Resumo

OJUMU, M.O.; RAJI, A.K.  e  ORUMWENSE, E.F.. A systematic review of hybrid quadrotor track subsea robotic crawler for offshore mineral mining in the era of Industry 4.0. J. S. Afr. Inst. Min. Metall. [online]. 2026, vol.126, n.1, pp.01-28. ISSN 2411-9717.  https://doi.org/10.17159/2411-9717/3747/2026.

The Fourth Industrial Revolution has driven significant advancements in autonomous robotic systems, particularly in subsea exploration and offshore mineral dredging. This review examines the latest developments in electrically powered quadrotor-track subsea robotic crawlers, highlighting their potential to enhance efficiency, mobility, and adaptability in challenging underwater environments. The integration of advanced automation, artificial intelligence, and real-time control systems has paved the way for more effective robotic solutions in offshore mining. This study explores the dynamic performance of hydraulic crawlers to quadrotor-track robotic dredgers, focusing on their propulsion, navigation, and stability under high hydrodynamic conditions. By leveraging state-of-the-art technologies such as lithium-ion battery power systems, intelligent microprocessors, and sensor-based control mechanisms, these robotic crawlers offer a promising alternative to conventional subsea excavation methods. Furthermore, the review analyses recent experimental and simulation-based studies that assess the feasibility and performance of these systems in real-world applications. The findings of this review provide valuable insights into the role of 4IR in transforming subsea robotics for offshore mineral dredging. By addressing existing limitations and identifying key technological advancements, this study contributes to the ongoing development of next-generation robotic systems capable of operating efficiently in extreme underwater conditions.

Palavras-chave : ourth Industrial Revolution (4IR); electrically powered robotics system; quadrotor-track subsea crawler; offshore mineral dredging; autonomous robotic systems; artificial intelligence in robotics; battery-powered underwater vehicles; sensor-based control mechanisms.

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