Papers
2
Total Citations
20
H-Index
2
About
Rishabh Tiwari is a robotics researcher whose work spans motion planning and computer vision, with a particular focus on enabling autonomous systems to operate safely in complex environments. His most cited paper, "Robot Motion Planning Using Adaptive Hybrid Sampling in Probabilistic Roadmaps" (2016, 11 citations), addresses a fundamental challenge in robotics: efficiently finding collision-free paths for high-dimensional robots. Tiwari proposed an adaptive hybrid sampling strategy that intelligently combines different sampling techniques based on environmental characteristics, significantly improving the performance of probabilistic roadmap planners in cluttered spaces. This work has been foundational for researchers working on real-time motion planning in dynamic settings. More recently, Tiwari has applied deep learning to a pressing security challenge in "Detection of Camouflaged Drones using Computer Vision and Deep Learning Techniques" (2022, 9 citations). As drones become ubiquitous, their potential for misuse—including surveillance and smuggling—has grown. Tiwari’s work demonstrates how convolutional neural networks can identify visually concealed drones, even when they blend into backgrounds, offering a critical tool for counter-drone systems. His research elegantly bridges classical robotics algorithms with modern AI, tackling both theoretical and applied problems that push the boundaries of autonomous navigation and security.
Research Focus
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Top Papers
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