Papers
5
Total Citations
117
H-Index
4
About
Pooja Agrawal is a leading researcher in autonomous robotics, specializing in safe navigation, vision-based control, and robust sliding mode control for nonholonomic mobile robots. Her major contributions include developing a vision-based guidance strategy integrated with a switching-based sliding mode controller, enabling safe robot navigation in unknown indoor environments within a cyber-physical framework (42 citations). She advanced this work with a multivariable event-triggered generalized super-twisting sliding-mode algorithm, further enhancing safety and efficiency (38 citations). Agrawal also pioneered a hybrid robust visual servoing approach combining reinforcement learning with finite-time adaptive fractional-order sliding mode control, eliminating the need for depth data (22 citations). Her research extends to robotic manipulators, where she proposed adaptive backstepping sliding mode control with nonlinear disturbance observers for precise trajectory tracking under uncertainties (14 citations). Most recently, she introduced a reward shaping method for end-to-end mapless navigation using deep reinforcement learning, pushing the boundaries of learning-based control. With over 100 total citations, Agrawal’s work is pivotal for advancing autonomous systems in complex, unstructured environments, earning her recognition as a key innovator in robotics and control theory.
Research Focus
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