Futuristic wave glider navigating the ocean with glowing AI circuits.

Wave Gliders: How AI-Powered Navigation Can Revolutionize Ocean Exploration

Rhea Morgan in Tech & Innovation August 2025 • 4 min read.

"Dive into the Future of Maritime Technology: Discover how artificial intelligence is optimizing unmanned wave gliders for efficient and eco-friendly ocean research."

Oceans, covering over 70% of our planet, remain largely unexplored. Traditional methods of ocean monitoring, reliant on fuel or batteries, are limiting due to energy constraints, high costs, and environmental concerns. This situation is driving the development of innovative technologies such as unmanned wave gliders (UWGs) to overcome these limitations.

Unmanned wave gliders represent a significant leap forward in maritime technology. They harness wave energy for propulsion, offering near-limitless endurance, reduced operational costs, and zero emissions. These advantages make them ideal platforms for various scientific and survey activities, from climate monitoring to marine resource management.

The integration of artificial intelligence (AI) is further enhancing the capabilities of UWGs. AI algorithms can optimize navigation, adapt to changing ocean conditions, and improve the accuracy of data collection. This article explores how AI is revolutionizing wave glider technology, making ocean exploration more efficient, sustainable, and insightful.

How AI is Transforming Wave Glider Navigation

Futuristic wave glider navigating the ocean with glowing AI circuits.

A significant challenge in UWG technology is motion control. Unlike traditional vessels, UWGs have unique structural and operational characteristics, making motion modeling and control particularly complex. Researchers are actively developing AI-powered solutions to address these challenges, focusing on optimizing navigation and adapting to unpredictable ocean conditions.

Traditional methods of modeling UWG motion often fall short due to the complex interplay of rigid and flexible components, as well as the influence of environmental forces. To overcome these limitations, a more accurate and adaptive approach is needed. That’s where the Artificial Fish Swarm Algorithm comes in.

Artificial Fish Swarm Algorithm (AFSA): Inspired by the foraging behavior of fish schools, AFSA is an AI technique used to optimize complex systems. In the context of UWGs, AFSA helps identify optimal parameters for heading control, allowing the glider to navigate efficiently.
Model Identification: The AI algorithm uses data from tank tests and real-world trials to create a dynamic model of the UWG's motion. This model accounts for various factors, such as wave conditions, glider dynamics, and control inputs.
Parameter Optimization: Once the model is established, AFSA optimizes the control parameters, enabling the UWG to maintain its desired heading with precision. This optimization process leads to improved energy efficiency and navigation accuracy.

By using AI algorithms, researchers can create more accurate and adaptive models of UWG motion, leading to improved control, energy efficiency, and overall performance. The Artificial Fish Swarm Algorithm offers a promising solution for optimizing UWG navigation, paving the way for more efficient and sustainable ocean exploration.

The Future of Ocean Exploration is Here

AI-powered wave gliders are poised to revolutionize ocean exploration, offering a sustainable and efficient alternative to traditional methods. As AI algorithms continue to evolve, we can expect even greater advancements in UWG technology, unlocking new possibilities for understanding and protecting our oceans. The combination of wave glider technology and AI not only improves operational efficiency but also promotes a more environmentally conscious approach to marine research.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1007/s11771-018-3902-9, Alternate LINK

Title: Unmanned Wave Glider Heading Model Identification And Control By Artificial Fish Swarm Algorithm

Subject: Metals and Alloys

Journal: Journal of Central South University

Publisher: Springer Science and Business Media LLC

Authors: Lei-Feng Wang, Yu-Lei Liao, Ye Li, Wei-Xin Zhang, Kai-Wen Pan

Published: 2018-09-01

Everything You Need To Know

What are unmanned wave gliders and how do they work?

Unmanned wave gliders (UWGs) are innovative maritime platforms designed for ocean exploration. They utilize wave energy for propulsion, offering near-limitless endurance and zero emissions. This wave-powered system enables them to operate for extended periods, making them ideal for various scientific and survey activities without the constraints of traditional fuel or battery-powered methods. They convert the kinetic energy of ocean waves into forward motion, allowing them to navigate the seas sustainably. Their unique design and operational characteristics set them apart from traditional vessels, necessitating advanced control methods.

How does artificial intelligence (AI) improve the navigation of wave gliders?

AI significantly enhances the capabilities of UWGs, particularly in navigation. The Artificial Fish Swarm Algorithm (AFSA), inspired by the behavior of fish schools, optimizes control parameters for heading. AI algorithms use data from tank tests and real-world trials to create dynamic models of the UWG's motion. This model accounts for factors such as wave conditions, glider dynamics, and control inputs. This is then used for parameter optimization, enabling the UWG to maintain its desired heading with precision, improving energy efficiency and navigation accuracy. The AI-driven approach allows UWGs to adapt to changing ocean conditions and navigate efficiently.

What are the primary challenges in wave glider motion control, and how is AI addressing them?

A major challenge in UWG technology is motion control, especially given their unique structural and operational characteristics. Traditional methods of modeling UWG motion often fall short due to the complex interplay of rigid and flexible components, and the influence of environmental forces. AI, specifically the Artificial Fish Swarm Algorithm (AFSA), addresses these challenges. AFSA helps identify optimal parameters for heading control. AI algorithms create dynamic models of the UWG's motion, accounting for various factors such as wave conditions and control inputs. By using AI, researchers can create more accurate and adaptive models, leading to improved control, energy efficiency, and overall performance.

Can you explain how the Artificial Fish Swarm Algorithm (AFSA) is used in wave glider navigation?

The Artificial Fish Swarm Algorithm (AFSA) plays a crucial role in optimizing wave glider navigation. Inspired by the foraging behavior of fish schools, AFSA is employed to identify the best control parameters. It optimizes these parameters based on dynamic models created from data, including wave conditions, glider dynamics, and control inputs. The optimization process enables the UWG to maintain its desired heading with precision. AFSA contributes to improved energy efficiency and overall performance, ensuring the glider navigates effectively and sustainably.

What is the potential impact of AI-powered wave gliders on the future of ocean exploration?

AI-powered wave gliders are poised to revolutionize ocean exploration. The integration of AI not only improves operational efficiency but also promotes a more environmentally conscious approach to marine research. The combination of wave glider technology and AI unlocks new possibilities for understanding and protecting our oceans. As AI algorithms continue to evolve, we can expect even greater advancements in UWG technology, providing a sustainable and efficient alternative to traditional methods, and leading to a deeper understanding of our oceans and their resources.