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Evaluierung von Navigationsmethoden für mobile Roboter

Evaluation of navigation methodologies for mobile robots

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Zusammenfassung

Mobile Manipulation ist das Kernstück eines hochflexiblen und autonomen Produktionssystems. Durch vernetzte und roboterbasierte Automatisierung ist eine individuell angepasste Fertigung möglich, wobei mobile Manipulatoren sowohl bei Transportaufgaben als auch bei der Werkstückbereitstellung eine signifikante Rolle spielen. Die Digitale Fabrik der FH Technikum Wien ist eine Forschungs- sowie Lehrplattform und dient der exemplarischen Erprobung neuer Technologien zur digitalen und flexiblen Produktion. Im Zuge mehrerer Forschungsarbeiten wurden mobile Manipulatoren in der Digitalen Fabrik integriert. Basierend darauf und auf einem konkreten und symbolischen Use Case diskutiert diese Arbeit verschiedene Methoden zur Navigation mobiler Manipulatoren. Es werden Positionierungsgenauigkeiten basierend auf unterschiedlichen Navigationsmethoden, Sicherheitsaspekte und Auswirkungen auf die Handhabung von Objekten diskutiert.

Abstract

Intelligent mobile robots and service robots are central parts of autonomous productions and flexible manufacturing. Interconnected industrial robot-based automation allows customized productions for which mobile robots are used for transport of material and tools. The digital factory of the UAS Technikum Wien is a research project which focuses on experimental evaluation of novel technologies for digital manufacturing. This paper discusses applications of mobile and service robots in the digital factory. Based on a production use case, we analyze several methods for navigation in terms of accuracy of those approaches and discuss safety aspects.

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Notes

  1. Siehe https://www.universal-robots.com/de/produkte/ur5-roboter/.

  2. Siehe https://www.mobile-industrial-robots.com/de/solutions/robots/mir100/.

  3. Siehe http://wiki.ros.org/navigation.

  4. Siehe https://www.mobile-industrial-robots.com/de/solutions/robots/mir-accessories/mir-charge-24v/.

Literatur

  1. Lehner, F., Schmid, J. (1992): Industrielle Wettbewerbsfähigkeit und flexible Produktionssysteme – Zukunftschancen der Fabrik (S. 13–28). Wiesbaden: VS Verlag für Sozialwissenschaften.

    Google Scholar 

  2. Westkämper, E., Spath, D., Constantinescu, C., Lentes, L. (2006): Digitale Produktion. Berlin: Springer.

    Google Scholar 

  3. Chebab, Z.-E., Fauroux, J.-C., Bouton, N., Mezouar, Y., Sabourin, L. (2015): Autonomous collaborative mobile manipulators: state of the art. In TrC-IFToMM symposium on theory of machines and mechanisms, Izmir, Turkey.

    Google Scholar 

  4. Thrun, S., Burgard, W., Fox, D. (2006): Probabilistic robotics. Cambridge: MIT Press.

    MATH  Google Scholar 

  5. Larsen, T. D., Hansen, K. L., Andersen, N. A., Ravn, O. (1999): Design of Kalman filters for mobile robots; evaluation of the kinematic and odometric approach. In Proceedings of the 1999 IEEE international conference on control applications (Cat. No. 99CH36328) (Bd. 2, S. 1021–1026).

    Google Scholar 

  6. Yu, B., Dong, L., Xue, D., Zhu, H., Geng, X., Huang, R., Wang, J. (2019): A hybrid dead reckoning error correction scheme based on extended Kalman filter and map matching for vehicle self-localization. J. Intell. Transp. Syst., 23(1), 84–98. [Online]. Available: https://doi.org/10.1080/15472450.2018.1527693.

    Article  Google Scholar 

  7. Kia, S. S., Rounds, S. F., Martínez, S. (2014): A centralized-equivalent decentralized implementation of extended Kalman filters for cooperative localization. In 2014 IEEE/RSJ international conference on intelligent robots and systems (S. 3761–3766).

    Google Scholar 

  8. Fox, D. (2001): Kld-sampling: adaptive particle filters and mobile robot localization. In Advances in neural information processing systems (NIPS).

    Google Scholar 

  9. Bishop, C. M. (2006): Pattern recognition and machine learning. New York: Springer.

    MATH  Google Scholar 

  10. Ko, J., Fox, D. (2008): GP-bayesfilters: Bayesian filtering using Gaussian process prediction and observation models. In IEEE/RSJ international conference on intelligent robots and systems, 2008. IROS 2008, Nice, France. New York: IEEE Press.

    Google Scholar 

  11. Reece, S., Roberts, S. (2010): An introduction to Gaussian processes for the Kalman filter expert. In 2010 13th conference on information fusion (FUSION). New York: IEEE Press.

    Google Scholar 

  12. Hartikainen, J., Särkkä, S. (2010): Kalman filtering and smoothing solutions to temporal Gaussian process regression models. In 2010 IEEE international workshop on machine learning for signal processing (MLSP). New York: IEEE Press.

    MATH  Google Scholar 

  13. Grisetti, G., Stachniss, C., Burgard, W. (2005): Improving grid-based SLAM with Rao-Blackwellized particle filters by adaptive proposals and selective resampling. In Proceedings of the IEEE international conference on robotics & automation (ICRA).

    Google Scholar 

  14. Labbé, M., Michaud, F. (2018): Rtab-map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and long-term online operation: Labbé and Michaud. J. Field Robot., 36, 10.

    Google Scholar 

  15. Russell, S., Norvig, P. (2010): Artificial intelligence: a modern approach. Aufl. 3. New York: Prentice Hall.

    MATH  Google Scholar 

  16. Garrido-Jurado, S., Munoz-Salinas, R., Madrid-Cuevas, F., Marin-Jiménez, M. (2014): Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recognit., 47, 06.

    Google Scholar 

  17. Goodfellow, I., Bengio, Y., Courville, A. (2016): Deep learning. Cambridge: MIT Press.

    MATH  Google Scholar 

  18. Pearl, J. (2009): Causality: models, reasoning and inference. Aufl. 2. New York: Cambridge University Press.

    MATH  Google Scholar 

  19. Pearl, J. (1988): Probabilistic reasoning in intelligent systems: networks of plausible inference. San Francisco: Morgan Kaufmann

    MATH  Google Scholar 

  20. Marcus, G. (2018): Deep learning: a critical appraisal. CoRR arXiv:1801.00631

  21. Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R. (2014): Intriguing properties of neural networks. In International conference on learning representations. [Online] Available: arXiv:1312.6199.

    Google Scholar 

  22. Su, J., Vargas, D. V., Sakurai, K. (2017): One pixel attack for fooling deep neural networks. CoRR arXiv:1710.08864.

  23. Selvaraju, R. R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., Batra, D. (2016): Grad-cam: Why did you say that? visual explanations from deep networks via gradient-based localization. CoRR arXiv:1610.02391.

  24. Pearl, J., Mackenzie, D. (2018): The book of why: the new science of cause and effect. Aufl. 1. New York: Basic Books

    MATH  Google Scholar 

  25. Sattinger, V., Papa, M., Stuja, K., Kubinger, W. (2019): Methodik zur entwicklung sicherer kollaborativer produktionssysteme im rahmen von industrie 4.0. E&I, Elektrotech. Inf.tech., 136, 10.

    Google Scholar 

  26. Papa, M., Kaselautzke, D., Stuja, K., Wölfel, W. (2018): Different safety certifiable concepts for mobile robots in industrial environments. In 29TH DAAAM international symposium on intelligent manufacturing and automation (Bd. 01, S. 0791–0800).

    Google Scholar 

  27. Markis, A., Papa, M., Kaselautzke, D., Rathmair, M., Sattinger, V., Brandstötter, M. (2019): Safety of mobile robot systems in industrial applications. In Proceedings of the joint ARW & OAGM workshop (Bd. 05).

    Google Scholar 

  28. Engelhardt-Nowitzki, C., Aburaia, M., Otrebski, R., Rauer, J., Orsolits, H. (2020): Research-based teaching in digital manufacturing and robotics – the digital factory at the UAS Technikum Wien as a case example. Proc. Manuf., 45, 164–170. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S2351978920311318.

    Google Scholar 

  29. Otrebski, R., Pospisil, D., Engelhardt-Nowitzki, C., Kryvinska, N., Aburaia, M. (2019): Flexibility enhancements in digital manufacturing by means of ontological data modeling. Proc. Comput. Sci., 155, 296–302. The 16th international conference on mobile systems and pervasive computing (MobiSPC 2019), The 14th international conference on future networks and communications (FNC-2019), The 9th international conference on sustainable energy information technology. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1877050919309573.

    Google Scholar 

  30. Rauer, J. N. (2019): Semi-automatic generation of training data for neural networks for 6D pose estimation and robotic grasping. Master’s thesis, University of Applied Sciences Technikum Wien, Vienna, Austria.

  31. Rauer, J. N., Wöber, W., Aburaia, M. (2019): An autonomous mobile handling robot using object recognition. In Proceedings of the joint ARW & OAGM workshop (S. 38–43).

    Google Scholar 

  32. Parungao, L., Hein, F., Lim, W. (2018): Dijkstra algorithm based intelligent path planning with topological map and wireless communication. J. Eng. Appl. Sci., 13, 04.

    Google Scholar 

  33. Quigley, M., Conley, K., Gerkey, B. P., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A. Y. (2009): Ros: an open-source robot operating system. In ICRA workshop on open source software.

    Google Scholar 

  34. Kumra, S., Kanan, C. (2017): Robotic grasp detection using deep convolutional neural networks. In IEEE international conference on intelligent robots and systems – IROS (S. 769–776).

    Google Scholar 

  35. Tobin, J., Biewald, L., Duan, R., Andrychowicz, M., Handa, A., Kumar, V., McGrew, B., Schneider, J., Welinder, P., Zaremba, W., Abbeel, P. (2017): Domain randomization and generative models for robotic grasping. In IEEE/RSJ international conference on intelligent robots and systems – IROS (S. 3482–3489).

    Google Scholar 

  36. Du, G., Wang, K., Lian, S. (2019): Vision-based robotic grasping from object localization, pose estimation, grasp detection to motion planning: A review. CoRR.

  37. Fu, M., Zhou, W. (2019): DeepHMap++: combined projection grouping and correspondence learning for full DoF pose estimation. Sensors, 19(5), 1032.

    Google Scholar 

  38. Fischler, M. A., Bolles, R. C. (1981): Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, 24(6), 381–395.

    MathSciNet  Google Scholar 

  39. Bohg, J., Morales, A., Asfour, T., Kragic, D. (2014): Data-driven grasp synthesis: a survey. IEEE Trans. Robot., 30(2), 289–309.

    Google Scholar 

  40. Tremblay, J., To, T., Sundaralingam, B., Xiang, Y., Fox, D., Birchfield, S. (2018): Deep object pose estimation for semantic robotic grasping of household objects. In 2nd annual conference on robot learning – CoRL (S. 306–316).

    Google Scholar 

  41. Steigl, D., Aburaia, M., Wöber, W. (2020): Autonomous grasping of known objects using depth data and the pca. In Proceedings of the joint ARW & OAGM workshop.

    Google Scholar 

  42. Andreas, K., Wöber, W. (2020): Vision-based docking of a mobile robot. In Proceedings of the joint ARW & OAGM workshop.

    Google Scholar 

  43. Sutton, R. S., Barto, A. G. (2018): Reinforcement learning: an introduction. Cambridge: MIT Press.

    MATH  Google Scholar 

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Danksagung

Diese Arbeit wurde teilweise durch die MA23 der Stadt Wien im Rahmen der Projekte 26-04 „AI Anwenden und Verstehen (AIAV)“ sowie 22-04 „Engineering goes International (ENGINE)“ und 19-05 „Sicherheit in intelligenten Produktionsumgebungen (SIP4.0)“ unterstützt.

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Authors and Affiliations

  1. Fachhochschule Technikum Wien, Höchstädtplatz 6, 1200, Wien, Österreich

    Wilfried Wöber, Johannes Rauer, Maximilian Papa, Ali Aburaia, Simon Schwaiger, Georg Novotny, Mohamed Aburaia & Wilfried Kubinger

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  1. Wilfried Wöber
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  2. Johannes Rauer
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  3. Maximilian Papa
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  4. Ali Aburaia
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  6. Georg Novotny
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  7. Mohamed Aburaia
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  8. Wilfried Kubinger
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Correspondence to Wilfried Kubinger.

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Wöber, W., Rauer, J., Papa, M. et al. Evaluierung von Navigationsmethoden für mobile Roboter. Elektrotech. Inftech. 137, 316–323 (2020). https://doi.org/10.1007/s00502-020-00820-x

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