Researchers from Aarhus University (AU) and the Technical University of Denmark (DTU) have collaborated on a project that aims to decrease the costs of measuring and documenting gravel and limestone quarries, while at the same time being faster and easier than the traditional method.
The project included the use of artificial intelligence (AI), which took over the traditionally human-controlled drones that are currently relied on to complete the task.
Erdal Kayacan is an associate professor and expert in artificial intelligence and drones at the Department of Engineering at Aarhus University.
“We’ve made the entire process completely automatic. We tell the drone where to start, and the width of the wall or rock face we want to photograph, and then it flies zig-zag all the way along and lands automatically,” says Kayacan.
Limitations of Human-Controlled Drones
The current method of measuring and documenting gravel and limestone quarries, cliff faces, and other natural and human-made formations relies on drones to photograph the area. A computer then receives the recordings and automatically converts everything and creates a 3D terrain model.
One of the downsides of this method is that drone pilots cost a lot, and the measurements are time-consuming. In an excavation, the drone pilot has to make sure that the drone keeps a constant distance from the wall. At the same time, the drone camera has to be kept perpendicular to the wall, making it a complex and difficult task.
In order for the computer to convert and create a 3D figure out of the images, there has to be a specific overlap in the images. This is the main process that was automated by artificial intelligence, and it drastically reduced the complexity of completing the task.
“Our algorithm ensures that the drone always keeps the same distance to the wall and that the camera constantly repositions itself perpendicular to the wall. At the same time, our algorithm predicts the wind forces acting on the drone body,” says Kayacan.
AI Overcomes Wind Problem
The artificial intelligence also helps overcome the wind, which is one of the biggest challenges with autonomous drone flight.
Mohit Mehndiratta is a visiting Ph.D. student in the Department of Engineering at Aarhus University.
“The designed Gaussian process model also predicts the wind to be encountered in the near future. This implies that the drone can get ready and take the corrective actions beforehand,” says Mehndiratta.
When a human-controlled drone is completing this task, even a light breeze can alter the course of it. With the new technology, wind gusts and the overall wind speed can be accounted for.
“The drone doesn’t actually measure the wind, it estimates the wind on the basis of input it receives as it moves. This means that the drone responds to the force of the wind, just like when we human beings correct our movements when we are exposed to a strong wind,” says Kayacan.
The research was completed in collaboration with the Danish Hydrocarbon Research and Technology Centre at DTU, and the results of the project will be presented in May 2020 at the European Control Conference.