Papers
12
Total Citations
127
H-Index
7
About
Panchanand Jha is a robotics and automation researcher whose work spans human-machine interaction, robot kinematics, and intelligent computing systems. With a career rooted in solving some of robotics' most fundamental challenges, Jha has made sustained contributions to the field of inverse kinematics — the complex problem of determining joint configurations for a desired end-effector position — applying neural networks, hybrid computational models, and adaptive neuro-fuzzy inference systems to deliver practical solutions for industrial manipulators. His early foundational work, including "A Neural Network Approach for Inverse Kinematic of a SCARA Manipulator" (2014, 26 citations) and related studies on 5-DOF and 4-DOF manipulators, established him as a pioneer in applying soft computing techniques to robot control problems. His research on sensor-integrated robotic end-effectors and vision-guided assembly systems further demonstrates a commitment to bridging intelligent computation with real-world manufacturing automation. More recently, Jha's most-cited work (34 citations, 2024) on human-machine interaction and humanoid robot control reflects his evolution toward cutting-edge human-robot collaboration. Collectively accumulating over 120 citations, his body of work offers students and researchers a rich resource at the intersection of artificial intelligence, mechatronics, and industrial robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2A Neural Network Approach for Inverse Kinematic of a SCARA Manipulator26 citations · 2014
- 3Inverse Kinematic Solution of Robot Manipulator Using Hybrid Neural Network14 citations · 2014
- 4Multiple Sensor Integrated Robotic End-effectors for Assembly11 citations · 2014
- 5
- 6Inverse Kinematic Analysis of Robot Manipulators9 citations · 2015
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