Hierarchical planning-based crowd formation
Team formation with realistic crowd simulation behavior is a challenge in computer graphics, multiagent control, and social simulation. In this study, we propose a framework of crowd formation via hierarchical planning, which includes cooperative-task, coordinated-behavior, and action-control planni...
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sg-ntu-dr.10356-1507542021-08-02T02:17:55Z Hierarchical planning-based crowd formation Liu, Na Wang, Xingce Liu, Shaolong Wu, Zhongke He, Jiale Cheng, Peng Miao, Chunyan Thalmann, Nadia Magnenat School of Computer Science and Engineering Engineering::Computer science and engineering Fuzzy Logic Control Gaze-movement Angle Team formation with realistic crowd simulation behavior is a challenge in computer graphics, multiagent control, and social simulation. In this study, we propose a framework of crowd formation via hierarchical planning, which includes cooperative-task, coordinated-behavior, and action-control planning. In cooperative-task planning, we improve the grid potential field to achieve global path planning for a team. In coordinated-behavior planning, we propose a time–space table to arrange behavior scheduling for a movement. In action-control planning, we combine the gaze-movement angle model and fuzzy logic control to achieve agent action. Our method has several advantages. (1) The hierarchical architecture is guaranteed to match the human decision process from high to low intelligence. (2) The agent plans his behavior only with the local information of his neighbor; the global intelligence of the group emerges from these local interactions. (3) The time–space table fully utilizes three-dimensional information. Our method is verified using crowds of various densities, from sparse to dense, employing quantitative performance measures. The approach is independent of the simulation model and can be extended to other crowd simulation tasks. The authors sincerely thank the referees and anonymous reviewers for their helpful comments and suggestions. This research was supported by the National Key Research and Development Program of China (2017YFB1002604, 2017YFB100-2600, and 2017YFB1402100), the National Key Cooperation between the BRICS of China (2017YFE0100500), and the Beijing Municipal Natural Science Foundation of China (4172033). 2021-08-02T02:17:55Z 2021-08-02T02:17:55Z 2019 Journal Article Liu, N., Wang, X., Liu, S., Wu, Z., He, J., Cheng, P., Miao, C. & Thalmann, N. M. (2019). Hierarchical planning-based crowd formation. Computer Animation and Virtual Worlds, 30(6), e1875-. https://dx.doi.org/10.1002/cav.1875 1546-4261 0000-0002-4572-4155 https://hdl.handle.net/10356/150754 10.1002/cav.1875 2-s2.0-85065093945 6 30 e1875 en Computer Animation and Virtual Worlds © 2019 John Wiley & Sons, Ltd. All rights reserved. |
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Engineering::Computer science and engineering Fuzzy Logic Control Gaze-movement Angle Liu, Na Wang, Xingce Liu, Shaolong Wu, Zhongke He, Jiale Cheng, Peng Miao, Chunyan Thalmann, Nadia Magnenat Hierarchical planning-based crowd formation |
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Team formation with realistic crowd simulation behavior is a challenge in computer graphics, multiagent control, and social simulation. In this study, we propose a framework of crowd formation via hierarchical planning, which includes cooperative-task, coordinated-behavior, and action-control planning. In cooperative-task planning, we improve the grid potential field to achieve global path planning for a team. In coordinated-behavior planning, we propose a time–space table to arrange behavior scheduling for a movement. In action-control planning, we combine the gaze-movement angle model and fuzzy logic control to achieve agent action. Our method has several advantages. (1) The hierarchical architecture is guaranteed to match the human decision process from high to low intelligence. (2) The agent plans his behavior only with the local information of his neighbor; the global intelligence of the group emerges from these local interactions. (3) The time–space table fully utilizes three-dimensional information. Our method is verified using crowds of various densities, from sparse to dense, employing quantitative performance measures. The approach is independent of the simulation model and can be extended to other crowd simulation tasks. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Liu, Na Wang, Xingce Liu, Shaolong Wu, Zhongke He, Jiale Cheng, Peng Miao, Chunyan Thalmann, Nadia Magnenat |
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Article |
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Liu, Na Wang, Xingce Liu, Shaolong Wu, Zhongke He, Jiale Cheng, Peng Miao, Chunyan Thalmann, Nadia Magnenat |
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Liu, Na |
title |
Hierarchical planning-based crowd formation |
title_short |
Hierarchical planning-based crowd formation |
title_full |
Hierarchical planning-based crowd formation |
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Hierarchical planning-based crowd formation |
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Hierarchical planning-based crowd formation |
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hierarchical planning-based crowd formation |
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2021 |
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https://hdl.handle.net/10356/150754 |
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1707050406599720960 |