Advertisement

Risk Based Navigation Decisions

  • Anne Spalanzani
  • Jorge Rios-Martinez
  • Christian Laugier
  • Sukhan Lee

Abstract

This chapter addresses autonomous navigation in populated and dynamic environments. Unlike static or controlled environments where global path planning approaches are suitable, dealing with highly dynamic and uncertain environments requires to address simultaneously many difficult issues: the detection and tracking of the moving obstacles, the prediction of the future state of the world, and the online motion planning and navigation. In the last few years, the problem of incomplete, uncertain, and changing information in the navigation problem domain has gained even more interest in the robotic community and probabilistic frameworks aiming to integrate and elaborate properly such information have been developed. This chapter is divided into three sections: First section introduces the main challenge of this approach. Section 2 focuses on navigation using prediction of the near future and Sect. 3 discusses on integrating human in the navigation decision scheme.

Keywords

Gaussian Process Personal Space Social Convention Autonomous Navigation Occupancy Grid 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Aoude GS, Luders BD, Levine DS, How JP (2010) Threat-aware path planning in uncertain urban environments. In: Proceedings of the 2010 IEEE/RSJ international conference on intelligent robots and systems, Taipei, 2010Google Scholar
  2. Bennewitz M, Burgard W (2003) Adapting navigation strategies using motion patterns of people. In: Proceedings of the IEEE international conference on robotics and automation, Taipei, pp 2000–2005. IEEEGoogle Scholar
  3. Bennewitz M, Burgard W, Cielniak G, Thrun S (2005) Learning motion patterns of people for compliant robot motion. Int J Robot Res 24(1):31–48CrossRefGoogle Scholar
  4. Chung SY, Huang HP (2010) A mobile robot that understands pedestrian spatial behaviors. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, pp 5861–5866Google Scholar
  5. Ciolek M, Kendon A (1980) Environment and the spatial arrangement of conversational encounters. Sociol Inq 50:237–271CrossRefGoogle Scholar
  6. Elfes A (1989) Using occupancy grids for moble robot perception and navigation. Computer, vol 22, pp 4657CrossRefGoogle Scholar
  7. Fulgenzi C (2009) Autonomous navigation in dynamic uncertain environment using probabilistic models of perception and collision risk prediction. PhD thesis, Institut National Polytechnique de Grenoble – INPG. http://tel.archives-ouvertes.fr/tel-00398055/en/
  8. Fulgenzi C, Tay C, Spalanzani A, Laugier C (2008) Probabilistic navigation in dynamic environment using rapidly-exploring random trees and gaussian processes. In: IEEE/RSJ 2008 international conference on intelligent robots and systems, France Nice, 2008. http://hal.inria.fr/inria-00332595/en/
  9. Fulgenzi C, Spalanzani A, Laugier C (2009) Probabilistic motion planning among moving obstacles following typical motion patterns. In: IEEE/RSJ international conference on intelligent robots and systems, St. Louis, Missouri États-Unis d’Amérique, 2009. http://hal.inria.fr/inria-00398059/en/
  10. Gockley R, Forlizzi J, Simmons R (2007) Natural person following behavior for social robots. Proceeding of HRI07, 2007Google Scholar
  11. Hall ET (1966) The hidden dimension: man’s use of space in public and private. The Bodley Head, LondonGoogle Scholar
  12. Hansen ST, Svenstrup M, Andersen HJ, Bak T (2009) Adaptive human aware navigation based on motion pattern analysis. In: The 18th IEEE international symposium on robot and human interactive communication, Toyama, 2009Google Scholar
  13. Junejo I, Javed O, Shah M (2004) Multi feature path modeling for video surveillance. In: Proceedings of the 17th international conference on pattern recognition, Cambridge, UK. August 2004. vol 2. pp 716–719Google Scholar
  14. Kendon A (2010) Spacing and orientation in co-present interaction. In: Development of multimodal interfaces: active listening and synchrony. Lecture notes in computer science, vol 5967. Springer, Berlin/Heidelberg, pp 1–15CrossRefGoogle Scholar
  15. Kirby R, Simmons R, Forlizzi J (2009) Companion: a constraint-optimizing method for person acceptable navigation. In: The 18th IEEE international symposium on robot and human interactive communication, Toyama, Japan, 2009Google Scholar
  16. Laga H, Amaoka T (2009) Modeling the spatial behavior of virtual agents in groups for non-verbal communication in virtual worlds. In: IUCS ’09, Tokyo, Japan, 2009Google Scholar
  17. LaValle S, Kuffner JJ Jr (2007) Randomized kinodynamic planning. Int J Robot Res 26:997–1024Google Scholar
  18. LaValle SM, Sharma R (1997) On motion planning in changing, partially-predictable environments. Int J Robot Res 16:775–805CrossRefGoogle Scholar
  19. Makris D, Ellis T (2001) Finding paths in video sequences abstract. In: British machine vision conference, Manchester, 2001, pp 263–272Google Scholar
  20. Meng Keat Christopher T (2009) Analysis of dynamic scenes: application to driving assistance. PhD in computer science, Institut National Polytechnique de Grenoble (INPG)Google Scholar
  21. Petti S, Fraichard T (2005) Safe motion planning in dynamic environments. In: Proceedings of the 2005 IEEE/RSJ international conference on intelligent robots and systems, Alberta, Canada, 2005Google Scholar
  22. Rios-Martinez J, Spalanzani A, Laugier C (2011) Probabilistic autonomous navigation using rist-rrt approach and models of human interaction. In: Proceedings of the 2011 IEEE/RSJ international conference on intelligent robots and systems, San Francisco, USA, 2011Google Scholar
  23. Sisbot EA, Marin-Urias LF, Alami R, Simeon T (2007) A human aware mobile robot motion planner. IEEE Trans Robot 23(5):874–883CrossRefGoogle Scholar
  24. Tay C, Laugier C (2007) Modelling paths using gaussian processes. In: Proceedings of the international conference on field and service robotics, Chamonix, 2007. http://emotion.inrialpes.fr/bibemotion/2007/TL07
  25. Trung-Dung Vu, Olivier Aycard NA (2007) Online localization and mapping with moving object tracking in dynamic outdoor environments. In: IEEE intelligent vehicles symposium, Istanbul, 2007Google Scholar
  26. Vasquez Govea DA (2007) Incremental Learning for Motion Prediction of Pedestrians and Vehicles. PhD thesis, Institut National Polytechnique de Grenoble, Grenoble (Fr)Google Scholar

Copyright information

© Springer-Verlag London Ltd. 2012

Authors and Affiliations

  • Anne Spalanzani
    • 1
  • Jorge Rios-Martinez
    • 2
  • Christian Laugier
    • 2
  • Sukhan Lee
    • 3
  1. 1.UPMF-Grenoble 2/INRIA Rhône-Alpes/Lig UMRGrenobleFrance
  2. 2.e-Motion Project-TeamINRIA Rhône-AlpesSaint Ismier CedexFrance
  3. 3.School of Information and Communication Engineering, Department of Interaction ScienceISRI (Intelligent Systems Research Institute), Sungkyunkwan UniversityJangan-guRep. of Korea (South Korea)

Personalised recommendations