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About

Roger Skjetne is a leading figure in marine cybernetics and autonomous robotic systems, with a research focus on underwater and surface vehicle navigation, fault diagnosis, and cable-driven parallel robots for hydrodynamic testing. His most influential work, a 2014 paper on particle filter-based fault diagnosis and robust navigation for underwater robots (94 citations), introduced a switching-mode hidden Markov model to handle sensor and thruster failures, significantly advancing autonomous underwater vehicle reliability. Skjetne also made key contributions to force allocation for overconstrained cable-driven robots (45 citations), developing continuously differentiable solutions that improve computational efficiency for marine applications. He led a landmark 2016 experiment integrating heterogeneous autonomous vehicles—AUVs, UAVs, and USVs—for marine research, demonstrating networked operations that have shaped modern ocean exploration. His work on lidar-based SLAM for autonomous surface vessels and real-time hybrid model testing using cable-driven parallel robots further underscores his impact, with applications ranging from Arctic sea-ice mapping to offshore engineering. With over 200 citations across his top papers, Skjetne’s research bridges theoretical control systems and practical marine robotics, earning him recognition as a professor at NTNU and a key contributor to Norway’s autonomous marine vehicle ecosystem.