, Ancona), Costa, Daniele (Università eCampus), Scaradozzi, David (Università Politecnica delle Marche)

AI-Driven Detection and Control in a Bioinspired Robotic Fish

Scheduled for presentation during the Regular Session "Genetic and evolutionary computation" (WeAD), Wednesday, June 11, 2025, 10:50−11:10, Room C

33rd Mediterranean Conference on Control and Automation, June 10-13, 2025, Tangier, Morocco

This information is tentative and subject to change. Compiled on May 9, 2025

Abstract

This paper presents a bioinspired robotic fish adaptable to diverse applications, integrating artificial intelligence for real-time recognition of marine objects and a biomimetic locomotion system based on a 2 Degrees of Freedom (DoFs) tail. The onboard neural network processes visual data to identify specific underwater objects, such as mussels and clams. At the same time, a real-time communication system transmits findings remotely via a Telegram-based API. Performance evaluation, including accuracy, loss, confusion matrix, and Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) analysis, confirms the model's reliability in classification tasks. Additionally, key operational metrics such as waterproof integrity, response time, manoeuvrability, and regime speed were measured, demonstrating its practicability. The proposed system offers a versatile and cost-effective solution suitable for applications in marine biology, underwater monitoring, and autonomous exploration.