doktorska disertacija
Abstract
V doktorski disertaciji predstavljamo učenje finih robotskih gibov z uporabo pristopa
kinestetičnega učenja. Kinestetično učenje je uveljavljen pristop učenja z
demonstracijo saj operaterjem omogoča intuitivno izvedbo giba brez dodatnih krmilnih
naprav. Operater lahko namreč izvede želen gib tako, da prime posamezne segmente
robota in jih premakne v želeno lego. Primernost kinestetičnega učenja je tako že
bila preučena v sklopu aplikacij, ki so zahtevale grobe gibe, ne pa v sklopu aplikacij,
ki so zahtevale učenje finih gibov. Fini gibi so namreč gibi, pri katerih je zahtevana
natančnost pozicioniranja znotraj velikostnega reda milimetra, za kar se pogosto uporablja
teleoperacijo in kooperativno robotsko orodje, ki sta uveljavljena pristopa za
demonstracijo finih gibov.
Tako smo v prvem delu disertacije delovanje kinestetičnega učenja primerjali z
omenjenima pristopoma, pri čemer smo pristope primerjali na dveh skrbno načrtovanih
nalogah. Nalogi sta se razlikovali glede na tip giba, pri čemer je prva zahtevala natančen
premik od točke do točke, druga pa natančno sledenje referenčni poti. Cilj študije je
bila, poleg določanja primernosti posameznega pristopa za demonstracijo finih gibov,
tudi analiza vpliva vizualnih modalitet na natančnost izvedbe. Razvili smo namreč
neke vrste virtualni mikroskop, ki je omogočal slikovno povečavo delovnega območja
pod vrhom robota in posledično izboljšal vizualno zaznavanje pozicijskih odstopanj med
izvajanjem demonstracije. Operaterji so tako izvedli demonstracije brez in z uporabo
vizualne povečave.
V sklopu te študije smo vzporedno pripravili tudi manjšo študijo osredotočeno na
izvajanje finih dinamičnih gibov. Pri teh gibih je za uspešno izvedbo potreben ustrezen
dinamičen potek, pri čemer se sam gib izvede na relativno kratki prostorski razdalji
velikostnega reda centimetra. Ugotovitve te študije so predstavljene v sklopu priloge
Dodatek A, saj ugotovitve niso tako pomembne kot v primeru ostalih študij.
Nadalje smo v drugem delu doktorske disertacije preučili metode zapisa finih gibov.
Poleg primerne demonstracije je ustrezen zapis demonstracij namreč druga ključna
stvar pri pristopu učenja z demonstracijo. Med seboj smo primerjali metodi DMP in
GMM, ki sta uveljavljeni metodi zapisa demonstracij. Dodatno smo predlagali tudi
nadgradnjo metode GMM na podlagi frekvenčne analize, ki omogoča ustrezen zapis
finih gibov brez občutnega povečanja računske kompleksnosti metode.
Disertacija se zaključi s preizkusom kinestetičnega učenja finih gibov na realnem
procesu v kliničnem mikrobiološkem okolju. Z vpeljavo sodelujočih robotov v to delovno
okolje je namreč potrebno vedeti, ali njihova uporaba omogoča primerljive rezultate
v primerjavi z izkušenimi delavci. Tako smo uporabili sodelujočega robota za
postopek zaznave, odvzema in nanosa bakterijskih kolonij v sklopu procesa identifikacije
bakterijskih kolonij z uporabo masne spektrometrije. Delovanje sistema smo
ocenili na podlagi rezultatov posameznega vmesnega postopka ter uspešnosti identifikacije
kolonij, rezultate pa primerjali z objavljenimi podatki o uspešnosti izkušenih
laboratorijskih tehnikov. Na podlagi prvih dveh študij, pa smo za namen te aplikacije
pripravili tudi t.i. učni vmesnik. Vmesnik je bil sestavljen iz dveh ločenih delov. Prvi
del je predstavljal obogateno okolje delovnega prostora, ki je operaterju, z uporabo
očal za navidezno resničnost, omogočil bolj natančno izvedbo demonstracije. Drugi del
pa je predstavljal sistem, ki je na podlagi metode DMP zapisal izvedeno demonstracijo
ter jo prilagodil glede na trenutne zahteve procesa.
Keywords
kinestetično učenje;fini gibi;učenje z demonstracijo;sodelujoči roboti;disertacije;
Data
Language: |
Slovenian |
Year of publishing: |
2022 |
Typology: |
2.08 - Doctoral Dissertation |
Organization: |
UL FE - Faculty of Electrical Engineering |
Publisher: |
[A. Baumkircher] |
UDC: |
007.52(043.3) |
COBISS: |
129216771
|
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8 |
Downloads: |
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Metadata: |
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Other data
Secondary language: |
English |
Secondary title: |
Augmented kinaesthetic teaching of precise robot movements |
Secondary abstract: |
Kinesthetic teaching is a well-established learning by demonstration (LfD) approach
as it allows operators to intuitively generate the robot motion without additional
control devices. That is so because the operator can perform the desired motion
by grasping individual robot segments and moving them to the desired pose. The
performance of kinesthetic teaching has thus already been studied in the context of
an application requiring coarse movements, while the performance of generating fine
movements has yet to be studied. Fine movements require high positional precision, for
which teleoperation and cooperative robot tool are the two established LfD approaches.
Thus, in the first part of the thesis, we compare the performance of kinesthetic
teaching to the two approaches mentioned above. For comparison, we carefully designed
two tasks based on the required motion, with the first task requiring a precise
movement from point to point and the second task requiring a precise tracking of a
reference trajectory. In addition, to determine the suitability of each LfD approach for
fine movements, we also analyzed the influence of visual modalities on the operator’s
performance. Specifically, we developed a visual enhancement tool that allowed us
to visually zoom in on the work area under the robot’s end-effector and consequently
improve the visual detection of positioning errors during the demonstration. Thus,
operators performed demonstrations using each LfD approach with and without the
use of the visual enhancement tool.
As part of this study, a smaller parallel study which focused on the execution of
fine dynamic movements was also performed. For these movements, the dynamics of
the movement have to be appropriate in order for successful demonstration. Usually,
these movements are also generated over a relatively short distance. The findings of
this study are presented in Appendix A, as the findings are not as significant as it is
the case with other studies.
In the second part of the thesis, we analyzed the performance of different methods
which are used for demonstration generalization. Apart from an appropriate
demonstration, motion generalization is the other important issue regarding LfD. We
compared DMP and GMM, which are both established methods for movement generalization.
Additionally, we proposed a novel addition to the GMM method that improves
the generalization of fine movements without increasing the computational complexity
of the model.
The thesis concludes with a study that implements kinesthetic teaching into a
real-world environment. We used a collaborative robot to detect, collect and deposit
bacterial colonies as part of a bacterial colony identification process using mass
spectrometry. The system’s performance was evaluated based on each intermediate
procedure’s results and colony identification’s success rate. The identification results
were then compared with published data on the success rate of experienced laboratory
technicians. We have also developed a so-called teaching agent for this application
based on the findings from the first two studies. The agent consisted of two separate
functionalities. The first was an augmented reality environment that allowed the operator
to perform the demonstration more precisely using the virtual reality ovals. The
second functionality was a system that, based on the DMP method, generalized the
given demonstration and adapted it to the current process requirements. |
Secondary keywords: |
kinesthetic teaching;fine movement;learning by demonstration;collaborative robots; |
Type (COBISS): |
Doctoral dissertation |
Study programme: |
1000319 |
Embargo end date (OpenAIRE): |
1970-01-01 |
Thesis comment: |
Univ. v Ljubljani, Fak. za elektrotehniko |
Pages: |
XIV, 113 str. |
ID: |
17080945 |