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, an autonomous underwater vehicle, addresses specific unmet needs for observing and sampling a variety of phenomena in the ocean's midwaters. The midwater hosts a vast biomass, has a role in regulating climate, and may soon be exploited commercially, yet our scientific understanding of it is incomplete. has the ability to survey and track slow-moving animals and to correlate the animals' movements with critical environmental measurements. will complement existing oceanographic assets such as towed, remotely operated, and autonomous vehicles; shipboard acoustic sensors; and net tows. Its potential to perform behavioral studies unobtrusively over long periods with substantial autonomy provides a capability that is not presently available to midwater researchers. The 250-kilogram marine robot can be teleoperated through a lightweight fiber optic tether and can also operate untethered with full autonomy while minimizing environmental disturbance. We present recent results illustrating the vehicle's ability to automatically track free-swimming hydromedusae ( sp.) and larvaceans () at depths of 200 meters in Monterey Bay, USA. In addition to these tracking missions, the vehicle can execute preprogrammed missions collecting image and sensor data while also carrying substantial auxiliary payloads such as cameras, sonars, and samplers.
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http://dx.doi.org/10.1126/scirobotics.abe1901 | DOI Listing |
Sci Rep
August 2025
School of Science, Jimei University, Xiamen, 361021, China.
Underwater imagery frequently exhibits low clarity and is subject to significant color distortion as a result of the inherent conditions of the marine environment and variations in illumination. Such degradation in image quality fundamentally undermines the efficacy of marine ecological monitoring and the detection of underwater targets. To address this issue, we present a Mamba-Convolution network for Underwater Image Enhancement (MC-UIE).
View Article and Find Full Text PDFSensors (Basel)
August 2025
National Deep Sea Center, Qingdao 266237, China.
With advances in underwater exploration and intelligent ocean technologies, wheeled underwater mobile robots are increasingly used for seabed surveying, engineering, and environmental monitoring. However, complex terrains centered on seabed soft slopes-characterized by wheel slippage due to soil deformability and force imbalance arising from slope variations-pose challenges to the accuracy and robustness of trajectory tracking control systems. Model predictive control (MPC), known for predictive optimization and constraint handling, is commonly used in such tasks.
View Article and Find Full Text PDFBiomimetics (Basel)
August 2025
Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China.
Aiming at the cooperative path-planning problem of multiple autonomous underwater vehicles in underwater three-dimensional terrain and dynamic ocean current environments, a hybrid algorithm based on the Improved Multi-Objective Particle Swarm Optimization (IMOPSO) and Dynamic Window (DWA) is proposed. The traditional particle swarm optimization algorithm is prone to falling into local optimization in high-dimensional and complex marine environments. It is difficult to meet multiple constraint conditions, the particle distribution is uneven, and the adaptability to dynamic environments is poor.
View Article and Find Full Text PDFSci Rep
August 2025
Research Organization for Nano & Life Innovation, Waseda University, Shinjuku, Tokyo, Japan.
Passive acoustic monitoring is essential for assessing the impact of anthropogenic noise on marine ecosystems and detecting vocalizing marine life. While acoustic event recorders are widely used to record odontocete echolocation due to their low power and memory demands, conventional detection algorithms are often unsuitable for analyzing datasets composed of complex pulse events. Here, we developed a hybrid analytical framework combining a rule-based filter with a random forest model to efficiently detect narrow-ridged finless porpoise (Neophocaena asiaeorientalis) click trains and vessel noise events using data from the pulse event recorder.
View Article and Find Full Text PDFISA Trans
August 2025
School of Electrical Engineering and Automation, Hubei Normal University, Huangshi 435005, PR China.
This paper focuses on solving the time-varying formation tracking (TVFT) problem for surface-underwater hybrid networked marine systems (HNMSs) under denial of service (DoS) attacks and physical attacks. The system comprises multiple autonomous surface vehicles (ASVs) and autonomous underwater vehicles (AUVs). To address this challenge, a prescribed-time hierarchical control (PTHC) framework is proposed.
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