Distributed control for geometric pattern formation of large-scale multirobot systems

TL;DR

This paper introduces a distributed control method enabling large multi-agent systems to form geometric patterns like lattices with minimal sensors and no communication, enhancing robustness and ease of tuning.

Contribution

It presents a novel displacement-based control law for pattern formation that requires low sensors and no inter-agent communication, along with an adaptive gain tuning mechanism.

Findings

Successfully achieves lattice formations in simulations and experiments.

Reduces sensor requirements and communication needs compared to existing methods.

Improves robustness and simplifies control gain tuning.

Abstract

Geometric pattern formation is crucial in many tasks involving large-scale multi-agent systems. Examples include mobile agents performing surveillance, swarm of drones or robots, or smart transportation systems. Currently, most control strategies proposed to achieve pattern formation in network systems either show good performance but require expensive sensors and communication devices, or have lesser sensor requirements but behave more poorly. Also, they often require certain prescribed structural interconnections between the agents (e.g., regular lattices, all-to-all networks etc). In this paper, we provide a distributed displacement-based control law that allows large group of agents to achieve triangular and square lattices, with low sensor requirements and without needing communication between the agents. Also, a simple, yet powerful, adaptation law is proposed to automatically tune the control gains in order to reduce the design effort, while improving robustness and flexibility. We show the validity and robustness of our approach via numerical simulations and experiments, comparing it with other approaches from the existing literature.