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.

翻译：本文针对大规模机器人集群在缺乏外部定位系统环境下的形态形成问题展开研究。仅依靠机载测量有效完成该任务目前仍鲜有探索，且面临若干实际挑战。为解决这一难题，我们提出以下创新成果：首先，为估计相邻机器人间的相对位置，提出一种基于并发学习的估计器。该方法放宽了经典估计器（如最小二乘估计器）所需的持续激励条件。其次，引入有限时间一致性协议以确定形态位置，通过估计每个机器人与随机指定的种子机器人之间的相对位置实现。种子机器人的初始位置即标记为形态位置。第三，基于相对定位的理论成果，设计了一种新颖的基于行为的控制策略。该策略不仅能够实现大规模机器人的自适应形态形成，还能增强机器人间相对定位的可观测性。数值仿真结果验证了所提策略相较于现有先进方法的性能优势。此外，真实机器人的户外实验进一步证明了本方法的实际有效性与鲁棒性。