Ninad Mehendale

About

Ninad Mehendale is a researcher whose work sits at the intersection of robotics, machine vision, and acoustic sensing. He is best known for his contributions to autonomous systems, particularly in developing a firefighting robot that leverages deep learning and machine vision to detect and extinguish fires, a project that has garnered over 40 citations. His research extends to sound source localization, where his comprehensive review paper has accumulated more than 85 citations, establishing him as a key voice in this domain. Mehendale has also explored advanced robotic motion planning, introducing the Probabilistic Collision Hidden Markov Model (PCHMM) for contactless drug delivery, and developed a robotic system for non-destructive crack detection on railway tracks. With a portfolio that spans practical robotics applications and theoretical frameworks, his work demonstrates a clear focus on creating intelligent, real-world solutions. His research has been cited over 130 times, reflecting its growing influence in the fields of robotics and automation.

Research Focus

Key Achievements

Top Papers

1. 1
   A Review on Sound Source Localization Systems

   83 citations · 2022
2. 2
   Firefighting robot with deep learning and machine vision

   37 citations · 2021
3. 3
   Fire Fighter Robot with Deep Learning and Machine Vision

   7 citations · 2020
4. 4
   A Review on Sound Source Localization Systems

   4 citations · 2021
5. 5
   Robotic Motion Planning Using Probabilistic Collision Hidden Markov Model (PCHMM) for Contactless Drug Delivery

   2 citations · 2021
6. 6
   Development of a Robotic System for Non-Destructive Crack Detection on Railway Tracks

   2 citations · 2023

Key Collaborators

Contact & Links