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Informative path planning algorithms are of paramount importance inapplications like disaster management to efficiently gather information througha priori unknown environments. This is, however, a complex problem thatinvolves finding a globally optimal path that gathers the maximum amount ofinformation (e.g., the largest map with a minimum travelling distance) whileusing partial and uncertain local measurements. This paper addresses thisproblem by proposing a novel heuristic algorithm that continuously estimatesthe potential mapping gain for different sub-areas across the partially createdmap, and then uses these estimations to locally navigate the robot.Furthermore, this paper presents a novel algorithm to calculate a benchmarksolution, where the map is a priori known to the planar, to evaluate theefficacy of the developed heuristic algorithm over different test scenarios.The findings indicate that the efficiency of the proposed algorithm, measuredin terms of the mapped area per unit of travelling distance, ranges from 70% to80% of the benchmark solution in various test scenarios. In essence, thealgorithm demonstrates the capability to generate paths that come close to theglobally optimal path provided by the benchmark solution.

A Heuristic Informative-Path-Planning Algorithm for Autonomous Mapping of Unknown Areas