Student Projects


How to apply

To apply, please send your CV, your Ms and Bs transcripts by email to all the contacts indicated below the project description. Since Prof. Davide Scaramuzza is affiliated with ETH, there is no organizational overhead for ETH students. Custom projects are occasionally available. If you would like to do a project with us but could not find an advertized project that suits you, please contact Prof. Davide Scaramuzza directly to ask for a tailored project (sdavide at ifi.uzh.ch).


Upon successful completion of a project in our lab, students may also have the opportunity to get an internship at one of our numerous industrial and academic partners worldwide (e.g., NASA/JPL, University of Pennsylvania, UCLA, MIT, Stanford, ...).



Projects related to an international robotics competition - Available

RPG is participating in an international robotics challenge (http://www.mbzirc.com/, https://www.youtube.com/watch?v=oVz2Sp3W468), and we have several work packages available as student projects, involving visual perception, MAV control and multi-robot collaboration. One sub-challenge is landing a MAV on a moving platform and another is one where multiple objects need to be retrieved from a large area using a collaborating group of MAVs.


Thesis Type: Semester Project / Master Thesis

Contact: Michael Gassner (gassner at ifi.uzh.ch)



Drake-ROS Integration for Quadrotor Simulation and Control - Available

Drake is an open source Control, Simulation and Analysis library supported by the Toyota Research Institute (TRI) and MIT. With its emphasis on state-of-art computational tools for the analysis of systems and development of nonlinear controllers, it is effective in tackling some of the toughest robotics planning and control problems. One interesting ongoing development work concerns the interface to the widely used ROS (Robot Operating System).

The proposed student project consists of two parts:

1) Designing and implementing an effective interface between Drake and ROS for the control and simulation of the Robotics and Perception Group (RPG) quadrotors.

2) Implementing an off-board higher level controller (using existing Drake and ROS tools) for trajectory control of the RPG quadrotors with visual feedback.

This project will involve close collaboration with TRI and MIT.


Thesis Type: Semester Project / Master Thesis

Contact: Naveen Kuppuswamy (naveen.kuppuswamy at tri.global), Davide Falanga (falanga at ifi.uzh.ch)



Nonlinear Control for Slung-load Throwing Using Quadrotors - Available

With recent advances in visually guided quadrotors and optimization based nonlinear control methods, the feasibility of tackling advanced control problems in various applications is increasing. One such interesting application in the search and rescue domain is that of utilising Quadrotors to carry loads by grabbing onto them with some kind of tether and to sling them towards a target (e.g. https://www.youtube.com/watch?v=08K_aEajzNA).

In this thesis topic, the student will be expected to tackle this challenge by designing an effective nonlinear controller and implement it on the Robotics and Perception Group (RPG) Quadrotor robots; Model Predictive Control (MPC) is one suggested framework for tackling this problem. The controller will be developed using Drake, an open source simulation, planning and control library supported by the Toyota Research Institute (TRI) and MIT.


Thesis Type: Master Thesis

Contact: Naveen Kuppuswamy (naveen.kuppuswamy at tri.global), Davide Falanga (falanga at ifi.uzh.ch)



Aerial monocular vision-based ball catching - Available

The goal of this project is to enable vision-based drones to catch a ball thrown by hand without relying on any external sensor or motion capture system. This project will consist of the following work packages, among which the student can choose according to his interests and skills.

1) Development of a real-time trajectory planning algorithm for micro aerial vehicles. The algorithm must be fast enough to be implemented and run onboard and must keep into account both dynamical and perception-related constraints.

2) Monocular vision-based fast ball detection. The first part will consist in studying the literature for fast ball detection, then the goal will be to implement a robust ball detection and tracking algorithm that can run onboard.

3) Ball trajectory estimation with uncertainty propagation. The goal is to predict the motion of the ball based on physical considerations and measurements from the onboard camera.

For the first package, knwoledge in control theory and optimization are required; the second one requires knowledge in computer vision; the third, finally, is suitable to students with knwoledge in recursive estimation.


Thesis Type: Semester Project or Master Thesis, according to the package(s) chosen

Contact: Davide Falanga (falanga at ifi.uzh.ch), Henri Rebecq (rebecq at ifi.uzh.ch), Zichao Zhang (zzhang at ifi.uzh.ch)



Optimal Sensor Placement - Available

The goal of this project is to investigate how sensor type, position, and orientation affect the performance of a visually-guided agile MAV. Part of the project will be designing a photorealistic simulation environment in which to test different camera arrangements on a flying platform, so experience with blender, Unreal Engine, etc. will be a bonus. The project will consist of evaluating these sensor setups in offline simulations of several tasks (e.g. fast outdoor flight, flying through a forest or through a window), and time permitting, performing further evaluations in a closed-loop physics simulation. Inspiration for candidate arrangements should come from examples in both the robotics literature and nature.


Thesis Type: Semester Thesis / Master Thesis

Contact: Zichao Zhang (zzhang at ifi.uzh.ch), Henri Rebecq (rebecq at ifi.uzh.ch)



Physical Threshold Detection - Available

In human environments, windows and doors represent thresholds between spaces. For a robot exploring an unknown environment, these portals offer new frontiers, but they can be challenging for a robot to safely traverse. This project deals with designing a system to robustly detect windows/doors/thresholds that a quadrotor could fly through, using cameras and range sensors. This system would need to detect these portals via appearance and geometry, and evaluate the feasibility of traversal with a very low false-positive rate using machine learning techniques and geometric constraints. The ultimate goal is to deploy this system on a quadrotor for a live demo.


Thesis Type: Semester Thesis / Master Thesis

Contact: Jeff Delmerico (jeffdelmerico at ifi.uzh.ch), Michael Gassner (gassner at ifi.uzh.ch)



Active Visual Saliency Mapping - Available

This project is motivated by a search and rescue scenario wherein a flying robot is searching for a "victim" in an outdoor environment. The goal is to identify and investigate areas of the environment that look different than their surroundings. The robot will use computer vision and machine learning techniques to build a visual saliency map, and will be controlled by a path planner that interactively evaluates this map and chooses the most visually distinct regions to investigate more closely.


Thesis Type: Semester Thesis / Master Thesis

Contact: Jeff Delmerico (jeffdelmerico at ifi.uzh.ch), Christian Forster (forster at ifi.uzh.ch)



Discovering Safe Regions for Reconstruction - Available

This project builds on a previous student work that discovered safe regions for vision-based flight (i.e. over textured areas) and prevented the quadrotor from moving into the unsafe areas. The goal of this project is to develop methods for detecting potential vertical obstacles (e.g. walls) in the peripheral view of the quadrotor using computer vision, geometry, and machine learning. With detected potential obstacles, the quadrotor can then reason about the structure of the environment to limit the flight of the robot to safe regions that have both good texture and are free of obstacles.


Thesis Type: Semester Thesis / Master Thesis

Contact: Jeff Delmerico (jeffdelmerico at ifi.uzh.ch)