PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Algorithms for the Safe Management of Autonomous Vehicles

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Konferencja
Federated Conference on Computer Science and Information Systems (16 ; 02-05.09.2021 ; online)
Języki publikacji
EN
Abstrakty
EN
We deal here with a fleet of autonomous vehicles, devoted to internal logistics inside some protected area. This fleet is ruled by a hierarchical supervision architecture, which, at the top level distributes and schedules the tasks, and, at the lowest level, ensures local safety. We focus here on the top level, while introducing a time dependent estimation of the risk induced by the traversal of any arc. We set a model, state structural results, and design a bi-level algorithm and a A* routing/scheduling algorithms which both aim at a well-fitted compromise between speed and risk and rely on reinforcement learning.
Rocznik
Tom
Strony
153--162
Opis fizyczny
Bibliogr. 23 poz., il., tab.
Twórcy
  • LIMOS Lab. CNRS/UCA, Clermont-Ferrand, France
  • Labex IMOBS3, LIMOS Lab. CNRS/UCA, Clermont-Ferrand, France
  • Labex IMOBS3, LIMOS Lab. CNRS/UCA, Clermont-Ferrand, France
  • HEUDYASIC Lab.CNRS and UTC, Compiègne, France
autor
  • LIMOS and InstitutPascal Labs UCA/CNRS, Clermont-Ferrand, France
Bibliografia
  • 1. Amazon.com, inc. amazon prime air. [online]., Available http://www.amazon.com/primeair (2013).
  • 2. C. Artigues, E. Hébrard, A. Quilliot, H. Toussaint: “Models and algorithms for natural disaster evacuation problems”. Proceedings of the 2019 FEDCSIS WCO Conference, p 143-146, (2019). http://dx.doi.org/http://dx.doi.org/10.15439/978-83-952357-8-8
  • 3. B. Bakker, S. Whiteson, L. J. Kester, F. Groen: “Traffic light control by multi-agent reinforcement learning systems”; In Interactive Collaborative Information Systems, (2010). http://dx.doi.org/10.1007/978-3-642-11688-9_18
  • 4. B. Berbeglia, J-F. Cordeau, J-F., I. Gribkovskaïa, G. Laporte: “Static pick up and delivery problems : a classification scheme and survey”. TOP: An Official Journal of the Spanish Society of Statistics and Operations Research 15, p 1-31, (2007). http://dx.doi.org/10.1007/s11750-007-0009-0
  • 5. L. Chen, C. Englund: “Cooperative intersection management: a survey”; IEEE Transactions on Intelligent Transportation Systems 17-2, p 570-586, (2016). http://dx.doi.org/10.1109/TITS.2015.2471812
  • 6. D. Duque, L.Lozano, A.L.Medaglia. An exact method for the biobjective shortest path problemfor large-scale road network. EJOR 242, p 788-795, (2015). http://dx.doi.org/10.1016/j.ejor.2014.11.003
  • 7. S. Fidanova, O. Roeva, M. Ganzha: “Ant colony optimization algorithm for fuzzy transport modelling”. Proceedings of the 2020 FEDCSIS WCO Conference, p 237-240, (2020). http://dx.doi.org/ http://dx.doi.org/10.15439/978-83-955416-7-4
  • 8. A. Franceschetti, E. Demir, D. Honhon, T. Van Woensel, G. Laporte, and M. Stobbe. “A metaheuristic for the time dependent pollution- routing problem”; European Journal of Operational Research, 259 (3): 972 – 991, (2017). http://dx.doi.org/10.1016/j.ejor.2016.11.026
  • 9. S. Bsaybes, A.Quilliot, A.Wagler: “Fleet management for autonomous vehicles using multicommodity coupled flows in time-expanded networks”; 17th International Symposium on Experimental Algorithms (SEA 2018) (LIPIcs) 103, (2018). http://dx.doi.org/10.4230/LIPIcs.SEA.2018.25
  • 10. M.Krzyszton: “Adapative supervison: method of reinforcement learning fault elimination by application of supervised learning”. Proceedings of the 2018 FEDCSIS AI Conference, p 139-149, (2018). http://dx.doi.org/http://dx.doi.org/10.15439/978-83-949419-5-6
  • 11. J. Kumar, V. V. Ranga: “Multi-robot coordination analysis, taxonomy, challenge and future scope”; Journal of Intelligent and Robotic Systems, 102:10, (2021). https://doi.org/10.1007/s10846-021-01378-2
  • 12. T. Le-Anh, M. B. De Koster:: “A review of design and control of automated guided vehicle systems” European Journal of Operational Research, 171, 1-23, (2006). https://doi.org/10.1016/j.ejor.2005.01.036
  • 13. Y.Li, E.Fadda, D.Manerba, R.Tadei, O.Terzo: “ Reinforcement learning algorithms for online single machine scheduling“. Proceedings of the 2020 FEDCSIS WCO Conference, p 277-283, (2020). http://dx.doi.org/http://dx.doi.org/10.15439/978-83-949419-5-6
  • 14. Nilsson, J.: Artificial Intelligence; SpringerY, (1982). ISBN 978-3-540-11340-9
  • 15. Philippe, C., Adouane, L., Tsourdos, A., Shin, H.S., Thuilot, B. : “Probability collective algorithm applied to decentralized coordination of autonomous vehicles”; 2019 IEEE Intelligent Vehicles Symp., 1928–34. IEEE, Paris (2019). http://dx.doi.org/10.1109/IVS.2019.8813827
  • 16. V. Pimenta, A. Quilliot, H. Toussaint, D. Vigo: “Models and algorithms for reliability oriented DARP with autonomous vehicles”; European Journ. of Operat. Res., 257, 2, p 601-613, (2016). http://dx.doi.org/10.1016/j.ejor.2016.07.037.
  • 17. Y. Rizk, M. Awad, E. Tunstel: “Cooperative heterogenous mutlti-robot systems: a survey”; ACM Computing Surveys 29, (2019). https://doi.org/10.1145/3303848
  • 18. C. Ryan, F. Murphy, F., Mullins, M.: “Spatial risk modelling of behavioural hotspots: Risk aware paths planning for autonomous vehicles”; Transportation Research A 134, p 152-163 (2020). http://dx.doi.org/10.1016/j.tra.2020.01.024
  • 20. K.Stoilova, T.Stoilov: “Bi-level optimization application for urban traffic management”. Proceedings of the 2020 FEDCSIS WCO Conference, p 327-336, (2020). http://dx.doi.org/http://dx.doi.org/10.15439/978-83-949419-5-6
  • 21. K. C. Vivaldini, G. Tamashiro, J. Martins Junior, M. Becker: “Communication infrastructure in the centralized management system for intelligent warehouses”. In: Neto, P., Moreira, A.P., et al. (eds.) WRSM 2013. CCIS, vol. 371, pp. 127–136. Springer, (2013)
  • 22. I. F. Vis: “Survey of research in the design and control of AGV systems”. European Journal of Operations Research 170:677–709, (2016). http://dx.doi.org/10.1016/j.ejor.2004.09.020
  • 23. J. Wojtuziak, T. Warden, O. Herzog: “Machine learning in agent based stochastic simulation: Inferential theory and evaluation in transportation logistics”; Computer and Mathematics with Applications 64, p 3658-3665, (2012). https://doi.org/10.1016/j.camwa.2012.01.079
  • 25. M. Zhang, R. Batta, R. Nagi R (2008): “Modeling of workflow congestion and optimization of flow routing in a manufacturing/warehouse facility”. Management Sciences 55:267–280, (2008). http://dx.doi.org/10.1287/mnsc.1080.0916
Uwagi
1. Present work was funded by French ANR: National Agency for Research, and Labex IMOBS3, as well as by Region AURA: Auvergne Rhône Alpes.
2. Track 1: Artificial Intelligence in Applications
3. Session: 14th International Workshop on Computational Optimization
4. Błędna numeracja bibliografii
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-dc9ac680-3ea9-48d1-a74b-b8b52ea49a60
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.