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Forschung im Ingenieurwesen

, Volume 83, Issue 2, pp 137–147 | Cite as

Decentralized path planning for cooperating autonomous mobile units

  • Simon RothfußEmail author
  • Rinat Prezdnyakov
  • Michael Flad
  • Sören Hohmann
Originalarbeiten/Originals
  • 194 Downloads

Abstract

In various domains, e.g. robotics or autonomous driving, automated path planning for conflict-free movements of the participating vehicles, i.e. robots, cars or other mobile units is an essential task on the navigation level. Especially in crowded scenarios in which many vehicles share a common operation area together with other static or dynamic obstacles, finding a set of conflict-free paths for all vehicles is a challenging navigation task that is crucial for fully autonomous vehicles. In this work we propose a decentralized path planning algorithm for such scenarios which focuses on the cooperative negotiation of conflict-free paths. The path planning on navigation level is realized with an innovative graph search algorithm based on A* that incorporates dynamic obstacles (e.g. manually operated vehicles and other autonomous mobile units) and enables the autonomous vehicles to change speed. Furthermore, the framework suggests a decentralized approach, in which each vehicle performs its own path planning locally. Communication between the mobile units allows them to cooperatively negotiate conflict-free paths for all autonomous vehicles participating in the framework. The resulting iterative process of calculating new paths and negotiating a feasible solution set for all vehicles is designed to yield a deterministic solution within a finite number of iterations. We furthermore provide promising simulation results for this framework with test scenarios involving many autonomous vehicles and challenging obstacle formations.

Dezentrale Pfadplanung für kooperierende autonome mobile Einheiten

Zusammenfassung

In vielen Domänen wie der Robotik oder dem autonomen Fahren ist die automatisierte Planung von konfliktfreien Pfaden der teilnehmenden mobilen Einheiten, wie zum Beispiel Roboter oder Autos, ein essentieller Arbeitsschritt der Navigationsebene. Vor allem in dichtem Gedränge, wenn sich viele Fahrzeuge einen gemeinsamen Arbeitsbereich mit anderen statischen oder dynamischen Hindernissen teilen, ist die Bestimmung von konfliktfreien Pfaden für alle Fahrzeuge eine herausfordernde aber notwendige Navigationsaufgabe für vollautonome Fahrzeuge. In dieser Arbeit stellen wir einen dezentralen Pfadplanungsalgorithmus für solche Szenarien vor und konzentrieren uns auf die kooperative Verhandlung der konfliktfreien Pfade. Die Pfadsuche auf Navigationsniveau erfolgt mithilfe eines innovativen Graphen-Suchalgorithmus basierend auf A*, der dynamische Hindernisse, bspw. manuell gesteuerte Fahrzeuge oder andere autonome Fahrzeuge, und auch Geschwindigkeitsänderungen der Fahrzeuge berücksichtigt. Für die Verhandlung der konfliktfreien Pfade schlägt das Framework einen dezentralen Ansatz mit lokaler Pfadplanung jedes Fahrzeugs vor. Durch Kommunikation zwischen den mobilen Einheiten wird das kooperative Verhandeln von konfliktfreien Pfaden aller teilnehmenden Fahrzeugen ermöglicht. Der resultierende Prozess aus der Berechnung neuer Pfade und der Verhandlung einer zulässigen Lösungsmenge für alle Fahrzeuge wurde so entwickelt, dass eine deterministische Lösung mit einer endlichen Iterationsanzahl gefunden wird. Außerdem präsentieren wir vielversprechende Simulationsergebnisse des Frameworks in Testszenarien, die viele autonome Fahrzeuge und herausfordernde Hindernisformationen umfassen.

Notes

Acknowledgements

This publication was written in the framework of the “Profilregion Mobilitätssysteme Karlsruhe” funded by the Ministry of Science, Research and the Arts and the Ministry of Economic Affairs, Labour and Housing of Baden-Württemberg, Germany.

References

  1. 1.
    Althoff M, Stursberg O, Buss M (2009) Model-based probabilistic collision detection in autonomous driving. IEEE Trans Intell Transportation Syst 10(2):299–310.  https://doi.org/10.1109/TITS.2009.2018966 CrossRefGoogle Scholar
  2. 2.
    van den Berg JP, Overmars MH (2005) Prioritized motion planning for multiple robots. IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 430–435  https://doi.org/10.1109/IROS.2005.1545306 Google Scholar
  3. 3.
    Goette C, Keller M, Nattermann T, Hass C, Glander K-H, Bertram T (2017) Spline-based motion planning for automated driving. Proceedings of the 20th IFAC World Congress.CrossRefGoogle Scholar
  4. 4.
    Cichella V, Choe R, Mehdi SB, Xargay E, Hovakimyan N, Dobrokhodov V, Kaminer I, Pascoal AM, Pedro Aguiar A (2016) Safe coordinated maneuvering of teams of multirotor unmanned aerial vehicles: A cooperative control framework for multivehicle, time-critical missions. IEEE Control Syst 36(4):59–82 (https://doi.org/10.1109/MCS.2016.2558443)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Donges E (1999) A conceptual framework for active safety in road traffic. Veh Syst Dyn 32(2-3):113–128.  https://doi.org/10.1076/vesd.32.2.113.2089 CrossRefGoogle Scholar
  6. 6.
    During M, Lemmer K (2016) Cooperative maneuver planning for cooperative driving. IEEE Intell Transportation Syst Mag 8(3):8–22.  https://doi.org/10.1109/MITS.2016.2549997 CrossRefGoogle Scholar
  7. 7.
    During M, Pascheka P (2014) Cooperative decentralized decision making for conflict resolution among autonomous agents. IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings, pp 154–161  https://doi.org/10.1109/INISTA.2014.6873612 Google Scholar
  8. 8.
    Ellis D, Sommerlade E, Reid I (2009) Modelling pedestrian trajectory patterns with gaussian processes. IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pp 1229–1234  https://doi.org/10.1109/ICCVW.2009.5457470 Google Scholar
  9. 9.
    Janjos F, Reichart R, Niermeyer P (2017) Smooth path-generation around obstacles using quartic splines and rrts. Proceedings of the 20th IFAC World Congress.CrossRefGoogle Scholar
  10. 10.
    Firmansyah ER, Masruroh SU, Fahrianto F (2016) Comparative analysis of a* and basic theta* algorithm in android-based pathfinding games. 6th International Conference on Information and Communication Technology for The Muslim World (ICT4M), pp 275–280  https://doi.org/10.1109/ICT4M.2016.063 Google Scholar
  11. 11.
    Frese C (2012) Planung kooperativer Fahrmanöver für kognitive Automobile. Ph.D. thesis, Karlsruhe Institute of Technology (KIT)Google Scholar
  12. 12.
    Hausler AJ, Saccon A, Aguiar AP, Hauser J, Pascoal AM (2016) Energy-optimal motion planning for multiple robotic vehicles with collision avoidance. IEEE Trans Control Syst Technol 24(3):867–883.  https://doi.org/10.1109/TCST.2015.2475399 CrossRefGoogle Scholar
  13. 13.
    Karaman S, Walter MR, Perez A, Frazzoli E, Teller S (2011) Anytime motion planning using the rrt*. IEEE International Conference on Robotics and Automation. vol 2011, pp 1478–1483  https://doi.org/10.1109/ICRA.2011.5980479 Google Scholar
  14. 14.
    Kuffner JJ, LaValle SM (2000) Rrt-connect: An efficient approach to single-query path planning. Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065). vol 2, pp 995–1001  https://doi.org/10.1109/ROBOT.2000.844730 Google Scholar
  15. 15.
    Kuwata Y, Teo J, Fiore G, Karaman S, Frazzoli E, How JP (2009) Real-time motion planning with applications to autonomous urban driving. Ieee Trans Control Syst Technol 17(5):1105–1118.  https://doi.org/10.1109/TCST.2008.2012116 CrossRefGoogle Scholar
  16. 16.
    LaValle SM (2006) Planning Algorithms. Cambridge University Press, CambridgeCrossRefzbMATHGoogle Scholar
  17. 17.
    Likhachev M, Gordon GJ, Thrun S (2004) Ara* : Anytime a* with provable bounds on sub-optimality. In: Thrun S, Saul LK, Schölkopf B (eds) Advances in Neural Information Processing Systems, vol 16. MIT Press, pp 767–774 (http://papers.nips.cc/paper/2382-ara-anytime-a-with-provable-bounds-on-sub-optimality.pdf)Google Scholar
  18. 18.
    Mendonca P, Goodwin S (2015) C‑theta*: Cluster based path-planning on grids. 2015 International Conference on Computational Science and Computational Intelligence (CSCI), pp 605–608  https://doi.org/10.1109/CSCI.2015.92 Google Scholar
  19. 19.
    Čáp M, Novák P, Vokřínek J, Pěchouček M (2013) Multi-agent rrt*: Sampling-based cooperative pathfinding. Autonomous Robots and Multirobot Systems Workshop at AAMAS, 2013Google Scholar
  20. 20.
    Russell SJ, Norvig P (1995) Artificial intelligence – a modern approach. Prentice Hall series in artificial intelligence. Prentice Hall, Englewood CliffszbMATHGoogle Scholar
  21. 21.
    Samek J, Sislak D, Volf P, Pechoucek M (2007) Multi-party collision avoidance among unmanned aerial vehicles. In: IEEE/WIC/ACM International Conference on Intelligent Agent Technology. IAT ’07, 2007, pp 403–406  https://doi.org/10.1109/IAT.2007.58 Google Scholar
  22. 22.
    Tazaki Y, Suzuki T (2014) Multi-robot scheduling and trajectory planning using state roadmap. Proceedings of the SICE Annual Conference (SICE), 2014, pp 1272–1277  https://doi.org/10.1109/SICE.2014.6935263 (IEEE)Google Scholar
  23. 23.
    Yang C, Hu Q, Shi C (2007) Automated collision resolution for vessel traffic management by using cooperative multi-agent negotiation. In: IEEE 7th International Conference on ITS Telecommunications, 06.06.2007–08.06.2007. vol 6. p 1  https://doi.org/10.1109/ITST.2007.4295919 Google Scholar

Copyright information

© Springer-Verlag GmbH Deutschland, ein Teil von Springer Nature 2019

Authors and Affiliations

  1. 1.Karlsruhe Institute of Technology (KIT)Institute for Control Systems (IRS)KarlsruheGermany

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