Results 41 to 50 of about 12,575 (196)

A Real-time Motion Planning Algorithm for AUV based on IDVD Method

水下无人系统学报
To enhance the intelligence of autonomous undersea vehicles(AUVs), this paper proposed a real-time motion planning algorithm based on the inverse dynamic virtual domain(IDVD) method, ensuring safe navigation in unknown environments with obstacles.
Guoshun LIU, Wei GUO, Yanjun LAN, Yifan FU   +3 more
doaj   +1 more source

Research on the Influence of Turbulent Flow Induced by Dunes on AUVs

Applied Sciences, 2023
The demand for oceanic resource exploration and development is increasing, and autonomous underwater vehicles (AUVs) have emerged as potential tools for ocean exploration.
Yu Guo, Peng Li, Hongde Qin, Zhongyu Lin, Jiaqi Guo   +4 more
doaj   +1 more source

Design of Sliding Mode Autopilot with Steady-state Error Elimination for Autonomous Underwater Vehicles [PDF]

, 2006
Autonomous Underwater Vehicles (AUVs) have nonlinear and time-varying behaviour and unmodelled dynamics. This paper describes the design, development and evaluation of nonlinear sliding mode autopilot system for an AUV to control the speed, steering and ...
Shi, Juan
core   +1 more source

Virtual Elastic Tether: A New Approach for Multi‐Agent Navigation in Confined Aquatic Environments

Journal of Field Robotics, EarlyView.
ABSTRACT Underwater navigation is a challenging area in the field of mobile robotics due to inherent constraints in self‐localization and communication in underwater environments. Some of these challenges can be mitigated by using collaborative multi‐agent teams.
Kanzhong Yao, Xueliang Cheng, Keir Groves, Barry Lennox, Ognjen Marjanovic, Simon Watson   +5 more
wiley   +1 more source

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

Multi-Underwater Target Interception Strategy Based on Deep Reinforcement Learning

水下无人系统学报
In the context of multiple autonomous undersea vehicles(AUVs) executing underwater target interception missions, AUVs are required to make precise decisions based on both enemy and partner information, navigating the dual challenges of competition and ...
Wenhao GAN, Yunfei PENG, Lei QIAO
doaj   +1 more source

Integrated navigation for autonomous underwater vehicles in aquaculture: A review

Information Processing in Agriculture, 2020
Aquaculture is the world’s fastest growing sector within the food industry, supplying humans with over half their aquatic products. Water quality monitoring or cage inspection is an indispensable part in aquaculture and is usually done manually ...
Jianhua Bao, Daoliang Li, Xi Qiao, Thomas Rauschenbach   +3 more
doaj   +1 more source

A Natural Language Interface with Relayed Acoustic Communications for Improved Command and Control of AUVs

, 2018
Autonomous underwater vehicles (AUVs) are being tasked with increasingly complex missions. The acoustic communications required for AUVs are, by the nature of the medium, low bandwidth while adverse environmental conditions underwater often mean they are
Garcia, Francisco J. Chiyah, Hastie, Helen, Laskov, Atanas, Liu, Xingkun, Patron, Pedro, Petillot, Yvan R., Robb, David A., Valeyrie, Nicolas, Willners, Jonatan Scharff   +8 more
core   +1 more source

Acoustic positioning and tracking in Portsmouth Harbour, New Hampshire [PDF]

, 2007
Portsmouth Harbor, New Hampshire, is frequently used as a testing area for multibeam and sidescan sonars, and is the location of numerous ground-truthing studies. Having the ability to accurately position underwater sensors is an important aspect of this
Huff, Lloyd C, Mayer, Larry A., McGillicuddy, Glenn, Schmidt, Val E., Weber, Thomas C., Weirathmueller, Michelle   +5 more
core   +2 more sources

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