M. Rugarli
Papers
2
Total Citations
6
H-Index
2
About
M. Rugarli’s research centers on mobile robotics and adaptive control, with a particular focus on compensating tracking errors through neural network-based methods. Their most notable contribution is the development of an on-line tuning framework for neural networks to correct trajectory deviations in mobile robots, a problem critical for autonomous navigation in dynamic environments. This work, detailed in their 1995 paper, demonstrates how real-time learning can enhance robot precision without requiring exhaustive pre-programming. Although the paper has garnered 3 citations, its conceptual foundation—bridging neural adaptation and kinematic control—remains a relevant precursor to modern learning-based robotics. Rugarli’s approach highlights the practical value of integrating artificial intelligence with mechanical systems, offering a pathway to more robust and flexible mobile platforms. Their research underscores the importance of adaptive error correction in achieving reliable autonomous movement, a challenge that continues to drive innovation in robotics today.
Research Focus
Key Achievements
Top Papers
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- 2