Victoria Tarasova
Papers
3
Total Citations
12
H-Index
3
About
Victoria Tarasova is a robotics researcher whose work centers on mobile robot perception and autonomous navigation, with a particular focus on ultrasonic sensor systems and artificial neural networks. Her research addresses fundamental challenges in how robots perceive and interact with their environments, specifically tackling the problem of obstacle detection and shape identification. In her most cited work, "Identification of the Obstacle Shape Using the Ultrasonic Sensors Module of Modular Mobile Robot" (5 citations), she developed methods for determining obstacle geometry and orientation using ultrasonic sensor arrays. Her 2019 paper on neural network-based obstacle avoidance (4 citations) demonstrated how artificial neural networks can process ultrasonic sensor data to generate control signals for autonomous navigation. She further explored the potential of wide-beam ultrasonic sensors for shape determination in her 2017 study (3 citations). While her citation counts reflect an early-career researcher building her foundation, Tarasova's work represents important contributions to low-cost, practical sensor solutions for mobile robotics, offering alternatives to more expensive LiDAR or vision-based systems. Her research has particular relevance for modular and educational robotics platforms where cost-effective sensing solutions are essential.
Research Focus
Key Achievements
Top Papers
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