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Artificial Intelligence in Autonomous Systems. A Collection of Projects in Six Problem Classes

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Real-time and Autonomous Systems 2022 (Real-Time 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 674))

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Abstract

The paper presents a collection of projects on autonomous mobile systems with a focus on artificial intelligence technologies. Basic technologies necessary for autonomous mobile behaviour are described. Current methods and application fields of SLAM, feature extraction based on various sensory data, working with simulations, and gripping with robot manipulators will be discussed. Some ideas on solutions for problems in real world applications will be presented. The area of application is a four wheeled robotic platform with an integrated 6-DOF manipulator. Focus will be set on machine learning methods, in particular object detection and reinforcement learning. The paper gives an overview of required technologies for autonomous robotic systems and current state of the art methods. Some aspects and problems arising in applications will be discussed in more detail. The projects have been carried out in student projects at the autosys research lab at the Department Computer Science of University of Applied Sciences Hamburg [1].

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References

  1. Pareigis, S., Tiedemann, T., Stelldinger, P., Schönherr, N., Mang, M.: autosys research lab (2022). https://autosys.informatik.haw-hamburg.de. Accessed 30 Nov 2022

  2. Tiedemann, T., Schwalb, L., Kasten, M., Grotkasten, R., Pareigis, S.: Miniature autonomy as means to find new approaches in reliable autonomous driving AI method design. Front. Neurorobot. 16, 846355 (2022)

    Article  Google Scholar 

  3. Adept MobileRobots: Pioneer and Pioneer-compatible platforms (2022). http://wiki.ros.org/Robots/AMR_Pioneer_Compatible. Accessed 30 Nov 2022

  4. ROS: Robot Operating System (2022). https://www.ros.org. Accessed 30 Nov 2022

  5. Casagrande, M.: Robust setup of an autonomous mobile robot research platform with multi-sensor integration in ROS. B.S. thesis, University of Applied Sciences Hamburg (2018)

    Google Scholar 

  6. ROS: gmapping (2022). http://wiki.ros.org/gmapping. Accessed 30 Nov 2022

  7. ROS: hector SLAM (2022). http://wiki.ros.org/hector_slam. Accessed 30 Nov 2022

  8. ROS: cartographer (2022). http://wiki.ros.org/cartographer. Accessed 30 Nov 2022

  9. Jenning, E.: Systemidentifikation eines autonomen Fahrzeugs mit einer robusten, kamerabasierten Fahrspurerkennung in Echtzeit. B.S. thesis, University of Applied Sciences Hamburg (2008)

    Google Scholar 

  10. Nikolov, I.: Verfahren zur Fahrbahnverfolgung eines autonomen Fahrzeugs mittels Pure Pursuit und Follow-the-carrot. B.S. thesis, University of Applied Sciences Hamburg (2009)

    Google Scholar 

  11. Stark, F.: Monokulare visuelle Odometrie auf einem autonomen Miniaturschiff. B.S. thesis, University of Applied Sciences Hamburg (2020)

    Google Scholar 

  12. Schnirpel, T.: Lokalisierung eines Miniaturschiffs durch Distanzsensoren. University of Applied Sciences Hamburg, Internal paper (2020)

    Google Scholar 

  13. Shan, T., Englot, B., Meyers, D., Wang, W., Ratti, C., Daniela, R.: LIO-SAM: tightly-coupled lidar inertial odometry via smoothing and mapping. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5135–5142 (2020)

    Google Scholar 

  14. Mihajlov, D.: Autonome 3D-Objektmodellierung durch aktive Szenenexploration mit einem UR5 Greifarm. B.S. thesis, University of Applied Sciences Hamburg (2021)

    Google Scholar 

  15. Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14, 239–256 (1992)

    Article  Google Scholar 

  16. Wang, L., Sun, X.: Comparisons of iterative closest point algorithms. In: Park, J.J.J.H., Pan, Y., Chao, H.-C., Yi, G. (eds.) Ubiquitous Computing Application and Wireless Sensor. LNEE, vol. 331, pp. 649–655. Springer, Dordrecht (2015). https://doi.org/10.1007/978-94-017-9618-7_68

    Chapter  Google Scholar 

  17. Darcis, M., Swinkels, W., Güzel, A.E., Claesen, L.: PoseLab: a levenberg-marquardt based prototyping environment for camera pose estimation. In: 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1–6 (2018). https://doi.org/10.1109/CISP-BMEI.2018.8633112

  18. Xu, X., Harada, K.: Automatic surface reconstruction with alpha-shape method. Vis. Comput. 19(7), 431–443 (2003). https://doi.org/10.1007/s00371-003-0207-1

    Article  Google Scholar 

  19. Kazhdan, M.M., Bolitho, M., Hoppe, H.: Poisson surface reconstruction. In: Eurographics Symposium on Geometry Processing (2006)

    Google Scholar 

  20. Denecke, E.: Autonome Nutzung von Aufzügen durch einen Industrieroboter. B.S. thesis, University of Applied Sciences Hamburg (2022)

    Google Scholar 

  21. Kasper, J.: Autonome Erkennung und Interaktion mit Türen in der Robotik. B.S. thesis, University of Applied Sciences Hamburg (2022)

    Google Scholar 

  22. Tran, J.: Robust door detection using deep learning in visual based robot navigation. B.S. thesis, University of Applied Sciences Hamburg (2022)

    Google Scholar 

  23. Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS, pp. 91–99 (2015). http://dblp.uni-trier.de/db/conf/nips/nips2015.html

  24. Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: You only look once: unified, real-time object detection. CoRR (2015). https://arxiv.org/abs/1506.02640

  25. Liu, W., et al.: SSD: single shot multibox detector. CoRR (2015). https://arxiv.org/abs/1512.02325

  26. Google: Coral (2022). https://coral.ai. Accessed 30 Nov 2022

  27. COCO Common Objects in Context (2022). https://cocodataset.org/. Accessed 30 Nov 2022

  28. ImageNet (2022). https://www.image-net.org. Accessed 30 Nov 2022

  29. Bradski, G.: The openCV library. Dr. Dobb’s J. Softw. Tools 25(11), 120–123 (2000)

    Google Scholar 

  30. Bloice, M.D., Stocker, C., Holzinger, A.: Augmentor: an image augmentation library for machine learning. arXiv (2017). https://doi.org/10.48550/ARXIV.1708.04680. https://arxiv.org/abs/1708.04680

  31. Ramôa, J.G., Lopes, V., Alexandre, L.A., Mogo, S.: Real-time 2D–3D door detection and state classification on a low-power device. SN Appl. Sci. 3(5), 1–13 (2021). https://doi.org/10.1007/s42452-021-04588-3

    Article  Google Scholar 

  32. Colledanchise, M., Ögren, P.: Behavior trees in robotics and AI: an introduction. CoRR (2017). https://arxiv.org/abs/1709.00084

  33. Avola, D., Cinque, L., Foresti, G.L., Mercuri, C., Pannone, D.: A practical framework for the development of augmented reality applications by using ArUco markers. In: Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - ICPRAM, pp. 645–654 (2016). https://doi.org/10.5220/0005755806450654

  34. Koide, K., Miura, J., Menegatti, E.: A portable three-dimensional LIDAR-based system for long-term and wide-area people behavior measurement. Int. J. Adv. Robot. Syst. 16 (2019). https://doi.org/10.1177/1729881419841532

  35. Abdelkarim, A.: Human-machine interaction on an autonomous robot through visual signals. B.S. thesis, University of Applied Sciences Hamburg (2022)

    Google Scholar 

  36. Sentler, D.: Detektion von Sprachbefehlen auf Edge-Geräten unterstützt durch automatisierte Trainingsdaten-Synthese für eine Not-Halt-Anwendung. B.S. thesis, University of Applied Sciences Hamburg (2022)

    Google Scholar 

  37. Zhang, Y., Suda, N., Lai, L., Chandra, V.: Hello edge: keyword spotting on microcontrollers. CoRR (2017). https://arxiv.org/abs/1711.07128

  38. Koch, S.: Hochgenaue Positionsbestimmung mit Ultra-wideband und Reinforcement Learning. B.S. thesis, University of Applied Sciences Hamburg (2021)

    Google Scholar 

  39. Zhang, E., Masoud, N.: Increasing GPS localization accuracy with reinforcement learning. IEEE Trans. Intell. Transp. Syst. 22(5), 2615–2626 (2021)

    Article  Google Scholar 

  40. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (2018)

    MATH  Google Scholar 

  41. Mnih, V., et al.: Asynchronous methods for deep reinforcement learning (2016). https://arxiv.org/abs/1602.01783

  42. Warden, P.: Speech commands: a dataset for limited-vocabulary speech recognition (2018)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department Computer Science, University of Applied Sciences Hamburg, Berliner Tor 7, 20099, Hamburg, Germany

    Stephan Pareigis, Tim Tiedemann, Nils Schönherr, Denisz Mihajlov, Eric Denecke, Justin Tran, Sven Koch, Awab Abdelkarim & Maximilian Mang

Authors
  1. Stephan Pareigis
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  2. Tim Tiedemann
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  3. Nils Schönherr
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  4. Denisz Mihajlov
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  5. Eric Denecke
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  6. Justin Tran
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  7. Sven Koch
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  8. Awab Abdelkarim
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  9. Maximilian Mang
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Corresponding author

Correspondence to Stephan Pareigis .

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Editors and Affiliations

  1. Lehrgebiet Kommunikationsnetze, FernUniversität in Hagen, Hagen, Germany

    Herwig Unger

  2. Lehrgebiet Kommunikationsnetze, FernUniversität in Hagen, Hagen, Germany

    Marcel Schaible

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Pareigis, S. et al. (2023). Artificial Intelligence in Autonomous Systems. A Collection of Projects in Six Problem Classes. In: Unger, H., Schaible, M. (eds) Real-time and Autonomous Systems 2022. Real-Time 2022. Lecture Notes in Networks and Systems, vol 674. Springer, Cham. https://doi.org/10.1007/978-3-031-32700-1_14

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