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TowardsASP-basedScheduling forIndustrialTransportVehicles
Felicitas Fabricius
MarcoDeBortoli
GeraldSteinbauer
GrazUniversityofTechnology
{mbortoli,steinbauer}@ist.tugraz.at MaximilianSelmair
BMWGroup
maximilian.selmair@bmw.de
MichaelReip
Incubed IT
m.reip@incubedit.com MartinGebser
Universita¨t Klagenfurt
martin.gebser@aau.at
Abstract.
The increasing number of robots and autonomous
vehicles involved in logistics applications leads to
new challenges to face for the community of Arti-
ficial Intelligence. Web-shop giants, like Amazon
or Alibaba for instance, brought this problem to a
new level, with huge warehouses and a huge num-
ber of orders to deliver with strict deadlines. Coor-
dinating and scheduling such high quantity of tasks
over a fleet of autonomous robots is a really com-
plex problem: neither simple imperative greedy al-
gorithms, which compromises over the quality of the
solution, nor precise enumeration techniques, which
make compromises over the solving time, are any-
more feasible to tackle such problems. In this work,
weuseAnswerSetProgrammingtotacklereal-world
logistics problems, involving both dynamic task as-
signment and planning, at the BMWGroup and In-
cubedIT. Different strategies are tried, and com-
pared to the original imperativeapproach.
1. Introduction
Industry 4.0 is bringing more and more interest
toward the digitalization of all productive stages in
the industrial field. Even before that, we all have
been witnesses of the big impact robotics had in
industry, by the automatization of repetitive tasks.
In the last years, thanks to the increasing computa-
tionalpower,Artificial Intelligence (AI) is spreading
as well, leading to the next step of the integration
between robots and production: the automatization
of complex tasks requiring reasoning. In this per- spective, optimization of logistics is crucial for large
companies, in order to save both time and money.
Still we are dealing with a production environment
which considers a fleet of robots floating around, ef-
ficiently performing tasks and carrying goods where
to model such NP-hard domains a high number of
constraints is needed. For this reason, a imperative
approach become more and more difficult to main-
tain, and cannot benefit from the numerous meta-
heuristics and optimizations (if not manually imple-
mented) already encoded inside the solvers of other
programming paradigms, like declarative program-
ming. Answer Set Programming (ASP) is a fast and
intuitive logic language,whichalreadyhasmanyap-
plications both in industry and in research (see Sec-
tion 2). In this paper we are going to investigate the
difference between the two paradigms, by replacing
theimperativepartof thetaskschedulersusedbytwo
companies, theBMWGroupandIncubedIT,withan
ASP implementation. While classical languages are
well suited for greedy algorithms implementation,
declarative programming has other advantages: first
of all, the focus is on the description of the problem,
leaving all the solving details to the external solver.
Moreover, most solvers are configurable with a lot
of meta-heuristics to cut the search space: the user
hasonly tofindtheonewhichfits theproblembetter,
without implementing anything. Then, since logic
languages are basically based on enumeration tech-
niques, an ASP solver looks for the best solution,
or at least the best one in a given time. Depending
on the size of the instance, this behaviour leads to
hugecomputational timewith respect toagreedyal-
34
Joint Austrian Computer Vision and Robotics Workshop 2020
- Title
- Joint Austrian Computer Vision and Robotics Workshop 2020
- Editor
- Graz University of Technology
- Location
- Graz
- Date
- 2020
- Language
- English
- License
- CC BY 4.0
- ISBN
- 978-3-85125-752-6
- Size
- 21.0 x 29.7 cm
- Pages
- 188
- Categories
- Informatik
- Technik