Xiang, W, Yao, X, Wang, H orcid.org/0000-0002-2281-5679 et al. (1 more author) (2020) FASTSWARM: A Data-driven FrAmework for Real-time Flying InSecT SWARM Simulation. Computer Animation and Virtual Worlds, 31 (4-5). e1957. ISSN 1546-4261
Abstract
Insect swarms are common phenomena in nature and therefore have been actively pursued in computer animation. Realistic insect swarm simulation is difficult due to two challenges: high‐fidelity behaviors and large scales, which make the simulation practice subject to laborious manual work and excessive trial‐and‐error processes. To address both challenges, we present a novel data‐driven framework, FASTSWARM, to model complex behaviors of flying insects based on real‐world data and simulate plausible animations of flying insect swarms. FASTSWARM has a linear time complexity and achieves real‐time performance for large swarms. The high‐fidelity behavior model of FASTSWARM explicitly takes into consideration the most common behaviors of flying insects, including the interactions among insects such as repulsion and attraction, self‐propelled behaviors such as target following and obstacle avoidance, and other characteristics such as random movements. To achieve scalability, an energy minimization problem is formed with different behaviors modeled as energy terms, where the minimizer is the desired behavior. The minimizer is computed from the real‐world data, which ensures the plausibility of the simulation results. Extensive simulation results and evaluations show that FASTSWARM is versatile in simulating various swarm behaviors, high fidelity measured by various metrics, easily controllable in inducing user controls and highly scalable.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 John Wiley & Sons, Ltd. This is the peer reviewed version of the following article: Xiang, W, Yao, X, Wang, H et al. (1 more author) (2020) FASTSWARM: A Data-driven FrAmework for Real-time Flying InSecT SWARM Simulation. Computer Animation and Virtual Worlds. e1957. ISSN 1546-4261, which has been published in final form at http://doi.org/10.1002/cav.1957. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. |
Keywords: | collective behavior; data‐driven; insect swarm simulation; optimization; real time |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 21 Jul 2020 15:03 |
Last Modified: | 01 Sep 2021 00:38 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1002/cav.1957 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:163467 |