The project involves designing a UAV system for mango farms, creating a field database, developing and fine-tuning deep learning models, conducting cost-benefit analysis with domain experts and farmers, commercializing the product, generalizing it fo
The project focuses on designing and developing a vehicular communication platform with indigenous hardware supporting DSRC and C-V2X technologies (OBU and RSU) and a software SDK including AGL, DSRC stack, and SAE J2735 message formats. It involves
The project aims to develop a battery-operated, compact, crewless Autonomous Surface Vehicle capable of operating in cluttered environments without human intervention. Key features include GPS/IRNSS-based navigation, long-endurance operation, modular
The project aims to pilot traffic control strategies using energy-efficient smart EDGE devices. It involves identifying challenges in dense urban traffic, developing robust datasets for monitoring and forecasting, applying approximate computing to op
The project develops an autonomous drone charging station with CNC-based battery swapping, precision landing using IR beacons and computer vision, and a balanced LiPo charger. It includes a solar-powered charger with MPPT for uninterrupted power, a d
The research develops an autonomous UAV-based bird scaring system for agriculture. It includes bird flock detection using machine learning and image segmentation (K-means clustering), automatic height adjustment to avoid obstacles, and activation of
The project aims to develop a Visual Autonomous Navigation Network (VANNet) for ground robots to navigate unstructured agricultural fields, and a Crop Health Monitoring Network (CHMNet) for detecting diseases and pests. An adjustable sensing platform
The project develops a UAV-based relay network, evaluating performance benefits in communication range, latency, and reliability using realistic ground-to-air (G2A) and air-to-ground (A2G) channels and mobility models. It includes designing a relay-s
The project focuses on situational awareness for self-driving cars using state-of-the-art perception algorithms, including 2D/3D object detection, sensor fusion, and multi-object tracking. Algorithms will be tested on datasets like IDD, KITTI, NuScen
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