An IndoorGeoBML Model Based IORP Algorithm for Indoor Operation

Mingzhan Su, Guangxia Wang, Lingyu Chen and Xin Zhang
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Mingzhan Su: Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China
Guangxia Wang: Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China
Lingyu Chen: Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China
Xin Zhang: Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China

Sustainability, 2022, vol. 14, issue 10, 1-24

Abstract: Indoor military operations play a vital part in modern urban warfare. Decision making in indoor operations is quite complicated due to the complex of the indoor spatial environment. However, the study of the characteristics and features of indoor operations is scarce. To help commanders make decisions in indoor operations, a model to represent the information of the building and an algorithm to perform route planning is needed. There have been some studies in the field of search and rescue problems, but these did not study the enemy force, which has a lot of uncertainties and plays a vital role in indoor operations. To solve this problem, this paper first proposes an innovative IndoorGeoBML (Indoor Geospatial Battle Management Language) model to accurately describe the indoor environment. We define six categories of information in IndoorGeoBML model: geometry information, navigation information, semantic information, outdoor information, intelligence information, and event information, which accurately, dynamically, and comprehensively describe the environment in the building. Then based on the IndoorGeoBML model, this paper researches the route planning in indoor operations. There are two types of indoor route planning problems. One is single destination route planning, the other is a searching route planning, which needs to plan paths to search the whole building. To deal with these two kinds of route planning problems, based on IndoorGeoBML model, this paper introduces a new algorithm: the IORP (Indoor Operation Route Planning) algorithm. Finally, this paper implements some experiments on a building with IORP algorithm dealing with the two kinds of route planning problems. For single destination route planning, the result shows that the enemy capability, traversing time, and own casualties of our proposed algorithm are 779.2, 801, and 12.5, which is at least 9.9%, 9.2%, and 7.5% lower compared to other algorithms. For searching route planning, the result shows that the whole time for searching decreases from 3044 s to 2673 s, and the number of squads decreases from 8 to 5. The evaluation of the model and algorithm shows a significant improvement in time and casualties, which will help commanders make better decision in indoor operation.

Keywords: indoor operation; BML; GeoBML; IndoorGeoBML; indoor route planning; indoor operation route planning algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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