The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.02 - Feb. (2014 vol.20)
pp: 159-171
Brian C. Ricks , Brigham Young University, Provo
Parris K. Egbert , Brigham Young University, Provo
ABSTRACT
Recent crowd simulation algorithms do path planning on complex surfaces by breaking 3D surfaces into a series of 2.5D planes. This allows for path planning on surfaces that can be mapped from 3D to 2D without distortion, such as multistory buildings. However, the 2.5D approach does not handle path planning on curved surfaces such as spheres, asteroids, or insect colonies. Additionally, the 2.5D approach does not handle the complexity of dynamic obstacle avoidance when agents can walk on walls or ceilings. We propose novel path planning and obstacle avoidance algorithms that work on surfaces as a whole instead of breaking them into a 2.5D series of planes. Our "whole surfaceâ approach simulates crowds on both multistory structures and highly curved topologies without changing parameters. We validate our work on a suite of 30 different meshes, some with over 100,000 triangles, with crowds of 1,000 agents. Our algorithm always averaged more than 40 FPS with virtually no stalling.
INDEX TERMS
Path planning, Collision avoidance, Navigation, Buildings, Topology, Heuristic algorithms, Solid modeling, path planning, Path planning, Collision avoidance, Navigation, Buildings, Topology, Heuristic algorithms, Solid modeling, obstacle avoidance, Crowd simulation, 3D crowd simulation
CITATION
Brian C. Ricks, Parris K. Egbert, "A Whole Surface Approach to Crowd Simulation on Arbitrary Topologies", IEEE Transactions on Visualization & Computer Graphics, vol.20, no. 2, pp. 159-171, Feb. 2014, doi:10.1109/TVCG.2013.110
REFERENCES
[1] F. Lamarche and S. Donikian, “Crowd of Virtual Humans: A New Approach for Real Time Navigation in Complex and Structured Environments,” Computer Graphics Forum, vol. 23, no. 3, pp. 509-518, 2004.
[2] S. Singh, M. Kapadia, B. Hewlett, G. Reinman, and P. Faloutsos, “A Modular Framework for Adaptive Agent-Based Steering,” Proc. Symp. Interactive 3D Graphics and Games, 2011.
[3] J. Sethian, “Fast Marching Methods,” SIAM Rev., vol. 41, pp. 199-235, 1999.
[4] R. Geraerts and M. Overmars, “The Corridor Map Method: A General Framework for Real-Time High-Quality Path Planning,” Computer Animation and Virtual Worlds, vol. 18, no. 2, pp. 107-119, 2007.
[5] M. Kallmann, “Navigation Queries from Triangular Meshes,” Proc. Third Int'l Conf. Motion in Games, pp. 230-241, 2010.
[6] A. Kamphuis, M. Mooijekind, D. Nieuwenhuisen, and M. Overmars, “Automatic Construction of Roadmaps for Path Planning in Games,” Proc. Int'l Conf. Computer Games: Artificial Intelligence, Design and Education, pp. 285-292, 2004.
[7] J. Pettré, P. Ciechomski, J. Maïm, B. Yersin, J. Laumond, and D. Thalmann, “Real-Time Navigating Crowds: Scalable Simulation and Rendering,” Proc. Computer Animation and Virtual Worlds, vol. 17, nos. 3/4, pp. 445-455, 2006.
[8] D. Hale, “A Growth-Based Approach to the Automatic Generation of Navigation Meshes,” dissertation, The Univ. of North Carolina Charlotte, 2012.
[9] S. Curtis, J. Snape, and D. Manocha, “Way Portals: Efficient Multi-Agent Navigation with Line-Segment Goals,” Proc. ACM SIGGRAPH Symp. Interactive 3D Graphics and Games, pp. 15-22, 2012.
[10] J. Mitchell, D. Mount, and C. Papadimitriou, “The Discrete Geodesic Problem,” SIAM J. Computing, vol. 16, no. 4, pp. 647-668, 1987.
[11] J. Chen and Y. Han, “Shortest Paths on a Polyhedron,” Proc. Sixth Ann. Symp. Computational Geometry, pp. 360-369, 1990.
[12] D. Martínez, L. Velho, and P. Carvalho, “Computing Geodesics on Triangular Meshes,” Computers and Graphics, vol. 29, no. 5, pp. 667-675, 2005.
[13] R. Kimmel and J. Sethian, “Computing Geodesic Paths on Manifolds,” Proc. Nat'l Academy of Sciences of the United States of Am., vol. 95, no. 15, p. 8431, 1998.
[14] C. Reynolds, “Flocks, Herds and Schools: A Distributed Behavioral Model,” Proc. ACM SIGGRAPH Computer Graphics, vol. 21, no. 4, pp. 25-34, 1987.
[15] D. Helbing and P. Molnar, “Social Force Model for Pedestrian Dynamics,” Physical Rev., vol. 51, no. 5, pp. 4282-4286, 1995.
[16] P. Fiorini and Z. Shiller, “Motion Planning in Dynamic Environments Using Velocity Obstacles,” The Int'l J. Robotics Research, vol. 17, no. 7, p. 760, 1998.
[17] J. Van den Berg, M. Lin, and D. Manocha, “Reciprocal Velocity Obstacles for Real-Time Multi-Agent Navigation,” Proc. Robotics and Automation Conf., pp. 1928-1935, 2008.
[18] S. Guy, J. Chhugani, S. Curtis, P. Dubey, M. Lin, and D. Manocha, “Pledestrians: A Least-Effort Approach to Crowd Simulation,” Proc. ACM SIGGRAPH/Eurographics Symp. Computer Animation, pp. 119-128, 2010.
[19] N. Courty and S. Musse, “Fastcrowd: Real-Time Simulation and Interaction with Large Crowds Based on Graphics Hardware,” Proc. ACM SIGGRAPH/EuroGraphics Symp. Computer Animation, pp. 177-187, 2004.
[20] R. Narain, A. Golas, S. Curtis, and M. Lin, “Aggregate Dynamics for Dense Crowd Simulation,” ACM Trans. Graphics, vol. 28, no. 5, p. 122, 2009.
[21] I. Karamouzas, P. Heil, P. van Beek, and M. Overmars, “A Predictive Collision Avoidance Model for Pedestrian Simulation,” Proc. Second Int'l Workshop Motion in Games, pp. 41-52, 2009.
[22] S. Singh, M. Kapadia, G. Reinman, and P. Faloutsos, “Footstep Navigation for Dynamic Crowds,” Computer Animation and Virtual Worlds, vol. 22, nos. 2/3, pp. 151-158, 2011.
[23] N. Pelechano, J. Allbeck, and N. Badler, “Controlling Individual Agents in High-Density Crowd Simulation,” Proc. ACM SIGGRAPH/Eurographics Symp. Computer Animation, pp. 99-108, 2007.
[24] A. Treuille, S. Cooper, and Z. Popović, “Continuum Crowds,” ACM Trans. Graphics, vol. 25, no. 3, pp. 1160-1168, 2006.
[25] H. Jiang, W. Xu, T. Mao, C. Li, S. Xia, and Z. Wang, “Continuum Crowd Simulation in Complex Environments,” Computers and Graphics, vol. 34, no. 5, pp. 537-544, 2010.
[26] M. Kapadia, M. Wang, S. Singh, G. Reinman, and P. Faloutsos, “Scenario Space: Characterizing Coverage, Quality, and Failure of Steering Algorithms,” Proc. ACM SIGGRAPH/Eurographics Symp. Computer Animation, pp. 53-62, 2011.
[27] M. Kapadia, M. Wang, G. Reinman, and P. Faloutsos, “Improved Benchmarking for Steering Algorithms,” Proc. Fourth Int'l Conf. Motion in Games, pp. 266-277, 2011.
[28] N. Pelechano, C. Stocker, J. Allbeck, and N. Badler, “Being a Part of the Crowd: Towards Validating VR Crowds Using Presence,” Proc. Seventh Int'l Joint Conf. Autonomous Agents and Multiagent Systems, pp. 136-142, 2008.
[29] S. Singh, M. Kapadia, P. Faloutsos, and G. Reinman, “SteerBench: A Benchmark Suite for Evaluating Steering Behaviors,” Computer Animation and Virtual Worlds, vol. 20, nos. 5/6, pp. 533-548, 2009.
[30] S. Singh, M. Naik, M. Kapadia, P. Faloutsos, and G. Reinman, “Watch Out! A Framework for Evaluating Steering Behaviors,” Proc. Conf. Motion in Games, pp. 200-209, 2008.
[31] S. Singh, M. Kapadia, P. Faloutsos, and G. Reinman, “An Open Framework for Developing, Evaluating, and Sharing Steering Algorithms,” Proc. Conf. Motion in Games, pp. 158-169, 2009.
[32] D. Thalmann and S.R. Musse, Crowd Simulation. Wiley Online Library, 2007.
[33] N. Pelechano, J. Allbeck, and N. Badler, “Virtual Crowds: Methods, Simulation, and Control,” Synthesis Lectures on Computer Graphics and Animation, vol. 3, no. 1, pp. 1-176, 2008.
[34] W. Shao and D. Terzopoulos, “Autonomous Pedestrians,” Proc. ACM SIGGRAPH/Eurographics Symp. Computer Animation, pp. 19-28, 2005.
[35] F. Lamarche, “Topoplan: A Topological Path Planner for Real Time Human Navigation under Floor and Ceiling Constraints,” Computer Graphics Forum, vol. 28, no. 2, pp. 649-658, 2009.
[36] H. Jiang, W. Xu, T. Mao, C. Li, S. Xia, and Z. Wang, “A Semantic Environment Model for Crowd Simulation in Multilayered Complex Environment,” Proc. 16th ACM Symp. Virtual Reality Software and Technology, pp. 191-198, 2009.
[37] L. Deusdado, A. Fernandes, and O. Belo, “Path Planning for Complex 3D Multilevel Environments,” Proc. 24th Spring Conf. Computer Graphics, pp. 187-194, 2008.
[38] W. van Toll, A. Cook, and R. Geraerts, “A Navigation Mesh for Dynamic Environments,” Proc. Computer Animation and Virtual Worlds, vol. 23, no. 6, pp. 535-546, 2012.
[39] S. Rodriguez, J. Denny, A. Mahadevan, J. Vu, J. Burgos, T. Zourntos, and N. Amato, “Roadmap-Based Pursuit Evation in 3D Structures,” Proc. Computer Animation and Social Agents (CASA), 2011.
[40] N. Sturtevant, “A Sparse Grid Representation for Dynamic Three-Dimensional Worlds,” Proc. Seventh AAAI Conf. Artificial Intelligence and Interactive Digital Entertainment, pp. 73-78, 2011.
[41] T. Stoyanov, M. Magnusson, H. Andreasson, and A. Lilienthal, “Path Planning in 3D Environments Using the Normal Distributions Transform,” Proc. IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS), pp. 3263-3268, 2010.
[42] T. Jund, P. Kraemer, and D. Cazier, “A Unified Structure for Crowd Simulation,” Computer Animation and Virtual Worlds, vol. 23, nos. 3/4, pp. 311-320, 2012.
[43] S. Levine, Y. Lee, V. Koltun, and Z. Popović, “Space-Time Planning with Parameterized Locomotion Controllers,” ACM Trans. Graphics, vol. 30, no. 3, p. 23, 2011.
[44] R. Torchelsen, L. Scheidegger, G. Oliveira, R. Bastos, and J. Comba, “Real-Time Multi-Agent Path Planning on Arbitrary Surfaces,” Proc. ACM SIGGRAPH Symp. Interactive 3D Graphics and Games, pp. 47-54, 2010.
[45] B. Ricks and P. Egbert, “Improved Obstacle Relevancy, Distance, and Angle for Crowds Constrained to Arbitrary Manifolds in 3D Space,” Proc. Eurographics, pp. 73-76, 2012.
[46] J. Ondřej, J. Pettré, A. Olivier, and S. Donikian, “A Synthetic-Vision Based Steering Approach for Crowd Simulation,” ACM Trans. Graphics, vol. 29, no. 4, pp. 123:1-123:9, 2010.
[47] Massive, http:/www.massivesoftware.com/, URL Nov. 2012.
[48] P. Hart, N. Nilsson, and B. Raphael, “A Formal Basis for the Heuristic Determination of Minimum Cost Paths,” IEEE Trans. Systems Science and Cybernetics, vol. SSC-4, no. 2, pp. 100-107, July 1968.
269 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool