Papers
12
Total Citations
71
H-Index
6
About
Konstantin Mironov is a robotics researcher whose work centers on intelligent control, motion planning, and the dynamic task of robotic throwing and catching for material transportation. His most significant contributions lie in developing data-driven methods for predicting the trajectory of thrown objects, replacing traditional physical models with approaches like k-nearest neighbors and genetic programming. These algorithms, which fuse stereo camera tracking with machine learning, enable robots to accurately forecast and intercept flying objects in real time—a capability with direct applications in logistics and automation. His research on stereo camera accuracy and image processing for spherical object tracking (e.g., tennis balls) has been foundational, with several papers accumulating 8–10 citations each. More recently, Mironov has advanced into intelligent control for mobile manipulators in human-oriented environments, proposing a Neural Potential Field for obstacle-aware local motion planning and model predictive control strategies that respect torque constraints. His 2024 work on STRL Robotics integrates localization, mapping, and motion planning into a cohesive architecture for safe human-robot interaction. With a growing citation footprint and a trajectory from precise object tracking to holistic robotic autonomy, Mironov’s research is shaping the future of agile, perception-driven robotics.
Research Focus
Key Achievements
Top Papers
- 1Fast kNN-based prediction for the trajectory of a thrown body10 citations · 2016
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- 3Accuracy of positioning spherical objects with a stereo camera system8 citations · 2015
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- 6Neural Potential Field for Obstacle-Aware Local Motion Planning6 citations · 2024
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