Papers
20
Total Citations
287
H-Index
9
About
Maria Makarov is a robotics researcher whose work centers on motion control, human-robot interaction, and safe collaborative robotics. Her most significant contributions lie in developing advanced model-based control strategies for elastic-joint robot manipulators, particularly under real-world conditions of model uncertainty. Her 2016 paper on H∞ control design for elastic-joint robots (85 citations) established a landmark framework combining system identification with robust preview control using only motor-side sensors — a practically important constraint in industrial settings. Complementing this, her earlier work on adaptive filtering for impact detection (48 citations, 2014) and collision detection under modeling uncertainties (22 citations, 2013) addressed the critical challenge of ensuring safe physical human-robot interaction without requiring external force sensing. More recently, she has applied neural network classification to disambiguate intended from unintended human-robot contact (29 citations, 2019), pushing collaborative robotics toward greater contextual intelligence. Beyond manipulators, Makarov has contributed to UAV modeling and robotics education, co-developing an open online course on drone systems. Her 2023 review of intelligent cobots reflects a broadening engagement with Industry 4.0 challenges. Across her career, her research consistently bridges theoretical rigor with practical deployment in safety-critical robotic environments.
Research Focus
Key Achievements
Top Papers
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- 3Using Neural Networks for Classifying Human-Robot Contact Situations29 citations · 2019
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- 10Evaluation of intelligent collaborative robots: a review8 citations · 2023