In this article, autonomous ground vehicle (AGV) path planning is considered. The AGV is assumed to be equipped with receivers capable of producing pseudorange measurements to overhead global navigation satellite systems (GNSS) satellites and to cellular base stations in its environment. Parameters of the cellular pseudoranges related to the transmitter clock bias are estimated in an initialization step in an open-sky environment. The AGV fuses these pseudoranges to produce an estimate about its own states. The AGV is also equipped with a three-dimensional building map of the environment. Starting from a known starting point, the AGV desires to reach a known target point by taking the shortest distance, while minimizing the AGV's position estimation error and guaranteeing that the AGV's position estimation uncertainty is below a desired threshold. Toward this objective, a so-called signal reliability map is first generated, which provides information about regions where large errors due to poor GNSS line-of-sight or cellular signal multipath are expected. The vehicle uses the signal reliability map to calculate the position mean-squared error (MSE). An analytical expression for the AGV's state estimates is derived, which is used to find an upper bound on the position bias due to multipath. An optimal path planning generation approach, which is based on Dijkstra's algorithm, is developed to optimize the AGV's path while minimizing the path length and position MSE, subject to keeping the position estimation uncertainty and position estimation bias due to multipath below desired thresholds. The path planning approach yields the optimal path together with a list of feasible paths and reliable GNSS satellites and cellular base stations to use along these paths.

中文翻译：

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在城市环境中使用 GNSS 和蜂窝信号可靠性地图进行自主地面车辆路径规划：模型和算法

在本文中，考虑了自主地面车辆 (AGV) 路径规划。假定 AGV 配备了接收器，该接收器能够对高架全球导航卫星系统 (GNSS) 卫星及其环境中的蜂窝基站进行伪距测量。在开放天空环境中的初始化步骤中估计与发射机时钟偏差相关的蜂窝伪距参数。AGV 融合这些伪距以产生关于其自身状态的估计。AGV还配备了环境的三维建筑图。从已知的起点出发，AGV希望以最短的距离到达已知的目标点，同时最小化AGV的位置估计误差，保证AGV' s 位置估计不确定性低于所需阈值。为实现这一目标，首先生成所谓的信号可靠性图，该图提供有关由于 GNSS 视距或蜂窝信号多径不良而导致大误差的区域的信息。车辆使用信号可靠性图来计算位置均方误差 (MSE)。推导出 AGV 状态估计的解析表达式，用于找到由于多径引起的位置偏差的上限。开发了一种基于 Dijkstra 算法的最优路径规划生成方法，以优化 AGV 的路径，同时最小化路径长度和位置 MSE，同时保持位置估计不确定性和由于多路径导致的位置估计偏差低于所需阈值。