Sediment Sampling in Estuarine Mudflats with an Aerial-Ground Robotic Team

This paper presents a robotic team suited for bottom sediment sampling and retrieval in mudflats, targeting environmental monitoring tasks. The robotic team encompasses a four-wheel-steering ground vehicle, equipped with a drilling tool designed to be able to retain wet soil, and a multi-rotor aeria...

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Bibliographic Details
Main Authors: Pedro Deusdado, Magno Guedes, André Silva, Francisco Marques, Eduardo Pinto, Paulo Rodrigues, André Lourenço, Ricardo Mendonça, Pedro Santana, José Corisco, Susana Marta Almeida, Luís Portugal, Raquel Caldeira, José Barata, Luis Flores
Format: Article
Language:English
Published: MDPI AG 2016-09-01
Series:Sensors
Subjects:
UGV
UAV
Online Access:http://www.mdpi.com/1424-8220/16/9/1461
Description
Summary:This paper presents a robotic team suited for bottom sediment sampling and retrieval in mudflats, targeting environmental monitoring tasks. The robotic team encompasses a four-wheel-steering ground vehicle, equipped with a drilling tool designed to be able to retain wet soil, and a multi-rotor aerial vehicle for dynamic aerial imagery acquisition. On-demand aerial imagery, properly fused on an aerial mosaic, is used by remote human operators for specifying the robotic mission and supervising its execution. This is crucial for the success of an environmental monitoring study, as often it depends on human expertise to ensure the statistical significance and accuracy of the sampling procedures. Although the literature is rich on environmental monitoring sampling procedures, in mudflats, there is a gap as regards including robotic elements. This paper closes this gap by also proposing a preliminary experimental protocol tailored to exploit the capabilities offered by the robotic system. Field trials in the south bank of the river Tagus’ estuary show the ability of the robotic system to successfully extract and transport bottom sediment samples for offline analysis. The results also show the efficiency of the extraction and the benefits when compared to (conventional) human-based sampling.
ISSN:1424-8220