Hi there, my name is Rajesh Doddegowda.
A passionate engineer with a Master's degree in Electrical Systems Engineering from Universität Paderborn,
specializing in Signals and Information Processing, and a Bachelor's in Electronics and Communication Engineering
from Sri Siddhartha Institute of Technology (SSIT), Tumkur, Karnataka, India. My academic and project experiences have
enabled me to build a unique blend of knowledge in embedded systems, signal processing, machine learning,
and software development.
Driven by curiosity and creativity, I have contributed to diverse industries such as smart home automation, robotics,
web application development, and drone-based logistics systems. At Loxone, I worked on developing core logic and visualization
systems for embedded smart devices, while at Flowsta I built interactive Qt/QML-based UI applications to enhance digital user experiences.
My work in academia includes reinforcement learning-based route planning for drones and simulator for swarm robot with real-time UI control.
I thrive at the intersection of software and embedded systems, and I’m particularly interested in building tools and applications that are robust,
scalable, and user-friendly. Whether I’m optimizing algorithms, designing visual interfaces, or integrating software with hardware, I bring a systems-level
approach and a collaborative mindset to every project.
A one-year master’s project involving behavior control implementation using the ROSPlan framework in C++, along with developing a QtWidget-based GUI to visualize action execution.
Developed a C++ application utilizing the Qt framework to graphically represent Binary Search Trees (BST). It features node manipulation, tree traversal visualizations, and zoom functionality, with support for saving BSTs as text or image files, aiming for educational demonstrations.
Developed a clone of the classic Color Lines game using Qt/QML. The project involved creating a 9x9 grid game logic, handling dynamic ball placement, and implementing a scoring system. Built with CMake and dependencies on Qt6, it showcases advanced use of Qt Quick and SQL components
Implemented a LQR optimal control algorithm for precise navigation of a differential drive Husarion robot to a specified goal, optimizing state and input costs. The setup involved ROS 2 integration, Husarion’s robot configuration, and custom LQR development for successful autonomous goal-oriented movement with targeting.
The above link contains my résumé based on my GitHub repos/activity. Also, you can check out the code for this résumé here.