This paper introduces a set of customizable and novel cost functions that enable the user to easily specify desirable robot formations, such as a ``high-coverage'' infrastructure-inspection formation, while maintaining high relative pose estimation accuracy. The overall cost function balances the need for the robots to be close together for good ranging-based relative localization accuracy and the need for the robots to achieve specific tasks, such as minimizing the time taken to inspect a given area. The formations found by minimizing the aggregated cost function are evaluated in a coverage path planning task in simulation and experiment, where the robots localize themselves and unknown landmarks using a simultaneous localization and mapping algorithm based on the extended Kalman filter. Compared to an optimal formation that maximizes ranging-based relative localization accuracy, these formations significantly reduce the time to cover a given area with minimal impact on relative pose estimation accuracy.

翻译：本文提出一组可定制的新型代价函数，使用户能够便捷地指定理想的机器人编队（例如高覆盖度的基础设施检测编队），同时保持较高的相对位姿估计精度。该综合代价函数在两类需求间取得平衡：一方面机器人需保持近距离以确保基于测距的相对定位精度，另一方面需完成特定任务（如最小化对给定区域的检测时间）。通过最小化聚合代价函数获得的编队，在仿真与实验中基于扩展卡尔曼滤波的同时定位与地图构建算法进行覆盖路径规划任务评估——机器人利用该算法实现自身定位与未知地标探测。相较于最大化测距相对定位精度的最优编队，这些编队能在对相对位姿估计精度影响最小化的前提下，显著缩短给定区域的覆盖时间。