AgroPixel AI

Implemented a semantic segmentation network to detect leaf pruning points for automated plant maintenance, boosting accuracy by 30%.

Developed point cloud processing algorithms and machine learning models for point cloud registration, increasing the 3D reconstruction accuracy of small objects by 40% over conventional approaches.

Neupeak Robotics

Visual Navigation Deep Learning Model for Zero-Shot, Robot-Independent Navigation

• Developed an end-to-end navigation model using transformers and diffusion networks to enable navigation via RGB camera feed, designed to work on different mobile robots regardless of their physical attributes.
• The goal-conditioned model can navigate to the target without reliance on additional sensors for 3D scene reconstruction, thereby significantly reducing the overall cost and increasing reliability in the field.
• Utilized a large open-source dataset to train the model on human navigational strategies, enhancing its ability to adapt to new environments and lowering the cost of collecting new training datasets.

Robotics Simulation for 7-Axis Manipulator and Mobile Robot Base

• Developed realistic custom simulation environments in Isaac Sim, integrating soft-body and rigid-body physics to create interactions nearly indistinguishable from real-world scenarios, thereby reducing the dependency on real-world testing.
• Integrated a custom 7-Axis manipulator and custom gripper with intricate linear and revolute joints, implemented along with Omnigraph ROS nodes for motion control using our developed ROS stack.

Real-Time Segmentation Networks

• Implemented real-time three-branch deep learning segmentation network (PIDNet) to detect greenhouse specific objects to help navigate inside compact farm rows.
• Developed a greenhouse simulation environment in Isaac Sim to collect semantic segmentation datasets using synthetic data generation tools, significantly reducing the need for manual dataset labeling and decreasing the time required to collect and train the model.

Void Robotics

• Implemented a ROS2 micro-ros agent on Jetson Nano using an ARM-based Docker container. Integrated the BlynkEdgent C++ package into ESP32 for multi-WiFi connectivity and set up Docker-based VPN services to enable access to ROS2 systems across different networks.

Children’s National Research Center

• Led a team to design and develop a modular navigation system that enabled autonomous functionality for a wheelchair, reducing prototype cost by 52% compared to market alternatives.
• Developed a ROS stack utilizing RTabMap for visual SLAM using Intel RealSense stereo camera, enabling autonomous navigation in dense and narrow environments with a 70% success rate.
• Implemented YOLO for object detection and integrated Open3D for obstacle distance estimation, reducing collisions with dynamic obstacles and enhancing navigation safety.
• Deployed on a Jetson Nano using docker containerization, maintaining power consumption below 10W.

Bariflo Labs

• Developed a ROS-based automation system for a Water Aeration Device, reducing manual intervention.
• IDesigned and deployed a Random Forest regression model on the cloud to analyze real-time sensor data with 160ms latency, ensuring 89% accuracy in aeration control predictions
• Integrated an ESP32-based Wi-Fi controller with a mobile application using MQTT and RESTful APIs, enabling remote monitoring and real-time control, reducing on-site maintenance costs by 40%.
• Optimized edge-to-cloud communication, reducing data transmission latency and ensuring 96% uptime for continuous aeration system monitoring.

National Institute of Ocean Technology

• Designed and fabricated a soft robotic gripper using flexible 3D-printed materials, enabling secure handling of fragile objects up to 500g.
• Developed and simulated the complete mechanism in SolidWorks, optimizing structural integrity through stress analysis.
• Developed mechanical linkages for underwater manipulation and integrated an ESP32-controlled stepper motor, ensuring precise actuation with a ±0.5mm tolerance.
• Optimized the gripper’s design, reducing overall weight by 30% while maintaining high grip strength, enhancing efficiency for underwater applications.

University of Maryland, College Park, MD, USA

Robotics Engineering

Kongu Engineering College, TN, India

Mechatronics Engineering