BSwarm: Biologically-Plausible Dynamics Model of Insect Swarms


ACM SIGGRAPH/Eurographics Symposium on Computer Animation'2015, Los Angeles, CA, August 7-9.

Xinjie Wang, Jiaping Ren,  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.


A variety of insect swarms can be simulated by our approach: (left) a swarm of fruitflies in a huge glass box, (middle) a swarm of male flies compete for a female (the green one), and (right) a large swarm of migratory locusts passes through a village.


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 hybrid formulation that combines a force-based model to capture different interactions between the insects with a data-driven noise model, and computes collision-free trajectories. We introduce a quantitative metric to evaluate the accuracy of such multi-agent systems and model the inherent noise. We highlight the performance of our dynamics model for simulating large flying swarms of midges, fruit fly, locusts and moths. In practice, our approach can generate many collective behaviors, including aggregation, migration, phase transition, and escape responses, and we highlight the benefits over prior methods.



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