Autonomous boat to improve underwater terrain mapping
Autonomous boat with independent decision-making, efficient control, and long-range navigation capabilities to improve bathymetric surveys. Researchers from the University of Texas, El Paso construct a fully autonomous vessel to carry out reliable and economical bathymetric surveys, which involves mapping the depth and terrain of water bodies. The researchers’ main objective being to provide a methodological framework for other researchers to build their own autonomous boat which will assist with reconnaissance missions.
The study, published in Sensors, details the unmanned surface vehicle (USV) proof-of-concept system which combines an autonomous navigation framework, environmental sensors, and a multibeam echosounder. The system is designed to collect submerged topography, temperature, and wind speed data for comprehensive analysis.
Bathymetric surveying plays a fundamental role in the analysis of geomorphologic features of water bodies. These surveys can be used in the management of water resources, to study ocean environments and biological processes, and for flood forecasting.
Lead author on the paper, Laura Alvarez started developing the USV several years ago, aiming to simplify the bathymetric survey process with the robotic system, a process that usually takes a crew of individuals to complete.
“The reason we wrote the paper was so that anyone can reproduce it by themselves,” Alvarez says. “It serves as an effective guideline to get them started”.
Real-time sampling and monitoring of water environments are fundamental for surface characterisation, material identification, and object detection. Autonomous systems, such as the researcher’s USV, provide rapid and secure environmental information, outperforming human-based methods. These systems also enable surveillance of inaccessible regions and effectively cover diverse spatial and temporal scales, enhancing overall effectiveness.
“If you want to work in water-related studies, you need to know the shape and landscape of bodies of water. For example, you might want to map a reservoir to learn about water supply for electrical demand, or a river to learn about river evolution or flow patterns,” Alvarez says.
Over the course of a year, Fernando Sotelo-Torres, an author on the paper, helped refine the USV. It comprises a 3x3-foot circular watercraft made of aluminium, resting on a sturdy black inner tube. The researchers tested the USV in different environments, aiming to enhance its operational hours, reliability, and achieve full autonomy.
The USV now features a failsafe mechanism that detects low battery levels or high wind gusts, initiating a return-to-base function. With a solar panel and lithium battery, it can operate for up to four hours, covering an area of 472,400 square feet.
Simultaneously, the USV employs a multibeam echosounder, a sonar system that emits sound waves from beneath the boat. By measuring the time it takes for the sound wave to reach the seafloor and return, water depth can be determined. The returning sound enables the identification of seafloor material, enhancing the system's ability to characterise underwater terrain.
To establish proof-of-concept, the team successfully created 2D and 3D maps of portions of Ascarate Lake in El Paso, Texas and Grindstone Lake in Ruidoso, New Mexico.
“My goal was to make the boat state-of-the-art and I think I did that. Of course, there’s always room to improve,” says Sotelo. “But the system works and for now, I hope it can make it easier for scientists to conduct their research”.
The researchers will put the USV to use for the first time later this year to study the Rio Grande River’s flow and depth.
The research team achieved success in creating a self-powered, reliable, and cost-effective autonomous system. This system serves as a foundational step towards intelligent reconnaissance that integrates field robotics and machine learning, enabling adaptive decision-making in unknown aquatic environments.
If you're interested in learning more about this research, you can access the paper published in Sensors here: https://t.ly/BA_y
Autonomous boat with independent decision-making, efficient control, and long-range navigation capabilities to improve bathymetric surveys. Researchers from the University of Texas, El Paso construct a fully autonomous vessel to carry out reliable and economical bathymetric surveys, which involves mapping the depth and terrain of water bodies. The researchers’ main objective being to provide a methodological framework for other researchers to build their own autonomous boat which will assist with reconnaissance missions.
The study, published in Sensors, details the unmanned surface vehicle (USV) proof-of-concept system which combines an autonomous navigation framework, environmental sensors, and a multibeam echosounder. The system is designed to collect submerged topography, temperature, and wind speed data for comprehensive analysis.
Bathymetric surveying plays a fundamental role in the analysis of geomorphologic features of water bodies. These surveys can be used in the management of water resources, to study ocean environments and biological processes, and for flood forecasting.
Lead author on the paper, Laura Alvarez started developing the USV several years ago, aiming to simplify the bathymetric survey process with the robotic system, a process that usually takes a crew of individuals to complete.
“The reason we wrote the paper was so that anyone can reproduce it by themselves,” Alvarez says. “It serves as an effective guideline to get them started”.
Real-time sampling and monitoring of water environments are fundamental for surface characterisation, material identification, and object detection. Autonomous systems, such as the researcher’s USV, provide rapid and secure environmental information, outperforming human-based methods. These systems also enable surveillance of inaccessible regions and effectively cover diverse spatial and temporal scales, enhancing overall effectiveness.
“If you want to work in water-related studies, you need to know the shape and landscape of bodies of water. For example, you might want to map a reservoir to learn about water supply for electrical demand, or a river to learn about river evolution or flow patterns,” Alvarez says.
Over the course of a year, Fernando Sotelo-Torres, an author on the paper, helped refine the USV. It comprises a 3×3-foot circular watercraft made of aluminium, resting on a sturdy black inner tube. The researchers tested the USV in different environments, aiming to enhance its operational hours, reliability, and achieve full autonomy.
The USV now features a failsafe mechanism that detects low battery levels or high wind gusts, initiating a return-to-base function. With a solar panel and lithium battery, it can operate for up to four hours, covering an area of 472,400 square feet.
Simultaneously, the USV employs a multibeam echosounder, a sonar system that emits sound waves from beneath the boat. By measuring the time it takes for the sound wave to reach the seafloor and return, water depth can be determined. The returning sound enables the identification of seafloor material, enhancing the system’s ability to characterise underwater terrain.
To establish proof-of-concept, the team successfully created 2D and 3D maps of portions of Ascarate Lake in El Paso, Texas and Grindstone Lake in Ruidoso, New Mexico.
“My goal was to make the boat state-of-the-art and I think I did that. Of course, there’s always room to improve,” says Sotelo. “But the system works and for now, I hope it can make it easier for scientists to conduct their research”.
The researchers will put the USV to use for the first time later this year to study the Rio Grande River’s flow and depth.
The research team achieved success in creating a self-powered, reliable, and cost-effective autonomous system. This system serves as a foundational step towards intelligent reconnaissance that integrates field robotics and machine learning, enabling adaptive decision-making in unknown aquatic environments.
If you’re interested in learning more about this research, you can access the paper published in Sensors here: https://t.ly/BA_y
