Industrial Challenges when Planning and Preparing Collaborative and Intelligent Automation Systems for Final Assembly Stations

Hanna, Atieh; Bengtsson, Kristofer; Dahl, Martin; Erős, Endre; Götvall, Per-Lage; Ekström, Mikael

doi:10.1109/etfa.2019.8869014

Cited by 12 publications

(2 citation statements)

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“…Increased industrial competitiveness requires fundamental changes in automation. As companies introduce collaborative, intelligent and flexible systems into production to meet the variability, quality and punctuality needs of the modern customer, production requirements make it evident that traditional automation solutions can't solve all challenges [1].…”

Section: Introductionmentioning

confidence: 99%

“…Even though SAT-based planning was first proposed by Kautz and Selman already in 1992 [5], the interest of planning *This work has been supported by Unification and Unicorn, VINNOVA. 1 Endre Erős, Martin Dahl, Petter Falkman and Kristofer Bengtsson are with Chalmers University of Technology, Electrical Engineering, Systems and Control Department, Automation Research Group, Gothenburg, Sweden. endree@chalmers.se, martin.dahl@chalmers.se, petter.falkman@chalmers.se, kristofer.bengtsson@chalmers.se researchers in SAT-based planning methods was limited up until recently.…”

Section: Introductionmentioning

confidence: 99%

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Towards compositional automated planning

Erős

Dahl

Falkman

et al. 2020

2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

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The development of efficient propositional satisfiability problem solving algorithms (SAT solvers) in the past two decades has made automated planning using SAT-solvers an established AI planning approach. Modern SAT solvers can accommodate a wide variety of planning problems with a large number of variables. However, fast computing of reasonably long plans proves challenging for planning as satisfiability. In order to address this challenge, we present a compositional approach based on abstraction refinement that iteratively generates, solves and composes partial solutions from a parameterized planning problem. We show that this approach decomposes the monolithic planning problem into smaller problems and thus significantly speeds up plan calculation, at least for a class of tested planning problems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%