Papers
14
Total Citations
322
H-Index
10
About
Signe Moe is a robotics researcher whose work spans inverse kinematics, autonomous manipulation, underwater robotics, and additive manufacturing. She is perhaps best known for her foundational contributions to set-based control within the singularity-robust multiple task-priority inverse kinematics framework, a methodology that enables robotic systems to simultaneously execute prioritized tasks while elegantly avoiding kinematic singularities. Her 2016 paper on this topic has accumulated 100 citations, reflecting its significant influence on the robotics community. Beyond theoretical formulation, Moe has consistently validated her approaches experimentally, lending practical credibility to her algorithms. Her research extends into diverse application domains: she has developed path-following and obstacle avoidance strategies for underwater snake robots, advanced energy-efficient autonomous spray painting systems, and pioneered robot-based wire-arc additive manufacturing techniques capable of depositing material on overhanging structures — a longstanding challenge in the field. Her early work on intuitive human-robot interaction using Microsoft Kinect demonstrates a sustained interest in making robotic systems more accessible and adaptable. Collectively, Moe's portfolio reflects a researcher who bridges rigorous mathematical control theory with impactful real-world robotics applications, making her work highly relevant to both academic and industrial audiences.
Research Focus
Key Achievements
Top Papers
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- 3Set-Based Control for Autonomous Spray Painting29 citations · 2018
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