Inherent Noise-Aware Insect Swarm Simulation

Abstract

Collective behaviour of winged insects is a wondrous and familiar phenomenon in the real world. In this paper, we introduce a highly efficient field-based approach to simulate various insect swarms. Its core idea is to construct a smooth yet noise-aware governing velocity field that can be further decomposed into two sub-fields: (i) a divergence-free curl-noise field to model noise-induced movements of individual insects in a swarm, and (ii) an enhanced global velocity field to control navigational paths in a complex environment along which all the insects in a swarm fly. Through simulation experiments and comparisons with existing crowd simulation approaches, we demonstrate that our approach is effective to simulate various insect swarm behaviours including aggregation, positive phototaxis, sedation, mass-migrating, and so on. Besides its high efficiency, our approach is very friendly to parallel implementation on GPUs (e.g. the speedup achieved through GPU acceleration is higher than 50 if the number of simulated insects is more than 10 000 on an off-the-shelf computer). Our approach is the first multi-agent modelling system that introduces curl-noise into agents' velocity field and uses its non-scattering nature to maintain non-colliding movements in 3D crowd simulation.Collective behavior of winged insects is a wondrous and familiar phenomenon in the real world. In this paper, we introduce a highly efficient field-based approach to simulate various insect swarms. Its core idea is to construct a smooth yet noise-aware governing velocity field that can be further decomposed into two sub-fields: (i) a divergence-free curl noise field to model noise-induced movements of individual insects in a swarm, and (ii) an enhanced global velocity field to control navigational paths in a complex environment along which all the insects in a swarm fly.