Universities are increasingly considering the implementation possibilities of drones for students, teachers and staff on campus.
Whether used for campus safety and security, research or student recruitment, unmanned aerial vehicles (UAVs) have popped up as the new normal. While some university teams / programs build their own drones, others look toward open-architecture drones they can tinker with and test to their liking.
The QDrone from Quanser is an example of an open-architecture, research-grade quadrotor, which was developed specifically for indoor autonomous robotics research.
The QDrone has a durable, light-weight carbon-fiber frame that makes it highly maneuverable and capable of withstanding high-impact applications with little downtime required for repairs. Its powerful on-board processor, RGB-D and optical flow cameras enable high-quality on-board video processing, as well as streaming for real-time monitoring.
You can see the QDrones in action here:
The Quanser QBall 2 quadrotor is another example of a UAV suitable for a wide variety of research applications. It is an open-architecture, indoor rotary wing platform on which you can add other off-the-shelf sensors. Researchers can quickly develop and apply controllers and control algorithms without having to integrate disparate hardware and software resources.
So how exactly are universities using these types of drones?
Flight Control and Simulation
At Concordia University, Dr. Youmin Zhang’s groups in the Diagnosis, Flight Control & Simulation (DFCS) and Networked Autonomous Vehicles (NAV) Labs have used QBalls to work on fault-tolerant control, collaborative control, and several other topics you can find on Dr. Zhang’s Google Scholar profile. He has also incorporated them into in his Flight Control Systems undergraduate courses.
Monitoring Power Lines
The Autonomous Vehicles Systems Lab (AVS) at the University of the Incarnate Word (UIW) in San Antonio, Texas is partnering with the local power company to help them monitor transmission lines. They want to deploy drones to fly to the transmission towers, take pictures or videos, and send them to the operation center for analysis. Having previous experience with the QBall quadrotors in their lab, the QDrone was a logical next choice.
The Qdrone in the video is searching for a tower, in this case, a black PVC pipe structure with a piece of bright white foam placed on top. Once the QDrone takes off, it goes into a scanning mode, looking for the tower. The drone’s optical flow camera captures a gray-scale image of the environment. A binary threshold algorithm running onboard identifies bright pixel values corresponding to the piece of foam. Another blob-detection algorithm then finds all the blobs in the image, draws a box around them, and identifies the center of the box. The pixel coordinates of the center of the blob represent the tower’s location. The Qdrone sends these coordinates to the mission server running on the ground station PC.
Identifying the “tower” initiates the exploratory phase of the mission. The QDrone approaches the tower using visual serving and takes pictures. With that accomplished, the drone returns to scan the environment, searching for the next tower. After repeating a certain number of scanning loops, the drone returns to its home position and lands.
Read more about the specifics of this project and the next phase UIW’s AVS Lab has planned here.
Have you implemented drones within your programs? How are you using them? Do you prefer to develop your own or utilize open-architecture platforms? Tell us in the comments section below.
