Advertisement

The Visual Computer

, Volume 28, Issue 12, pp 1209–1227 | Cite as

Parallelized egocentric fields for autonomous navigation

  • Mubbasir KapadiaEmail author
  • Shawn Singh
  • William Hewlett
  • Glenn Reinman
  • Petros Faloutsos
Original Article

Abstract

In this paper, we propose a general framework for local path-planning and steering that can be easily extended to perform high-level behaviors. Our framework is based on the concept of affordances: the possible ways an agent can interact with its environment. Each agent perceives the environment through a set of vector and scalar fields that are represented in the agent’s local space. This egocentric property allows us to efficiently compute a local space-time plan and has better parallel scalability than a global fields approach. We then use these perception fields to compute a fitness measure for every possible action, defined as an affordance field. The action that has the optimal value in the affordance field is the agent’s steering decision. We propose an extension to a linear space-time prediction model for dynamic collision avoidance and present our parallelization results on multicore systems. We analyze and evaluate our framework using a comprehensive suite of test cases provided in SteerBench and demonstrate autonomous virtual pedestrians that perform steering and path planning in unknown environments along with the emergence of high-level responses to never seen before situations.

Keywords

Affordance Egocentric Steering Space-time planning 

Notes

Acknowledgements

The work in this paper was partially supported by Intel through a Visual Computing grant, and the donation of a 32-core Emerald Ridge system with Xeon processors X7560. In particular, we would like to thank Randi Rost, Scott Buck, and Mitchell Lum from Intel for their support.

Supplementary material

(MP4 57.7 MB)

References

  1. 1.
    Altun, K., Koku, A.: Evaluation of egocentric navigation methods. In: IEEE International Workshop on Robot and Human Interactive Communication (ROMAN 2005), pp. 396–401 (2005). doi: 10.1109/ROMAN.2005.1513811 CrossRefGoogle Scholar
  2. 2.
    Arkin, R.: Motor schema based navigation for a mobile robot: An approach to programming by behavior. In: IEEE International Conference on Robotics and Automation. Proceedings, 1987, vol. 4, pp. 264–271 (1987). doi: 10.1109/ROBOT.1987.1088037 Google Scholar
  3. 3.
    Boulic, R.: Relaxed steering towards oriented region goals. In: Motion in Games, First International Workshop, pp. 176–187 (2008) CrossRefGoogle Scholar
  4. 4.
    Chao, G., Dyer, M.: Concentric spatial maps for neural network based navigation. In: Ninth International Conference on Artificial Neural Networks (ICANN 99) (Conf. Publ. No. 470), vol. 1, pp. 144–149 (1999) CrossRefGoogle Scholar
  5. 5.
    Chenney, S.: Flow tiles. In: Proceedings of the ACM SIGGRAPH/EG Symposium on Computer Animation (2004). doi:http://doi.acm.org/10.1145/1028523.1028553 Google Scholar
  6. 6.
    Clements, R.R., Hughes, R.L.: Mathematical modelling of a medieval battle: the battle of Agincourt, 1415. Math. Comput. Simul. 64(2), 259–269 (2004) MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Dechter, R., Pearl, J.: Generalized best-first search strategies and the optimality of a*. J. ACM 32(3), 505–536 (1985). doi:http://doi.acm.org/10.1145/3828.3830 MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Farenc, N., Schweiss, E., Kallmann, M., Aune, O., Boulic, R., Thalmann, D.: A paradigm for controlling virtual humans in urban environment simulations. Appl. Artif. Intell. 14, 69–91 (1999) CrossRefGoogle Scholar
  9. 9.
    Fiorini, P., Shiller, Z.: Motion planning in dynamic environments using velocity obstacles. Int. J. Robot. Res. 17(7), 760–772 (1998). doi: 10.1177/027836499801700706. URL http://ijr.sagepub.com/cgi/content/abstract/17/7/760 CrossRefGoogle Scholar
  10. 10.
    Fleming, P.: Implementing a robust 3 dimensional egocentric navigation system. Master’s thesis, Graduate School of Vanderbilt University (2005) Google Scholar
  11. 11.
    Gibson, J.J.: The theory of affordances. In: Perceiving, Acting, and Knowing (1977) Google Scholar
  12. 12.
    Goldenstein, S., Karavelas, M., Metaxas, D., Guibas, L., Aaron, E., Goswami, A.: Scalable nonlinear dynamical systems for agent steering and crowd simulation (2001) Google Scholar
  13. 13.
    Greeno, J.G.: Gibson’s affordances. Psychol. Rev. 336–342 (1994) Google Scholar
  14. 14.
    Hart, P., Nilsson, N., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968). doi: 10.1109/TSSC.1968.300136 CrossRefGoogle Scholar
  15. 15.
    Hart, P.E., Nilsson, N.J., Raphael, B.: Correction to “a formal basis for the heuristic determination of minimum cost paths”. SIGART Bull. 37, 28–29 (1972). doi:http://doi.acm.org/10.1145/1056777.1056779 CrossRefGoogle Scholar
  16. 16.
    Heck, R., Gleicher, M.: Parametric motion graphs. In: Proceedings of the 2007 Symposium on Interactive 3D Graphics and Games, I3D ’07, pp. 129–136. ACM, New York (2007). doi:http://doi.acm.org/10.1145/1230100.1230123 CrossRefGoogle Scholar
  17. 17.
    Helbing, D., Molnár, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282–4286 (1995). doi: 10.1103/PhysRevE.51.4282 CrossRefGoogle Scholar
  18. 18.
    Helbing, D., Buzna, L., Johansson, A., Werner, T.: Self-organized pedestrian crowd dynamics: Experiments, simulations, and design solutions. Transp. Sci. 39(1), 1–24 (2005). doi: 10.1287/trsc.1040.0108 CrossRefGoogle Scholar
  19. 19.
    Hoogendoorn, S.P.: Pedestrian travel behavior modeling. In: 10th International Conference on Travel Behavior Research, Lucerne, pp. 507–535 (2003) Google Scholar
  20. 20.
    Kant, K., Zucker, S.W.: Planning collision-free trajectories in time-varying environments: a two-level hierarchy. Vis. Comput. 3(5), 304–313 (1988) CrossRefGoogle Scholar
  21. 21.
    Kapadia, M., Singh, S., Hewlett, W., Faloutsos, P.: Egocentric affordance fields in pedestrian steering. In: Proceedings of the 2009 Symposium on Interactive 3D Graphics and Games, I3D ’09, pp. 215–223. ACM, New York (2009). doi:http://doi.acm.org/10.1145/1507149.1507185 CrossRefGoogle Scholar
  22. 22.
    Lamarche, F., Donikian, S.: Crowd of virtual humans: a new approach for real time navigation in complex and structured environments. In: Computer Graphics Forum, vol. 23 (2004) Google Scholar
  23. 23.
    Lee, K.H., Choi, M.G., Hong, Q., Lee, J.: Group behavior from video: a data-driven approach to crowd simulation. In: Proceedings of the ACM SIGGRAPH/EG Symposium on Computer Animation, pp. 109–118 (2007) Google Scholar
  24. 24.
    Lerner, A., Chrysanthou, Y., Lischinski, D.: Crowds by example. Comput. Graph. Forum 26(3), 655–664 (2007) CrossRefGoogle Scholar
  25. 25.
    Li, T.Y., Chen, P.F., Huang, P.Z.: Motion planning for humanoid walking in a layered environment. In: Proceedings of IEEE ICRA, vol. 3, pp. 3421–3427 (2003). doi: 10.1109/ROBOT.2003.1242119 Google Scholar
  26. 26.
    Loscos, C., Marchal, D., Meyer, A.: Intuitive crowd behaviour in dense urban environments using local laws. In: TPCG ’03: Proceedings of the Theory and Practice of Computer Graphics 2003, p. 122. IEEE Comput. Soc., Washington (2003) CrossRefGoogle Scholar
  27. 27.
    Lovas, G.: Modeling and simulation of pedestrian traffic flow. In: Transportation Research Record, pp. 429–443 (1994) Google Scholar
  28. 28.
    Michael, D., Chrysanthou, Y.: Automatic high level avatar guidance based on affordance of movement. In: Eurographics 2003. Eurographics Association, Geneva (2003) Google Scholar
  29. 29.
    Milazzo, J., Rouphail, N., Hummer, J., Allen, D.: The effect of pedestrians on the capacity of signalized intersections. In: Transportation Research Record, pp. 37–46 (1998) Google Scholar
  30. 30.
    Paris, S., Pettré, J., Donikian, S.: Pedestrian reactive navigation for crowd simulation: a predictive approach. In: EUROGRAPHICS 2007, vol. 26, pp. 665–674 (2007) Google Scholar
  31. 31.
    Paris, S., Gerdelan, A., O’Sullivan, C.: Ca-lod: Collision avoidance level of detail for scalable, controllable crowds. In: MIG ’09: Proceedings of the 2nd International Workshop on Motion in Games, pp. 13–28. Springer, Berlin (2009) Google Scholar
  32. 32.
    Park, S.I., Shin, H.J., Shin, S.Y.: On-line locomotion generation based on motion blending. In: Proceedings of the 2002 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA ’02, pp. 105–111. ACM, New York (2002). doi:http://doi.acm.org/10.1145/545261.545279 CrossRefGoogle Scholar
  33. 33.
    Pelechano, N., Allbeck, J.M., Badler, N.I.: Controlling individual agents in high-density crowd simulation. In: Proceedings of the ACM SIGGRAPH/EG Symposium on Computer Animation, pp. 99–108 (2007) Google Scholar
  34. 34.
    Pelechano, N., Allbeck, J., Badler, N.: Virtual Crowds: Methods, Simulation, and Control. Synthesis Lectures on Computer Graphics and Animation. Morgan & Claypool Publishers, San Francisco (2008) Google Scholar
  35. 35.
    Quinn, M.J., Metoyer, R.A., Hunter-zaworski, K.: Parallel implementation of the social forces model. In: Proceedings of the Second International Conference in Pedestrian and Evacuation Dynamics, pp. 63–74 (2003) Google Scholar
  36. 36.
    Reynolds, C.W.: Flocks, herds and schools: A distributed behavioral model. In: Proceedings of ACM SIGGRAPH, pp. 25–34. ACM, New York (1987) Google Scholar
  37. 37.
    Reynolds, C.: Steering behaviors for autonomous characters. In: Game Developers Conference (1999) Google Scholar
  38. 38.
    Rodrigues, R.A., Lima Bicho, A., Paravisi, M., Jung, C.R., Magalhães, L.P., Musse, S.R.: Tree paths: A new model for steering behaviors. In: Proceedings of the 9th International Conference on Intelligent Virtual Agents, IVA ’09, pp. 358–371. Springer, Berlin (2009) Google Scholar
  39. 39.
    Rudomín, I., Millán, E., Hernández, B.: Fragment shaders for agent animation using finite state machines. Simul. Model. Pract. Theory 13(8), 741–751 (2005) CrossRefGoogle Scholar
  40. 40.
    Shao, W., Terzopoulos, D.: Autonomous pedestrians. In: Proceedings of the ACM SIGGRAPH/EG Symposium on Computer Animation, pp. 19–28 (2005) CrossRefGoogle Scholar
  41. 41.
    Shao, W., Terzopoulos, D.: Autonomous pedestrians. Graph. Models 69, 246–274 (2007). doi: 10.1016/j.gmod.2007.09.001. URL http://portal.acm.org/citation.cfm?id=1323742.1323926 CrossRefGoogle Scholar
  42. 42.
    Shapiro, A., Kallmann, M., Faloutsos, P.: Interactive motion correction and object manipulation. In: I3D ’07: Proceedings of the 2007 Symposium on Interactive 3D Graphics and Games, pp. 137–144. ACM, New York (2007). doi:http://doi.acm.org/10.1145/1230100.1230124 CrossRefGoogle Scholar
  43. 43.
    Shimoda, S., Kuroda, Y., Iagnemma, K.: Potential field navigation of high speed unmanned ground vehicles on uneven terrain. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation (ICRA 2005), pp. 2828–2833 (2005) CrossRefGoogle Scholar
  44. 44.
    Singh, S., Kapadia, M., Faloutsos, P., Reinman, G.: An open framework for developing, evaluating, and sharing steering algorithms. In: MIG ’09: Proceedings of the 2nd International Workshop on Motion in Games, pp. 158–169. Springer, Berlin (2009) Google Scholar
  45. 45.
    Singh, S., Kapadia, M., Faloutsos, P., Reinman, G.: Steerbench: a benchmark suite for evaluating steering behaviors. In: Computer Animation and Virtual Worlds, pp. 533–548 (2009) Google Scholar
  46. 46.
    Singh, S., Kapadia, M., Reinmann, G., Faloutsos, P.: On the interface between steering and animation for autonomous characters. In: Workshop on Crowd Simulation, Computer Animation and Social Agents, Saint-Malo, France (2010) Google Scholar
  47. 47.
    Singh, S., Kapadia, M., Hewlett, W., Reinmann, G., Faloutsos, P.: A modular framework for adaptive agent-based steering. In: Proceedings of the 2011 Symposium on Interactive 3D Graphics and Games, I3D ’11. ACM, New York (2011) Google Scholar
  48. 48.
    Sud, A., Gayle, R., Andersen, E., Guy, S., Lin, M., Manocha, D.: Real-time navigation of independent agents using adaptive roadmaps. In: VRST ’07: Proceedings of the 2007 ACM Symposium on Virtual Reality Software and Technology, pp. 99–106. ACM, New York (2007) CrossRefGoogle Scholar
  49. 49.
    Surasmith, S.: Preprocessed solution for open terrain navigation. In: AI Game Programming Wisdom, pp. 161–170 (2002) Google Scholar
  50. 50.
    Takeuchi, R., Unuma, M., Amakawa, K.: Path planning and its application to human animation system. In: Creating and Animating the Virtual World, pp. 163–175. Springer, New York (1992). URL http://portal.acm.org/citation.cfm?id=141248.141259 CrossRefGoogle Scholar
  51. 51.
    Tecchia, F., Loscos, C., Conroy, R., Chrysanthou, Y.: Agent behaviour simulator (abs): A platform for urban behaviour development. In: GTEC’2001, pp. 17–21 (2001) Google Scholar
  52. 52.
    Thalmann, D., Musse, S.R.: Crowd Simulation. Springer, Berlin (2007) Google Scholar
  53. 53.
    Torrens, D.P.M.: Behavioral intelligence for geospatial agents in urban environments. In: IAT ’07: Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, pp. 63–66. IEEE Comput. Soc., Washington (2007). doi: 10.1109/IAT.2007.37 CrossRefGoogle Scholar
  54. 54.
    Treuille, A., Cooper, S., Popović, Z.: Continuum crowds. ACM Trans. Graph. 25(3), 1160–1168 (2006). doi:http://doi.acm.org/10.1145/1141911.1142008 CrossRefGoogle Scholar
  55. 55.
    Trovato, K.I., Dorst, L.: Differential a*. IEEE Trans. Knowl. Data Eng. 14(6), 1218–1229 (2002). doi: 10.1109/TKDE.2002.1047763 CrossRefGoogle Scholar
  56. 56.
    Tsubouchi, T., Kuramochi, S., Arimoto, S.: Iterated forecast and planning algorithm to steer and drive a mobile robot in the presence of multiple moving objects. In: IROS ’95: Proceedings of the International Conference on Intelligent Robots and Systems, vol. 2, p. 2033. IEEE Comput. Soc., Washington (1995) Google Scholar
  57. 57.
    Turner, A., Penn, A.: Encoding natural movement as an agent-based system: an investigation into human pedestrian behaviour in the built environment. Environ. Plan. B, Plan. Des. 29, 473–490 (2002). http://eprints.ucl.ac.uk/73/ CrossRefGoogle Scholar
  58. 58.
    van den Berg, J., Lin, M.C., Manocha, D.: Reciprocal velocity obstacles for real-time multi-agent navigation. In: IEEE International Conference on Robotics and Automation, pp. 1928–1935. IEEE Press, New York (2008) CrossRefGoogle Scholar
  59. 59.
    van den Berg, J., Patil, S., Sewall, J., Manocha, D., Lin, M.: Interactive navigation of multiple agents in crowded environments. In: SI3D ’08: Proceedings of the 2008 Symposium on Interactive 3D Graphics and Games, pp. 139–147. ACM, New York (2008) CrossRefGoogle Scholar
  60. 60.
    Warren, C.: Global path planning using artificial potential fields. In: Proceedings of IEEE ICRA, vol. 1, pp. 316–321 (1989). doi: 10.1109/ROBOT.1989.100007 Google Scholar
  61. 61.
    Warren, C.: Multiple robot path coordination using artificial potential fields. In: Proceedings of IEEE ICRA, vol. 1, pp. 500–505 (1990). doi: 10.1109/ROBOT.1990.126028 Google Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Mubbasir Kapadia
    • 1
    • 2
    Email author
  • Shawn Singh
    • 1
    • 4
  • William Hewlett
    • 1
  • Glenn Reinman
    • 1
  • Petros Faloutsos
    • 1
    • 3
  1. 1.University of CaliforniaLos AngelesUSA
  2. 2.University of PennsylvaniaPhiladelphiaUSA
  3. 3.York UniversityTorontoCanada
  4. 4.Google Inc.Mountain ViewUSA

Personalised recommendations