Deepti Aggarwal
Papers
3
Total Citations
85
H-Index
3
About
Deepti Aggarwal is a leading researcher in robotics and computational intelligence, with a primary focus on solving the complex inverse kinematics problems that underpin industrial robotic manipulation. Her work addresses a fundamental challenge: finding optimal, multiple solutions to the transcendental equations that govern a robot’s joint movements. Aggarwal’s major contribution lies in pioneering the use of real-coded genetic algorithms and evolutionary strategies to resolve these multimodal functions. Her most cited paper, "An evolutionary approach for solving the multimodal inverse kinematics problem of industrial robots" (2006, 69 citations), established a robust framework for incorporating performance criteria—such as minimizing total joint displacement—directly into the solution scheme. This approach marked a significant departure from traditional numerical methods. In her subsequent work, Aggarwal systematically compared different niching strategies, including tournament selection and simulated binary crossover, to enhance the algorithm’s ability to locate and preserve multiple viable solutions. Her research has provided a powerful, nature-inspired toolkit for roboticists, enabling more efficient and dexterous control of manipulators in manufacturing and automation.
Research Focus
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Top Papers
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