Papers
2
Total Citations
40
H-Index
2
About
Dr. Jigar Sarda’s research bridges the foundational principles of neural control with the cutting-edge demands of autonomous robotics. His early seminal work, "Dynamic control of a robot arm using CMAC neural networks" (1997), with 31 citations, established a robust framework for adaptive robotic manipulation, demonstrating how cerebellar model articulation controllers could enable precise, real-time motion in complex environments. This contribution laid crucial groundwork for intelligent robotic systems. More recently, Dr. Sarda has advanced the field of computer vision for autonomous platforms through his 2025 study, "Optimizing object detection for autonomous robots: a comparative analysis of YOLO models," which has already garnered 9 citations. By systematically evaluating and optimizing YOLO architectures for robotic perception, he provides a practical roadmap for deploying efficient, high-accuracy detection in resource-constrained systems. His work uniquely spans from classical neural control to modern deep learning, reflecting a deep commitment to solving real-world autonomy challenges. Dr. Sarda’s research continues to influence both academic theory and applied robotics, making him a key figure in the evolution of intelligent, perceptive machines.
Research Focus
Key Achievements
Top Papers
- 1Dynamic control of a robot arm using CMAC neural networks31 citations · 1997
- 2