Risk Based Navigation Decisions

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


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.


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.


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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)

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