Kushal Madane
Papers
1
Total Citations
11
H-Index
1
About
Kushal Madane is a researcher at the forefront of visible light sensing (VLS) and robotics, where he leverages machine learning to transform everyday lighting into intelligent sensing platforms. His most-cited work, "Machine Learning Assisted Visible Light Sensing of the Rotation of a Robotic Arm" (2021, 11 citations), demonstrates how LED-based luminaires—originally designed for illumination—can be repurposed to detect and track robotic motion with high precision. This contribution bridges the gap between optical communication, positioning, and robotics, offering a low-cost, non-intrusive alternative to traditional sensors. By integrating machine learning algorithms, Madane’s approach enables robust, real-time sensing even in complex environments, paving the way for smarter factories and autonomous systems. His research underscores the untapped potential of ambient light as a versatile sensing medium, earning recognition for its innovation and practical impact. With a growing citation footprint, Madane continues to push the boundaries of visible light sensing, inspiring new applications in human-robot interaction and cyber-physical systems. For students and researchers, his work exemplifies how interdisciplinary thinking can unlock novel solutions from existing technologies.
Research Focus
Key Achievements
Top Papers
- 1