Mahesh A. Makwana

Papers

Total Citations

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About

Mahesh A. Makwana is a robotics researcher whose work focuses on solving the complex kinematic challenges of parallel manipulators, particularly the delta robot. His primary research areas include forward and inverse kinematics, neural network optimization, and model-based motion simulation. Makwana’s major contribution lies in developing a novel hybrid approach that combines Artificial Neural Networks (MLP) with Genetic Algorithms to efficiently solve the notoriously difficult forward kinematics problem for delta robots—a task that is far more challenging than its inverse counterpart. His most cited paper (6 citations) introduces this innovative method, while a second highly cited work (5 citations) presents a model-based motion simulation of a delta parallel robot using Simulink’s SimScape environment, enabling accurate virtual prototyping and motion planning. These contributions are significant for advancing the practical application of delta robots in industries requiring high-speed precision, such as pick-and-place operations. Makwana’s work demonstrates a clear ability to bridge theoretical robotics challenges with practical simulation tools, making him a valuable researcher in the field of parallel robotics and intelligent control systems.