Alexey A. Munishkin
Papers
2
Total Citations
18
H-Index
2
About
Alexey A. Munishkin’s research focuses on the intersection of stochastic optimal control and safe autonomous navigation, particularly in multi-vehicle environments. His major contributions lie in developing computationally efficient algorithms for real-time path planning under uncertainty, addressing the critical challenge of avoiding unsafe configurations when vehicles may be non-cooperative. His most-cited work, “Scalable Markov chain approximation for a safe intercept navigation in the presence of multiple vehicles” (2018, 11 citations), introduces a method to overcome the computational bottlenecks of long-horizon planning, enabling practical, real-time implementation. This builds on his earlier foundational paper, “Stochastic optimal control navigation with the avoidance of unsafe configurations” (2016, 7 citations), which tackled the problem of two nonholonomic vehicles navigating safely over extended time horizons. Munishkin’s work is notable for bridging theoretical control frameworks with real-world applicability, offering scalable solutions that are vital for the future of autonomous systems, from drones to self-driving cars. His research continues to influence the development of robust, safety-critical navigation algorithms.
Research Focus
Key Achievements
Top Papers
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