Intelligent automated, rapid and safe landmine and Unexploded Ordnance (UXO) detection using multiple sensor modalities mounted on autonomous drones

Kuru, Kaya orcid iconORCID: 0000-0002-4279-4166, Ansell, Darren orcid iconORCID: 0000-0003-2818-3315, Jon Watkinson, Benjamin, Jones, David, Sujit, Aadithya orcid iconORCID: 0000-0002-6744-5472, Pinder, John Michael and Tinker-Mill, Claire Louisa orcid iconORCID: 0000-0002-1981-3111 (2023) Intelligent automated, rapid and safe landmine and Unexploded Ordnance (UXO) detection using multiple sensor modalities mounted on autonomous drones. IEEE Transactions on Geoscience and Remote Sensing . (Submitted)

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Official URL: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?pu...

Abstract

Detecting and clearing legacy landmines using human force or animals is tremendously risky and labour-intensive. Mapping millions of buried landmines rapidly and removing them cost-effectively is prime important to avoid their potential risks and to ease this labour-intensive task. Deploying uninhabited vehicles and robots equipped with multiple remote sensing modalities seems an excellent option for performing this task using a geophysical investigative method in a non-invasive fashion. In this study, several remote sensing modalities are mounted on an advanced autonomous uninhabited aerial vehicle (UAV), the so-called LMUAV, to increase the probability of detection and decrease the false alarm rate. The likely landmine spots determined by these modalities are analysed by a novel intelligent data fusion technique to construct a geospatial landmine map. Additionally, several autonomous robotic ground vehicles (ARGVs) with GPR are incorporated into the system to ensure the specific locations of the landmines based on the constructed geospatial landmine map. These ARGVs can be safely carried by other UAVs designed for this purpose to particular landmine locations to perform their tasks while the LMUAV is searching for the landmines in other adjacent locations. The performance of the particular modalities was evaluated in field tests in Croatia and Cambodia. The feasibility of the full system has been tested in the UK in a landmine field. The results based on the data sets acquired in the outdoor minefields confirm the viability of the techniques and approaches for detecting legacy landmines efficiently. Furthermore, as future work, the demining task with an excavation ability is aimed to be incorporated into ARGVs to increase the automation level of the overall system.


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