A Service-oriented Robotic Manufacturing System: Lessons Learned from Participating in the World Robot Challenge 2018

Kröger, Ole; Guhl, Jan; Heimann, Oliver; Katanacho, Manuel; Niebuhr, Carsten; Rudorfer, Martin; Özkaya, Tayyip; Wassermann, Jonas; Hügle, Johannes; Pannen, Tessa J.; Krüger, Jörg

doi:10.1007/978-3-662-59317-2_5

Cited by 3 publications

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“…Although robot competitions have incited teams to push their systems to remarkable heights, the knowledge they accumulated often dissipates after the end of the contest, when teams decompress from the heat of the competition, members leave the group and the remainder struggles to find a venue to publish their results. To our knowledge, only one team published an article presenting their system and experiences from the WRC (BerlinAUTs [10]).…”

Section: Introductionmentioning

confidence: 99%

Robots assembling machines: learning from the World Robot Summit 2018 Assembly Challenge

et al. 2019

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The Industrial Assembly Challenge at the World Robot Summit was held in 2018 to showcase the state-of-the-art of autonomous manufacturing systems. The challenge included various tasks, such as bin picking, kitting, and assembly of standard industrial parts into 2D and 3D assemblies. Some of the tasks were only revealed at the competition itself, representing the challenge of "level 5" automation, i. e., programming and setting up an autonomous assembly system in less than one day. We conducted a survey among the teams that participated in the challenge and investigated aspects such as team composition, development costs, system setups as well as the teams' strategies and approaches. An analysis of the survey results reveals that the competitors have been in two camps: those constructing conventional robotic work cells with off-the-shelf tools, and teams who mostly relied on custom-made end effectors and novel software approaches in combination with collaborative robots. While both camps performed reasonably well, the winning team chose a middle ground in between, combining the efficiency of established play-back programming with the autonomy gained by CAD-based object detection and force control for assembly operations.

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Section: Introductionmentioning

confidence: 99%