Nidhi Prasad

Papers

Total Citations

H-Index

About

Nidhi Prasad is a researcher at the forefront of autonomous robotics and real-time computer vision. Her work focuses on integrating deep learning with embedded systems to enable intelligent, low-latency perception for robots operating in dynamic environments. Prasad’s most-cited paper, "Real-time deep learning–based image processing for pose estimation and object localization in autonomous robot applications" (2022), has garnered 9 citations, establishing a foundation for efficient neural network deployment on resource-constrained hardware. This contribution addresses a critical bottleneck in robotics: balancing computational accuracy with real-time performance. By developing lightweight architectures for pose estimation and object localization, Prasad’s research directly supports applications in warehouse automation, drone navigation, and human-robot collaboration. Her approach emphasizes practical, deployable solutions that bridge the gap between theoretical deep learning advances and real-world robotic systems. As the field moves toward edge AI and autonomous decision-making, Prasad’s work provides essential building blocks for robots that can perceive and interact with their surroundings swiftly and reliably. Her growing citation record reflects the increasing relevance of her methods in both academic and industrial contexts.