The SeaClear system: An intelligent multi-robot solution for autonomous cleanup of marine debris on the seabed

Marine debris poses an alarming threat to ocean environments. Conventional methods of sea and ocean cleaning rely heavily on manual collection, a process that has repeatedly demonstrated its inefficiency and extensive demand for resources. This paper presents the SeaClear system, a novel multi-robot platform designed to autonomously detect and collect marine debris, thereby offering a more efficient solution to this environmental challenge. An overview of the system is presented, followed by a detailed description of each robot’s capabilities. Leveraging artificial intelligence, the system employs the deep-learning-based computer vision algorithm You Only Look Once (YOLO) for the detection of underwater litter, addressing the challenges of poor visibility and hydrodynamic disturbances of underwater environments. Additionally, the paper explores the implemented navigation and control methodologies, which are an essential part of the workflow of the system. The performance of the designed system is validated via field tests conducted in a real-world underwater environment. Finally, directions for future work are proposed. • The infrastructure of a multi-robot system for autonomous underwater debris detection, mapping, and collection is presented. • The suitability of YOLO-based deep learning for real-time debris detection in shallow waters is validated. • The sensing and control scheme that enables the operation of the multi-robotic platform is presented. • The practical performance of the system is confirmed with field experiments to validate the feasibility of AI-driven underwater operations.