ICUAS 2020 Paper Abstract

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Paper ThC3.5

Velasco, Omar (Wageningen University and Research), Valente, Joćo (Wageningen University & Research), Mersha, Abeje Yenehun (Saxion University of Applied Sciences)

An Adaptive Informative Path Planning Algorithm for Real-Time Air Quality Monitoring Using UAVs

Scheduled for presentation during the Regular Session "UAS Applications III" (ThC3), Thursday, September 3, 2020, 18:20−18:40, Edessa

2020 International Conference on Unmanned Aircraft Systems (ICUAS), September 1-4, 2020 (Postponed from June 9-12, 2020), Athens, Greece

This information is tentative and subject to change. Compiled on September 25, 2020

Keywords UAS Applications, Path Planning, Smart Sensors

Abstract

Environmental monitoring is a heavily data driven task where data sample efficiency is paramount due to the shear volumes of gathered data. In particular, air monitoring strongly depends on sensor location. Since the recent past, Unmanned Aerial Vehicles (UAVs) present themselves as a prospective solution for flexible and better air quality data gathering. In this paper, we present a novel adaptive Informative Path Planning (IPP) approach that enables UAVs navigate through a sample utility map based on adaptive Statistical Gas Distribution Models (GDM) for efficient surveying. The presented adaptive IPP approach maximises the amount of gathered information per mission within the system constraints in known and unknown environments with near optimal performance. The effectiveness of the algorithm is tested through extensive simulation. The results showed high quality sample collection, low computational costs and better model prediction metrics against other surveying strategies. Although framed in an air environmental monitoring context, the developed solution can be used for any generic IPP problem by adapting the sample utility map to the particular application.

 

 

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