Go Big or Go Home: Simulating Mobbing Behavior with Braitenbergian Robots

TL;DR

This paper simulates mobbing predator behavior in Braitenberg robots using Webots, analyzing how call range and group size affect mobbing success, with implications for artificial life and autonomous agent design.

Contribution

It introduces a simulation of mobbing behavior in robots, exploring the effects of communication range and group size on cooperative predator defense.

Findings

Larger mobbing call range improves mobbing success.

Increased group size enhances the effectiveness of mobbing.

Both call range and group size significantly influence mobbing outcomes.

Abstract

We used the Webots robotics simulation platform to simulate a dyadic avoiding and mobbing predator behavior in a group of Braitenbergian robots. Mobbing is an antipredator adaptation used by some animals in which the individuals cooperatively attack or harass a predator to protect themselves. One way of coordinating a mobbing attack is using mobbing calls to summon other individuals of the mobbing species. We imitated this mechanism and simulated Braitenbergian robots that use mobbing calls when they face a light source (representing an inanimate predator) and mob it if they can summon allies, otherwise, they escape from it. We explore the effects of range of mobbing call (infinite range, mid-range and low-range) and the size of the robot group (ten robots vs three) on the overall success of mobbing. Our results suggest that both variables have significant impacts. This work has implications for simulations of action selection in artificial life and designing control architectures for autonomous agents.