KAJAL DAMJI GADA – Robotics Software Engineer & Youtuber | Los Angeles, CA
COMPUTER SKILLS:
- PROGRAMMING
Python • C++ • Matlab • LaTeX - SOFTWARE
Libraries: Robot Operating System (ROS) • OpenCV • NumPy
Simulation Environment: Webots • V-Rep • Gazebo • RViz
Tools: Git • Microsoft Visual Studio • Eclipse
Operating System: Windows • Linux
EDUCATION:
- Master of Engineering, Robotics – May 2017
University of Maryland, College Park, MD - Master of Business Administration, Technology Management – May 2015
NMIMS University, Mukesh Patel School of Technology Management & Engineering - Bachelor of Technology, Electronics and Telecommunication – May 2014
NMIMS University, Mukesh Patel School of Technology Management & Engineering
WORK EXPERIENCE:
- Brain Corp – Engineer (June 2018 – June 2020)
• Lead collaborative efforts amongst multiple teams to resolve mission critical issues for timely software releases.
• Incorporated ToF camera data for better situational awareness to enhance performance in tight spaces.
• Worked jointly with R&D to develop & integrate advance path planning functionality that improved performance by 20%.
• Entrusted with investigating safety concerns to root cause software bugs and resolve them promptly.
• Reduced sensor breakage & replacement costs by designing protective casing for LiDAR with hardware & mechanical team.
• Combined efforts with QA team to develop a suite of tests to evaluate platform robustness for each software release.
• Created and managed a team to review field data to identify problem areas and prioritize based on frequency of occurrence.
• Proactively launched efforts for knowledge sharing by means of bi-weekly lecture series and documentation.
- Qualcomm Technologies Inc. – Engineer (January 2018 – May 2018)
• Developed applications as software module in C++ and implemented on aerial and ground robots using ROS.
• Built software module for Qualcomm chip 8×96 using docker for cross-compilation.
• Debugged hardware interfaces and real-time protocols between software modules for smooth application runs.
- Reality Analytics, Inc. – AI Engineer (June 2017 – December 2017) & AI Intern (Jan – May’ 17)
• Applied machine learning techniques for object detection and classification in images and videos using MATLAB.
• Developed a function to extract various objects from images to create a database of templates.
• Machine Learning techniques – normalized cross-correlation, SVM and neural network.
- Autonomy, Robotics, and Cognition Laboratory – Graduate Assistant, University of Maryland (Jan – May’ 16)
• Experimentally evaluated path planning algorithms – RRT and RRT* with Open Motion Planning Library (OMPL).
• Tested the algorithm to move Baxter’s arm with collision avoidance using motion planning framework Move-It on ROS.
PROJECT – Vision and Perception:
- Texture Synthesis by non-parametric sampling (May 2016)
• Created a large image of a given texture by placing close match pixel neighbours around the sample over multiple iterations.
• Tested using multiple texture samples for varying window size and threshold values. - Panorama image by image stitching using SIFT features (March 2016)
• Identified the matching features (SIFT) between images to obtain the affine transform that aligns the adjecent images.
• Evaluated the best affine transform using RANSAC and used it to stitch the images for creating a panorama.
• Enhanced the final image to a single intensity using normalization and filled up the empty spaces using bi-linear interpolation.
PROJECT – Path Planning and Navigation:
- Localizing a robot using particle filter approach with Python (April 2016)
• Implemented particle filter on the laser scan data for estimating the robot’s position and orientation in the map.
• Reduced the sensing noise by comparing the actual vs expected laser scan data, computed using robot’s odometer reading. - Map building from laser scan data and vizualized in Rviz (April 2016)
• Implemented an occupancy grid-based approach to compute the map of the environment for a mobile robot.
• Identified the occupied (or obstacles) cells on the map by combining the laser scan data with the robot’s odometry.
• Used RViz in Robot Operating System (ROS) to visualize the mobile robot and the mapped environment. - Path Planning and Control for Turtle-bot using ROS (March 2016)
• Developed an algorithm to compute path for the robot and designed a proportional controller to drive the robot.
• Implemented the algorithms in ROS using Python and tested its performance in Gazebo simulation environment.
• Evaluated the performance of the developed algorithms by implementing it on a physical Turtle-bot.
PROJECT – Machine Learning:
- Teaching a computer to play Tic-Tac-Toe with Q-learning and Neural Network (December 2016)
• Programmed to learn the winning strategy of Tic-Tac-Toe by playing the game with itself on a reward-based methodology.
• Compared the result with win-loss based method of neural network and found both to be effective. - Learning a function based on input-output point using CMAC (September 2016)
• Implemented an Cerebellar Model Arithmetic Computer (CMAC) method to approximate a function using.
• Designed based on input and output data points over various iterations to drive error to zero.