Difference between revisions of "2nd Level Theses"

Methods for indoor layout reconstruction must be significantly more tolerant to missing data than their outdoor counterparts, since environments such as offices and apartments exhibit extremely high levels of clutter, which typically results in heavy occlusions of walls and other structures of interest, large-scale artifacts, noise and missing data. The proposed work will be developed in collaboration with IRALAB, the Robotics Lab of University of Milano Bicocca.

The work will be based on an existing project, Free Your Camera (http://www.ira.disco.unimib.it/research/robotic-perception-research/free-your-camera-3d-indoor-scene-understanding-from-arbitrary-camera-motion/) and will be part of a robotic framework based on with ROS and in development at IRALAB.

While browsing the Web, a user should be able to visualize the annotations relative to the page they are visiting, and to add new annotations as well.

References:

• Drivatar in Forza Motorsport (http://gar.eecs.ucf.edu/)
• TORCS (http://torcs.sourceforge.net/)
• CIG@PoliMI (http://cig.dei.polimi.it/)
• Daniele Loiacono's AI&R Lab Projects (http://home.dei.polimi.it/loiacono/uploads/Teaching/HomePage/AIRLab_AA20092010.pdf)

• increase the number of markers supported by the system (ARTag + QR codes)
• increase the accuracy of the detection and localization of the marker
• test different algorithms for the solution of the perspective from n points problem

Material:

• papers on PnP algorithms, OpenCV,
• Matlab code with three PnP algorithms implementations
• C++ libraries for marker detection (to be found and evaluated)

Expected outcome:

• C++ library to the robust localization of artificial markers
• a ROS node performing accurate ARTag localization
• a comparison of Tags and algorithms in a real world scenario
• The use of this library in a SLAM framework (Thesis)

Required skills or skills to be acquired:

• background on computer vision and image processing
• C++ programming under Linux

The purpose of this project is to develop translation tools for different social annotation systems, collect their data in a common format (expressed using an ontology), and show them through a unique user interface, able to display different annotations (i.e. geo coordinates, dates, tags, etc.) in different ways. Moreover, tests and evaluations should be performed on this aggregator to show how efficient the queries are when performed on-the-fly or from an intermediate knowledge base.

The thesis will explore some of the aspects of the problem, eventually participating to the production of prototypes to be tested with real users. The thesis gives the possibility to develop a robot with special requirements, and to work with gesture interpretation by using devices such as Kinect or the WII Mote. The work is done in an interdisciplinary group including care givers and designers.

Picture taken from http://gar.eecs.ucf.edu/

References

• Galactic Arm Race (http://gar.eecs.ucf.edu/)
• TORCS (http://torcs.sourceforge.net/)
• CIG@PoliMI (http://cig.dei.polimi.it/)
• J. Togelius, R. De Nardi and S. Lucas. Towards automatic personalised content creation for racing games (http://julian.togelius.com/Togelius2007Towards.pdf)
• Daniele Loiacono's AI&R Lab Projects (http://home.dei.polimi.it/loiacono/uploads/Teaching/HomePage/AIRLab_AA20092010.pdf)

Material

• C++ modules for Extended Kalman Filtering
• libraries for automatic differentiation (http://www.autodiff.org/)

Expected outcome: New modules implementations based on automatic differentiation A comparison between the old stuff and new approach

Required skills or skills to be acquired:

• C++ programming under Linux

The robot first exhibition has been at Robotica 2009, within HI-Tech Expo at Fiera di Milano, on November 23-25, 2009. Here, the robot had to go around in an area delimited by a white stripe and contact verbally and with gestures people entering the area, in order to attract them to the booth.

Behaviors and gestures have still to be developed to come to an interesting and robust demo at next Robotica, or at other ehibits (e.g. at the Museo della Scienza of Milan).

• L. E. Baum and J. A. Eagon. An inequality with applications to statistical estimation for probabilistic functions of markov processes and to a model for ecology. Bull. Amer. Math. Soc, 73(73):360–363, 1967.
• P. W. Colgan. Quantitative Ethology. John Wiley & Sons, New York, 1978.
• A. Stolcke and S. M. Omohundro. Hidden markov model induction by bayesian model merging. In Stephen Jos é Hanson, Jack D. Cowan, and C. Lee Giles, editors, Advances in Neural Information Processing Systems, volume 5. Morgan Kaufmann, San Mateo, CA, 1993.
• Zoubin Ghahramani. Learning dynamic bayesian networks. Lecture Notes in Computer Science, 1387:168, 1998.
• A. Stolcke and S. M. Omohundro. Best-first model merging for hidden markov model induction. Technical Report TR-94-003, 1947 Center Street, Berkeley, CA, 1994.

Material:

• papers from major journals and conferences
• kinet SDK for the extraction of body poses

Expected outcome:

• general framework for the recognition of behaviors from time series
• toolkit for behavior segmentation and recognition from time series
• running prototype based on data coming from the Microsoft kinect sensor

Required skills or skills to be acquired:

• understanding of techniques for behavior recognition
• background on pattern recognition and stochastic models
• basic understanding of computer vision
• C++ programming under Linux or Matlab

No special skills are required, except basic c and object oriented programming.

analysis of different algorithms for the optimization of pseudo-Boolean functions, i.e., functions defined over binary variables with values in R. These functions have been studied a lot in the mathematical programming literature, and different algorithms have been proposed (1). More recently, the same problems have been faced in evolutionary computations, with the use of genetic algorithms, and in particular estimation of distribution algorithms (2,3). Estimation of distribution algorithms are a recent meta-heuristic, where classical crossover and mutation operators used in genetic algorithms are replaced with operators that come from statistics, such as sampling and estimation.

The focus will be on the implementation of a new algorithm able to combine different approaches (estimation and sampling, from one side, and exploitation of prior knowledge about the structure of problem, on the other), together with the comparison of the results with existing techniques that historically appear in different (and often separated) communities. Good coding (C/C++) abilities are required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses. The project could require some extra effort in order to build and consolidate some background in math, especially in Bayesian statistics and MCMC techniques, such as Gibbs and Metropolis samplers (4).

The project can be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work. Possible ideas may concern the proposal of new algorithms able to learn existing dependencies among the variables in the function to be optimized, and exploit them in order to increase the probability to converge to the global optimum.

Picture taken from http://www.ra.cs.uni-tuebingen.de/

Bibliography

1. Boros, Endre and Boros, Endre and Hammer, Peter L. (2002) Pseudo-boolean optimization. Discrete Applied Mathematics.
2. Pelikan, Martin; Goldberg, David; Lobo, Fernando (1999), A Survey of Optimization by Building and Using Probabilistic Models, Illinois: Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-Champaign.
3. Larrañga, Pedro; & Lozano, Jose A. (Eds.). Estimation of distribution algorithms: A new tool for evolutionary computation. Kluwer Academic Publishers, Boston, 2002.
4. Image Analysis, Random Fields Markov Chain Monte Carlo Methods

estimation.

The focus will be on the implementation of new hybrid algorithms able to combine estimation of distribution algorithms with different approaches available in the evolutionary computation literature, such as genetic algorithms and evolutionary strategies, together with other local search techniques. Good coding (C/C++) abilities are required. Some background in combinatorial optimization form the "Fondamenti di Ricerca Operativa" is desirable. The project could require some effort in order to build and consolidate some background in MCMC techniques, such as Gibbs and Metropolis samplers (4). The project could be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work.

Computer vision provides a large number of optimization problems, such as new-view synthesis, image segmentation, panorama stitching and texture restoration, among the others, (6). One common approach in this context is based on the use of binary Markov Random Fields and on the formalization of the optimization problem as the minimum of an energy function expressed as a square-free polynomial, (5). We are interested in the proposal, comparison and evaluation of different Estimation of Distribution Algorithms for solving real world problems that appear in computer vision.

Pictures taken from http://www.genetic-programming.org and (6)

Bibliography

1. Pelikan, Martin; Goldberg, David; Lobo, Fernando (1999), A Survey of Optimization by Building and Using Probabilistic Models, Illinois: Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-Champaign.
2. Larrañga, Pedro; & Lozano, Jose A. (Eds.). Estimation of distribution algorithms: A new tool for evolutionary computation. Kluwer Academic Publishers, Boston, 2002.
3. Lozano, J. A.; Larrañga, P.; Inza, I.; & Bengoetxea, E. (Eds.). Towards a new evolutionary computation. Advances in estimation of distribution algorithms. Springer, 2006.
4. Pelikan, Martin; Sastry, Kumara; & Cantu-Paz, Erick (Eds.). Scalable optimization via probabilistic modeling: From algorithms to applications. Springer, 2006.
5. Image Analysis, Random Fields Markov Chain Monte Carlo Methods
6. Carsten Rother, Vladimir Kolmogorov, Victor Lempitsky, Martin Szummer. Optimizing Binary MRFs via Extended Roof Duality, CVPR 2007

Material

• a huge corpus of literature on the topic
• libraries on visual odometry (http://www.cvlibs.net/datasets/kitti/index.php)
• C++ library for image processing and computer vision (OpenCV)

Expected outcome:

• a set of running algorithms performing visual odometry

Required skills or skills to be acquired:

• computer vision and 3D reconstruction
• C++ programming under Linux

• J.R. Wolpaw et al. Brain-computer interfaces for communication and control (http://tinyurl.com/yhq27pq)

Several theses and projects are available on this topic and involve different games: TORCS, Unreal Tournament and Quake Live. Please contact us for additional information.

References

• TORCS (http://torcs.sourceforge.net/)
• QuakeLive (http://www.quakelive.com)
• Daniele Loiacono's AI&R Lab Projects (http://home.dei.polimi.it/loiacono/uploads/Teaching/HomePage/AIRLab_AA20092010.pdf)

• J.R. Wolpaw et al. Brain-computer interfaces for communication and control (http://tinyurl.com/yhq27pq)

• B. Dal Seno, M. Matteucci, and L. Mainardi. "A genetic algorithm for automatic feature extraction in P300 detection" (http://home.dei.polimi.it/dalseno/papers/2008/ijcnn08.pdf)
• B. Dal Seno, M. Matteucci, L. Mainardi, F. Piccione, and S. Silvoni. "Single-trial P300 detection in healthy and ALS subjects by means of a genetic algorithm" (http://home.dei.polimi.it/dalseno/papers/2008/grazGa08.pdf)

• L. E. Baum and J. A. Eagon. An inequality with applications to statistical estimation for probabilistic functions of markov processes and to a model for ecology. Bull. Amer. Math. Soc, 73(73):360–363, 1967.
• P. W. Colgan. Quantitative Ethology. John Wiley & Sons, New York, 1978.
• A. Stolcke and S. M. Omohundro. Hidden markov model induction by bayesian model merging. In Stephen Jos é Hanson, Jack D. Cowan, and C. Lee Giles, editors, Advances in Neural Information Processing Systems, volume 5. Morgan Kaufmann, San Mateo, CA, 1993.
• Zoubin Ghahramani. Learning dynamic bayesian networks. Lecture Notes in Computer Science, 1387:168, 1998.
• A. Stolcke and S. M. Omohundro. Best-first model merging for hidden markov model induction. Technical Report TR-94-003, 1947 Center Street, Berkeley, CA, 1994.

Material:

• papers from major journals and conferences
• kinet SDK for the extraction of body poses

Expected outcome:

• general framework for the recognition of behaviors from time series
• toolkit for behavior segmentation and recognition from time series
• running prototype based on data coming from the Microsoft kinect sensor

Required skills or skills to be acquired:

• understanding of techniques for behavior recognition
• background on pattern recognition and stochastic models
• basic understanding of computer vision
• C++ programming under Linux or Matlab

analysis of different algorithms for the optimization of pseudo-Boolean functions, i.e., functions defined over binary variables with values in R. These functions have been studied a lot in the mathematical programming literature, and different algorithms have been proposed (1). More recently, the same problems have been faced in evolutionary computations, with the use of genetic algorithms, and in particular estimation of distribution algorithms (2,3). Estimation of distribution algorithms are a recent meta-heuristic, where classical crossover and mutation operators used in genetic algorithms are replaced with operators that come from statistics, such as sampling and estimation.

The focus will be on the implementation of a new algorithm able to combine different approaches (estimation and sampling, from one side, and exploitation of prior knowledge about the structure of problem, on the other), together with the comparison of the results with existing techniques that historically appear in different (and often separated) communities. Good coding (C/C++) abilities are required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses. The project could require some extra effort in order to build and consolidate some background in math, especially in Bayesian statistics and MCMC techniques, such as Gibbs and Metropolis samplers (4).

The project can be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work. Possible ideas may concern the proposal of new algorithms able to learn existing dependencies among the variables in the function to be optimized, and exploit them in order to increase the probability to converge to the global optimum.

Picture taken from http://www.ra.cs.uni-tuebingen.de/

Bibliography

1. Boros, Endre and Boros, Endre and Hammer, Peter L. (2002) Pseudo-boolean optimization. Discrete Applied Mathematics.
2. Pelikan, Martin; Goldberg, David; Lobo, Fernando (1999), A Survey of Optimization by Building and Using Probabilistic Models, Illinois: Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-Champaign.
3. Larrañga, Pedro; & Lozano, Jose A. (Eds.). Estimation of distribution algorithms: A new tool for evolutionary computation. Kluwer Academic Publishers, Boston, 2002.
4. Image Analysis, Random Fields Markov Chain Monte Carlo Methods

estimation.

The focus will be on the implementation of new hybrid algorithms able to combine estimation of distribution algorithms with different approaches available in the evolutionary computation literature, such as genetic algorithms and evolutionary strategies, together with other local search techniques. Good coding (C/C++) abilities are required. Some background in combinatorial optimization form the "Fondamenti di Ricerca Operativa" is desirable. The project could require some effort in order to build and consolidate some background in MCMC techniques, such as Gibbs and Metropolis samplers (4). The project could be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work.

Computer vision provides a large number of optimization problems, such as new-view synthesis, image segmentation, panorama stitching and texture restoration, among the others, (6). One common approach in this context is based on the use of binary Markov Random Fields and on the formalization of the optimization problem as the minimum of an energy function expressed as a square-free polynomial, (5). We are interested in the proposal, comparison and evaluation of different Estimation of Distribution Algorithms for solving real world problems that appear in computer vision.

Pictures taken from http://www.genetic-programming.org and (6)

Bibliography

1. Pelikan, Martin; Goldberg, David; Lobo, Fernando (1999), A Survey of Optimization by Building and Using Probabilistic Models, Illinois: Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-Champaign.
2. Larrañga, Pedro; & Lozano, Jose A. (Eds.). Estimation of distribution algorithms: A new tool for evolutionary computation. Kluwer Academic Publishers, Boston, 2002.
3. Lozano, J. A.; Larrañga, P.; Inza, I.; & Bengoetxea, E. (Eds.). Towards a new evolutionary computation. Advances in estimation of distribution algorithms. Springer, 2006.
4. Pelikan, Martin; Sastry, Kumara; & Cantu-Paz, Erick (Eds.). Scalable optimization via probabilistic modeling: From algorithms to applications. Springer, 2006.
5. Image Analysis, Random Fields Markov Chain Monte Carlo Methods
6. Carsten Rother, Vladimir Kolmogorov, Victor Lempitsky, Martin Szummer. Optimizing Binary MRFs via Extended Roof Duality, CVPR 2007

Information Geometry has been recently applied in different fields, both to provide a geometrical interpretation of existing algorithms, and more recently, in some contexts, to propose new techniques to generalize or improve existing approaches. Once the student is familiar with the theory of Information Geometry, the aim of the project is to apply these notions to existing machine learning algorithms.

Possible ideas are the study of a particular model from the point of view of Information Geometry, for example as Hidden Markov Models, Dynamic Bayesian Networks, or Gaussian Processes, to understand if Information Geometry can give useful insights with such models. Other possible direction of research include the use of notions and ideas from Information Geometry, such as the mixed parametrization based on natural and expectation parameters (3) and/or families of divergence functions (2), in order to study model selection from a geometric perspective. For example by exploiting projections and other geometric quantities with "statistical meaning" in a statistical manifold in order to chose/build the model to use for inference purposes.

Since the project has a theoretical flavor, mathematical inclined students are encouraged to apply. The project requires some extra effort in order to build and consolidate some background in math, partially in differential geometry, and especially in probability and statistics.

Bibliography

1. Shun-ichi Amari, Hiroshi Nagaoka, Methods of Information Geometry, 2000
2. Shun-ichi Amari, Information geometry of its applications: Convex function and dually flat manifold, Emerging Trends in Visual Computing (Frank Nielsen, ed.), Lecture Notes in Computer Science, vol. 5416, Springer, 2009, pp. 75–102
3. Shun-ichi Amari, Information geometry on hierarchy of probability distributions, IEEE Transactions on Information Theory 47 (2001), no. 5, 1701–1711.

One of the common hypothesis in regression analysis is that the noise introduced in order to model the linear relationship between regressors and dependent variable has a Gaussian distribution. A generalization of this hypothesis leads to a more general framework, where the geometry of the regression task is no more Euclidean. In this context different estimation criteria, such as maximum likelihood estimation and other canonical divergence functions do not coincide anymore. The target of the project is to compare the different solutions associated to different criteria, for example in terms of robustness, and propose generalization of LASSO and LARS in non Euclidean contexts.

The project will focus on the understanding of the problem and on the implementation of different algorithms, so (C/C++ or Matlab or R) coding will be required. Since the project has also a theoretical flavor, mathematical inclined students are encouraged to apply. The project may require some extra effort in order to build and consolidate some background in math, especially in probability and statistics.

Picture taken from (2)

Bibliography

1. Tibshirani, R. (1996), Regression shrinkage and selection via the lasso. J. Royal. Statist. Soc B., Vol. 58, No. 1, pages 267-288
2. Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani, Least Angle Regression, 2003

variables associated to both internal nodes and leaves of the tree.

The project will focus on the understanding of the problem and on the implementation of different algorithms, so (C/C++ or Matlab or R) coding will be required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses.

The project is thought to be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work. Possible ideas may concern the proposal and implementation of new algorithms, based on recent approaches to phylogenetic inference available in the literature, as in (3) and (4). In this case the thesis requires some extra effort in order to build and consolidate some background in math in oder to understand some recent literature, especially in (mathematical) statistics and, for example, in the emerging field of algebraic statistics (5).

Other possible novel applications of phylogenetic trees have been proposed in contexts different from biology, as in (6). Malware (malicious software) is software designed to infiltrate a computer without the owner's informed consent. Often malwares are related to previous programs thought evolutionary relationships, i.e., new malwares appear as small mutations of previous softwares. We are interested in the use of techniques from phylogenetic trees to create a taxonomy of real world malwares.

Picture taken from http://www.tolweb.org/tree/ and http://www.blogscienze.com

Bibliography

1. Felsenstein 2003: Inferring Phylogenies
2. Semple and Steel 2003: Phylogenetics: The mathematics of phylogenetics
3. Louis J. Billera, Susan P. Holmes and and Karen Vogtmann Geometry of the space of phylogenetic trees. Advances in Applied Math 27, 733-767 (2001)
4. Evans, S.N. and Speed, T.P. (1993). Invariants of some probability models used in phylogenetic inference. Annals of Statistics 21, 355-377.
5. Lior Pachter, Bernd Sturmfels 2005, Algebraic Statistics for Computational Biology.
6. A. Walenstein, E-Md. Karim, A. Lakhotia, and L. Parida. Malware Phylogeny Generation Using Permutations of Code, Journal in Computer Virology, v1.1, 2005.

References:

• Drivatar in Forza Motorsport (http://gar.eecs.ucf.edu/)
• TORCS (http://torcs.sourceforge.net/)
• CIG@PoliMI (http://cig.dei.polimi.it/)
• Daniele Loiacono's AI&R Lab Projects (http://home.dei.polimi.it/loiacono/uploads/Teaching/HomePage/AIRLab_AA20092010.pdf)

Picture taken from http://gar.eecs.ucf.edu/

References

• Galactic Arm Race (http://gar.eecs.ucf.edu/)
• TORCS (http://torcs.sourceforge.net/)
• CIG@PoliMI (http://cig.dei.polimi.it/)
• J. Togelius, R. De Nardi and S. Lucas. Towards automatic personalised content creation for racing games (http://julian.togelius.com/Togelius2007Towards.pdf)
• Daniele Loiacono's AI&R Lab Projects (http://home.dei.polimi.it/loiacono/uploads/Teaching/HomePage/AIRLab_AA20092010.pdf)

Several theses and projects are available on this topic and involve different games: TORCS, Unreal Tournament and Quake Live. Please contact us for additional information.

References

• TORCS (http://torcs.sourceforge.net/)
• QuakeLive (http://www.quakelive.com)
• Daniele Loiacono's AI&R Lab Projects (http://home.dei.polimi.it/loiacono/uploads/Teaching/HomePage/AIRLab_AA20092010.pdf)

Our projects and theses on this topic involve two different games: Unreal Tournament 2004 and TORCS. Please contact us for additional information.

References

• Drivatar in Forza Motorsport (http://research.microsoft.com/en-us/projects/drivatar/default.aspx)
• TORCS (http://torcs.sourceforge.net/ TORCS)
• Botprize (http://botprize.org/)
• Daniele Loiacono's AI&R Lab Projects (http://home.dei.polimi.it/loiacono/uploads/Teaching/HomePage/AIRLab_AA20092010.pdf)

While browsing the Web, a user should be able to visualize the annotations relative to the page they are visiting, and to add new annotations as well.

The purpose of this project is to develop translation tools for different social annotation systems, collect their data in a common format (expressed using an ontology), and show them through a unique user interface, able to display different annotations (i.e. geo coordinates, dates, tags, etc.) in different ways. Moreover, tests and evaluations should be performed on this aggregator to show how efficient the queries are when performed on-the-fly or from an intermediate knowledge base.

Aim of this project is to expand a traditional search engine with semantic information, so that also documents containing words related to the ones in the query can be retrieved. Existing thesauri and ontologies can be used, as well as more dynamic and collaborative sources of knowledge such as user tags and wikipedia pages and categories.

Starting points for this work can be the projects "SeQuEx - Semantic Query Expansion" and "Enriching search results with semantic metadata".

In the project "Wikipedia Category Map" a tool has been developed to extract the graph of Wikipedia categories, to store it in RDF format and to interactively visualize and explore it. Aim of this project is to analyze the resulting graph for the extraction of semantic relationships; for example it is possible to define metrics of distance between topics in the graph, which can be useful for various purposes in information retrieval.

Aim of this project is to design and develop a software tool to extract this kind of information from a single social network platform (decided by the student). It may be required also some kind of analysis or visual representation of the collected data.

One possible example is the annotation of mp3 files available on the Web. They can be classified in genres or associated to datatype properties, such as rating, title, length and release date... also exploiting data already available in http://musicbrainz.org/

Methods for indoor layout reconstruction must be significantly more tolerant to missing data than their outdoor counterparts, since environments such as offices and apartments exhibit extremely high levels of clutter, which typically results in heavy occlusions of walls and other structures of interest, large-scale artifacts, noise and missing data. The proposed work will be developed in collaboration with IRALAB, the Robotics Lab of University of Milano Bicocca.

The work will be based on an existing project, Free Your Camera (http://www.ira.disco.unimib.it/research/robotic-perception-research/free-your-camera-3d-indoor-scene-understanding-from-arbitrary-camera-motion/) and will be part of a robotic framework based on with ROS and in development at IRALAB.

The thesis will explore some of the aspects of the problem, eventually participating to the production of prototypes to be tested with real users. The thesis gives the possibility to develop a robot with special requirements, and to work with gesture interpretation by using devices such as Kinect or the WII Mote. The work is done in an interdisciplinary group including care givers and designers.

Material

• C++ modules for Extended Kalman Filtering
• libraries for automatic differentiation (http://www.autodiff.org/)

Expected outcome: New modules implementations based on automatic differentiation A comparison between the old stuff and new approach

Required skills or skills to be acquired:

• C++ programming under Linux

The robot first exhibition has been at Robotica 2009, within HI-Tech Expo at Fiera di Milano, on November 23-25, 2009. Here, the robot had to go around in an area delimited by a white stripe and contact verbally and with gestures people entering the area, in order to attract them to the booth.

Behaviors and gestures have still to be developed to come to an interesting and robust demo at next Robotica, or at other ehibits (e.g. at the Museo della Scienza of Milan).

No special skills are required, except basic c and object oriented programming.

Material:

• papers about Manifold based optimization and space representations
• C++ framework for EKF-SLAM

Expected outcome:

• An extended Kalman filter which uses this new representation

Required skills or skills to be acquired:

• Good mathematical background
• C++ programming under Linux

In a context of smart modules that have to cooperate by exchanging data to reach their common goal, the communication protocol and middleware are core components. This project is about the middleware component, a publish/subscribe system that takes care of managing topics, publisher and subscribers, and of marshaling data before sending it. This project aims at porting the LCM marshaling and middleware library, developed at MIT and used in the Grand Challenge competition, to embedded systems, in order to exploit the existing LCM tools and to be compliant with an existing and efficient technology.

The project consists in:

• stripping non necessary features of LCM to match the constraints of an embedded system and of the communication protocol
• adding necessary features, like the concept of deadline (and priority as a consequence), that are mandatory for a real time distributed system
• building a gateway, on an embedded platform, that acts as gateway between the standard-LCM world and the embededd-LCM network

The projects has to be developed in ANSI C, and experience with embedded platforms is a plus.

Material

• lots of theoretical background and material
• an existing (and working) C++ implementation of the framework

Expected outcome:

• a C++ library for the implementation of generic EKF-SLAM algorithms

Required skills:

• Experienced C++ programming under Linux

The aim of the project is first to improve the current design of the PIC-based board, and realize a new working prototype, and then to implement and evaluate different algorithms able to estimate more precisely the x,y and theta odometric data from the mice readings. Experience with PIC-based systems and some experience with electronics circuits is a plus. Students are supposed to redesign the electronic board, improve the firmware of the PIC, and work on the algorithm that estimates the robot position on the PC. It would be also interesting to evaluate the possibility to embed the optimization and estimation algorithms in the firmware of the PIC in order to produce a stand-alone device.

Ask the tutors of the project for extra material, such as data-sheets and other documentation.

• implementing embedded algorithms for the estimation of the IMU attitude to be compared with the actual one (e.g., Kalman filter, DCM, Madgwick, etc.)
• developing a, easy to use, procedure for the calibration of IMU parameters
• making a comparison with commercial units using a robot arm as testbed
• validate the accuracy of the IMU on a flying platform
• integrate the measurements from a GPS to reduce drift and provide accurate positiong (this will make it definitely a MS thesis)

Material

• electronic board and eclipse based C development toolkit for ARM processors
• papers describing the algorithms we are interested in implementing

Expected outcome:

• few different AHRS algorithms with comparative results
• user-friendly procedure to calibrate the IMU
• a sistem which integrated IMU and GPS to provide accurate positioning

Required skills or skills to be acquired:

• C programming on ARM microcontroller
• background on kalman filtering and attitude estimation

Material

• A framework for multisensor SLAM using the world centric approach
• Papers and report about robocentric slam

Expected outcome:

• a fully functional robocentric version of the MoonSLAM framework

Required skills or skills to be acquired:

• Basic background in computer vision
• Background in Kalman filtering
• C++ programming under Linux

Material:

• a MS thesis which describes the scan matching algorithms
• a BS thesis which implements a prototype of the system

Expected outcome:

• a complete system that build maps integrating laser scan and visual informtion

Required skills or skills to be acquired:

• Background on Kalman filtering
• C++ programming under Linux

Material:

• datasets with real data
• a few odometric system implementations
• C++ libraries for non linear optimization

Expected outcome:

• software for the self calibration of a set of odometry systems mounted on the same robot

Required skills or skills to be acquired:

• C++ programming under Linux

The proposed project is aimed at extending USARSim along two directions:

• Development of a tool for automatically generating realistic 3D worlds for robot testing. This task involves work on 3D game modelling and development using the UDK engine.
• Create test arenas and perform experiment in USARSim. Simulation tools as USARSim are powerful tools for robotics, but in order to consider the results obtained in a virtual environment also valid in the real case, simulated environments must be validated through experiments performed both in virtually and with real robots.

Results will be included in the USARSim project and used by the RoboCup community.

• Implementation of mechanical and electronical parts of the robots for the management of the ball and kicking
• Design of robot behaviors (fuzzy systems)
• Coordination of robots
• New sensors

These projects allow to experiment with real mobile robots. Participation to the championships is a unique experience (2000 people, with 800 robots playing all sort of games...)

The project can be turned into a thesis by facing different problems in depth.

TiltOne is a robot with only two wheels that can stand in vertical position following an unstable equilibrium point. The control is applied by commanding an amount of torque to the wheels, allowing the robot to mantain it's gravity center vertical aligned to the wheel axis.

The aim of the project proposal is to implement and compare different control solutions, based on classical approach (as PID and LQR control) and Machine Learning approach (as Reinforcement Learning control policies), that allow the robot to move following a given trajectory at a given speed.