Kumar Panigrahi
Papers
1
Total Citations
10
H-Index
1
About
Kumar Panigrahi is a distinguished researcher in the fields of robotics, artificial intelligence, and neural network-based control systems. His work focuses on developing intelligent navigation algorithms for autonomous mobile robots, particularly in unknown and dynamic environments. Panigrahi’s most notable contribution is his pioneering comparative study of Radial Basis Function Neural Networks (RBFNN) and a novel Wavelet Neural Network (WNN) approach for robotic path planning. In his highly cited 2016 paper, which has garnered 10 citations, he demonstrated how different activation functions within the WNN framework can significantly enhance the performance and adaptability of autonomous robotic agents. This research has provided a foundational benchmark for future work in intelligent controller design, offering a more efficient and robust alternative to traditional neural network models. Panigrahi’s work is particularly valuable for students and researchers exploring the intersection of wavelet theory and neural computation for real-time robotic applications. His contributions continue to influence the development of smarter, more autonomous systems capable of navigating complex, unstructured environments with greater precision and reliability.
Research Focus
Key Achievements
Top Papers
- 1