Papers
38
Total Citations
506
H-Index
10
About
Konstantin Yakovlev is a prominent researcher at the intersection of autonomous robotics, path planning, and artificial intelligence, whose work has significantly advanced how robots perceive, map, and navigate complex environments. His research spans grid-based pathfinding, deep reinforcement learning, kinodynamic motion planning, and topological mapping — areas critical to modern mobile robotics and multi-agent systems. Yakovlev's most influential contribution explores applying deep reinforcement learning to classical grid-based path planning, a field traditionally dominated by heuristic search methods like A*, earning over 150 citations and opening new directions for learned navigation policies. He has further pushed boundaries through transformer-based heuristic learning (TransPath) and extensions to safe interval path planning with kinodynamic constraints, addressing real-world robot dynamics. His work on multilayer cognitive architectures for UAV control and policy optimization for dynamic obstacle avoidance reflects a sustained commitment to intelligent, adaptive autonomous systems. Beyond algorithmic contributions, Yakovlev has made practical impacts through tools for simulation environment construction and topological mapping frameworks such as PRISM-TopoMap, which support scalable robot deployment in large, real-world spaces. With over 350 cumulative citations across diverse robotics subfields, his body of work represents a rich and growing contribution to the foundations of autonomous robot navigation.
Research Focus
Key Achievements
Top Papers
- 1Grid Path Planning with Deep Reinforcement Learning: Preliminary Results157 citations · 2018
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- 3Multilayer cognitive architecture for UAV control46 citations · 2016
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- 5TransPath: Learning Heuristics for Grid-Based Pathfinding via Transformers19 citations · 2023
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- 7Safe Interval Path Planning with Kinodynamic Constraints13 citations · 2023
- 8Evaluation of Topological Mapping Methods in Indoor Environments11 citations · 2023
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