Developed MHA-VAE-LSTM for respiratory forecasting and anomaly detection, achieving >95% detection accuracy and ≥10% better reconstruction than VAE-LSTM. Integrated sensors, real-time inference, and ESP-NOW communication into an end-to-end monitoring system with sustained latency below 50 ms.

Trained and evaluated GraspCNN1 & GraspCNN2 and PoseCNN models for robotic arm manipulation, designing grasp synthesis strategies and validating the grasping pipeline in simulated and real-world settings.

Implemented LiDAR-based SLAM with ICP alignment, A* path planning, Bezier-curve trajectory generation, and PID control to achieve frontier-based autonomous UGV navigation and exploration.

Implemented real-time trajectory planning for quadrotors, including tuning PID controller, minimum-snap trajectory generation and obstacle-aware path planning (A*, Dijkstra), to achieve smooth and safe autonomous navigation. Developed vision-based localization and state estimation pipelines: applied PnP pose estimation, stereo visual odometry, and fused IMU & vision data via augmented-state EKF, and validated in simulation and real-world flight tests.

Developed and programmed an autonomous driving framework for a fast robot car from scratch, with sensing (multi-sensor fusion), localization (Bayes filter-based localization), environment mapping, control (PID), path planning, and navigation on a microcontroller board (SparkFun RedBoard Artemis Nano).