Simulating Flying Insects Using Dynamics and Data-Driven Noise Modeling to Generate Diverse Collective Behaviors

 

PLoS ONE, 2016, 11(5): e0155698. doi:10.1371/journal.pone.0155698

http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0155698

Jiaping Ren,  Xinjie Wang, Xiaogang Jin, Dinesh Manocha

Overview of our biologically-driven insect swarm model (illustrated in 2D view). We highlight different components of our algorithm used to calculate the position of each insects at each time step, including two sets of forces: interaction forces and self-propulsion forces. Interaction forces are represented by individual-based zones: insects follow forces that are represented in concentric zones of repulsion, alignment, and attraction to their neighbors. We use these forces to compute the acceleration and preferred velocity for each insect, and use velocity obstacles to perform collision avoidance and compute the actual velocity. The parameter estimation step is performed to compute the optimal parameters for our model.

 

Aggregation. (a) and (b) simulated swarms of midges moving in the same space with 500 and 3,000 midges, respectively.  (c) a photo captured using a camera.

Abstract

We present a biologically plausible dynamics model to simulate swarms of flying insects. Our formulation, which is based on biological conclusions and experimental observations, is designed to simulate large insect swarms of varying densities. We use a force-based model that captures different interactions between the insects and the environment and computes collision-free trajectories for each individual insect. Furthermore, we model the noise as a constructive force at the collective level and present a technique to generate noise-induced insect movements in a large swarm that are similar to those observed in real world trajectories. We use a data-driven formulation that is based on pre-recorded insect trajectories. We also present a novel evaluation metric and a statistical validation approach that takes into account various characteristics of insect motions. In practice, the combination of Curl noise function with our dynamics model is used to generate realistic swarm simulations and emergent behaviors. We highlight its performance for simulating large flying swarms of midges, fruit fly, locusts and moths and demonstrate many collective behaviors, including aggregation, migration, phase transition, and escape responses.

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