The initial focus is on improving online battery status estimation for unmanned aerial systems (UAS) as battery level is a main limiting factor in the operational use of UAS. Current approaches tend to be overly conservative in harsh environments, such as windy and icing conditions. The new methods are expected to enhance energy consumption prediction accuracy, extend UAS operation times, and potentially enable new types of missions.
Moreover, these methods have broader applicability in enhancing the performance of defense technologies operating under challenging conditions and uncertainties. As the defense sector increasingly relies on autonomous systems, innovative approaches to address real-world uncertainties are crucial. Dissemination efforts will focus on maximizing impact, targeting FFI and the Norwegian Armed Forces. Based on good experience and positive feedback from similar projects, knowledge transfer to the defence sector will be facilitated by numerous seminars, webinars, and hands-on tutorials. Additionally, the project team intends to explore synergies with ongoing FFI projects to further extend the impact of the project, both in terms of knowledge sharing and in terms of establishing new constellations for future collaboration.
The primary objective of the PERTINENCE project is to transfer the successful methods of physics-informed learning
from TAPI to the defence sector.
