Hitesh Jangid
Papers
1
Total Citations
16
H-Index
1
About
Hitesh Jangid is a robotics researcher whose work focuses on the kinematic control and path planning of complex mobile manipulator systems (MMS). His key research areas include inverse kinematics, machine learning for robotics, and autonomous navigation on uneven terrain. Jangid’s most notable contribution is the development of a two-stage Support Vector Machine (SVM) approach to solve the inverse kinematics problem for a 14-degree-of-freedom (DOF) mobile manipulator—a system that lacks a closed-form IK solution. By decoupling the mobile base and arm motions, his method enables precise end-effector path control on uneven terrain, with applications in rescue operations, space exploration, and warehouse automation. This work, published in 2019, has garnered 16 citations and demonstrates a practical fusion of kinematics and machine learning. Jangid’s research addresses a critical gap in robotics: achieving coordinated, stable motion for high-DOF systems in challenging environments. His contributions are valuable for students and researchers interested in advancing autonomous robotic systems for real-world deployment.
Research Focus
Key Achievements
Top Papers
- 1