Controlling the movement of crowds in computer graphics by using the mechanism of particle swarm optimization

This paper presents a uniform conceptual model to co-operate with particle swarm optimization (PSO) for controlling the movement of crowds in computer graphics. According to the PSO mechanism, each particle in the swarm adopts the information to automatically find a path from the initial position to the optimum. However, PSO aims to obtain the optimal solution instead of the searching path, while the purpose of this work concentrates on the control of the crowd movement, which is composed of the generated searching paths of particles. Hence, in order to generate seemingly natural, appropriate paths of people in a crowd, we propose a model to work with the computational facilities provided in PSO. Compared to related approaches previously presented in the literature, the proposed model is simple, uniform, and easy to implement. The results of the conducted simulations demonstrate that the coupling of PSO and the proposed technique can generate appropriate non-deterministic, non-colliding paths for the use in computer graphics for several different scenarios, including static and dynamic obstacles, moving targets, and multiple crowds.

[1]  Pavel Slavik,et al.  Proceedings of the Eurographics workshop on Virtual environments and scientific visualization '96 , 1996 .

[2]  Christian Jacob,et al.  Evolutionary exploration of dynamic swarm behaviour , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[3]  J. Bishop Stochastic searching networks , 1989 .

[4]  Daniel Thalmann,et al.  Towards Interactive Real‐Time Crowd Behavior Simulation , 2002, Comput. Graph. Forum.

[5]  Daniel Thalmann,et al.  Real-time display of virtual humans: levels of details and impostors , 2000, IEEE Trans. Circuits Syst. Video Technol..

[6]  David C. Brogan,et al.  Group Behaviors for Systems with Significant Dynamics , 1997, Auton. Robots.

[7]  R. Steele Optimization , 2005 .

[8]  Daniel Thalmann,et al.  Virtual humans: thirty years of research, what next? , 2005, The Visual Computer.

[9]  Adrien Treuille,et al.  Continuum crowds , 2006, SIGGRAPH 2006.

[10]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[11]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[12]  Daniel Thalmann,et al.  MuscleBuilder: A modeling tool for human anatomy , 2008, Journal of Computer Science and Technology.

[13]  Dirk Helbing,et al.  Simulating dynamical features of escape panic , 2000, Nature.

[14]  Yiorgos Chrysanthou,et al.  Real-Time Rendering of Densely Populated Urban Environments , 2000, Rendering Techniques.

[15]  Seiichi Shin,et al.  Self-organization of Decentralized Swarm Agents Based on Modified Particle Swarm Algorithm , 2006, J. Intell. Robotic Syst..

[16]  Eric Bouvier,et al.  Crowd simulation in immersive space management , 1996 .

[17]  Craig W. Reynolds Steering Behaviors For Autonomous Characters , 1999 .

[18]  Jing Wang,et al.  Swarm Intelligence in Cellular Robotic Systems , 1993 .

[19]  Craig W. Reynolds Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[20]  Stylianou Soteris,et al.  Crowd self-organization, streaming and short path smoothing , 2006 .