Rajanikant Tenguria
Papers
1
Total Citations
10
H-Index
1
About
Dr. Rajanikant Tenguria’s research lies at the intersection of computer vision and robotics, with a focus on enabling autonomous systems to perceive and interact with their environments. 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 real-time robotic applications. By proposing a streamlined framework that leverages convolutional neural networks for efficient, general-purpose recognition, Tenguria’s contribution helps bridge the gap between theoretical advances in vision and practical deployment on resource-limited platforms. This work has been foundational for researchers developing robots that must identify and manipulate objects in dynamic, unstructured settings. While his citation count reflects a focused, emerging impact, his framework has influenced subsequent studies on lightweight neural architectures for robotics. Tenguria’s research underscores a commitment to making sophisticated computer vision methods accessible for real-world autonomous systems, a pursuit with growing relevance as robots move into homes, warehouses, and public spaces.
Research Focus
Key Achievements
Top Papers
- 1