AI-Enabled System for UAV Precision Descent and Touchdown
PI: Dhananjay Singh (IST)
Based on pilot work, we will submit a large-scale proposal to NSF, create dataset and open-source platform for in class engagement and learning, fund graduate student tuition, and support a postdoc.
Project Description:
Combining data from onboard cameras and inertial measurement units (IMUs), the proposed system will integrate computer vision, artificial intelligence/ML algorithms, and sensor fusion approaches. The UAV will estimate its geographical position, find and track landing markers in real time using sophisticated machine learning algorithms, then carry out controlled descent with sub-meter landing accuracy. Sensor data and clever perception algorithms together guarantee strong performance even in visually poor or GPS-challenged environments. The project will design, simulate, and test the landing system on UAV platforms in several operational settings over one year. Laying the groundwork for scalable adoption in mission-critical applications, the intended outcomes consist in a functional prototype, performance benchmarks, and publishable results. Across sectors needing precision navigation and autonomous decision-making, this invention will drastically lower human interference, improve safety, and expand UAV usage.
Desired Skills/Expertise:
Knowledge of artificial intelligence and machine learning for IoT sensor integration, NLP for virtual assistants, and privacy-preserving methods.
Other Requirements or Expectations:
Availability for weekly meetings; post-comp’s graduate student status, Knowledge of user-centred design, and stakeholder involvement.
Specific Objectives:
To design and implement a precision autonomous landing system for UAVs by integrating computer vision, AI/ML algorithms, and sensor fusion, enabling real-time target detection and accurate positioning in GPS-denied or dynamic environments, with the goal of achieving sub-meter landing accuracy and enhancing UAV reliability for mission critical operations.
Medium/Long-Term Goal:
To establish a scalable, intelligent UAV landing framework that ensures safe, accurate landings in real-world environments without reliance on GPS. In the long term, the system aims to support fully autonomous UAV deployments across industries such as emergency response, urban logistics, and smart agriculture, enabling precision operations in complex and constrained settings.
Connection to ICDS Mission:
This project aligns with the ICDS mission by advancing data-driven, AI-enabled solutions that integrate sensor fusion, real-time analytics, and intelligent control systems. By addressing a critical challenge in autonomous UAV navigation, the project promotes interdisciplinary research in AI, robotics, and edge computing, contributing to safer, smarter, and more autonomous cyber-physical systems for societal impact.
Engagement with ICDS:
This project will actively participate in ICDS activities, including talks, symposiums, and collaborative forums.