ロボット群の形状形成における同時学習に基づく相対位置推定
Concurrent-Learning Based Relative Localization in Shape Formation of Robot Swarms (Extended version)
外部測位システムが使えない環境で、機体間の相対位置を同時学習で推定し、群れ全体で形状を形成する制御手法を提案した論文。
著者: Jinhu Lü, Kunrui Ze, Shuoyu Yue, Kexin Liu, Wei Wang, Guibin Sun
分類: cs.RO, cs.MA
原文アブストラクト
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.
