Projects for Master Thesis and/or Independent Study

Flight Control and Motion Planning for a Class of VTOL UAVs
Supervisors: Kostas Alexis
Available
This thesis aims to develop optimized flight control and motion planning algorithms to enable the  robust and high performance flight of a class of Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicles (UAVs) capable of convertible flight, namely from rotorcraft mode to fixed-wing mode. The research will emphasize on robust flight control especially against wind disturbances, alongside the generation of optimized trajectories that account for estimates of the wind field across the direction of motion. The method will be implemented on the PX4 open-source autopilot software and deployed onboard a prototype VTOL UAV. Details here. 

Resilient Flight Control for a Collision-tolerant Aerial Robot
Supervisors: Kostas Alexis
Available
This thesis aims to develop a novel control strategy tailored to a Resilient Micro Flyer implementing a collision-tolerant frame. The method should account and exploit the collision tolerance of not only to passively mitigate the risks of a collision but also to a) actively modify the control commands when a collision-event is detected, and b) to intentionally decide if the robot can navigate an environment better by maintaining smooth contact with a surrounding surface. Alongside the controller design, the implementation of a collision detection algorithm relying on the onboard Inertial Measurement Unit data is also necessary. 

Blazing Fast Exploration Path Planning for Subterranean Flying Robots
Supervisors: Kostas Alexis
Assigned
​This project aims to develop a path planning strategy for autonomous aerial robots in order to explore large-scale underground environments, such as mines and cave networks, at rapid speeds. Building on top of a set of previously developed planning methods of our team, the goal is to address two core limitations relating to the environment representation and the information gain formulation used to derive optimized exploration paths and thus allow to efficiently calculate dynamic and agile trajectories allowing high-speed exploration. The latter is a necessary condition if battery- and broadly resource-constrained robots are to be able to efficiently explore the often km-long underground environments. Check examples of our previous work. 

Collision Resilient Navigation for Aerial Robots in Confined Environments
Supervisors: Kostas Alexis, Christos Papachristos
Available
This research aims to enhance the navigation abilities of aerial robots in confined/narrow environments by enabling them to stably establish contact with surfaces of their environment. More specifically, a Micro Aerial Vehicle will be enhanced with specialized mechanisms for physical interaction (extensions) and a software framework for control in confined spaces by exploiting contact. Force feedback at the end effectors will facilitate stable and sustainable physical interaction. This research direction plans to radically change how flying robots navigate through narrow environments such as ore-passes or manholes.

Reactive Collision Avoidance for Aerial Robots Navigating in Underground Environments
Supervisors: Kostas Alexis
Available
This research aims to investigate a last-resort collision-avoidance mechanism that is implemented onboard an aerial robot aiming to navigate complex underground settings. The method should employ different sensing modalities in order to provide robustness and satisfactory performance even in visually-degraded conditions. In particular, the combination of visible camera data and LiDAR ranging is considered as a starting point. Ideally, the designed solution should not assume that a reliable pose and map estimate is available and enable reactive avoidance even in the most degenerate cases.

On the role of Levy Flights towards Efficient Memoryless Robotic Search
Supervisors: Kostas Alexis
Available
Autonomous robots are commonly tasked with the problem of area exploration and search for certain targets or artifacts of interest to be tracked. Traditionally, the problem formulation considered is that of complete search and thus - ideally - identification of all targets of interest. An important problem however which is not often addressed is that of time-efficient memoryless search under sparse rewards that may be worth visited any number of items. In this work we want to address the largely understudied problem of optimizing the "time-of-arrival" or "time-of-detection" to robotically search for sparsely distributed rewards (detect targets of interest) within large-scale environments and subject to memoryless exploration. 

​Relevant "Working" Paper: Download

Simulation Environment for Subterranean Robotics
Supervisors: Kostas Alexis, Christos Papachristos
Available
This project aims to develop a comprehensive simulation environment for testing ground and flying robots for autonomous navigation in subterranean environments. It will be based on Gazebo and ideally should also allow for some type of Hardware-In-the-Loop functionality. It will develop on top of the Gazebo-based simulator of subterranean environments provided by DARPA. 

Object Detection and Classification in Thermal Vision
Supervisors: Kostas Alexis
Assigned
In a large variety of degraded visual environments, robotic classification of objects of interests becomes particularly hard due to the impaired sensor data. Especially in the case of darkness and obscurants, classical visible spectrum cameras and associated techniques are rendered ineffective. The goal of this project is to investigate the potential of using thermal vision camera data for the purposes of object detection and classification. Thermal vision is unaffected by darkness and can penetrate certain types of obscurants therefore offering a viable alternative. The envisioned research relates to the associated machine learning methods for high accuracy results on thermal vision. It involves both the steps of data annotation, alongside neural network design and training. 

Leg-wheel Robot for Underground Navigation
Supervisors: Kostas Alexis
Available
In this project we consider the problem of developing a small and cost efficient robot for undergound navigation. A leg-wheel robotic design is proposed on the basis of the combined simplicity and ability to overcome rough terrain that these mechanisms present. The research and development tasks of the project relate to the mechatronic design, automated and control and autonomous navigation solution for such a robotic platform. 

Understanding the Vision System of Underground Species
Supervisors: Kostas Alexis
Available
In this project we question what is special and specific to the vision system of specials (from animals to insects) living underground. Good examples include the wolf spider or beavers. The emphasis is on literature study on the specific domain and derivation of conclusions on how certain principles may apply to robotic vision systems both in the sense of hardware and algorithms. It corresponds to a project that will lay the ground for many subsequent investigations to follow. 

Deep Learning for Monocular Thermal Vision to Depth Estimation
Supervisors: Kostas Alexis, Shehryar Khattak
Available
Lightweight depth perception is critical for underground navigation as it allows small systems (which can go through tight spaces) to proceed autonomously. Camera-based solutions are ideal but depth perception on monocular is ill-conditioned. In fact, visible-light cameras soon become degraded in the underground domain due to issues such as darkness and extreme dust. On the contrary, thermal vision penetrates darkness and obscurants. The goal of this project is to employ supervised deep learning to allow a pixel-wise depth value prediction from the thermal image given training in analogous environments. 

Collaborative Visual-Inertial Localization and Mapping
Supervisors: Kostas Alexis 
Assigned
Robotic systems are not bound to operate alone. In this project the goal is to enable two robots to perform GPS-denied localization in a collaborative manner and simulatneously map their environment. Both robots will be equipped with a visual-inertial sensor. The problems of data association and establishing a common reference frame will therefore have to be addressed. 

Localization and Mapping using a Thermal Camera
Supervisors: Kostas Alexis 
Assigned
​Thermal imaging has certain properties that make it interesting as a sensing modality to assist the problem of localization and mapping for robitc systems. In many environments that good visual features may not be available, the thermal image may turn up being rich in information. However, certain challenges also exist due to the different charactecteristics of thermal imaging and how different viewpoints do not lead to the same differences in image information as in visible-spectrum imaging. The goal of this project is to develop a Simultaneous Localization and Mapping optimized for the case of thermal imaging onboard robotic systems. 

Autonomous Driving Off-Road, on Snow and Ice
Supervisors: Kostas Alexis 
Available
For the autonomous cars to be able to satisfy their promise, they have to be able to deal with all environments. Among others, driving on snow and ice corresponds to a great challenge for the control, planning and perception loops of the robot. In this project, the goal is to utilize a small ground robot in order to develop the relevant control and planning algorithms and deliver a library for autonomous driving on snow and ice. 

Own ideas - high risk projects!

Do you have your own idea about a robotics project? Are you willing to discuss a high-risk project with the understanding that things might not always work? Contact me and schedule a meeting!