Papers
7
Total Citations
30
H-Index
3
About
M. Dev Anand is a robotics and automation researcher whose work centers on robot kinematics, neural network-based control systems, and industrial robot design. His most significant contributions lie in solving the complex computational challenges of robot manipulator kinematics, particularly for multi-joint systems. Anand has pioneered the application of artificial neural networks — including feedforward networks, Radial Basis Function networks, and Elman networks — to tackle both forward and inverse kinematics problems for five-joint robot manipulators, work that has garnered his most cited recognition with 9 and 5 citations respectively. His research extends into practical industrial applications, including vision-based quality control systems using moment algorithms and neuro-fuzzy approaches for intelligent mobile robot control. Notably, Anand has contributed to the structural and CAD-based analysis of the Scorbot-ER Vu Plus industrial robot, bridging theoretical modeling with real-world engineering validation. By integrating computational intelligence with mechanical design, his body of work offers valuable tools for advancing robotic automation in manufacturing environments, making his research particularly relevant for engineers and students exploring intelligent robotics and human-machine systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Robotics in online inspection and quality control using moment algorithm6 citations · 2012
- 3
- 4
- 5Structural analysis of Scorbot-ER Vu plus industrial robot manipulator3 citations · 2014
- 6
- 7