Custom UAV with Model Predictive Control for Autonomous Static and Dynamic Trajectory Tracking in Agricultural Fields

Published at Frontiers in Robotics and AI [paper] 

This study introduces a custom-built uncrewed aerial vehicle (UAV) designed for precision agriculture, emphasizing modularity, adaptability, and affordability. Unlike commercial UAVs that are restricted by proprietary systems, the proposed platform integrates a Cube Blue flight controller for low-level control with a Raspberry Pi 4 companion computer that runs a Model Predictive Control (MPC) algorithm for high-level trajectory optimization. The system also incorporates Kalman filtering to enable adaptive mission planning and real-time coordination with a moving uncrewed ground vehicle (UGV), offering greater flexibility in changing field conditions. It consistently achieved root mean square error values between 8 and 20 centimeters during autonomous operations, with slightly higher errors in more complex trajectories. The UAV successfully followed the UGV along nonlinear, curved paths, confirming its suitability for real-world agricultural applications.

Cooperative Localization of UAVs in Multi-Robot Systems Using Deep Learning-Based Detection

Published at AIAA SCITECH 2025 Forum [paper] 

This paper introduces a novel cooperative localization framework designed to enhance localization accuracy in multi-robot systems comprising UAVs and Unmanned Ground Vehicles (UGVs). The proposed method leverages deep learning-based detection, specifically utilizing the YOLOv8 convolutional neural network, to enable real-time object detection and localization. By integrating perception with Kalman Filtering (KF), the approach achieves improved localization accuracy, even in challenging environments, thus advancing the state-of-the-art in cooperative multi-robot systems.

Digital soil mapping of available phosphorus using a smartphone-integrated RGB imaging device and ascorbic acid extraction method

Published at Smart Agricultural Technology [paper] 

To take quick remedial actions, it is critical to map the non-germinated mulch cells at a high throughput capacity which is aimed in this study. We compared the performance of different object detection models to find the missing index of mulch planting. 

Estimated the maximum resolution of testing images and calculated the maximum height for the drone flight. Targetting Computers and Electronics in Agriculture Journal.

A Two-stage Deep-learning Model for Detection and Occlusion-based Classification of Kashmiri Orchard Apples for Robotic Harvesting.

Published at Journal of Biosystems Engineering  [paper] 

Proposed a novel two-stage deep-learning-based approach that can detect the apples using YOLOv7 and utilized EfficientNet that can classify the apple's occlusion condition.

From Goals, Waypoints Paths To Long Term Human Trajectory Forecasting

Published at ReScience C Journal [paper] [code]

Undertaken as part of the Machine Learning Reproducibility Challenge 2021 , we reviewed the above-accepted ICCV 2021 publication for its reproducibility and verification of its empirical claims. Our major contributions include implementing new sampling methods, creating new visualizations, achieving better results than the original paper, and discovering the generalizing power of the model over other datasets.