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Abstract
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| In this paper, we present a fully-autonomous aerial robotic solution, for executing complex SAR missions in unstructured indoor environments. The proposed system is based on the combination of a complete hardware configuration and a flexible system architecture which allows the execution of high-level missions in a fully unsupervised manner (i.e. without human intervention). In order to obtain flexible and versatile behaviors from the proposed aerial robot, several learning-based capabilities have been integrated for target recognition and interaction. The target recognition capability includes a supervised learning classifier based on a computationally-efficient Convolutional Neural Network (CNN) model trained for target/background classification, while the capability to interact with the target for rescue operations introduces a novel Image-Based Visual Servoing (IBVS) algorithm which integrates a recent deep reinforcement learning method named Deep Deterministic Policy Gradients (DDPG). In order to train the aerial robot for performing IBVS tasks, a reinforcement learning framework has been developed, which integrates a deep reinforcement learning agent (e.g. DDPG) with a Gazebo-based simulator for aerial robotics. | |
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International
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Si |
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JCR
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Title
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Journal of Intelligent & Robotic Systems |
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ISBN
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0921-0296 |
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Impact factor JCR
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1,583 |
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Impact info
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Datos JCR del año 2017 |
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Volume
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10.1007/s10846-018-0898-1 |
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Journal number
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From page
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1 |
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To page
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27 |
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Month
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JULIO |
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Ranking
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