Specializing in Ground Control Systems (GCS), autonomous drone software, and UAV systems integration.
Software Engineer specializing in Ground Control Systems and autonomous drone software. Experienced in QGroundControl (QGC) customization, MAVLink integration, and cross-platform development using C++, Qt, and QML. Strong background in real-time video systems, 3D mission planning, secure UAV communication, and AI-based object detection using YOLO and OpenCV. Proven ability to deliver reliable, production-grade UAV software for real-world operations.
- Control Systems & GCS: QGroundControl, Mission Planner, Mavlink, PyMavlink, ArduPilot, PX4
- RF & Communications: Microhard & Doodle Labs RF radios, Aerial Data Relay (ADR) setups
- APIs & Mapping: Google Elevation API, ArcGIS, Weather API, Defense Series Map GeoTIFF integrations
- Deployment: Inno-Setup, Cross-platform deployment (Windows, Linux, Android)
- QGroundControl Customization: Tailored QGC frontend and backend using C++, Qt, and QML for autonomous drone operations.
- 3D Mission Planning & Path Following: Built 3D autonomous navigation with elevation data integration and Polyline-based 3D path following.
- Offline Mapping & RF Integration: Integrated high-resolution Defense Series Map GeoTIFF layers for offline rendering; configured Microhard/Doodle Labs RF radios for Aerial Data Relay (ADR).
- Security & Failover: Implemented MAVLink message signing to prevent spoofing and seamless video stream failover (Link 1 to Link 2) to prevent operator interruption.
- Actuation & Payloads: Built a GPS-based striking mechanism for PX4 and direct GCS servo controls for actuator operations.
- Computer Vision: Integrated YOLOv8 (ONNX) for real-time target detection, classification, and camera stream integration (MAVLink & TCP streams).
- Built the company website and developed an AI-based system for plant disease detection using image analysis.
- Developed a replica of Tesla’s infotainment UI using Qt and QML with fluid animations, responsive layouts, and clean interactive controls.
- Tech Stack: Qt, QML, C++
- Implemented a real-time object tracking pipeline using YOLO and SORT (Simple Online and Realtime Tracking) with Kalman Filter-based motion prediction and IoU association.
- Tech Stack: C++, OpenCV, YOLO, SORT, Kalman Filter, GStreamer, CMake
