Shubham Prasad
Papers
1
Total Citations
11
H-Index
1
About
Shubham Prasad is a roboticist whose research centers on motion planning and sampling-based algorithms for high-dimensional spaces. His most cited work, "Robot Motion Planning Using Adaptive Hybrid Sampling in Probabilistic Roadmaps" (2016, 11 citations), addresses a fundamental challenge in robotics: efficiently navigating complex environments. Prasad introduced an adaptive hybrid sampling strategy that intelligently combines different sampling techniques based on the specific characteristics of the environment, improving the performance and reliability of probabilistic roadmap methods. This contribution is particularly valuable for robots operating in cluttered or dynamic settings where traditional uniform sampling falls short. By tailoring the sampling approach to the scenario at hand, Prasad’s work helps robots find collision-free paths more quickly and robustly. His research bridges theory and practice, offering practical solutions for autonomous navigation in real-world applications. With 11 citations, this paper has influenced subsequent work in adaptive motion planning, demonstrating Prasad’s ability to identify and address critical gaps in robotic pathfinding. His focus on adaptive methods continues to inspire researchers seeking more intelligent and efficient motion planning strategies.
Research Focus
Key Achievements
Top Papers
- 1