Atanu Panda
Papers
1
Total Citations
8
H-Index
1
About
Atanu Panda is a researcher specializing in robotics, control systems, and artificial intelligence, with a focus on enhancing the performance and resilience of robotic manipulators. His major contributions lie at the intersection of deep learning and model predictive control, particularly in developing adaptive frameworks that enable robots to maintain precise tracking under adverse conditions, such as actuator failures. His most-cited work, "Integrating attention-based GRU with event-driven NMPC to enhance tracking performance of robotic manipulator under actuator failure" (2024, 8 citations), introduces a novel approach that combines attention-based gated recurrent units with nonlinear model predictive control, allowing for real-time fault tolerance and improved trajectory accuracy. This work has garnered attention for its practical implications in industrial automation and autonomous systems, where reliability is critical. Panda’s research demonstrates a strong ability to bridge theoretical advances with real-world applications, making his contributions valuable for students and engineers working on robust robotic control. His ongoing efforts continue to push the boundaries of intelligent, failure-resistant robotic systems.
Research Focus
Key Achievements
Top Papers
- 1