Fernanda Coutinho
Papers
11
Total Citations
94
H-Index
6
About
Fernanda Coutinho’s research lies at the intersection of robotic manipulation, adaptive control, and industrial automation, with a particular focus on environment stiffness estimation for compliant robotic tasks. Her most influential work, “Online stiffness estimation for robotic tasks with force observers” (2013, 32 citations), introduced a novel approach that leverages force data to overcome the limitations of traditional position-based methods, enabling more precise and stable contact interactions in unknown environments. This contribution is foundational for applications requiring dynamic consistency, such as haptic telepresence and industrial assembly. Coutinho further advanced the field with the Candidate Observers Algorithm (COBA) and adaptive estimation techniques using stochastic disturbance models, collectively cited over 20 times. In recent years, she has applied her expertise to the automotive industry, leading case studies within the GreenAuto project that integrate 3D vision systems with collaborative robots and develop fleet management software for multi-brand mobile robots—work that has already garnered 13 citations since 2025. Her research not only addresses fundamental challenges in robotic control but also delivers tangible solutions for enhancing industrial efficiency.
Research Focus
Key Achievements
Top Papers
- 1Online stiffness estimation for robotic tasks with force observers32 citations · 2013
- 2Choosing paths that prevent network partitioning in mobile ad-hoc networks11 citations · 2005
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- 5System stiffness estimation with the candidate observers algorithm7 citations · 2010
- 6Environment stiffness estimation with multiple observers7 citations · 2009
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- 10Force-based stiffness estimation for robotic tasks3 citations · 2012