We propose a path planning methodology for a mobile robot navigating through an obstacle-filled environment to generate a reference path that is traceable with moderate sensing efforts. The desired reference path is characterized as the shortest path in an obstacle-filled Gaussian belief manifold equipped with a novel information-geometric distance function. The distance function we introduce is shown to be an asymmetric quasi-pseudometric and can be interpreted as the minimum information gain required to steer the Gaussian belief. An RRT*-based numerical solution algorithm is presented to solve the formulated shortest-path problem. To gain insight into the asymptotic optimality of the proposed algorithm, we show that the considered path length function is continuous with respect to the topology of total variation. Simulation results demonstrate that the proposed method is effective in various robot navigation scenarios to reduce sensing costs, such as the required frequency of sensor measurements and the number of sensors that must be operated simultaneously.

翻译：我们建议了移动机器人在一个充满障碍的环境中航行的路径规划方法,以产生一条可以用适度的遥感努力追踪的参考路径。理想的参考路径被描述为在一个充满障碍的高森信仰区中最短的路径,该障碍区装配了一个新的信息地理距离功能。我们引入的距离函数被证明是一种不对称的准模拟函数,可以解释为指导高西亚信仰所需的最低信息收益。一个基于 RRT* 的数字解决方案算法用于解决所拟订的最短路径问题。为了深入了解拟议的算法的无孔不入的最佳性,我们表明所考虑的路径长度功能在全面变异的表层方面是连续的。模拟结果表明,拟议的方法在各种机器人导航情景中是有效的,可以降低遥感成本,例如所需的传感器测量频率和必须同时运行的传感器数量。