Sangeetha Krishnan
Papers
1
Total Citations
9
H-Index
1
About
Sangeetha Krishnan’s research lies at the intersection of robotics, computer vision, and intelligent automation, with a focus on optimizing multi-manipulator systems for real-world object detection and sorting. Her most cited work, “Optimization of target acquisition and sorting for object-finding multi-manipulator based on open MV vision” (2022, 9 citations), introduces a novel method that integrates OpenMV visual programming with deep learning detection algorithms to enhance robotic arm capture strategies. This contribution addresses a critical challenge in industrial automation: enabling robots to efficiently identify, classify, and manipulate objects in dynamic environments. By combining low-cost vision hardware with advanced neural network approaches, Krishnan’s research offers a scalable solution for smart manufacturing and logistics. Her work is particularly notable for bridging the gap between theoretical deep learning models and practical robotic control systems, demonstrating how accessible visual platforms can be leveraged for high-precision tasks. With growing citation impact, Krishnan is establishing herself as an emerging voice in applied robotics, and her findings hold promise for advancing autonomous sorting systems in warehouses, recycling facilities, and assembly lines.
Research Focus
Key Achievements
Top Papers
- 1