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- ZeitschriftenartikelEpisodic Memories for Safety-Aware Robots(KI - Künstliche Intelligenz: Vol. 33, No. 2, 2019) Bartels, Georg; Beßler, Daniel; Beetz, MichaelIn the factories and distribution centers of the future, humans and robots shall work together in close proximity and even physically interact. This shift to joint human–robot teams raises the question of how to ensure worker safety. In this manuscript, we present a novel episodic memory system for safety-aware robots. Using this system, the robots can answer questions about their actions at the level of safety concepts. We built this system as an extension of the KnowRob framework and its notion of episodic memories. We evaluated the system in a safe physical human–robot interaction (pHRI) experiment, in which a robot had to sort surgical instruments while also ensuring the safety of its human co-workers. Our experimental results show the efficacy of the system to act as a robot’s belief state for online reasoning, as well as its ability to support offline safety analysis through its fast and flexible query interface. To this end, we demonstrate the system’s ability to reconstruct its geometric environment, course of action, and motion parameters from descriptions of safety-relevant events. We also show-case the system’s capability to conduct statistical analysis.
- ZeitschriftenartikelFrom Research to Market: Building the Perception Systems for the Next Generation of Industrial Robots(KI - Künstliche Intelligenz: Vol. 33, No. 2, 2019) Bartels, Georg; Beetz, Michael
- ZeitschriftenartikelTowards Explainable Process Predictions for Industry 4.0 in the DFKI-Smart-Lego-Factory(KI - Künstliche Intelligenz: Vol. 33, No. 2, 2019) Rehse, Jana-Rebecca; Mehdiyev, Nijat; Fettke, PeterWith the advent of digitization on the shopfloor and the developments of Industry 4.0, companies are faced with opportunities and challenges alike. This can be illustrated by the example of AI-based process predictions, which can be valuable for real-time process management in a smart factory. However, to constructively collaborate with such a prediction, users need to establish confidence in its decisions. Explainable artificial intelligence (XAI) has emerged as a new research area to enable humans to understand, trust, and manage the AI they work with. In this contribution, we illustrate the opportunities and challenges of process predictions and XAI for Industry 4.0 with the DFKI-Smart-Lego-Factory. This fully automated factory prototype built out of LEGO $$^\circledR$$ ® bricks demonstrates the potentials of Industry 4.0 in an innovative, yet easily accessible way. It includes a showcase that predicts likely process outcomes and uses state-of-the-art XAI techniques to explain them to its workers and visitors.
- ZeitschriftenartikelVision-Based Solutions for Robotic Manipulation and Navigation Applied to Object Picking and Distribution(KI - Künstliche Intelligenz: Vol. 33, No. 2, 2019) Roa-Garzón, Máximo A.; Gambaro, Elena F.; Florek-Jasinska, Monika; Endres, Felix; Ruess, Felix; Schaller, Raphael; Emmerich, Christian; Muenster, Korbinian; Suppa, MichaelThis paper presents a robotic demonstrator for manipulation and distribution of objects. The demonstrator relies on robust 3D vision-based solutions for navigation, object detection and detection of graspable surfaces using the rc _ visard , a self-registering stereo vision sensor. Suitable software modules were developed for SLAM and for model-free suction gripping. The modules run onboard the sensor, which enables creating the presented demonstrator as a standalone application that does not require an additional host PC. The modules are interfaced with ROS, which allows a quick implementation of a fully functional robotic application.
- ZeitschriftenartikelPerception-Guided Mobile Manipulation Robots for Automation of Warehouse Logistics(KI - Künstliche Intelligenz: Vol. 33, No. 2, 2019) Bartels, Georg; Beetz, Michael
- ZeitschriftenartikelEditorial(KI - Künstliche Intelligenz: Vol. 33, No. 2, 2019) Ragni, Marco
- ZeitschriftenartikelNews(KI - Künstliche Intelligenz: Vol. 33, No. 2, 2019)
- ZeitschriftenartikelCatering to Real-Time Requirements of Cloud-Connected Mobile Manipulators(KI - Künstliche Intelligenz: Vol. 33, No. 2, 2019) Walter, Christoph; Scholle, Julian-Benedikt; Elkmann, NorbertIn this contribution, we explore real-time requirements of mobile manipulators, a class of intelligent robots, in the context of the ongoing fast-robotics ( https://de.fast-zwanzig20.de/industrie/fast-robotics/ ) project. The project aims at implementing such robots based on (edge-) cloud-services using wireless communication in order to make them more capable and efficient. Instead of trying to universally achieve hard real-time in such a system, we present a mixed real-time approach with an application centered fault tolerance scheme based on transition points and pre-computed alternate plans. We argue that deliberatively addressing uncertainties in timing is similarly important than handling uncertainties e.g. in perception for future intelligent robots.
- ZeitschriftenartikelOn Cognitive Reasoning for Compliant Manipulation Tasks in Smart Production Environments(KI - Künstliche Intelligenz: Vol. 33, No. 2, 2019) Leidner, DanielHighly automated smart production environments require robots with autonomous planning mechanisms as well as effect-based performance inference methods. This report discusses the possibilities of cognitive reasoning for compliant manipulation tasks to satisfy these demands. The article builds on the representations for compliant wiping actions and their effects which are fundamental to many tasks in industrial manufacturing. It is described how these actions can be planned, executed, and interpreted by means of generic action descriptions and qualitative models.
- ZeitschriftenartikelA Jumpstart Framework for Semantically Enhanced OPC-UA(KI - Künstliche Intelligenz: Vol. 33, No. 2, 2019) Katti, Badarinath; Plociennik, Christiane; Schweitzer, MichaelDecentralization is the norm of future smart production as it assists in contextual dynamic decision-making and thereby increases the flexibility required to produce highly customized products. When manufacturing business software is operated as a cloud based solution, it experiences network latency and connectivity issues. To overcome these problems, the production control should be delegated to the manufacturing edge layer and hence, the argument of decentralization is even more applicable to this narrative. In order to accomplish the assigned manufacturing task effectively, the edge layer is required to possess contextual awareness to make run-time decisions in production. Semantic technologies, on the other hand, assist in discerning the meaning, reasoning and drawing inferences from the data. There are several specifications and frameworks to automate the discovery, orchestration and invocation of web services; the prominent are OWL-S, SAWSDL and WSMO. This paper derives a hybrid approach that integrates OWL-S and SAWSDL specifications to overcome the downsides, yet retain the benefits of both approaches to the OPC-UA application methods. Consequently, the proposed semantically enriched OPC-UA concept enables the edge layer to create flexible production orchestration plans in a manufacturing scenario controlled by cloud MES. Furthermore, the derived hybrid approach is applied to a real use case to demonstrate its feasibility in industrial environments.