2 citations · 6 across the 10 of their papers we have counts for
7 papers · 1 filter
REACT: Real-time Entanglement-Aware Coverage Path Planning for Tethered Underwater Vehicles
Abdelhakim Amer, Mohit Mehindratta, Yury Brodskiy +2
Inspection of underwater structures with tethered underwater vehicles is often hindered by the risk of tether entanglement. We propose REACT (real-time entanglement-aware coverage…
AUV trajectory optimization with hydrodynamic forces for Icy Moon Exploration
Lukas Rust, Shubham Vyas, Bilal Wehbe
To explore oceans on ice-covered moons in the solar system, energy-efficient Autonomous Underwater Vehicles (AUVs) with long ranges must cover enough distance to record and collect…
Spatial Acoustic Projection for 3D Imaging Sonar Reconstruction
Sascha Arnold, Bilal Wehbe
In this work we present a novel method for reconstructing 3D surfaces using a multi-beam imaging sonar. We integrate the intensities measured by the sonar from different viewpoints…
Deep Reinforcement Learning for Continuous Docking Control of Autonomous Underwater Vehicles: A Benchmarking Study
Mihir Patil, Bilal Wehbe, Matias Valdenegro-Toro
Docking control of an autonomous underwater vehicle (AUV) is a task that is integral to achieving persistent long term autonomy. This work explores the application of state-of-the-…
Results from the Robocademy ITN: Autonomy, Disturbance Rejection and Perception for Advanced Marine Robotics
Matias Valdenegro-Toro, Mariela De Lucas Alvarez, Mariia Dmitrieva +7
Marine and Underwater resources are important part of the economy of many countries. This requires significant financial resources into their construction and maintentance. Robotic…
A Framework for On-line Learning of Underwater Vehicles Dynamic Models
Bilal Wehbe, Marc Hildebrandt, Frank Kirchner
Learning the dynamics of robots from data can help achieve more accurate tracking controllers, or aid their navigation algorithms. However, when the actual dynamics of the robots c…
Learning of Multi-Context Models for Autonomous Underwater Vehicles
Bilal Wehbe, Octavio Arriaga, Mario Michael Krell +1
Multi-context model learning is crucial for marine robotics where several factors can cause disturbances to the system's dynamics. This work addresses the problem of identifying mu…