Golak Bihari Mohanta

National Institute of Technology Rourkela

Papers

Total Citations

H-Index

About

Golak Bihari Mohanta is a researcher specializing in robotics, automation, and intelligent optimization methods, with a particular focus on trajectory planning and assembly sequence optimization for industrial robotic systems. His most significant contribution lies in developing efficient computational approaches to enhance the precision and performance of robotic operations in manufacturing environments. Mohanta's most cited work, "Optimal time-jerk trajectory planning of 6 axis welding robot using TLBO method" (2018, 26 citations), introduced the Teaching-Learning-Based Optimization (TLBO) algorithm to address end-effector deviation challenges during robotic welding — a critical problem affecting weld quality and process reliability. Building on this foundation, his 2019 study extended trajectory planning to incorporate both kinematic and dynamic constraints, simultaneously minimizing torque rate and joint jerks to achieve smoother, more accurate robotic motion. His work on assembly sequence optimization using the Improved Cuckoo Search Algorithm further demonstrates his commitment to applying bio-inspired metaheuristics to real-world manufacturing challenges. Collectively, Mohanta's research bridges the gap between theoretical optimization and practical industrial robotics, offering solutions that improve manufacturing efficiency, product quality, and robot performance. His work serves as a valuable reference for researchers and engineers seeking intelligent approaches to robotic motion planning and automated assembly.