Arabinda Ghosh
Papers
4
Total Citations
23
H-Index
2
About
Arabinda Ghosh is a rising researcher in robotics and control systems, whose work focuses on solving complex challenges in manipulator kinematics and underactuated system control. His research spans inverse kinematics, nature-inspired optimization algorithms, and controller design for nonlinear systems. Ghosh’s most cited paper, “Inverse Kinematic Solution of a 7 DOF Robotic Manipulator using Boundary Restricted Particle Swarm Optimization” (2022, 13 citations), addresses the critical need for high-precision end-effector positioning in industrial, medical, and space applications. He has advanced the application of the Gravitational Search Algorithm (GSA) in robotics and automatic voltage regulator systems, proposing novel frameworks for agent-based optimization within parameter boundaries. His work on underactuated systems, including the rotary double inverted pendulum, demonstrates practical controller designs with experimental verification. Ghosh’s contributions are particularly notable for bridging theoretical optimization methods with real-world robotic implementations, offering efficient solutions to computationally intensive problems. With a growing citation record and publications in 2022-2025, he is establishing himself as an innovative voice in intelligent control and robotic systems engineering.
Research Focus
Key Achievements
Top Papers
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