Informative path planning algorithms are of paramount importance in applications like disaster management to efficiently gather information through a priori unknown environments. This is, however, a complex problem that involves finding a globally optimal path that gathers the maximum amount of information (e.g., the largest map with a minimum travelling distance) while using partial and uncertain local measurements. This paper addresses this problem by proposing a novel heuristic algorithm that continuously estimates the potential mapping gain for different sub-areas across the partially created map, and then uses these estimations to locally navigate the robot. Furthermore, this paper presents a novel algorithm to calculate a benchmark solution, where the map is a priori known to the planar, to evaluate the efficacy of the developed heuristic algorithm over different test scenarios. The findings indicate that the efficiency of the proposed algorithm, measured in terms of the mapped area per unit of travelling distance, ranges from 70% to 80% of the benchmark solution in various test scenarios. In essence, the algorithm demonstrates the capability to generate paths that come close to the globally optimal path provided by the benchmark solution.

翻译：信息路径规划算法在灾害管理等应用中至关重要，用于在未知环境先验信息缺失的情况下高效收集数据。然而，这是一个复杂问题，需要利用局部且不确定的测量结果，找到一条能最大化信息获取量（例如，以最小行驶距离构建最大地图）的全局最优路径。本文通过提出一种新型启发式算法来解决该问题，该算法持续估算已部分构建地图中不同子区域的潜在绘图增益，并利用这些估算值局部引导机器人导航。此外，本文提出一种新颖的基准解计算方法——假定平面地图已知先验信息，以评估所提启发式算法在不同测试场景中的效能。结果表明，以单位行驶距离测绘面积衡量，该算法在不同测试场景中的效率可达基准解的70%至80%。本质上，该算法能够生成接近基准解所提供全局最优路径的路径。