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Projects / Programmes source: ARIS

Development of a self-learning system for optimizing the driving rules of autonomous transport vehicles and their temporally and spatially coordinated activities

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Research activity

Code Science Field Subfield
2.06.00  Engineering sciences and technologies  Systems and cybernetics   

Code Science Field
2.02  Engineering and Technology  Electrical engineering, Electronic engineering, Information engineering 
Keywords
intralogistics, multiagent path planning, automated optimization of the transport route map, autonomous guided vehicles, self-learning order dispatching, distributed control, path planning
Evaluation (rules)
source: COBISS
Evaluation of bibliographic research performance indicators according to ARIS methodology
Points
8,364.18
A''
2,205.4
A'
2,783.99
A1/2
4,960.61
CI10
6,193
CImax
223
h10
39
A1
25.92
A3
9.68
Data for the last 5 years (citations for the last 10 years) on April 25, 2024; A3 for period 2018-2022
Data for ARIS tenders ( 04.04.2019 – Programme tender, archive )
Citations Citations for bibliographic records in COBIB.SI that are linked to records in citation databases
source: WoS source: Scopus source: COBISS
Database Linked records Citations Pure citations Average pure citations
WoS  410  6,157  5,254  12.81 
Scopus  556  8,716  7,459  13.42 
Researchers (23)
no. Code Name and surname Research area Role Period No. of publications
1.  53522  Miloš Antić  Systems and cybernetics  Junior researcher  2021 - 2024  5 
2.  51907  Martina Benko Loknar  Systems and cybernetics  Junior researcher  2021 - 2022  12 
3.  16422  PhD Sašo Blažič  Systems and cybernetics  Researcher  2021 - 2024  328 
4.  51679  Aleš Bogovič  Computer intensive methods and applications  Researcher  2021 - 2024  0 
5.  31982  PhD Matevž Bošnak  Systems and cybernetics  Researcher  2021 - 2024  50 
6.  18327  PhD Drago Bračun  Manufacturing technologies and systems  Researcher  2021  237 
7.  39218  PhD Gregor Černe  Systems and cybernetics  Junior researcher  2021 - 2022  8 
8.  04107  PhD Janez Diaci  Manufacturing technologies and systems  Researcher  2021  363 
9.  55227  Tim Kambič  Computer intensive methods and applications  Researcher  2021 - 2024  0 
10.  20181  PhD Gregor Klančar  Systems and cybernetics  Head  2021 - 2024  318 
11.  53118  Nejc Kozamernik  Manufacturing technologies and systems  Junior researcher  2021 - 2024  8 
12.  38151  PhD Dominik Kozjek  Manufacturing technologies and systems  Researcher  2021 - 2024  39 
13.  50105  Andreja Malus  Manufacturing technologies and systems  Researcher  2021 - 2024  15 
14.  32338  PhD Vid Novak  Computer intensive methods and applications  Researcher  2021 - 2024  13 
15.  55226  Nejc Planinšek  Computer intensive methods and applications  Researcher  2021 - 2024  0 
16.  17059  PhD Primož Podržaj  Systems and cybernetics  Researcher  2021  201 
17.  10742  PhD Igor Škrjanc  Systems and cybernetics  Researcher  2021 - 2024  735 
18.  33467  PhD Gašper Škulj  Manufacturing technologies and systems  Researcher  2021 - 2024  52 
19.  35420  PhD Simon Tomažič  Systems and cybernetics  Researcher  2021 - 2024  39 
20.  30914  PhD Rok Vrabič  Manufacturing technologies and systems  Researcher  2021 - 2024  247 
21.  21454  PhD Viktor Zaletelj  Computer intensive methods and applications  Researcher  2021 - 2024  42 
22.  33167  PhD Andrej Zdešar  Systems and cybernetics  Researcher  2021 - 2024  56 
23.  50587  PhD Tena Žužek  Manufacturing technologies and systems  Researcher  2022 - 2024  28 
Organisations (3)
no. Code Research organisation City Registration number No. of publications
1.  1538  University of Ljubljana, Faculty of Electrical Engineering  Ljubljana  1626965  27,774 
2.  0782  University of Ljubljana, Faculty of Mechanical Engineering  Ljubljana  1627031  29,252 
3.  3862  EPILOG proizvodnja, trgovina in storitve d.o.o. (Slovene)  Ljubljana  5417104000  55 
Abstract
Managing internal logistics operations is becoming more and more demanding in increasingly dynamic modern production environments. The state-of-the-art solution in this area is the introduction of autonomous mobile robots (AMRs) which are a more advanced version of automatic guided vehicles (AGVs) and have advanced sensors that give them the ability to localize in space and to navigate around without a magnetic strip placed on the floor. The main goal of the project is the development of algorithms for an efficient and flexible multi-robot transport. Important novelties and advantages of the proposal according to the existing approaches in the industry will be three-fold. First, a better flexibility will be obtained by automatic building or adjusting the map configuration, which will enable a more efficient transport with AMRs (e.g. shorter transportation times, less congestion and fewer conflicts that would require solution adaptations when planning AMR routes). Second, an important novelty will be a self-learning module for tasks assignment to AMRs, which will adapt the rules to the actual situation (i.e. a current map, current statistics of the transportation orders, properties of the implemented route planning algorithm, etc.). It would therefore enable an improved performance over time through a more efficient planning and lower complexity (according to the combinatorial complexity of the simultaneous solving of task assignments and route planning). And third, there will also be advantages in a coordinated route planning for a group of AMRs by setting occupancy windows for resources in the map sections, considering transportation priorities with a minimum required coordination and without conflicts and collisions. The obtained transportation plans will facilitate a local control with fewer necessary corrections during transport. These algorithms will be tested, analyzed and demonstrated at several levels of fleet management. It will be shown that it is possible to achieve more efficient and robust solutions than the existing ones with the above-mentioned new approaches to the abstraction of the intralogistic problem, multi-robotic route planning and task assignment. The project is divided into six work packages which include activities related to the project management, appropriate configuration of the logistics system determination, coordinated multi-robot planning, self-learning task assignment, integration and establishment of a demonstration system, as well as the dissemination of the results. There is a high probability of the project goals being fully realized as they have been previously verified by the studies mentioned in the project description and because the participating company has all the necessary infrastructure in order to implement the application. The solutions will be validated on simulations and also by implementing the sample application on newly developed robots from Epilog d.o.o. Furthermore, the latter will serve for the purpose of performance demonstration and dissemination of the results. The results of the research will be efficiently and extensively used by Epilog d.o.o. and their partners in upgrading existing solutions.
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