Sreenatha G. Anavatti

About

Sreenatha G. Anavatti is a prominent researcher whose work spans autonomous systems, aerial robotics, computer vision, and multi-robot coordination. Based at the intersection of artificial intelligence and engineering, Anavatti has made substantial contributions to the development of intelligent control systems for unmanned aerial vehicles (UAVs), most notably through a highly cited 2017 survey on intelligent flight control systems (139 citations) and a 2016 examination of visual-inertial navigation and sensor fusion for micro-aerial vehicles (94 citations). His research into monocular depth estimation has proven particularly impactful, with a 2022 survey attracting 160 citations and complementary work introducing MobileXNet, an efficient convolutional neural network architecture optimized for real-time robotics applications. Anavatti has also advanced multi-robot systems research, pioneering frontier-led swarming algorithms for robust coverage of unknown and dynamic environments. His breadth extends to integrated guidance and control systems, autonomous ground vehicle path planning, and gas-source localization using flocking robots. More recently, his interests have expanded to sustainable technology, contributing a comprehensive review of digital solutions for net-zero energy futures (89 citations). Collectively, his publications reflect a career dedicated to bridging theoretical innovation with practical autonomous systems applications.

Research Focus

Key Achievements

Top Papers

1. 1
   Towards Real-Time Monocular Depth Estimation for Robotics: A Survey

   160 citations · 2022
2. 2
   State-of-the-Art Intelligent Flight Control Systems in Unmanned Aerial Vehicles

   139 citations · 2017
3. 3
   Visual–Inertial Navigation Systems for Aerial Robotics: Sensor Fusion and Technology

   94 citations · 2016
4. 4
   Digital technologies for a net-zero energy future: A comprehensive review

   89 citations · 2024
5. 5
   Dynamic Frontier-Led Swarming: Multi-Robot Repeated Coverage in Dynamic Environments

   38 citations · 2023
6. 6
   Frontier-led swarming: Robust multi-robot coverage of unknown environments

   33 citations · 2022
7. 7
   Path-planning modules for Autonomous Vehicles: Current status and challenges

   29 citations · 2015
8. 8
   Multi-gas source localization and mapping by flocking robots

   28 citations · 2022
9. 9
   MobileXNet: An Efficient Convolutional Neural Network for Monocular Depth Estimation

   27 citations · 2022
10. 10
    State-of-the-Art Integrated Guidance and Control Systems in Unmanned Vehicles: A Review

    26 citations · 2020

Key Collaborators

Contact & Links