Some of the topics are also suitable for bachelor thesis or a course project, if you are interested, contact directly the supervisor listed after each topic.
Probabilistic Localisation Model for Underwater Robot U-CAT
Description of Work: The U-CAT vehicle is equipped with 8 discrete rangefinders, a camera, and IMU which can be used to determine the rough odometry of the vehicle. During multiple dives of a single archaeological site, it would be advantageous for the robot, especially in semi-autonomous operation, to be able to self-localize based upon observable data using these sensors. Given both unknown and known environments, a scheme for probabilistic localization will be developed.
Learning Objectives and Topics: various probabilistic models will be a natural part of this project, sensor fusion, robot-SLAM
Importance: The sophistication of navigation and exploration schemes of the vehicle can be greatly improved by simply knowing, or deducing, more about an environment. The more sophisticated these techniques, the more thorough an investigation can be, and more useful the data. What’s more, these techniques come standard on more expensive vehicles - emulating these on a lower-cost vehicle like the U-CAT furthers the utility of such a vehicle to the field.
Simulation of Artificial Lateral Line sensors with the Robot Operating System (ROS)
Keywords: Sensor simulation, Robotic Operating system (ROS), C++, Python
Description of the work: Your task will be to program and simulate a virtual artificial lateral line (ALL) based on the real device developed in our lab. The ALL consists of small pressure sensors, which are used to detect flow speeds or the velocity of underwater vehicles. The simulation tool will be the Robot Operating System (ROS), which is a frequently used environment for a great variety of robotic applications. It supports different programming languages like C++ and Python. The first part of the assignment would consist of familiarizing with ROS and our ALL system. Afterwards you would be required to create the virtual ALL within the ROS environment. After successful implementation the validity of your virtual ALL has to be demonstrated in a simulation. As a possible bonus, the virtual ALL could be integrated in the simulation of the autonomous underwater vehicle (AUV) SPARUS II on which the real ALL system will be tested in the future.
What will you learn: You will: learn to work with and program in ROS, learn to model and simulate sensors for underwater robots, get to know the novel sensor system developed in our lab, get a basic knowledge in the field of underwater robotics and you will be able to apply your programming skills in this exciting field.
Why does it matter? Your work will be part of a collaboration project between the Centre for Biorobotics and the University of Girona in Spain. The goal of this collaboration is to implement our ALL sensors in the SPARUS II AUV to provide useful information for the control of the vehicle. Our sensor system is much smaller and cheaper compared to existing solutions and could therefore provide an important alternative for small or low cost underwater vehicles. By developing a simulation tool, you will help making the sensor development and implementation process less expensive and more efficient.
Contact: christian dot meurer at ttu dot ee
Make Turbulence beautiful
Description of the work: Your goal will be to take flow information from underwater measurements and visualize them in a unique way. The tasks you will need to perform include basic signal processing (e.g. converting signals from time to frequency domain) and visualization. The measurement data will be available as ASCII and Matlab binary format, and your job will be to turn the signal data into imagery which can aid fluid dynamics researchers in understanding turbulent flows.
What you will learn: You will gain practical experience in signal processing and data visualization, programming with Matlab.
Why does it matter? Flows in Nature are often very different from those in the laboratory. We are studying how those differences are important to biological organisms, especially fish. Many aquatic animals have developed advanced sensory systems which work in turbulent flows. Turbulence includes fast and slow, big and small vortices, and comparing laboratory and natural flows is cumbersome using standard methods. We want to turn our data into stunning pictures and videos which can help researchers study turbulence in a less technical, but more human way.
Contact: Dr. Jeffrey A. Tuhtan group leader of Environmental Sensing and Intelligence, Centre for Biorobotics:jeffrey dot tuhtan at ttu dot ee
Development of a user interface for flow sensors
Description of the work: Your task will be to develop a user interface for the use of different types of artificial lateral line (ALL) probes that we have developed in our lab. The final objective is to facilitate and extend the use of these measuring tools to those users with less advanced programing skills.
What will you learn? You will familiarize with ALL technology and you will have the opportunity of make real tests with it. In the same way, you will learn data treatment technics, programing, user interface programing, and how to represent effectively data.
Why does it matter? ALL is a new technology which is still in prototyping phase and, last years, has demonstrate many benefits for environmental monitoring. However, at the moment, data visualization and processing is being done with independent scripts which usually require a deep knowledge of the technology. Thus, it is necessary to unify these tools into a single software solution to extent the use of this technology to less advanced users.
Keywords: C#, Visual Studio, Artificial lateral lines, data treatment
Contact person: juan dot fuentes at ttu dot ee
Classification of natural flows
Description of the work: to apply/extend existing signal-processing techniques for classification of river sites based on field experiments. We are developing a multimodal bioinspired flow sensors, Hydromasts, for flow speed and flow characterization and we are looking into gathering more knowledge of the sensors abilities.
The assignment would include fieldwork on different rivers and river sites with an array of sensors. In addition, you will analyze recoded data in order to find distinguishable signatures of different river sites.
What will you learn: how to prepare and conduct field experiments, obtain experience in programming in MATLAB and learn methods of signal processing.
Why does it matter? The work will be done as a part of an international project (www.lakhsmi.eu) that will develop a new bio-inspired technology to make continuous and cost-effective measurements of the near field, large-scale hydrodynamic situation, for environmental monitoring in cabled ocean observatories, marine renewable energy and port/harbor security.
System for Real Time Robot Localization Using Acoustig Beacons
Description of work: creating software modules for realtime pinger localization. Currently, the UCAT robot is able to localize itself according to signals from acoustic beacons (pingers), but it does that with a considerable time lag. Receiving the signals from hydrophones and processing the received data are done, but there is no software which is able to relay the data at the time when the ping comes, and timestamp it. The pinger localization module uses the Beaglebone Black’s (BBB) programmable realtime units (PRUs), which are two high-speed microcontrollers that can be programmed in assembler language. This is connected to Python programs using Linux file system and Beaglebone’s Linux+PRU shared memory areas. The student can make use of ROS and its timestamps which are running and connected on Beaglebone and U-CAT’s main computer. The student can also suggest changes in hardware and software framework. Some programming skills and an understanding of how microcontrollers work, are necessary
What you will learn: how ROS works. Programming PRU microcontrollers and ARM computers. The student will see how the robot’s software and hardware work together, and how exactly embedded computers are working with custom-made electronic devices.
Why does it matter? The U-CAT robot is highly maneuverable, so it can be in a totally different position when the software receives the ping with a considerable time lag. With less time lag, the robot is able to position itself much faster and save valuable mission time and battery power in autonomous mode.