Winning the RoboCup Logistics League with Fast Navigation, Precise Manipulation, and Robust Goal Reasoning

Hofmann, Till; Limpert, Nicolas; Mataré, Victor; Ferrein, Alexander; Lakemeyer, Gerhard

doi:10.1007/978-3-030-35699-6_41

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…Another example in e-vehicle manufacturing covers sophisticated logistics robots used in modern modularized factories without assembly lines [25]. In such complex settings, sophisticated logistics robots controlled by autonomous agents can help to assemble products, integrating different AI methods [66,67]. Similar methods were used for autonomous agents communicating as programs with the WWW to realize Semantic Web applications [100,127].…”

Section: Model-integrated Artificial Intelligence With Autonomous Age...mentioning

confidence: 99%

A Computer Science Perspective on Digital Transformation in Production

Brauner

Dalibor

Jarke

et al. 2022

ACM Trans. Internet Things

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The Industrial Internet-of-Things (IIoT) promises significant improvements for the manufacturing industry by facilitating the integration of manufacturing systems by Digital Twins. However, ecological and economic demands also require a cross-domain linkage of multiple scientific perspectives from material sciences, engineering, operations, business, and ergonomics, as optimization opportunities can be derived from any of these perspectives. To extend the IIoT to a true Internet of Production , two concepts are required: first, a complex, interrelated network of Digital Shadows which combine domain-specific models with data-driven AI methods; and second, the integration of a large number of research labs, engineering, and production sites as a World Wide Lab which offers controlled exchange of selected, innovation-relevant data even across company boundaries. In this article, we define the underlying Computer Science challenges implied by these novel concepts in four layers: Smart human interfaces provide access to information that has been generated by model-integrated AI . Given the large variety of manufacturing data, new data modeling techniques should enable efficient management of Digital Shadows, which is supported by an interconnected infrastructure . Based on a detailed analysis of these challenges, we derive a systematized research roadmap to make the vision of the Internet of Production a reality.

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Section: Model-integrated Artificial Intelligence With Autonomous Age...mentioning

confidence: 99%

A Computer Science Perspective on Digital Transformation in Production

Brauner

Dalibor

Jarke

et al. 2022

ACM Trans. Internet Things

Self Cite

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“…The integration of robotic systems is more commonly discussed in the context of robotics competitions, e.g. [4]- [8], rather than for real-world industrial applications. These works focus on the architecture of the system, rather than on the adopted integration solutions.…”

Section: Introductionmentioning

confidence: 99%

A Methodology for Approaching the Integration of Complex Robotics Systems Illustrated through a Bi-manual Manipulation Case-Study

Triantafyllou¹,

Rodrigues²,

Chaikunsaeng³

et al. 2021

Preprint

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The multidisciplinarity of robotics creates a need for robust integration methodologies that can facilitate the adoption of state-of-the-art research components in an industrial application. Unfortunately, there are no clear, communityaccepted guidelines or standards that define the integration of such components in a single robotic system. In this paper, we propose a methodology that assesses the software components of a candidate system on the basis of the effort required to integrate them and the impact their integration will have on a target system. We demonstrate how this methodology can be applied using an industrial tool packing system as an example. The system integrates a wide range of both in-house and thirdparty research outputs and software components. We prove the effectiveness of our approach by evaluating system performance with an experimental benchmark that assesses the robustness, reliability and operational speed of the system for the given packing task. We also demonstrate how our methodology can be used to predict the amount of integration time required for a component. The proposed integration methodology can be applied to any robotic system to facilitate its transition from the research to an industrial environment.

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