Ashwin Kannan
Papers
1
Total Citations
11
H-Index
1
About
Ashwin Kannan is a robotics researcher whose work focuses on advancing motion planning algorithms for high-dimensional, complex environments. His primary contributions lie in sampling-based path planning, particularly through the development of adaptive hybrid sampling techniques for probabilistic roadmaps. In his most-cited work, "Robot Motion Planning Using Adaptive Hybrid Sampling in Probabilistic Roadmaps" (2016, 11 citations), Kannan addresses a critical challenge: efficiently generating collision-free trajectories in high-dimensional spaces where traditional sampling methods often struggle. By intelligently combining different sampling strategies based on environmental context, his approach improves both the speed and reliability of motion planning—a key enabler for autonomous systems operating in cluttered or dynamic settings. This work has been recognized for its practical relevance, bridging theoretical sampling theory with real-world robotic applications. Kannan's research continues to explore how adaptive algorithms can make robots more responsive and safer, contributing to the broader field of autonomous navigation and manipulation.
Research Focus
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Top Papers
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