Mukulika Ghosh
Papers
2
Total Citations
10
H-Index
2
About
Mukulika Ghosh is a robotics researcher specializing in motion planning, collision detection, and fault-tolerant systems. Her work addresses critical challenges in autonomous navigation and manipulation, particularly in dynamic or failure-prone environments. Her most cited paper, "Fast Collision Detection for Motion Planning Using Shape Primitive Skeletons" (2020, 8 citations), introduces an efficient method to accelerate collision detection by leveraging simplified geometric representations, enabling faster and more reliable motion planning for complex robotic systems. This contribution is foundational for real-time applications in manufacturing, autonomous vehicles, and service robotics. In her more recent work, "Minimal Path Violation Problem with Application to Fault Tolerant Motion Planning of Manipulators" (2023, 2 citations), Ghosh tackles the pressing issue of component failure during operation. She proposes a novel recovery technique that minimizes path deviations without expensive re-computation, ensuring robots can adapt to sudden changes in configuration space. This research is vital for robust, fail-safe robotic systems in industrial and hazardous environments. With a growing citation record and a focus on practical, resilient solutions, Ghosh is emerging as a key voice in advancing autonomous robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2