Multicluster Design and Control of Large-Scale Affine Formations

TL;DR

This paper introduces an efficient multicluster design and control framework for large-scale affine formations, significantly improving scalability, convergence speed, and communication efficiency for swarms of hundreds of agents.

Contribution

It presents a novel scalable optimal design method for interaction graphs and a multicluster control framework enabling partially independent motions in large swarms.

Findings

Achieves formation convergence with several hundred agents.

Outperforms existing methods in scalability and efficiency.

Demonstrates effective partial independence in swarm motions.

Abstract

Conventional affine formation control (AFC) empowers a network of agents with flexible but collective motions - a potential which has not yet been exploited for large-scale swarms. One of the key bottlenecks lies in the design of an interaction graph, characterized by the Laplacian-like stress matrix. Efficient and scalable design solutions often yield suboptimal solutions on various performance metrics, e.g., convergence speed and communication cost, to name a few. The current state-of-the-art algorithms for finding optimal solutions are computationally expensive and therefore not scalable. In this work, we propose a more efficient optimal design for any generic configuration, with the potential to further reduce complexity for a large class of nongeneric rotationally symmetric configurations. Furthermore, we introduce a multicluster control framework that offers an additional scalability improvement, enabling not only collective affine motions as in conventional AFC but also partially independent motions naturally desired for large-scale swarms. The overall design is compatible with a swarm size of several hundred agents with fast formation convergence, as compared to up to only a few dozen agents by existing methods. Experimentally, we benchmark the performance of our algorithm compared with several state-of-the-art solutions and demonstrate the capabilities of our proposed control strategies.