About

I am an Applied Scientist with a specialization in Robotics and Autonomy. After completing my PhD in Aerospace Engineering at UT Austin I have been working with the Navigation team at Amazon Scout, an autonomous sidewalk delivery robot delivering packages and groceries to the suburban United States. I graduated from UT Austin with a Masters in Mechanical Engineering and Bachelors from IIT Bombay in Mechanical Engineering. I worked with Maruthi Akella and Renato Zanetti on control theory, nonlinear estimation and robotics. My PhD research included Self Calibration of Sensors and Machine Learning for Autonomous Lunar Landing.

Work Experience

Applied Scientist, Amazon Scout

Jun, 2021 onwards

Navigation Team working on motion planning, trajectory tracking, using learning to enhance planning algorithms, robustness analysis, hardware-in-the-loop testing of autonomous robots.

Robotics Intern, Schlumberger-Doll Research Center

Applied Scientist Intern, Amazon Scout

Jun - Aug, 2020

Worked with the Navigation Team on collision detection and avoidance of the autonomous robot.

Robotics Intern, Schlumberger-Doll Research Center

Jun - Sep, 2019

Worked with the Intelligent Automation team to enable automation and remote operation of oil field services.

Talks

Recent Work

Semantic Segmentation for Autonomous Hazard Detection on the Lunar Surface

A real time machine learning based algorithm for Hazard Detection on the lunar surface to be deployed during the landing phase is presented. A computer vision technique called Semantic Segmentation is used to classify safe and hazardous landing spots for the spacecraft. Randomly sampled Lunar DEMs from the Lunar Reconnaissance Orbiter mission of 2009 are used to train, test, and validate the CNN. The ground truth is calculated according the mission requirements and use existing techniques to calculate slope and roughness. Data augmentation techniques are then used to artificially create additional DEMs by transforming the existing data set. This work is accepted in the AIAA SciTech conference and is to be presented in Jan 2020.

Rahul Moghe and Renato Zanetti
AIAA SciTech Conference 2020

Robot Soccer

Set up the code base for the Aldebaran Nao robot for perception and autonomous control. Tasks included writing color segmentation, blob formation and object detection, using Extended Kalman filters for ball pose estimation and Particle filters for localization. Also formulated controllers for high-level behaviors like aligning and walking to the ball on the soccer field. Perform goalkeeping and shooting actions with the robot. Stood fourth out of ten teams in the penalty kick tournament held as a part of the Autonomous Robots course. Implemented the Complete Coverage D* Lite algorithm for coverage path planning.

Soccer Video  •   Project Report

Covariance Matching Kalman Filter

Formulated a novel adaptive Kalman filtering technique to estimate unknown elements in the noise covariance matrices while simultaneously estimating the state of the system. Provided a comprehensive proof of convergence for the filter under conditions of uniform observability. The number of unknown elements in the covarianec matrix decides depends on the

Rahul Moghe, Renato Zanetti and Maruthi R. Akella
IEEE Conference on Decision and Control (CDC) 2018
Paper

Minimum Snap Trajectory Planning

Developed a real time minimum snap algorithm for Quadcopters using feedforward neural networks. The optimal minimum snap algorithm involves an expensive gradient descent calculation and cannot be performed in real time. Other techniques like the trapezoidal velocity profile methods are far from optimality. The networks trained using the output from optimal gradient descent methods result in a significant improvement in the optimality of the trajectories found while also allowing them to be generated in real time.

Marcelino M. de Almeida, Rahul Moghe, and Maruthi R. Akella
IEEE International Conference on Robotics and Automation (ICRA) 2019
Video

Paper

Variable Structure Controller for single pendulum

Designed a variable structure unstable energy controller for the single pendulum with angular feedback. Performed system identification to estimate the parameters of the motor by collecting data and applying regression techniques. The controller involved a unstable energy component which destabilized the pendulum from its bottom position. As the pendulum approached the top position a PID controller was used to maintain in at the top position. The PID controller was tuned using Ziegler Nichols tuning method and implemented the controller on the hardware.

Video  •   Code (GitHub)