Results 141 to 150 of about 35,722 (281)

Redefining Optimal Coverage Path Planning for FLS‐Equipped AUVs With Deep Reinforcement Learning

Journal of Field Robotics, EarlyView.
ABSTRACT Autonomous Underwater Vehicles (AUVs) have emerged as indispensable tools for a variety of subsea tasks, from habitat monitoring and seabed mapping to infrastructure inspection and mine countermeasures. A fundamental challenge in this field is Coverage Path Planning (CPP), the problem of ensuring complete and efficient area coverage.
Lorenzo Cecchi, Alberto Topini, Alessandro Bucci, Alessandro Ridolfi   +3 more
wiley   +1 more source

Towards Energy-Aware Feedback Planning for Long-Range Autonomous Underwater Vehicles. [PDF]

Front Robot AI, 2021
Alam T, Al Redwan Newaz A, Bobadilla L, Alsabban WH, Smith RN, Karimoddini A.   +5 more
europepmc   +1 more source

Mapping River Bed Topography in Whitewater Rapids Using Bathymetric LiDAR

River Research and Applications, EarlyView.
ABSTRACT Bathymetric LiDAR captures river topography efficiently for clear and shallow water, but for mountain rivers, whitewater rapids still pose challenges. This study proposes a novel method to enable the extraction of bottom returns specifically in turbulent whitewater sections.
Jan Rhomberg‐Kauert, Theresa Himmelsbach, Florian Pöppl, Lucas Dammert, Martin Pfennigbauer, Gottfried Mandlburger   +5 more
wiley   +1 more source

A Novel Routing Protocol Based on Elliptical Shaped Movement of Autonomous Underwater Vehicles in Data Gathering Process for Underwater Wireless Sensor Network. [PDF]

Sensors (Basel), 2022
Ali G, Ali T, Hassan IU, Shaf A, Irfan M, Nowakowski G, Kielkowicz K, Glowacz A, Alqhtani SM.   +8 more
europepmc   +1 more source

System and Method for Automated Rendezvous, Docking and Capture of Autonomous Underwater Vehicles [PDF]

A system for automated rendezvous, docking, and capture of autonomous underwater vehicles at the conclusion of a mission comprising of comprised of a docking rod having lighted, pulsating (in both frequency and light intensity) series of LED light strips
Clark, Evan, Kapit, Jason, Kimball, Peter, Paulus, Jeremy, Richmond, Kristof, Scully, Mark, Stone, William C.   +6 more
core   +1 more source

A UAV‐based deep learning pipeline for intertidal macrobenthos monitoring: Behavioral and age classification in Tachypleus tridentatus

Remote Sensing in Ecology and Conservation, EarlyView.
The endangered tri‐spine horseshoe crab (Tachypleus tridentatus), a “living fossil” crucial to coastal ecology and biomedical research, is experiencing severe population declines. Effective conservation requires efficient monitoring, which traditional methods cannot deliver at scale. We develop an integrated UAV deep learning framework tailored to this
Xiaohai Chen, Yuyuan Bao, Ziwei Ying, Mujiao Xie, Ting Li, Jixing Zou, Jun Shi, Xiaoyong Xie   +7 more
wiley   +1 more source

Active Fault Localization of Actuators on Torpedo-Shaped Autonomous Underwater Vehicles. [PDF]

Sensors (Basel), 2021
Liu F, Long Y, Luo J, Pu H, Duan C, Zhong S.   +5 more
europepmc   +1 more source

Robotics‐assisted acoustic surveys could deliver reliable, landscape‐level biodiversity insights

Remote Sensing in Ecology and Conservation, EarlyView.
Deploying and maintaining sensors is often a major bottleneck in collecting rapid biodiversity data. We tested whether autonomous hopping drones equipped with acoustic recorders could collect reliable biodiversity data in Costa Rica. Using 26,000+ hours of existing audio from 341 sites, with machine learning detections of 19 bird species and spider ...
Peggy A. Bevan, Cristina Banks‐Leite, Mirko Kovac, Jenna Lawson, Lorenzo Picinali, Sarab S. Sethi   +5 more
wiley   +1 more source

Experimental Validation of a Model-Free High-Order Sliding Mode Controller with Finite-Time Convergence for Trajectory Tracking of Autonomous Underwater Vehicles. [PDF]

Sensors (Basel), 2022
González-García J, Gómez-Espinosa A, García-Valdovinos LG, Salgado-Jiménez T, Cuan-Urquizo E, Escobedo Cabello JA.   +5 more
europepmc   +1 more source

Comparing convolutional neural network and random forest for benthic habitat mapping in Apollo Marine Park

Remote Sensing in Ecology and Conservation, EarlyView.
A comparison of Convolutional Neural Network (CNN) and Random Forest (RF) model predictions of benthic habitats within Apollo Marine Park. The CNN (left) and RF (right) classification maps show the spatial distribution of three habitat types: high energy circalittoral rock with seabed‐covering sponges, low complexity circalittoral rock with non‐crowded
Henry Simmons, Dang Nguyen, Benjamin Misiuk, Daniel Ierodiaconou, Sunil Gupta, Oli Dalby, Mary Young   +6 more
wiley   +1 more source