Visa version
Visa
< föregående
|
nästa >
Jämför
< föregående
|
nästa >
Thesis project proposals
Ego motion estimation on a drone using visual inertial sensors
One of the key components in a drone system is the ability to estimate the position. In this project we investigate methods for ego motion estimation with the aim to generate a position estimate that is robust and accurate for periods on the orders of tens of seconds. We want a solution that is light weight both in terms of physical weight and computational cost. The aim is of this algorithm to be part of the basic infrastructure for the drones used at KTH CAS. There are examples of this in the literature.The first part of the thesis would be about identifying the most promising of these and then to implement and evaluate it one drone at KTH CAS. The hope is that the algorithm will support ego motion estimation also for high speed motion. Given the current projects at KTH CAS, it is estimated that light weight and robust is more important that being able to handle high speeds.
Literature
- ICRA 2016 tutorial slides
- D. Scaramuzza and F. Fraundorfer, "Visual Odometry [Tutorial]: Part I - The First 30 Years and Fundamentals", IEEE Robotics and Automation Magazine, Volume 18, issue 4, 2011
- F. Fraundorfer and D. Scaramuzza, "Visual odometry: Part II - Matching, robustness, optimization, and applications", IEEE Robotics and Automation Magazine, Volume 19, issue 2, 2012
- C. Forster, M. Pizzoli, and D. Scaramuzza, "SVO: Fast Semi-Direct Monocular Visual Odometry", IEEE International Conference on Robotics and Automation, Hong Kong, China, May 2014
- C. Forster, L. Carlone, F. Dellaert, and D. Scaramuzza, "On-Manifold Preintegration for Real-Time Visual-Inertial Odometry", IEEE Transactions on Robotics, to appear, 2016
- M. Faessler, F. Fontana, C. Forster, E. Mueggler, M. Pizzoli, and D. Scaramuzza, "Autonomous, Vision-based Flight and Live Dense 3D Mapping with a Quadrotor Micro Aerial Vehicle", Journal of Field Robotics, 2015
Required skills: ROS, programming, DD2425
Resources: KTH CAS will supply a drone to work with
Where to work: It is foreseen that the majority of the time will be spent at KTH CAS.
Motion primitives for indoor drones
In this project we will investigate how the design and implementation of some basic motion primitives for drones moving in indoor office environments. Example are robust take-off and landing and flying through doors. The aim are algorithms that works on many different platforms. The output would be a set of minimum requirements in terms of sensing and computations such that they can be used on drone of all sizes and a set of motion primitives that can be used to help in the implementation of atoms drones.
Required skills: Programming, control theory
Resources: KTH CAS will supply a drone to work with
Where to work: It is foreseen that the majority of the time will be spent at KTH CAS.
User interface for safe navigation of a teleoperated drone
Current rules for operating drones dictate that you need to have line of sight to the drone. However, for many the applications that we foresee with drone this will not be the case, for example, when drones are used for search and rescue. In such applications the operator will not be able to see the drone and the decision for how to control it has to be based on the sensor data gathered by the robot. In this project we will start with the assumption that the operator has a video feed from the drone and the task is to be able to move safely in the environment. This means that the drone should not run into objects and the interface should provide intuitive feedback to assist the operator in the control of the motion. At the lowest level the project is about obstacle avoidance, but we foresee that the project would also include considerations for how to present the operator with information about the environment, the available controls and how to actually do the control. The system should be useable for drone with a minimum amount of sensors so one question to ask what is that minimum set of sensors to guarantee safety. Can higher safety and a less requirements on sensors be achieved by limiting the allowed types of motion. If so how? The output of this thesis is a method that can be used on a drone that has only local positioning (i.e. no global map) and limited sensing and which can be scaled up when more sensors and processed data (such as a map) are available.
Required skills: ROS, programming, DD2425
Resources: KTH CAS will supply a drone to work with
Where to work: It is foreseen that the majority of the time will be spent at KTH CAS.
Autonomous exploration/exploitation and data gathering with a drone
The scenario here is a drone that is sent into an environment to gather data. The environment is either completely unknown in which case the drone needs to explore or it is already known in which case it can use the prior information to define how to move. The robot project should output methods for how to move in a save way and in that way it is related to the project "User interface for safe navigation of a teleoperated drone" with the difference that here the drone has to make the decisions autonomously.
Required skills: ROS, programming, DD2425
Resources: KTH CAS will supply a drone to work with
Where to work: It is foreseen that the majority of the time will be spent at KTH CAS.
Semantic labeling and object recognition from a drone
In this project the aim is to investigate methods for semantically labelling indoor scenes using drone and to recognise objects in those scenes. The scenario is a drone that moves around in an indoor environment in need of renovation. The algorithms developed in this thesis has the aim of identifying the type of carpet/floor that is on the ground and what type/model of appliances (fridge, stove, oven, etc) are in an apartment so as to be able to assess the level of renovation needed. The assumption is that the drone moves through the environment and collects and stores the data. The processing of the data can be done offline. Insight for how to fly to collect good data for this processed is also an expected outcome of the project.
Required skills: Computer vision, machine learning, programming
Resources: KTH CAS will supply a drone to work with
Where to work: It is foreseen that the majority of the time will be spent at KTH CAS.
Control of a drone with faulty motors
In this project we study the control of a drone when one or more motors have failed. This would typically result in the drone crashing. However, it has been shown that the drone can stay in the air even when such failures occur and that the platform can detect such failures automatically. This project is about investigate this problem with the aim to come up with an algorithm that would allow a drone to handle a failure gracefully.
Literature
Required skills: Solid knowledge of control theory and systems modelling, programming,
Resources: KTH CAS will supply a drone to work with
Where to work: It is foreseen that the majority of the time will be spent at KTH CAS.
UWB positioning of drones
In this project we study ultra wideband (UWB) as a means for positioning of a drone. Examples in the literature show that it should be possible to use UWB combined with information from an IMU to position the drone well enough to control its flight. In this project we will study the use of different setups of the UWB system, to generate, for example, ToA or TDoA measurements. The study should consider i) scalability (can it be used for many drones or just one?), ii) update rate (how often can we get data?), iii) accuracy (how accurate are the measurements?), iv) reliability (how reliable is the signal that we get? are there man outliers?). If and in what situations an IMU is needed should also be investigated. The output of the project is expected to be one or several algorithms for positioning using UWB.
Literature
Required skills: Programming, Kalman filtering and similar, control theory
External collaboration: The project will be conducted in collaboration with the company Loligo which will be responsible for the UWB hardware.
Resources: KTH CAS will supply a drone to work with and Loligo will provide the UWB hardware
Where to work: It is foreseen that the majority of the time will be spent at KTH CAS.
Automatic recharging of drones for extended periods of operation
One of the common denominators of battery powered drones is that they can only operate for short periods of time, typically 10-15 and very rarely above 30min. Many applications would require longer periods of operation. Today the common way to handle this is to manually change the battery. To realise a fully autonomous system, a drone needs to be able to automatically recharge the batteries. This would then also cater for longer periods of operations, albeit with pauses for recharging. The aim the project is to came up with a safe and cost effective solution for autonomous charging of drones. There are several problems associated with this.The drone needs to be able to position itself with respect to the charging station. It needs to be able to land in such that charging can happen. A method for charging the drone in safe and efficient way is needed. The drone should ideally be able to calculate when t needs to return to the recharging station so that it reaches it without running out of battery power. The focus of the master thesis project will be on the first and second of these aspects, with some thoughts going into the fourth issue.
Required skills: Programming, control theory
External collaboration: The project will be conducted in collaboration with the company Loligo which will be responsible for the recharging solution and hardware.
Resources: KTH CAS will supply a drone to work with and Loligo will supply the recharging hardware.
Where to work: It is foreseen that the majority of the time will be spent at KTH CAS.