A Minimalist Controller for Autonomously Self-Aggregating Robotic Swarms: Enabling Compact Formations in Multitasking Scenarios
🤖 AI Summary
In multi-task scenarios, swarm robots struggle to achieve fully autonomous, high-density, and non-circular compact formations.
Method: This paper proposes a vision-based distributed self-aggregation approach, employing minimalist isomorphic robot controllers and local interaction rules that rely solely on line-of-sight (LoS) sensing—requiring no global information or external intervention—to enable group separation and stable aggregation.
Contribution/Results: The method achieves, for the first time, fully autonomous and scalable generation of multiple compact clusters. It effectively suppresses inter-cluster dynamic interference and demonstrates robustness across varying swarm sizes and cluster counts in simulation. Experimental results show clustering ratios comparable to state-of-the-art methods, yet with significantly higher cluster density, tighter morphology, and enhanced environmental adaptability and scalability.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Achieving compact formations in multitasking robotic swarms
Enabling autonomous self-aggregation using line-of-sight sensors
Improving scalability and performance in clustered robot groups
Innovation
Methods, ideas, or system contributions that make the work stand out.
Uses line-of-sight sensor for self-aggregation
Achieves compact clusters in multitasking scenarios
Scales well with homogeneous robotic swarms
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