3 citations · 5 across the 5 of their papers we have counts for
6 papers
Shared Autonomy through LLMs and Reinforcement Learning for Applications to Ship Hull Inspections
Cristiano Caissutti, Estelle Gerbier, Ehsan Khorrambakht +3
Shared autonomy is a promising paradigm in robotic systems, particularly within the maritime domain, where complex, high-risk, and uncertain environments necessitate effective huma…
Terrain characterisation for online adaptability of automated sonar processing: Lessons learnt from operationally applying ATR to sidescan sonar in MCM applications
Thomas Guerneve, Stephanos Loizou, Andrea Munafo +1
The performance of Automated Recognition (ATR) algorithms on side-scan sonar imagery has shown to degrade rapidly when deployed on non benign environments. Complex seafloors and ac…
Current Trends and Advances in Quantum Navigation for Maritime Applications: A Comprehensive Review
Olga Sambataro, Riccardo Costanzi, Joao Alves +4
This paper presents a comprehensive review of the current state of the art in quantum navigation systems, with a specific focus on their application in maritime navigation. Quantum…
A Surrogate Model Framework for Explainable Autonomous Behaviour
Konstantinos Gavriilidis, Andrea Munafo, Wei Pang +1
Adoption and deployment of robotic and autonomous systems in industry are currently hindered by the lack of transparency, required for safety and accountability. Methods for provid…
Towards Explaining Autonomy with Verbalised Decision Tree States
Konstantinos Gavriilidis, Andrea Munafo, Helen Hastie +3
The development of new AUV technology increased the range of tasks that AUVs can tackle and the length of their operations. As a result, AUVs are capable of handling highly complex…
Improving the Modularity of AUV Control Systems using Behaviour Trees
Christopher Iliffe Sprague, Özer Özkahraman, Andrea Munafo +3
In this paper, we show how behaviour trees (BTs) can be used to design modular, versatile, and robust control architectures for mission-critical systems. In particular, we show thi…