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Group chasing tactics: how to catch a faster prey

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Logo of Institute of Physics (IOP)Institute of Physics (IOP)
Logo of Deutsche Physikalische Gesellschaft (DPG)Deutsche Physikalische Gesellschaft (DPG)
 Janosov, Milán Virágh, Csaba Vásárhelyi, Gábor Vicsek, Tamás

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Group chasing tactics: how to catch a faster prey
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Abstract
We propose a bio-inspired, agent-based approach to describe the natural phenomenon of group chasing in both two and three dimensions. Using a set of local interaction rules we created a continuous-space and discrete-time model with time delay, external noise and limited acceleration. We implemented a unique collective chasing strategy, optimized its parameters and studied its properties when chasing a much faster, erratic escaper. We show that collective chasing strategies can significantly enhance the chasers' success rate. Our realistic approach handles group chasing within closed, soft boundaries—in contrast with the periodic ones in most published literature—and resembles several properties of pursuits observed in nature, such as emergent encircling or the escaper's zigzag motion.
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Transcript: English(auto-generated)
00:09
We describe the complex collective motion of slower chasers and faster escapers during group hunting using a system of equations that takes into account realistic assumptions in much more detail than in previous similar studies.
00:28
The effects we consider are random perturbations, zigzag escape tactics, time delays, and several others. One of the essential ingredients of our approach is that in a natural environment, all sorts of barriers are present.
00:47
These are represented in our approach by a circular arena. The system of nonlinear differential equations that we introduce is realistic. This comes at the price of having a higher number of parameters.
01:03
Some of these parameters can be measured, while some others, both concerning chasers and escapers, can be estimated only by optimization techniques.
01:24
Slower chasers can never catch the prey if their parameters are far from optimal or if they do not cooperate. Selecting interaction parameter values among the chasers intuitively leads to a catch, although the process takes a relatively long time.
01:57
With a suboptimal choice for the parameters, many chasers may encircle their prey.
02:04
This is a phenomenon that can be observed in natural systems as well. However, if the parameters are perfectly optimized for catching, even a group of three chasers can be successful in a relatively short time.
02:26
In three dimensions, the prey has an extra chance to escape. And, without optimizing the parameters, even 15 chasers cannot catch it within a reasonable amount of time.
02:42
However, with optimized parameters, even 5 chasers can succeed. Using the same simulation framework, we can also investigate the inverse situation, when the number of escapers is much higher than the number of chasers.
03:03
In three dimensions, if two chasers are targeting a group of faster escapers, they will never succeed. When three chasers are trying to catch a group of faster escapers, then the group of prey will
03:20
split and reunite in a completely emergent, life-like fashion, without introducing any specific splitting-related prey-prey interaction.