Raghav Sood
Papers
1
Total Citations
10
H-Index
1
About
Raghav Sood is a robotics researcher whose work focuses on motion planning and manipulation in high-dimensional, cluttered environments. His most-cited paper, "Search-based Path Planning for a High Dimensional Manipulator in Cluttered Environments Using Optimization-based Primitives" (2021, 10 citations), addresses the formidable challenge of planning paths for a 21-degree-of-freedom snake-like manipulator navigating inside a gas turbine for inspection. By fusing heuristic search with optimization-based primitives, Sood’s approach enables efficient and collision-free motion in spaces where traditional planners struggle. This contribution has direct implications for industrial maintenance, particularly in confined, high-value machinery. His work bridges theoretical planning algorithms with practical robotic applications, demonstrating how combinatorial search can be scaled to complex, real-world systems. With a growing citation record, Sood is establishing himself as a rising voice in robotic manipulation and path planning. His research not only advances the state of the art in high-dimensional motion planning but also offers tangible solutions for automating inspection tasks in hard-to-reach environments, making him a notable figure in contemporary robotics.
Research Focus
Key Achievements
Top Papers
- 1