Sushita Sharma
Papers
1
Total Citations
5
H-Index
1
About
Sushita Sharma’s research lies at the intersection of robotics and machine learning, with a focus on enabling autonomous systems to navigate complex environments. Her most-cited work, “Obstacle Avoidance of a Point-Mass Robot using Feedforward Neural Network” (2021), demonstrates a novel application of neural networks to real-time path planning, allowing robots to adaptively avoid obstacles without pre-programmed routes. This contribution addresses critical challenges in deploying robots for hazardous tasks—such as landmine detection, manufacturing, and healthcare—where adaptability and safety are paramount. Though early in her career, Sharma’s paper has already garnered 5 citations, signaling growing interest in her approach among robotics researchers. Her work bridges the gap between theoretical machine learning models and practical robotic control, offering scalable solutions for autonomous navigation in unpredictable settings. By integrating feedforward neural networks with point-mass robot dynamics, Sharma provides a foundation for future advances in intelligent, self-correcting robotic systems. Her research holds promise for safer, more efficient automation in high-risk industries, marking her as an emerging voice in the field of intelligent robotics.
Research Focus
Key Achievements
Top Papers
- 1