Prajwal S Hebbar
Papers
2
Total Citations
13
H-Index
2
About
Prajwal S Hebbar is a robotics researcher whose work centers on intelligent trajectory planning and inverse kinematics for industrial manipulators. His key contributions lie in applying machine learning and neural network techniques to solve complex, non-linear problems in robot motion—specifically, minimizing jerk to achieve smoother, more efficient paths for assembly and pick-and-place operations. In his most cited work, "Minimum Jerk Trajectory Planning of PUMA560 with Intelligent Computation using ANN" (2021, 10 citations), Hebbar demonstrated how artificial neural networks can overcome the non-linearity challenges inherent in robot manipulator variables, significantly improving performance in automated manufacturing. He extended this approach in "Trajectory Planning & Computation of Inverse Kinematics of SCARA using Machine Learning" (2021, 3 citations), where he integrated Cubic-B spline methods with machine learning algorithms to compute inverse kinematics while navigating static obstacles. Though early in his career, Hebbar’s work is notable for bridging classical robotics control with modern AI, offering practical solutions for industrial automation. His research holds particular relevance for students and engineers seeking to enhance robot precision and adaptability in constrained environments.
Research Focus
Key Achievements
Top Papers
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