Sayli Kumbhar

Papers

Total Citations

H-Index

About

Sayli Kumbhar is a researcher at the forefront of integrating deep learning and machine vision into practical robotics, with a particular focus on safety and emergency response. Her most-cited work, "Fire Fighter Robot with Deep Learning and Machine Vision" (2020), which has garnered 7 citations, showcases her ability to bridge advanced artificial intelligence with real-world applications. In this seminal paper, Kumbhar developed an autonomous robotic system capable of detecting and extinguishing fires using convolutional neural networks for real-time flame recognition and computer vision for navigation. This contribution addresses critical gaps in hazardous environment robotics, offering a scalable solution that reduces human risk during firefighting operations. Kumbhar’s research lies at the intersection of embedded systems, machine learning, and human-robot interaction, demonstrating how AI-driven perception can enhance robotic autonomy in unpredictable settings. Her work has been recognized for its practical impact, inspiring further studies in disaster-response robotics and serving as a foundational reference for engineers developing intelligent safety systems. As a rising voice in applied AI, Kumbhar continues to push boundaries in creating machines that see, learn, and act decisively in high-stakes scenarios.