Airat Migranov
Papers
1
Total Citations
5
H-Index
1
About
Airat Migranov’s research lies at the intersection of robotics, artificial intelligence, and multi-agent systems, with a particular focus on optimizing task distribution among teams of mobile robots. His most cited work, “Task Distribution Module for a Team of Robots Based on Genetic Algorithms: Synthesis Methodology and Testing” (2019, 5 citations), introduces a novel approach to a classic challenge in robotics: efficiently allocating tasks to robots performing single-syllable operations in a shared workspace. While market economy algorithms and neural networks have been applied to this problem, Migranov’s contribution lies in leveraging genetic algorithms to synthesize and test a robust task distribution module, offering a flexible and adaptive alternative. This work underscores his broader interest in developing scalable, bio-inspired solutions for autonomous systems. Though his citation count is modest, Migranov’s research is notable for its practical methodology and potential to improve coordination in real-world robotic teams, such as those used in manufacturing or logistics. His work continues to inform the design of intelligent, decentralized control systems, making him a thoughtful contributor to the field of multi-robot systems.
Research Focus
Key Achievements
Top Papers
- 1