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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Implementation of SQP for Nonlinear Optimal Control
A semi-autonomous litter-cleaning robot
Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 1
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portfolio
Monocular Depth Estimation
A semi-autonomous litter-cleaning robot
LitterBot
A semi-autonomous litter-cleaning robot
State Estimation and Optical Flow based Tracking of a Quadrotor
In this project we develop an Unscented Kalman Filter (UKF) for our IMU driven quadcopter model to fuse the inertial data and the vision-based pose and velocity estimation. 
Robust Quadcopter Control in Dynamic Windfield
Designed and benchmarked control architectures (PID, LQR, MPC, and Geometric Controller) for a quadcopter operating in variable windfields, modeling wind disturbances as Gaussian noise and accounting for aerodynamic drag at cruising speeds of 1–5 m/s. 
publications
From Concept to Reality: An In-Depth Review of Autonomous Driving Strategies and Their Implementation
Published in International Conference on Mechanical Design and Manufacturing (ICMDM), 2023
In this paper, we provide a comprehensive review of driving strategies for perception, prediction, and control, particularly emphasizing recent advances in deep learning-based approaches. We also discuss the benefits and limitations of each approach and highlight areas for future research.
Recommended citation: R. Verma, J. Phillip, M. Kotambkar. "2023 From Concept to Reality: An In-Depth Review of Autonomous Driving Strategies and Their Implementation" International Conference on Mechanical Design and Manufacturing 2023.
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Reactive Collision Avoidance for Safe Agile Navigation
Published in IEEE International Conference on Robotics and Automation (ICRA), 2025
The goal is to enable an agile aerial vehicle with an RGBD sensor to navigate safely from start to goal in unknown, dynamic environments. Our method combines nonlinear model predictive control with adaptive control barrier functions, directly linking perception-driven constraints to real-time planning and control
Recommended citation: A. Saviolo, N. Picello, J. Mao, R. Verma and G. Loianno, "Reactive Collision Avoidance for Safe Agile Navigation," 2025 IEEE International Conference on Robotics and Automation (ICRA), Atlanta, GA, USA, 2025, pp. 16125-16132, doi: 10.1109/ICRA55743.2025.11127284.
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talks
teaching
Workshop on Computer Aided Design
Workshop, Visvesvaraya National Institute of Technology, 2021
ROB-GY 6003 Foundations of Robotics
Graduate Course, Tandon School of Engineering, Department of Mechanical Engineering and Aerospace, 2024
