The Smart Sailing Twirre project aims to add automatic navigation to boats.
Twirre is a new architecture for mini-UAV platforms designed for autonomous flight in both GPS-enabled and GPS-deprived applications. The architecture consists of low-cost hardware and software components. High-level control software enables autonomous operation. Exchanging or upgrading hardware components is straightforward and the architecture is an excellent starting point for building low-cost autonomous systems for a variety of applications. Experiments with an implementation of the architecture are in development, and preliminary results demonstrate accurate navigation.
For Smart Sailing, the Twirre architecture will be utilized and adapted for automatic navigation of boats.
Students: Mechanical Engineering, Electrical Engineering, Information Science.
Deep Learning is one of the most important machine learning paradigms currently in use. This is currently the most researched field within artificial intelligence.
Big companies like Google, Facebook and Microsoft use this technique for data mining, classification and prediction. We currently research the application areas of this exiting new technology. In previous efforts with student we found great potential in using these technologies on practical machine vision applications.
You’ll be working on a project with deep learning on our special “deep learning machine” with multiple GPUs. For more information on the used software you can take a look at the caffe.berkeleyvision.org and NVIDIA DIGITS.
Students: 1 or 2
Most of the farmers inspect the state of their agricultural fields manually, which is not only an intensive and time consuming but also subjective task. This can be improved by taking pictures of the fields from the air. An Smart Civil Drone (or Unmanned Aerial Vehicle) with a computer on board can be used to take these pictures and automatically inspect the field using a hyperspectral or a multispectral camera (a camera which records more than the conventional three –red, green and blue- bands of the spectrum, in order to provide more information).
The goal is to detect potato plant anomalies and diseases from the images taken by an unmanned aerial vehicle. This consist of stitching, image analysis and data science.
Students: 1 or 2
The NHL Centre of Expertise in Computer Vision works on automating visual inspections using Smart Civil Drones (or Smart Unmanned Aerial Vehicles). The main goal is to research and develop a UAV which can automatically perform navigation tasks using computer vision and sensor fusion. For example: take off, approach and aligning with an object, hover at one position, return to the initial point, landing, line following and moving object following.
This enables applied research projects in a multitude of applications like wind turbine blade inspection and precision agriculture. Interest in learning C++ is required.
Students: 1 or 2 (Computer Science)
For the maintenance of windturbines, it is essential to detect the defects of the blades. Even small defects on these blades reduce the efficiency of the whole turbine. Currently, the blades are inspected by trained people who have to climb up in the wind turbine and inspect them manually. Smart Civil Drones (or Smart Unmanned Aerial Vehicles) can be used to inspect the wind turbine blades.
The goal is to automatically scan the windmill blades and perform image analysis of the defects on the blades using image analysis and data science.