A model for rolling swarms of locusts

Abstract

Abstract. We construct an individual-based kinematic model of rolling migratory locust swarms. The model incorporates social interactions, gravity, wind, and the effect of the impenetrable boundary formed by the ground. We study the model using numerical simulations and tools from statistical mechanics, namely the notion of H-stability. For a free-space swarm (no wind and gravity), as the number of locusts increases, it approaches a crystalline lattice of fixed density if it is H-stable, and in contrast becomes ever more dense if it is catastrophic. Numerical simulations suggest that whether or not a swarm rolls depends on the statistical mechanical properties of the corresponding free-space swarm. For a swarm that is H-stable in free space, gravity causes the group to land and form a crystalline lattice. Wind, in turn, smears the swarm out along the ground until all individuals are stationary. In contrast, for a swarm that is catastrophic in free space, gravity causes the group to land and form a bubble-like shape. In the presence of wind, the swarm migrates with a rolling motion similar to natural locust swarms. The rolling structure is similar to that observed by biologists, and includes a takeoff zone, a landing zone, and a stationary zone where grounded locusts can rest and feed.

Ring patterns and their bifurcations in a nonlocal model of biological swarms

“…Mathematical models for swarming, schooling, and other aggregative behavior in biology have given us many tools to understand the fundamental behavior of collective motion and pattern formation that occurs in nature [10,6,2,26,25,14,7,13,27,19,33,32,23,11,17,37,38,34,36,9,15,29,21,20,24,8]. One of the key features of many of these models is that the social communication between individuals (sound, chemical detection, sight, etc...) is performed over different scales and are inherently nonlocal [11,22,2].…”

Collective navigation of cargo-carrying swarms

“…The 'self-propelling particles' model [21], in which the motion of each individual is determined by the mean orientation of its local neighbourhood, shows phases of coherent motion and clustering, depending on the density of agents and the effect of noise. More interaction rules such as collision avoidance and attraction, preferential movement directions, and influences from the environment such as chemotaxis have also been investigated in [22][23][24][25][26][27][28][29].…”

Simulation in the Cloud Using Handheld Devices

“…Examples of such phenomena include swarm movements in rural areas, in which a field-expert needs to update the simulation models at run-time with current observed data (such as the wind) [1], or civil protection scenarios, like forest fire spreading, in which fire troopers can use simulation models to predict fire propagation in realtime, based on their field observations and weather forecast information [2]. Indeed, due to their complexity, most simulations require high performance computing infrastructures.…”