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Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot

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

Andrea Giusti¹, Gian Carlo Maffettone², Davide Fiore³

¹Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy.

Frontiers in Robotics and AI

|October 16, 2023

Summary

This study introduces a distributed control law for multi-agent systems to form geometric patterns like lattices. It requires minimal sensors and no communication, enhancing robustness and flexibility.

Keywords:

collective dynamics distributed control multiagent systems pattern formation swarm robotics

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Area of Science:

Robotics
Control Theory
Distributed Systems

Background:

Geometric pattern formation is essential for large-scale multi-agent systems (MAS) like drone swarms and smart transportation.
Existing control strategies often involve a trade-off between performance and sensor/communication requirements.
There is a need for efficient, low-cost control methods for MAS pattern formation.

Purpose of the Study:

To develop a distributed control law for achieving triangular and square lattice formations in MAS.
To design an adaptation law for automatic control gain tuning, improving robustness and flexibility.
To validate the proposed approach through simulations and experiments.

Main Methods:

A distributed displacement-based control law is proposed.
An adaptive control law is introduced for automatic gain tuning.
Numerical simulations and experimental validation are conducted.

Main Results:

The control law successfully enables large groups of agents to form triangular and square lattices.
The system demonstrates robustness and flexibility with low sensor requirements and no inter-agent communication.
The adaptation law effectively tunes control gains, reducing design effort.

Conclusions:

The proposed distributed control law offers an effective solution for geometric pattern formation in MAS.
The approach is validated as robust, flexible, and suitable for systems with limited resources.
This work contributes to the advancement of decentralized control strategies for coordinated multi-agent behavior.