Group chasing tactics: how to catch a faster prey?

TL;DR

This paper introduces a bio-inspired agent-based model for group chasing, demonstrating how collective strategies improve success rates against faster, erratic prey in realistic bounded environments.

Contribution

It presents a novel continuous-space, discrete-time model with local interaction rules, optimized parameters, and realistic boundary conditions for studying group chasing behavior.

Findings

Collective chasing strategies significantly increase success rates.

The model reproduces natural pursuit behaviors like encircling and zigzag motion.

The approach handles bounded environments unlike many previous models.

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 - contrasting most of those published in the literature with periodic ones -- and resembles several properties of pursuits observed in nature, such as the emergent encircling or the escaper's zigzag motion.