A Minimalist Controller for Autonomously Self-Aggregating Robotic Swarms: Enabling Compact Formations in Multitasking Scenarios

TL;DR

This paper introduces a minimalist, line-of-sight sensor-based controller enabling homogeneous robotic groups to self-aggregate into compact, scalable formations in multitask scenarios, improving cluster compactness and autonomy.

Contribution

It presents a novel multitask self-aggregation method that achieves fully autonomous, compact clustering using only line-of-sight sensors, addressing previous limitations.

Findings

Scalable self-aggregation into compact clusters demonstrated in simulations.

Improved cluster compactness compared to prior multitask aggregation methods.

Behavior maintains high proportion of clustered robots across configurations.

Abstract

The deployment of simple emergent behaviors in swarm robotics has been well-rehearsed in the literature. A recent study has shown how self-aggregation is possible in a multitask approach -- where multiple self-aggregation task instances occur concurrently in the same environment. The multitask approach poses new challenges, in special, how the dynamic of each group impacts the performance of others. So far, the multitask self-aggregation of groups of robots suffers from generating a circular formation -- that is not fully compact -- or is not fully autonomous. In this paper, we present a multitask self-aggregation where groups of homogeneous robots sort themselves into different compact clusters, relying solely on a line-of-sight sensor. Our multitask self-aggregation behavior was able to scale well and achieve a compact formation. We report scalability results from a series of simulation trials with different configurations in the number of groups and the number of robots per group. We were able to improve the multitask self-aggregation behavior performance in terms of the compactness of the clusters, keeping the proportion of clustered robots found in other studies.