If you are interested in one of the projects listed below, please contact the first assistant mentioned at the bottom of each project description by email, phone or in person. It is sometimes possible to have two students working on the same project, please discuss the formalities with your first assistant.
Si vous vous intéressez à l’un des projets ci-dessous, veuillez prendre contact directement avec le premier responsable indiqué à la fin de chacune des descriptions, soit par téléphone, soit par email, soit en passant au LIS. Il est parfois envisageable de travailler à deux sur un même projet de semestre. Veuillez en discuter les formalités avec l’assistant responsable.
La langue (souvent Anglais) utilisée dans les descriptions de nos projets n’a pas d’influence directe sur la langue utilisée dans les relations avec les assistants et pour les rapports de projets.
Analysis of the limitations of quadrotor swarms in the real world with Crazyflies
At the Laboratory of Intelligent Systems, we develop swarming algorithms for quadcopters. These algorithms are extensively tested in a dynamics simulator developed internally. The goal of this project is to integrate the current simulation setup and interface it with hardware to allow experimental testing (https://crazyswarm.readthedocs.io/en/latest/). The first phase of the project will involve the development of a Matlab/Simulink (or Python) program able to send velocity commands to a Crazyflie through ROS. The second step involves testing on hardware in an indoor room equipped with a Motion Tracking system. The robot should be able to accomplish a navigation mission thanks to the commands generated through Matlab/Simulink and the measurements coming from the OptiTrack. The integration of a second drone will allow to evaluate the swarming behavior of the robots in the established framework. A final step of analysis is necessary to assess the limitations of the tested algorithm. Previous experience with the cited software and hardware are required.
Drone Log Analyser
At the Laboratory of Intelligent Systems (LIS) at Ãcole Polytechnique FÃ©dÃ©rale de Lausanne (EPFL) we are developing drones for last-cm delivery. These delivery drones are fully autonomous with the help of a web-application framework of Dronistics. The first goal of this project is to develop a software that can receive logs from an autopilot of a drone after every delivery. The implementation of the same should be compatible with DroneCode SDK. This developed software should be capable of retrieving the logs from the drone and store it in the database (Mongo DB) of the server. The REST API should be then developed to provide specific data from logs based on users query that will be used in the next step. The second goal of this project is to implement a web-based Log Analyser that uses the REST API developed before. This implementation should analyze the corresponding logs and fetch the meaningful data to the user. This Log Analyser should be as generic as possible and should be capable of decoding logs of PX4 autopilot and Ardupilot (at the least). At the end of the implementation, the user should be capable of visualizing the logs in the form of interactive graphs such as a battery, altitude, velocity and other sensor reading over the timeframe. The third goal of the project is to implement a Machine Learning (or) Deep, Learning algorithm that can analyze the logs automatically and report/notify if an anomaly has been detected. Implementation should be supported by a strong state-of-the-art study and feasibility analysis. All the above features should be well documented, unit tested and should be made available on a web-based user interface that should part of Dronistics Software Framework.