Papers
1
Total Citations
11
H-Index
1
About
Apurv Khatri is a roboticist whose work focuses on advancing motion planning for high-dimensional robotic systems. His key research area lies in sampling-based algorithms, particularly the development of adaptive hybrid sampling strategies for probabilistic roadmaps. In his most-cited paper, "Robot Motion Planning Using Adaptive Hybrid Sampling in Probabilistic Roadmaps" (2016, 11 citations), Khatri addresses a critical challenge: selecting the optimal sampling technique for diverse environments. He proposes a novel approach that dynamically blends multiple sampling methods based on the scenario’s complexity, significantly improving collision-free trajectory generation in cluttered or narrow spaces. This contribution enhances the efficiency and reliability of motion planning for robots operating in real-world settings, from manufacturing floors to autonomous navigation. While his citation count reflects a focused, early-career impact, Khatri’s work demonstrates a deep understanding of the trade-offs between exploration and exploitation in sampling-based planners. His research is particularly valuable for students and engineers seeking to optimize robot autonomy in high-dimensional configuration spaces, offering a pragmatic framework for adapting algorithms to environmental constraints.
Research Focus
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Top Papers
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