Research on a behavior control technique of crowd simulation

With its imitating the perception and behaviors, scheming for instance, of human beings in virtual space, the crowd simulation technique aims at a truthful reflecting of crowd characteristics. However, for the crowd simulation, the behavior control is the crux. It introduces functions like perception, cognition, decision-making and so forth into motion control, and builds an autonomy model for virtual human, thus achieving the control of virtual crowd. To begin with, this paper introduces theories of behavior and behavior control. Based on it, the thesis presents a behavior control model, which involves perception model, path planning and behavior model. It goes on with the comparison of several behavior control models, and concludes the related control algorithm. Besides, based on the analysis of relevant researches, this paper ends with the indication about the aspects that more attention should be laid to in the field of crowd simulation in the near future.

[1]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

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

[3]  Emil M. Petriu,et al.  Behavior-based neuro-fuzzy controller for mobile robot navigation , 2003, IEEE Trans. Instrum. Meas..

[4]  Mark H. Overmars,et al.  The corridor map method: a general framework for real‐time high‐quality path planning , 2007, Comput. Animat. Virtual Worlds.

[5]  P. Maes How to Do the Right Thing , 1989 .

[6]  Li Yan-mei Survey on realistic behavior in crowd animation , 2010 .

[7]  D. Thalmann,et al.  A navigation graph for real-time crowd animation on multilayered and uneven terrain , 2005 .

[8]  J. Toner,et al.  Flocks, herds, and schools: A quantitative theory of flocking , 1998, cond-mat/9804180.

[9]  Dinesh Manocha,et al.  Reciprocal Velocity Obstacles for real-time multi-agent navigation , 2008, 2008 IEEE International Conference on Robotics and Automation.

[10]  Joan Batlle,et al.  Hybrid coordination of reinforcement learning-based behaviors for AUV control , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[11]  Jean-Claude Latombe,et al.  Fast synthetic vision, memory, and learning models for virtual humans , 1999, Proceedings Computer Animation 1999.

[12]  Jun Zhou,et al.  Survey on realistic behavior in crowd animation: Survey on realistic behavior in crowd animation , 2010 .

[13]  Daniel Thalmann,et al.  An Efficient and Flexible Perception Pipeline for Autonomous Agents , 1999, Comput. Graph. Forum.

[14]  Xiaoyuan Tu,et al.  Artificial Animals for Computer Animation: Biomechanics, Locomotion, Perception, and Behavior , 1999, Lecture Notes in Computer Science.

[15]  Daniel Thalmann,et al.  Synthetic Vision and Audition for Digital Actors , 1995, Comput. Graph. Forum.