Biologically‐Inspired Visual Simulation of Insect Swarms
Biologically‐Inspired Visual Simulation of Insect Swarms
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DOI：
10.1111/cgf.12572
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发表时间：
2015-05
影响因子：
2.5
通讯作者：
Weizi Li;D. Wolinski;J. Pettré;M. Lin
中科院分区：
文献类型：
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作者：
Weizi Li;D. Wolinski;J. Pettré;M. Lin
昆虫是地球上最普遍的生物体，代表了大多数现存动物。能够模拟昆虫群可以增强各种图形应用程序的视觉真实感。然而，昆虫行为的复杂性使其模拟成为一个具有挑战性的计算问题。为了解决这个问题，我们提出了一个通用的生物启发框架，用于昆虫群的可视化模拟。我们的方法受到昆虫在自然界中各种尺度上表现出涌现行为的观察的启发。在低级别，我们的框架自动选择和配置最合适的转向算法的本地避碰任务。在中间层，它将昆虫轨迹处理成分段线性段，并为采样路径点构建概率分布函数。这些航路点，然后评估的大都会-黑斯廷斯算法，以保持在高层次的昆虫群的全球结构。通过这种生物启发的数据驱动方法，我们能够模拟不同尺度的昆虫行为，并使用定性和定量指标评估我们的模拟。此外，由于昆虫数据可能很难获得，我们的框架可以作为计算机辅助动画工具，将草图般的输入解释为用户控制，并生成复杂昆虫群集现象的模拟。
Representing the majority of living animals, insects are the most ubiquitous biological organisms on Earth. Being able to simulate insect swarms could enhance visual realism of various graphical applications. However, the very complex nature of insect behaviors makes its simulation a challenging computational problem. To address this, we present a general biologically‐inspired framework for visual simulation of insect swarms. Our approach is inspired by the observation that insects exhibit emergent behaviors at various scales in nature. At the low level, our framework automatically selects and configures the most suitable steering algorithm for the local collision avoidance task. At the intermediate level, it processes insect trajectories into piecewise‐linear segments and constructs probability distribution functions for sampling waypoints. These waypoints are then evaluated by the Metropolis‐Hastings algorithm to preserve global structures of insect swarms at the high level. With this biologically inspired, data‐driven approach, we are able to simulate insect behaviors at different scales and we evaluate our simulation using both qualitative and quantitative metrics. Furthermore, as insect data could be difficult to acquire, our framework can be adopted as a computer‐assisted animation tool to interpret sketch‐like input as user control and generate simulations of complex insect swarming phenomena.