Saurabh Parkhedkar
Papers
1
Total Citations
10
H-Index
1
About
Saurabh Parkhedkar is a researcher in computer vision and robotics, with a focus on enabling real-time object recognition for autonomous systems. His most-cited work, "Design framework for general purpose object recognition on a robotic platform" (2017, 10 citations), addresses a critical challenge in the field: balancing the accuracy of deep learning-based object detection with the computational constraints of robotic platforms. Parkhedkar’s framework provides a structured approach for integrating convolutional neural networks into robotic systems, allowing for efficient, real-time identification of diverse objects without sacrificing performance. This contribution is particularly valuable for applications in autonomous navigation, manipulation, and human-robot interaction. By tackling the gap between high-accuracy computer vision methods and practical deployment on resource-limited hardware, Parkhedkar’s work has informed subsequent efforts to streamline object recognition pipelines. His research underscores the importance of designing scalable, general-purpose solutions that can adapt to varied environments, making his framework a foundational reference for engineers and researchers developing intelligent robotic platforms. With a focus on bridging theory and application, Parkhedkar continues to advance the integration of vision and robotics.
Research Focus
Key Achievements
Top Papers
- 1