Cognitive knowledge representation under uncertainty for autonomous underwater vehicles

Francesco Maurelli, Zeyn Saigol, David Michael Lane

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper presents an early approach for marine cognitive robots, in order to incorporate uncertainty into an ontological representation of the world. The proposed system is based on a signal processing module and an ontology-based knowledge framework, which is queried and updated according to the processed sensor data. It has been successfully demonstrated post-processing data from a mission of NessieAUV at The Under- water Centre, in Fort William, west of Scotland. The system shows its ability to process sensor data, identify basic features (lines and circles) and populate the ontology model. Additionally, from the ontology side, the basic information are elaborated in order to arrive to more complex concepts, like pillars and crossbeams.
Original languageEnglish
Title of host publicationIEEE International Conference on Robotics and Automation 2014: Workshop on Persistent Autonomy for Underwater Robotics
Publication statusPublished - 31 May 2014


Dive into the research topics of 'Cognitive knowledge representation under uncertainty for autonomous underwater vehicles'. Together they form a unique fingerprint.

Cite this