The question of whether robots will one day replace humans is not new and is highlighted whenever a new invention leads to job losses by performing the tasks it is designed for more efficiently and at a lower cost than the people it is believed to replace. Bio-inspired underwater vehicles equipped with artificial intelligence (AI) are advancing rapidly, offering new capabilities for exploring and monitoring aquatic environments. These vehicles, designed to mimic the locomotion, adaptability, and navigation abilities of marine creatures, can access areas that are challenging and potentially hazardous for divers. They can also perform repetitive or long-duration tasks without fatigue, which is a significant advantage over human capabilities. As an example of such machines already available to operators, we can refer to the “underwater swimming manipulator” concepts such as the one provided by Eelume, which has been previously described in an article called “About Underwater Swimming Manipulators” in the section “Equipment to Take into Consideration” on our website.

However, while this article provides an overview of the capabilities of these new-generation machines, it is advisable to consult the studies on this topic in the "Studies of ROV and AUV Concepts" section of our database or other relevant sources for a broader perspective. Given the wide range of studies available, it is wise to select a few that illustrate concepts such as "target detection and exploitation", "automatic path finding and selection", "manipulation", and "general design and propulsion".  "Target detection and exploitation" functions encompass abilities such as detecting, classifying, and enumerating devices, algae, fish, etc. The following documents, among others, may be taken as references to understand this point: • "A survey of AI techniques for control of underwater vehicles" by Pepijn van de Ven, Colin Flanagan, & Daniel Toal. • "Deep Learning on Underwater Marine Object Detection: A Survey" by MD Moniruzzaman, Syed Mohammed, Shamsul Islam, Mohammed Bennamoun, and Paul Lavery. • "Robust Underwater Object Detection with Autonomous Underwater Vehicle: A Comprehensive Study" by Dipta Gomes & Dip Nandi (American International University-Bangladesh) • "Underwater target recognition methods based on the framework of deep learning: A survey" by Bowen Teng and Hongjian Zhao. • "Deep learning with self-supervision and uncertainty regularization to count fish in underwater images" by Penny Tarling, Mauricio Cantor, Albert Clapés, Sergio Escalera. • "Automated Detection, Classification, and Counting of Fish in Fish Passages with Deep Learning" by Vishnu Kandimalla, Matt Richard, Frank Smith, Jean Quirion, Luis Torgo, and Chris Whidden. • "An Overview of Underwater Vision Enhancement: From Traditional Methods to Recent Deep Learning" by Kai Hu, Chenghang Weng, Yanwen Zhang, Junlan Jin, and Qingfeng Xia. • "Deep learning based deep-sea automatic image enhancement and animal species classification" by Vanesa Lopez-Vazquez, Jose Manuel Lopez-Guede, Damianos Chatzievangelou, and Jacopo Aguzzi.

"Automatic pathfinding and selection" is a function that enables an underwater vehicle to autonomously select its path to a target and return to its docking station or launching vessel. The following documents, among others, provide insight into the ongoing research concerning this aspect of artificial intelligence: • “Learning Dynamics and Trajectory optimization for octopus inspired Soft Robotic Manipulators”, by Thomas George, Thuruthel Egidio, Falotico, Federico Renda, and Cecilia Laschi. • “Adaptive Navigation Algorithm with Deep Learning for Autonomous Underwater Vehicle”, by Hui Ma, Xiaokai Mu, and Bo He. • “A digital twins enabled underwater intelligent internet vehicle path planning system via reinforcement learning and edge computing”, by Jiachen Yang, Meng Xi, Jiabao Wen, Yang Li, and Houbing Herbert Song • A Novel Bio-Inspired Path Planning for Autonomous Underwater Vehicle for Search and Tracing of Underwater Target, by Adnan Elahi Khan Khalil, Shahzad Anwar, Ghassan Husnain, Atif Elahi, Zhang Dong. • An Overview of Machine Learning Techniques in Local Path Planning for Autonomous Underwater Vehicles, by Chinonso E. Okereke, Mohd Murtadha Mohamad, Nur Haliza Abdul Wahab, Olakunle Elijah, Abdulaziz Al-Nahari, and S.Zaleha.

"Manipulation" refers to functions that enable remotely operated and autonomous underwater vehicles to grasp objects with full control, even if they are fragile and require careful handling. Manipulation also includes operating various mechanisms without damaging them. The following studies, among others, can be considered references for this topic: • “Soft Manipulators and Grippers: A Review”, by Josie Hughes, Utku Culha, Fabio Giardina, Fabian Guenther, Andre Rosendo, and Fumiya Iida. • “Control Strategies for Soft Robotic Manipulators: A Survey”, by Thomas George Thuruthel, Yasmin Ansari, Egidio Falotico, and Cecilia Laschi • “Model Based Reinforcement Learning for Closed Loop Dynamic Control of Soft Robotic Manipulators”, by Thomas George Thuruthel, Egidio Falotico, Federico Renda, and Cecilia Laschi. • “Learning from Northern clingfish (Gobiesox maeandricus): bioinspired suction cups attach to rough surfaces”, by Petra Ditsche, and Adam Summers. • “Research development of soft manipulator: A review”, by Lu Zongxing, Li Wanxin, and Zhang Liping. • Actuation Technologies for Soft Robot Grippers and Manipulators: A Review”, by Shadab Zaidi, Martina Maselli, Cecilia Laschi, & Matteo Cianchetti. • “Applications of Bioinspired Reversible Dry and Wet Adhesives: A Review”, by Minsu Kang, Kahyun Sun, Minho Seong , Insol Hwang, Hyejin Jang, Seongjin Park, Geonjun Choi, Sang-Hyeon Lee, Jaeil Kim and Hoon Eui Jeong. • “Bioinspired Underwater Adhesion to Rough Substrates by Cavity Collapse of Cupped Microstructures”, by Yue Wang, and René Hensel. • “Underwater Robot Manipulation: Advances, Challenges and Prospective Ventures” by Sara Aldhaheri, Giulia De Masi, `Eric Pairet, and Paola Ardon.

"General design and propulsion" refers to concepts that mimic the forms, propulsion methods, and behaviours of underwater creatures. This includes machines designed with bio-inspired exoskeletons that enable access to areas unreachable by conventional machines and mitigate collision-induced accelerations, thereby reducing stress on the vehicle's components and minimizing environmental impact. "General design and propulsion" also encompasses a new generation of software to control machines capable of performing tasks autonomously or designed to assist the operator efficiently. The selection below may be considered to enhance understanding of this future aspect of underwater vehicles: • “A hybrid dynamic model for bio-inspired robots with soft appendages - Application to a bio-inspired flexible flapping-wing micro air vehicle”, by Mathieu Porez, Frederic Boyer, and Ayman Belkhiri. • “Improved Lighthill fish swimming model for bio-inspired robots - Modeling, computational aspects, and experimental comparisons”, by Mathieu Porez, Frederic Boyer, and Auke Ijspeert. • “SAUV - A Bio-Inspired Soft-Robotic Autonomous Underwater Vehicle” by Fabian Plum, Susanna Labisch, and Jan-Henning Dirks. • “Motion control of unmanned underwater vehicles via deep imitation reinforcement learning algorithm” by Zhenzhong Chu, Bo Sun, Daqi Zhu, Mingjun Zhang, Chaomin Luo. • “A Perspective on Cephalopods Mimicry and Bioinspired Technologies toward Proprioceptive Autonomous Soft Robots” by Goffredo Giordano,Marco Carlotti, and Barbara Mazzolai. • “Underwater Soft Robotics: A Review of Bioinspiration in Design, Actuation, Modeling, and Control” by Samuel M. Youssef, MennaAllah Soliman, Mahmood A. Saleh, Mostafa A. Mousa, Mahmoud Elsamanty, and Ahmed G. Radwan. • “Recent Progress in Modeling and Control of Bio-Inspired Fish Robots”, by Boai Sun, Weikun Li, Zhangyuan Wang, Yunpeng Zhu, Qu He, Xinyan Guan, Guangmin Dai, Dehan Yuan, Ang Li, Weicheng Cui, and Dixia Fan. • “Advances in Autonomous Underwater Robotics Based on Machine Learning” by Antoni Burguera, and Francisco Bonin-Font. • “Reinforcement Learning for Autonomous Underwater Vehicles via Data-Informed Domain Randomization” by WenjieLu, Kai Cheng, and Manman Hu.