Concurrent-Learning Based Relative Localization in Shape Formation of Robot Swarms (Extended version)

TL;DR

This paper presents a novel approach for shape formation in robot swarms using onboard measurements, introducing a concurrent-learning estimator, a finite-time agreement protocol, and a behavior-based control strategy, validated through simulations and outdoor experiments.

Contribution

It introduces a concurrent-learning based relative localization method, a finite-time agreement protocol for shape positioning, and a new control strategy for adaptive swarm shape formation.

Findings

Effective relative localization without external systems

Successful shape formation demonstrated in simulations

Robust outdoor experiments validate practical applicability

Abstract

In this paper, we address the shape formation problem for massive robot swarms in environments where external localization systems are unavailable. Achieving this task effectively with solely onboard measurements is still scarcely explored and faces some practical challenges. To solve this challenging problem, we propose the following novel results. Firstly, to estimate the relative positions among neighboring robots, a concurrent-learning based estimator is proposed. It relaxes the persistent excitation condition required in the classical ones such as least-square estimator. Secondly, we introduce a finite-time agreement protocol to determine the shape location. This is achieved by estimating the relative position between each robot and a randomly assigned seed robot. The initial position of the seed one marks the shape location. Thirdly, based on the theoretical results of the relative localization, a novel behavior-based control strategy is devised. This strategy not only enables adaptive shape formation of large group of robots but also enhances the observability of inter-robot relative localization. Numerical simulation results are provided to verify the performance of our proposed strategy compared to the state-of-the-art ones. Additionally, outdoor experiments on real robots further demonstrate the practical effectiveness and robustness of our methods.