Mikhail V. Medvedev
Papers
2
Total Citations
5
H-Index
1
About
Mikhail V. Medvedev is a researcher focused on the intersection of artificial intelligence and robotics, with key contributions in motion planning and multi-robot coordination. His work addresses critical challenges in autonomous systems, particularly in developing efficient path planning methods using deep neural networks. His 2024 paper on "Method of Motion Path Planning Based on a Deep Neural Network with Vector Input" (4 citations) introduces novel approaches to two-dimensional navigation, tackling the computational demands and data requirements that limit the accuracy of deep learning-based path planning. In 2023, he explored "Study of Algorithms for Coordinating a Group of Autonomous Robots in a Formation" (1 citation), contributing to the growing field of swarm robotics and formation control. While his citation counts are modest, his research is timely, addressing fundamental bottlenecks in autonomous navigation and multi-agent coordination. Medvedev’s work is particularly relevant for students and researchers interested in practical AI applications for robotics, as it bridges theoretical deep learning methods with real-world robotic challenges. His focus on vector-input neural networks and formation algorithms positions him as an emerging voice in the development of more efficient and scalable autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2