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A UAV Path and Action Planning Algorithm for Indoor Localization Information Collection

How to efficiently and accurately locate people and objects in the indoor environment has always been a hotspot in indoor localization research. Channel State Information (CSI) fingerprint is a very effective method for indoor localization. At present most CSI information collection tasks are done by humans, which is labor-intensive and inefficient. To overcome the disadvantages of manual operation, researchers begin to use UAV to complete CSI information collection tasks in recent years. At present, the UAV localization fingerprint information collection algorithm has the following problems: (1) When planning the UAV path, the energy consumption of turning, ascending, descent, etc. are not taken into consideration, which leads to large energy consumption for completing tasks; (2) Data collection only at the two-dimensional level instead of three-dimension results in errors in data analysis. To address these problems, we model the three-dimensional indoor environment, the action, and energy consumption of the UAV, then convert the path and action planning problem into a Generalized Traveling Salesman Problems (GTSP). An energy-efficient UAV path and action planning algorithm (EEPAP) is proposed to collect three-dimension CSI information. Finally, through experiments, we evaluated the performance of EEPAP and proved that it outperforms the existing algorithm in terms of energy consumption.