Full Stack Software Engineer & Robotics Researcher. Architecting offline RL pipelines, autonomous aerial systems, and resilient production applications.
I am Debangshu Mounas, Pre-Final year student at National Institute of Technology (NIT) Silchar. Currently serving as the Treasurer (former Academic & Research Secretary) at the IEEE Robotics & Automation Society (RAS) Student Branch Chapter.
My work spans offline reinforcement learning for tendon-driven robots, vision-based aerial multispectral imaging, full-stack microservices architecture, and combat robotics platforms. I build real systems where physical mechanics and intelligent software tightly converge.
SAAS / AGENTIC
Full-stack event-driven document intelligence platform using Celery, Docker, pgvector, and Gemini AI for automated lease clause audit and sub-second retrieval.
MOBILE / ML
Real-time mobile audit terminal combining on-device computer vision (ML Kit) and Supabase backend to perform instant packaging sustainability classification.
CHAMPION
Custom CNC aluminium combat chassis designed in SolidWorks with high-torque brushless drive train and reinforced impact armor. Winner at Tecnoesis 2026.
AERIAL ROBOTICS
Custom autonomous aerial platform running on ArduPilot with onboard GPS-lock stabilization, ELRS low-latency control, and long-range telemetry downlink.
EMBEDDED NAV
High-speed autonomous maze mapping vehicle equipped with ToF laser arrays, 6-DoF IMU orientation stabilization, and flood-fill shortest-path optimization.
MECHATRONICS
Custom 3D-modeled robotic manipulator using high-torque metal gear servos, inverse kinematics positioning, and serial trajectory interpolation.
Engineered an end-to-end autonomous UAV framework synthesizing real-time multispectral vegetation index mapping (NDVI/NDRE) with onboard trajectory planning to enable localized micro-targeted agricultural intervention without ground station latency.
Hierarchical offline-RL navigation system (3D RRT planner + behavior-regularized Double DQN) trained on 21,102 real-robot transitions, deployed with zero online fine-tuning: 91.1% success rate across 90 physical trials in 30 unseen layouts.
Hybrid XGBoost/LSTM flood-forecasting pipeline (94% accuracy) linked to an automated A*-based dynamic traffic rerouting mechanism over LoRaWAN sensor networks.
Team Lead for 3-Autonomous Surface Vessel system for magnetic anomaly detection (sub-metre target precision).
Software & Design Lead for ROS2 autonomous rover navigation and SolidWorks chassis design under space constraints.
Tecnoesis 2026. Designed, engineered and piloted heavy combat chassis to 1st place victory.
Selected at Institute Level (NIT Silchar) for national innovation challenge.
National Level Finalist solving computational industry process optimization.
Active competitive algorithmic solver on arrays, dynamic programming, and graph theory.
Interested in discussing autonomous systems, offline RL, full-stack software development, or prospective internship opportunities? Leave a packet below or contact directly.