Go Big or Go Home: Simulating Mobbing Behavior with Braitenbergian Robots
Summary: arXiv:2604.00350v1 Announce Type: cross
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. In our study, 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.
Research Objectives
The primary objectives of this research include:
- To simulate the behavior of Braitenbergian robots in response to a simulated predator.
- To analyze the impact of mobbing call range on the effectiveness of mobbing behavior.
- To evaluate the influence of group size on the success of mobbing actions.
Methodology
We utilized the Webots robotics simulation platform, which allows for the creation and testing of complex robotic behaviors in a virtual environment. The Braitenbergian robots were programmed to react to a light source, which served as a stand-in for a predator. The robots were equipped with the ability to emit mobbing calls, which would attract other robots to join in their defense against the predator.
Key Variables
Our study focused on two main variables:
- Range of Mobbing Call: We tested three different ranges: infinite, mid-range, and low-range. Each range represented a different ability for the robots to communicate and summon allies.
- Size of Robot Group: We assessed two group sizes: ten robots versus three robots. This comparison enabled us to understand how group dynamics affect mobbing success.
Results
The results indicated that both the range of the mobbing call and the size of the robot group significantly impacted the overall success of the mobbing behavior. Robots that operated in larger groups and utilized infinite range mobbing calls were more effective in coordinating their actions against the simulated predator.
Implications
This work has important implications for simulations of action selection in artificial life and for designing control architectures for autonomous agents. By understanding how these variables interact, researchers can develop more sophisticated models that mimic natural behaviors in robotics.
Conclusion
Our simulation of mobbing behavior with Braitenbergian robots provides valuable insights into cooperative strategies employed by animals in the wild. The findings underscore the importance of communication and group dynamics in enhancing survival strategies against predators. As robotics technology continues to advance, integrating these biological principles may lead to more resilient and adaptive autonomous systems.