Papers
6
Total Citations
89
H-Index
5
About
Ruslan Agishev is a pioneering roboticist whose research bridges aerial and ground robotics, autonomous navigation, and human-robot interaction. His most impactful work, a high-precision UAV localization system using infrared marker pattern recognition (54 citations), enables seamless collaboration between unmanned aerial vehicles and ground robots for indoor environments—a critical capability for warehouse automation and industrial inspection. Agishev further advanced heterogeneous robotic systems through impedance-based control for soft UAV landings on moving ground platforms, achieving stable inter-robot docking essential for multi-agent missions. His recent contributions tackle the frontier of off-road autonomy: the MonoForce model (2024) uses self-supervised learning to predict robot-terrain interaction on deformable surfaces like grass, while his trajectory optimization work (2022) enables efficient exploration of large subterranean spaces. Agishev also innovates in perception, developing self-supervised depth correction for lidar (2023) to improve 3D mapping accuracy, and in teleoperation through AeroVr (2019), which integrates virtual reality with tactile feedback for aerial manipulation. His research, accumulating over 80 citations, systematically addresses the core challenges of deploying robots in unstructured, GPS-denied environments—from precise localization to adaptive control—making him a leading voice in next-generation autonomous systems.
Research Focus
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Top Papers
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