Papers
11
Total Citations
112
H-Index
6
About
Mikhail Posypkin is a leading researcher in global multi-objective optimization and the geometric analysis of parallel robots. His most influential work, a 2013 paper on a deterministic algorithm for multi-objective optimization with box constraints, has garnered 42 citations for its novel ability to not only approximate a Pareto frontier but to rigorously prove its ε-optimality. This foundational contribution is complemented by his pioneering use of space-filling curves for numerical approximation and visualization, applied to solve systems of nonlinear inequalities—a technique he has successfully used to determine the complex working areas of parallel robots like the planar DexTAR. With over 20 citations, this work is central to his broader impact. Posypkin’s research directly addresses critical engineering challenges: optimizing robot design by maximizing workspace area and the global dexterity index (GDI), and automating workspace approximation to replace error-prone manual methods. His recent work continues to refine these approaches, solidifying his reputation as a key figure in both theoretical optimization and practical robotics design.
Research Focus
Key Achievements
Top Papers
- 1A deterministic algorithm for global multi-objective optimization42 citations · 2013
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- 4Finding sets of solutions to systems of nonlinear inequalities9 citations · 2017
- 5Parallel algorithm for approximating the work space of a robot8 citations · 2019
- 6Constrained Multi-objective Robot’s Design Optimization6 citations · 2020
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- 8Automated Robot’s Workspace Approximation4 citations · 2019
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