论文标题
基于范围的移动机器人网络优化
Ranging-Based Localizability Optimization for Mobile Robotic Networks
论文作者
论文摘要
在依靠代理之间进行合作定位的嘈杂范围测量的机器人网络中，可实现的定位准确性在很大程度上很大程度上取决于网络几何形状。这激发了在此类多机器人系统中规划机器人轨迹的问题，以保持较高的本地化准确性。我们提出了基于潜在的计划方法，其中引入了本地性潜力来表征网络几何以进行合作位置估计的质量。这些电位基于Cramer RAO下限（CRLB），并在任何无偏置位置估计器都可以实现的误差协方差方面提供了理论下限。在此过程中，我们建立了CRLB与图形刚度理论之间的联系，该理论先前已用于计划机器人网络的运动。我们开发了适合大型网络的分散部署算法，并使用等于受限的CRLB将本地化性的概念扩展到方案，其中已知有关范围传感器相对位置的其他信息。我们通过模拟示例和实验说明了由此产生的机器人部署方法。
In robotic networks relying on noisy range measurements between agents for cooperative localization, the achievable positioning accuracy strongly strongly depends on the network geometry. This motivates the problem of planning robot trajectories in such multi-robot systems in a way that maintains high localization accuracy. We present potential-based planning methods, where localizability potentials are introduced to characterize the quality of the network geometry for cooperative position estimation. These potentials are based on Cramer Rao Lower Bounds (CRLB) and provide a theoretical lower bound on the error covariance achievable by any unbiased position estimator. In the process, we establish connections between CRLBs and the theory of graph rigidity, which has been previously used to plan the motion of robotic networks. We develop decentralized deployment algorithms appropriate for large networks, and we use equality-constrained CRLBs to extend the concept of localizability to scenarios where additional information about the relative positions of the ranging sensors is known. We illustrate the resulting robot deployment methodology through simulated examples and an experiment.
