sepideh pashami, victor hernandez, marco … pashami, victor hernandez, marco trincavelli, ... that...

Sepideh Pashami, Victor Hernandez, Marco Trincavelli, Dimitar Dimitrov, Achim J. Lilienthal

1 Initial Remarksall projects designed with ...

... plan Aa good MSc thesis

... and plan Ban exceptional MSc thesis

possibility to publish a paper

8 defined proposals ...

... and one open proposal

Agenda

1. Gas Dispersal Simulation in ROSSepideh Pashami, Achim Lilienthal

2. GPGPU Gas Distribution ModellingSepideh Pashami, Achim Lilienthal

3. Gas Discrimination with Electronic Noses Despite Long-Term Drift

Marco Trincavelli

4. Identification of Gases with Missing SensorsMarco Trincavelli

Agenda

5. Feature Selection for Stock Market ForecastingMarco Trincavelli

6. Signal Reconstruction for Gas Discrimination with the PTM E-Nose

Victor Hernandez Bennetts, Achim Lilienthal

7. NAO StabilizationDimitar Dimitrov

Agenda

8. Planning Motions Considering Constraints with Priority

Dimitar Dimitrov

9. Mixture Models for Modeling MotionsDimitar Dimitrov

10.Open MSc Project Proposal

11.Further, Sketchy MSc Project Proposals

further details on our Web page: http://aass.oru.se/Research/Learning/studentprojects/ LSL_MSc_Project_Proposal-2010_12_16.pdf

Agenda

Gas Dispersal Simulation in ROS

(30 ECTS, 1 Student)

Supervisors: Sepideh Pashami, Achim Lilienthal

1 Gas Dispersal Simulation in ROS

Backgroundexisting gas dispersal simulation engine

combines OpenFOAM fluid flow simulation ...• physics simulation



combines OpenFOAM fluid flow simulation ...

... with filament-based atmospheric dispersion model



combines OpenFOAM fluid flow simulation ...

... with filament-based atmospheric dispersion model• time-dependent simulation• models turbulent effects



OpenFOAM + filament-based atmospheric dispersion model

Project Goal: Integration of the Gas Dispersal Simulation Engine into the Robot Operating System (ROS)





interface to gas dispersal simulation engine• interface to ROS: import of environment model• interface to ROS: simulation of (passive) robots• gas sensor model• pre-selection of meaningful simulation parameters





Project Goal++: Mobile Robot Olfaction Experiments in Simulation (According to the Students' Preferences)

gas source localization (finding the gas source), sensor planning for gas source localization

gas distribution mapping (building a model of the gas distribution), sensor planning for gas distribution mapping


Work Plan (1 Student, 30 ECTS Points)study of available filament-based gas dispersal simulation engine

study of Robot Operating System (ROS)

integration of gas dispersal simulation engine with ROS? comparison with alternative simulation framework in Player/Stage (PlumeSim)

? adding 3D visualization for the gas dispersal simulation engine

? mobile robot olfaction experiments in simulation ? gas source localization (finding the gas source)

? sensor planning for gas source localization

? gas distribution mapping (building a model of the gas distribution)

? sensor planning for gas distribution mapping


RequirementsGood programming skills in C++

Additional Beneficial Skills (Not Mandatory)previous experience with ROS or Player/Stage

Contact PersonsSepideh Pashami [email protected]

Achim J. Lilienthal [email protected]

Achim

Sepideh

Agenda

GPGPU Gas Distribution Modelling


Supervisors: Sepideh Pashami, Achim Lilienthal

2 GPGPU Gas Distribution Modelling

Backgroundmodel gas distributions from a set of gas sensor measurements

statistically build predictive models




parallel implementation can speed up computation of these predictive models




parallel implementation can speed up computation of these predictive models

Project Goalparallel implementation of existing statistical gas distribution modelling methods


Work Plan (1 student, 30 ECTS)study of statistical gas distribution modelling approaches, focus on Kernel DM+V

implementation of Kernel DM+V in C++

study of CUDA parallel programming

implementation of Kernel DM+V in CUDA based on the C++ implementation in one GPU block

implementation of Kernel DM+V in more than one GPU block

? parallel implementation of other gas distribution modellingapproaches


Requirementsgood programming skills in C++ and Matlab

interest in algorithm design

Additional Beneficial Skills (Not Mandatory)parallel programming knowledge

programming skills in CUDA or OpenCL

Contact PersonsSepideh Pashami [email protected]


Achim

Sepideh

Agenda

Gas Discrimination with Electronic

Noses Despite Long-Term Drift


Supervisor: Marco Trincavelli

3 E-Nose Gas Discrimination Despite Drift

Backgroundmetal oxide gas sensors (MOX) very popular in mobile robot olfaction (science of smelling robots)

one of the main limitations of MOX gas sensors is the drift in the response

it is possible to cope with the effect of drift by designing an appropriatepattern recognition algorithmfor gas discrimination



one of the main limitations of MOX gas sensors is the drift in the response

compensation of the drift by designing an appropriate pattern recognition algorithm for gas discrimination possible

Project Goaldesign and implementation of a gas discrimination algorithm that performs well on a dataset collected over 3 years

large data set available (collected by Alexander Vergara at UCSD)


Work Plan (1 student, 30 ECTS)get familiar with MOX gas sensors

analyze the effect of the drift on MOX gas sensors response

design and implement a gas discrimination algorithm that is robust to drift changes

test gas discrimination algorithm on available dataset use large data set collected over 3 years by Alexander Vergara at UCSD


RequirementsBackground in Machine Learning

Knowledge of Linear Algebra

Additional Beneficial SkillsProgramming skills in MATLAB

Contact PersonsMarco Trincavelli [email protected]

Marco

Agenda

Identification of Gases

with Missing Sensors



4 Gas Discrimination with Missing Sensors

Backgroundgas sensing for Mobile Robot Olfaction, Air Pollution Monitoring, Exploration of Hazardous Areas

when a sensor array is deployed without sensing chamber only some of the sensors might be exposed to a gas



comparison of different approaches for performing gas identification when one or more sensors are missing (imputation, reduced-feature models, etc.)

analysis of the degradation of performance when sensors are missing (theoretical bounds?)



Programming skills in MATLAB

Contact PersonsMarco Trincavelli [email protected]

Marco

Agenda

Feature Selection

for Stock Market Forecasting



5 Feature Selection for Stock Market Forecasting

Backgroundfeature selection is the problem of selecting a subset of M features from a set of N (M<N) that is optimal w.r.t. a classification/regression task

feature selection is generally a hard combinatorial problem

various heuristic methods for feature selection proposed in the literature

filters, embedded, wrappers

convex optimizationguarantees a global maximum, efficiently solved


Backgroundfeature selection: selecting M out of N features that is optimal w.r.t. a classification/regression task

feature selection is generally a hard combinatorial problem

various heuristic methods for feature selection

convex optimization

Project Goaldesign a feature selection algorithm that chooses which stocks to monitor for predicting a given financial index


Work Plan (1 student, 30 ECTS)study feature selection

design and implement a feature selection algorithm

compare developed algorithm with existing approaches

? extend the approach to include additional constraints (e.g. feature grouping) on the selected feature set



Programming skills in MATLAB

Additional Beneficial SkillsKnowledge of Optimization Techniques

Contact PersonMarco Trincavelli [email protected]

Marco

Agenda

Signal Reconstruction for Gas

Discrimination with the PTM E-Nose


Supervisors: Victor Hernandez, Achim Lilienthal

6 Signal Reconstruction in the PTM E-Nose


characteristic of MOX gas sensors changes with the operating temperature

oper

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mpe

ratu

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sens

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BackgroundMOX characteristic changes with operating temperature

temperature modulation set of virtual sensors (with different characteristics)

open sampling systems exposed to intermittent patches of gas short odour pulses

high modulation frequency required to expose all virtual sensors to short odour pulses ...

... but with increasing temperature "the chemistry cannot follow" number of virtual sensors decreases

PTM e-nose – operate several MOX sensors in parallel


PTM E-NoseN MOX sensors of the same type measure 1/N of the modulation period T

concatenation of the N pieces

approximate reconstruction of the signal of one sensor over the full modulation period


PTM E-NoseN MOX sensors of the same type measure 1/N of the modulation period T

concatenation of the N pieces

approximate reconstruction of the signal of one sensor over the full modulation period

reconstructed signal is used to identify gases (gas discrimination)


PTM E-NoseN MOX sensors measure 1/N of the modulation period T

reconstruction of the signal over the full modulation period

reconstructed signal used to identify gases

Project Goalinvestigate the value of reconstructing the response of a single sensor for gas discrimination with the PTM e-nose



investigate correlation between reconstructed signal and the true signal over the full modulation period

does the reconstruction step help to improve classification?

which segment of the modulation sinusoid is most informative for classification?

investigate modulation signals other then sinusoids





study difference between transient and stationary signal

investigate value of signal reconstruction in the specific case of transient signals

transient signals are very important for real world applications (because odour pulses are short sensors do not reach the steady-state)





study difference between transient and stationary signal

investigate value of signal reconstruction in the specific case of transient signals

transient signals very important for real world applications

question: does reconstruction improve classification as compared to extracting features from the T/N segments individually?


RequirementsMachine Learning

Matlab

Additional Beneficial Skills (Not Mandatory)–

Contact PersonsVictor Hernandez Bennetts

[email protected]


Victor

Achim

Agenda

Nao Stabilization


Supervisor: Dimitar Dimitrov

7 NAO Stabilization

Background

In the laboratory we have two NAO humanoid robots. One problem related to the control of such robots is the generation of a “stable” walking gait.

Due to errors/uncertainty in the modeling of both the robot and environment, pre-computation of satisfactory motions offline is not feasible, hence strategies for generating them online are required.

7 NAO Stabilization

Background (continued)

Up to date, many stabilization schemes have been proposed. A particularly successful one (that we have been developing since 2007) is based on the use of Model Predictive Control (MPC).

Previous versions of this scheme have been successfully used for the online motion generation (planning) for humanoid robots like HRP-2 and NAO.

7 NAO Stabilization

The basic idea

Stable motion generation (online) using the whole dynamical model of complex systems (like a humanoid robot) is computationally very demanding task.

It is very common to use a simplified (often linear) model in combination with carefully chosen constraints that “guarantee” the generation/execution of stable motions.

7 NAO Stabilization

The basic idea (continued)

Linear model

Constraints

currentstate

+reference

profile

Prediction about the behavior in the “near future” as a function of

a set of control actions

Compute the set of optimal actions

7 NAO Stabilization

The basic idea (continued)The currently used control actions (which are computed according to a predefined optimality criterion in order to “guarantee” the robot’s stability) are:

position of the center of mass of the humanoid robot;

change of the reference positions of the feet on the ground (only when necessary). This is useful in the presence of “strong” disturbances.

The ability of the system to automatically perform repositioning of the feet if necessary, increases the robustness to unknown perturbations, however …

7 NAO Stabilization

Problem

From experience we know that our ability to preserve balance when pushed depends not only on how we reconfigure our body, but on the timing of the reaction.

Depending on when we react, the necessary reconfiguration (to preserve balance) could be completely different. Furthermore, in most cases we preserve our balance not only by repositioning of the feet.

7 NAO Stabilization

Problem (continued)

The current MPC scheme is based on a predefined discretization of the preview window (i.e., the time interval in the “near future” during which a prediction about the motion of the system is made).

As a result, the computation of both optimal time when to react, and optimal reaction is not performed.

7 NAO Stabilization

Envisioned contributions of the thesisTheoretical investigation and development of formulation that includes the time (of reaction) as a parameter of the optimization problem.

The reaction of the robot (to perturbation) should include not only foot repositioning but arm motion (which is not pre-defined) as well.

Implementation (and experimental work) demonstrating the validity of the developed approach on the humanoid robot NAO.

7 NAO Stabilization

The student would not start from scratch. The following main modules have already been developed:

Walking Motion Generation (WMG) library, with the ability to reposition foot placement (the implementation of the foot repositioning is still under development)

Inverse kinematics library for the execution of the control actions (computed by the WMG library).

A library for the application of control commands to the actuators of NAO (this is simply our C++ interface to using NAO)

7 NAO Stabilization

RequirementsVery good C++ programming skills!

Exposure to a linear algebra course

Additional Beneficial Skills (Not Mandatory)Exposure to a course in optimization

Matlab programming skills

Contact PersonDimitar Dimitrov [email protected]

Mitko

Agenda

Planning Motions Considering

Constraints with Priority



8 Planning motions considering constraints with priority

Background

When dealing with systems with many Degrees of Freedom (DoF) it is often possible/desirable to perform multiple tasks simultaneously. For example while a humanoid robot is walking towards a goal, it might be desirable to point the head-built-in camera in a given direction.

Planning motion that satisfy multiple tasks (constraints) is a relevant issue.


Problem

In the presence of multiple tasks, it is possible that some of them are conflicting. In such cases, a standard approach would be to satisfy all tasks “as much as possible” while minimizing a given cost. However, this would result in possibly violating “by some amount” all tasks.

In practice, however, it is often the case that certain tasks are more important (with higher priority) than others.


Problem (continued)

When the notion of more relevant and less relevant task is defined, it is often advisable to use approaches that can account for the priorities of the tasks.

For example, if a humanoid robot wants to grasp an object, one might impose as a constraint with highest priority for the robot to keep its balance, while the grasping of the object is performed only if possible


Recent development

In 2010 an interesting paper appeared in “The international Journal of Robotics Research”

O. Kanoun, et al., “Planning foot placements for a humanoid robot: A problem of inverse kinematics,”

The authors make a very nice use of the task priority concept in the context of planning general motions for a humanoid robot.


The main idea

Example scenario: GO and GRASP the ball

The main idea is to NOT treat the planning of the steps and the “reaching motions” for the arms separately.

A virtual manipulator that parameterizes the footstep locations is used (more details: during the presentation or in the paper).


Envisioned contributions of the thesisImplementation (and verification) of the proposed strategy using the NAO robot

Extension to cases when the floor is not assumed to be flat. For example walking on stairs.

Development of a C++ library for the efficient task priority resolution (at velocity level) – this is based on papers from 2009 and 2010.

Integration with the results from the walking stabilization project


RequirementsVery good C++ programming skills!

Exposure to a linear algebra course

Additional Beneficial Skills (Not Mandatory)Matlab programming skills


Mitko

Agenda

Mixture Models

for Modeling Motions



9 Mixture Models for Modeling Motions

BackgroundImagine a scenario where we are allowed to observe a given process and collect data describing its evolution.

A classical problem is to obtain a model of the process given the data we have collected.

The question: what model to use and how to fit it?

The answer: not clear (depends on the process and envisioned application).


To be more specificThe process to be observed will be the humanoid robot NAO walking (for example we can joystick it around the room for a while)

The observed variable could be for example the positions and velocities of the center of mass (CoM), the position of the zero moment point (ZMP), and positions and velocities of the the two legs.

Our goal would be to develop a statistical model of the stabile walking process because we would like to predict whether a give action would be (statistically) safe to perform


To be more specificThe purpose of the model would be twofold:

We could use it to simply detect abnormal behavior e.g., the system is perturbed;

Or to generate a reaction based on our statistical model, that would maximize the probability that the robot will maintain a stable configuration;

Essentially, we want to let NAO learn how to walk without using first principal models.

There are many related challenges, but an essential one is to find a (reasonably small) set of indicators (parameters) that would reflect the stability of the system.


Model to useWe will first put Gaussian Mixture Model (GMM) and Gaussian Mixture Regression (GMR) to the test ☺;

The reason: over the past few years many publications present the successful application of mixture models in a variety of different applications.

Using a statistical model instead of first principle models could be advantageous in this scenario (more during the presentation).


Envisioned contributions of the thesisThe theoretical investigation and development of a statistical model for the stability of NAO.

Experimental verification and comparison with MPC based control/planning schemes (the student need not be familiar with the later scheme).

Development of a scheme for online adaptation of the model (to be implemented in C++). The application is for example in case when the robot is walking on an inclined slope.


RequirementsVery good Matlab and C++ programming skills!

Exposure to a probability or statistics course

Additional Beneficial Skills (Not Mandatory)Exposure to a machine learning course


Mitko

Agenda

Open MSc Project Proposal


Supervisor(s): ?

Agenda

Further, Sketchy

MSc Project Proposals


Supervisor(s): ?

11 Further Project Ideas

Improved Gas Source Discrimination with a Mobile Robot By Modelling the Wind Distribution

contact Achim Lilienthal [email protected] or ...

... Marco Trincavelli [email protected]

Wind Mapping for Robot Localizationcontact Achim Lilienthal [email protected]


3D-NDT on the GPUcontact Martin Magnusson

[email protected]

3D Scan-based Localizationcontact Todor Stoyanov

[email protected]

Combined 3D Scanning and Gas Distribution Mappingcontact Achim Lilienthal

[email protected]


Combined 3D Scanning and Gas Distribution Mappinginput

registered 3D point cloud

gas concentration readings from 3D nose

tasksoftware that visualizes the fused information

• meshing the 3D point clouds• visualize mean concentration of the gas cloud (translucent)• simultaneously visualize the uncertainty about the gas

concentration• carry out further experiments with the smelling robot

in different environments


Combined 3D Scanning and Gas Distribution Mappingplan A

meshing

develop the GUI to display the mean concentration

plan Bvisualize also the uncertainty about the gas concentration

carry out further experiments with the smelling robot in different environments


QP solver on a GPUcontact Dimitar Dimitrov

[email protected]

Grasp planning in the presence of uncertaintiescontact Dimitar Dimitrov

[email protected]

Sepideh Pashami, Victor Hernandez, Marco Trincavelli, Dimitar Dimitrov, Achim J. Lilienthal

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