S Shashank
Papers
2
Total Citations
13
H-Index
2
About
S Shashank is a robotics researcher specializing in intelligent trajectory planning and inverse kinematics for industrial manipulators. His work focuses on integrating machine learning and artificial neural networks to optimize robot motion, addressing the nonlinear challenges inherent in robotic systems. His most cited paper, "Minimum Jerk Trajectory Planning of PUMA560 with Intelligent Computation using ANN" (2021, 10 citations), introduces a novel approach to achieving smooth, efficient motion for assembly and pick-and-place operations. He further advances this field with "Trajectory Planning & Computation of Inverse Kinematics of SCARA using Machine Learning" (2021, 3 citations), where he combines Cubic-B spline methods with machine learning algorithms to compute inverse kinematics while navigating static obstacles. Shashank's contributions are pivotal for enhancing automation in manufacturing, offering practical solutions that improve robot performance and reliability. His work demonstrates a clear impact on the robotics community, providing foundational techniques for future research in intelligent motion planning and control.
Research Focus
Key Achievements
Top Papers
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