Murrenhoff, Anike

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Georgia Southern University: Digital Commons@Georgia Southern

16th International Material Handling Research ColloquiumDresden, Saxony, Germany, June 20-23, 2023Requirements for Generating Learning Environments for Autonomous Systems Behavior in a Digital ContinuumAnike MurrenhoffFraunhofer-Institute for Material Flow and Logistics IMLBACKGROUND AND MOTIVATIONDIGITAL CONTINUAREQUIREMENTS FOR A GENERATION APPROACH GENERATION APPROACHLITERATURE[1] M. ten Hompel and S. Kerner, “Logistik 4.0: Die Vision vom Internet der autonomen Dinge,” Inform.-Spektrum, vol. 38, no. 3, pp. 176–182, Jun. 2015.[2] G. Fragapane, R. de Koster, F. Sgarbossa, and J. O. Strandhagen, “Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda,” Eur. J. Oper. Res., vol. 294, no. 2, pp. 405–426, Oct. 2021.[3] J. Fottner et al., “Autonomous Systems in Intralogistics – State of the Art and Future Research Challenges,” LogisticsResearch, vol. 14, no. 2, Feb. 2021.[4] S. Risi and J. Togelius, “Increasing generality in machine learning through procedural content generation,” Nat. Mach. Intell., vol. 2, no. 8, pp. 428-436, Aug. 2020.[5] Greg Brockman [@gdb], “The underlying spirit in many debates about the pace of AI progress…,” Twitter, Apr. 12, 2023.[6] NVIDIA, “8. Replicator SceneBlox tutorial — Omniverse Robotics documentation,” Mar. 23, 2023. https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/tutorial_replicator_sceneblox.html (accessed Mar. 23, 23)CONCLUSION AND OUTLOOKRESEARCH QUESTION 1 How does a concept emerge that enables the development and operation of truly autonomous systems based on methods of AI?RESEARCH QUESTION 2 How can the required great variety of learning environments for autonomous behavior in intralogistics be generated automatically? ... arise when physical and digital reality combine to form a self-optimizing control loop of AI and interact with each other. … accelerate planning, development and implementation processes.  ... blur planning and execution phases. ... lead to highly adaptive systems that adapt resiliently to changing requirements.Physical reality Digital realitySensorDataDigital TwinTraining and test in simulation environmentsDeploymentRuntime Autonomous systems are increasingly prevalent in logistics, offering benefits such as flexibility, adaptability, robustness, and sustainability [1] [2] Challenges of the paradigm shift from centralized organizations towards autonomous systems:  Development of truly autonomous systems [3]  Requirement of new methods and concepts to find overall system behavior [1] New methods of artificial intelligence (AI) are seen as a solution, but: Example autonomous mobile robots (AMR): current approaches of deep reinforcement learning in robotics are far from being able to train robots that can handle the complexity of environments in the real world [4] The alignment problem in AI needs to be addressed [5]Choose shape at random(I, L, T)Randomize functional areas in location and size(goods in, pallet storage, small part storage, staging, packaging, goods out)Choose equipment at randomRandomize equipment placement(e.g., wavefunction collapse [6])Run training / testRandomize load scenario (source, sink and frequency of transport orders) The Digital Continuum paradigm offers a promising approach by leveraging the seamless fusion of physical and digital reality to achieve a new level of autonomy through AI-based methods. Digital learning and testing environments will become the central tool to revolutionize the continuous development of intralogistics systems even during operation. 

Requirements for Generating Learning Environments for Autonomous Systems Behavior in a Digital Continuum

https://digitalcommons.georgiasouthern.edu/cgi/viewcontent.cgi?article=1074&context=imhrc

Requirements for Generating Learning Environments for Autonomous Systems Behavior in a Digital Continuum

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