Advertisement

A Novel Path Planning Approach for Robotic Navigation Using Consideration Within Crowds

  • Ross Walker
  • Tony J. Dodd
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9287)

Abstract

This paper presents a novel approach towards mobile robotic path planning within a pedestrian environment, focused on being considerate towards crowd members. Through predicting pedestrian movement, with ellipses used to encompass the uncertainty, a modified Voronoi diagram is proposed to create a roadmap through the environment. Predictions of pedestrian trajectories are used to generate collision-free paths that minimise congestion and are considerate to the overall crowd flow. The results demonstrate that the robot’s movement allows potential collisions to be recognised in advance and avoided before they can develop.

Keywords

Robotic navigation Pedestrian Crowds Path planning Voronoi diagram Consideration 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zeng, S., Weng, J.: Obstacle avoidance through incremental learning with attention selection. In: Proceedings of the 2004 IEEE International Conference on Robotics and Automation, ICRA 2004, vol. 1, pp. 115–121 (2004)Google Scholar
  2. 2.
    Trautman, P., Krause, A.: Unfreezing the robot: navigation in dense, interacting crowds. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 797–803 (2010)Google Scholar
  3. 3.
    Helbing, D.: Models for Pedestrian Behavior (1998). eprint arXiv:cond-mat/9805089
  4. 4.
    Pellegrini, S., et al.: You’ll never walk alone: modeling social behavior for multi-target tracking. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 261–268 (2009)Google Scholar
  5. 5.
    Jin, L., et al.: A sweepline algorithm for euclidean voronoi diagram of circles. Computer-Aided Design 38(3), 260–272 (2006)CrossRefGoogle Scholar
  6. 6.
    Emiris, I.Z., et al.: Exact voronoi diagram of smooth convex pseudo-circles: General predicates, and implementation for ellipses. Computer Aided Geometric Design 30(8), 760–777 (2013)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Lhner, R.: On the modeling of pedestrian motion. Applied Mathematical Modelling 34(2), 366–382 (2010)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Huber, M., et al.: Adjustments of speed and path when avoiding collisions with another pedestrian. PLoS ONE 9(2), e89589 (2014). http://dx.doi.org/10.1371%2Fjournal.pone.0089589
  9. 9.
    Pelechano, N., et al.: Virtual Crowds: Methods, Simulation, and Control. Morgan & Claypool Publishers (2008)Google Scholar
  10. 10.
    Hughes, R.L.: The flow of human crowds. Annual Review of Fluid Mechanics 35(1), 169–182 (2003)CrossRefGoogle Scholar
  11. 11.
    Bellomo, N., Dogbé, C.: On the modelling crowd dynamics from scaling to hyperbolic macroscopic models. Mathematical Models and Methods in Applied Sciences 18(supp01), 1317–1345 (2008)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Helbing, D., Molnár, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51, 4282–4286 (1995)CrossRefGoogle Scholar
  13. 13.
    Henry, P., et al.: Learning to navigate through crowded environments. In: IEEE International Conference on Robotics and Automation, pp. 981–986 (2010)Google Scholar
  14. 14.
    Choset, H., et al.: Principles of Robot Motion: Theory, Algorithms, and Implementations. MIT Press, Cambridge (2005)Google Scholar
  15. 15.
    Burgard, W., et al.: The interactive museum tour-guide robot. In: Proc. of the Fifteenth National Conference on Artificial Intelligence (AAAI 1998) (1998)Google Scholar
  16. 16.
    Thrun, S., et al.: Minerva: a second-generation museum tour-guide robot. In: Proceedings of the 1999 IEEE International Conference on Robotics and Automation, vol. 3, pp. 1999–2005 (1999)Google Scholar
  17. 17.
    Fox, D., et al.: A hybrid collision avoidance method for mobile robots. In: Proc. of the IEEE International Conference on Robotics and Automation 1998 (1998)Google Scholar
  18. 18.
    Yoshimi, T., et al.: Development of a person following robot with vision based target detection. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5286–5291, October 2006Google Scholar
  19. 19.
    Walters, M., et al.: The influence of subjects’ personality traits on personal spatial zones in a human-robot interaction experiment. In: IEEE International Workshop on Robot and Human Interactive Communication, pp. 347–352 (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Automatic Control and Systems EngineeringThe University of SheffieldSheffieldUK

Personalised recommendations