Browsing by Author "Florez-Lozano, Johana"

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    Cooperative and distributed decision-making in a multi-agent perception system for improvised land mines detection
    (Elsevier, 2020-06-25) Florez-Lozano, Johana; Caraffini, Fabio; Parra, Carlos; Gongora, Mario Augusto
    This work presents a novel intelligent system designed using a multi-agent hardware platform to detect improvised explosive devices concealed in the ground. Each agent is equipped with a different sensor, (i.e. a ground-penetrating radar, a thermal sensor and three cameras each covering a different spectrum) and processes dedicated AI decision-making capabilities. The proposed system has a unique hardware structure, with a distributed design and effective selection of sensors, and a novel multi-phase and cooperative decision-making framework. Agents operate independently via a customised logic adjusting their sensor positions - to achieve optimal acquisition; performing a preliminary “local decision-making” - to classify buried objects; sharing information with the other agents. Once sufficient information is shared by the agents, a collaborative behaviour emerges in the so-called “cooperative decision-making” process, which performs the final detection. In this paper, 120 variations of the proposed system, obtained by combining both classic aggregation operators as well as advanced neural and fuzzy systems, are presented, tested and evaluated. Results show a good detection accuracy and robustness to environmental and data sets changes, in particular when the cooperative decision-making is implemented with the neuroevolution paradigm.
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    A Robust Decision-Making Framework Based on Collaborative Agents
    (IEEE, 2020-08-14) Florez-Lozano, Johana; Caraffini, Fabio; Carlos, Parra; Gongora, Mario Augusto
    Making decisions under uncertainty is very challenging but necessary as most real-world scenarios are plagued by disturbances that can be generated internally, by the hardware itself, or externally, by the environment. Hence, we propose a general decision-making framework which can be adapted to optimally address the most heterogeneous real-world domains without being significantly affected by undesired disturbances. Our paper presents a multi-agent based structure in which agents are capable of individual decision-making but also interact to perform subsequent, and more robust, collaborative decisionmaking processes. The complexity of each software agent can be kept quite low without deterioration of the performance since an intelligent and robust-to-uncertainty decision-making behaviour arises when their locally produced measures of support are shared and exploited collaboratively. We show that by equipping agents with classic computational intelligence techniques, to extract features and generate measures of support, complex hybrid multi-agent software structures capable of handling uncertainty can be easily designed. The resulting multi-agent systems generated with this approach are based on a two-phases decision-making methodology which first runs parallel local decision making processes to then aggregate the corresponding outputs to improve upon the accuracy of the system. To highlight the potential of this approach, we provided multiple implementations of the general framework and compared them over four different application scenarios. Results are promising and show that having a second collaborative decisionmaking process is always beneficial.
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    Shallow Buried Improvised Explosive Device Detection Via Convolutional Neural Networks
    (IOS Press, 2020-07-03) Colreavy-Donnelly, S.; Caraffini, Fabio; Kuhn, Stefan; Gongora, Mario Augusto; Florez-Lozano, Johana; Parra, Carlos
    The issue of detecting improvised explosive devices, henceforth IEDs, in rural or built-up urban environments is a persistent and serious concern for governments in the developing world. In many cases, such devices are plastic, or varied metallic objects containing rudimentary explosives, which are not visible to the naked eye and are difficult to detect autonomously. The most effective strategy for detecting land mines also happens to be the most dangerous. This paper intends to leverage the use of a Convolutional Neural Network (CNN) to aid in the discovery of such IEDs. As part of a related project, an autonomous sensor array was used to detect the devices in terrains too hazardous for a human to survey. This paper presents a CNN and its training methodology, suitable to make use of the sensor system. This convolutional neural network can accurately distinguish between a potential IED and surrounding undergrowth and natural features of the environment in real-time. The training methodology enabled the CNN to successfully recognise the IEDs with an accuracy of 98.7%, in well-lit conditions. The results are evaluated against other convolutional neural systems as well as against a deterministic algorithm, showing that the proposed CNN outperforms its competitors including the deterministic method.
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    Training Data Set Assessment for Decision-Making in a Multiagent Landmine Detection Platform
    (IEEE, 2020-07-19) Florez-Lozano, Johana; Caraffini, Fabio; Parra, Carlos; Gongora, Mario Augusto
    Real-world problems such as landmine detection require multiple sources of information to reduce the uncertainty of decision-making. A novel approach to solve these problems includes distributed systems, as presented in this work based on hardware and software multi-agent systems. To achieve a high rate of landmine detection, we evaluate the performance of a trained system over the distribution of samples between training and validation sets. Additionally, a general explanation of the data set is provided, presenting the samples gathered by a cooperative multi-agent system developed for detecting improvised explosive devices. The results show that input samples affect the performance of the output decisions, and a decision-making system can be less sensitive to sensor noise with intelligent systems obtained from a diverse and suitably organised training set.