UAV-based 3D Structural Inspection
In this project, we are investigating algorithms for performing complete 3D coverage path planning of structures such as bridges, towers, buildings and other structures. Starting from a 3-D model of a structure, our proposed method uses slicing techniques to decompose the available surfaces of the structure into contiguous regions. Then, a graph-based algorithm is used to compute a collision-free navigation path for the UAV that maintains a specified offset of the UAV's coverage sensor (e.g., camera, laser or Lidar) from the surfaces of the structure. Our proposed algorithm guarantees complete 3D coverage of the structure while reducing the time and energy expended in performing the coverage.
Funding: Nebraska University Collaboration Initiative (2017-2018)
Collaborators: Prof. Ricky Wood, Civil Engg., UNL; Prof. Robin Gandhi, School of Interdisciplinary Informatics, UNO.
Autonomous Inventory Counting Using UAV
In this project, we developed a technique for a quadrotor type UAV to autonomously perform systematic coverage of a warehouse shelf-like structure for counting inventory on shelves. To localize the UAV at each shelf, we use markers called AprilTags that are perceived by the UAV's camera to calculate the 3D position of the UAV in real-time. Each marker also encodes the information for the next maneuver of the UAV. In this way, the entire flight path of the UAV to cover all the shelves in the warehouse is encoded in the markers
UAV-based Intelligent and Energy-Aware Traffic Monitoring
Check out our upcoming Workshop Website
This project is supported by a planning grant from NU Collaboration Initiative - System Science including collaborators from Electrical and Computer Engineering (Prof. Hamid Sharif) and Civil Engineering (Prof. Aemal Khattak).
The project's vision is to develop novel techniques and algorithms that will enable multiple, mobile air-borne sensors (e.g., camera, LIDAR) integrated on unmanned aerial vehicles (UAVs), and ground sensors located at strategic locations (e.g., busy traffic intersections, inside traffic tunnels) to autonomously and intelligently coordinate with each other and with central command and control centers via wireless communication, towards collecting real-time traffic information and using that information to pro-actively improve traffic congestion.
Our team's objective is to focus on three inter-disciplinary research areas towards achieving our vision:
- Artificial intelligence(AI)-based real-time coordination techniques between aerial and ground sensor nodes for dynamic asset (mobile sensor) allocation to achieve improved coverage and surveillance of traffic movement within a region of interest.
- Energy-aware, wireless communication strategies and protocols to enable robust, reliable and secure information exchange between aerial-ground sensor nodes for seamless operation of system in zero to low communication zones.
- Urban traffic data collection using the aforementioned techniques and analysis to proactively mitigate traffic congestion situations.
UAV-based Autonomous Monitoring of Crop Health
In this project, a multi-spectral sensor called Micasense Rededge, was integrated on an Asctec Pelican UAV. The UAV was autonomously flown utilizing its on-board flight controller over a small portion of a prairie. Spectral data was collected from the vegetation.
Other Ongoing UAV Projects
- Swarm Influencing and Counter-swarming
- UAV-based gas sensing