N N Jose
Papers
2
Total Citations
47
H-Index
2
About
N N Jose is a robotics researcher whose work bridges advanced system identification and intelligent navigation for autonomous platforms. His primary research areas include recursive estimation techniques, robotic manipulator control, and machine learning applications for mobile robotics. Jose’s most impactful contribution is the development of the Recursive Least Squares with Kalman Filter (RLS-KF) method for robotic manipulators, which significantly improves model accuracy by minimizing the discrepancy between real and simulated system outputs—a critical advancement for precision control in industrial automation. This work has garnered 41 citations, underscoring its influence in the field. More recently, Jose has turned his attention to agricultural robotics, proposing a Recurrent Neural Network (RNN)-based system to track mobile robot behavior and navigation patterns. This approach addresses key challenges in crop field automation, including obstacle detection, route planning, and localization. With a growing citation record and a focus on real-world applications—from factory floors to farmlands—Jose’s research exemplifies how adaptive algorithms and neural networks can enhance robotic autonomy in complex, unstructured environments.
Research Focus
Key Achievements
Top Papers
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- 2