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Autonomous Robots
Erschienen in: Autonomous Robots 7/2019

14.01.2019

Deep learning of structured environments for robot search

verfasst von: Jeffrey A. Caley, Nicholas R. J. Lawrance, Geoffrey A. Hollinger

Erschienen in: Autonomous Robots | Ausgabe 7/2019

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Abstract

Robots often operate in built environments containing underlying structure that can be exploited to help predict future observations. In this work, we present a framework based on convolutional neural networks to predict point of interest locations in structured environments. The proposed technique exploits the inherent structure of the environment to train a convolutional neural network that is leveraged to facilitate robotic search. We start by investigating environments where the full environmental structure is known, and then we extend the work to unknown environments. Experimental results show the proposed framework provides a reliable method for increasing the efficiency of current search methods across multiple domains. Finally, we demonstrate the proposed framework increases the search efficiency of a mobile robot in a real-world office environment.
Vorheriger Artikel Reinforcement learning and model predictive control for robust embedded quadrotor guidance and control
Nächster Artikel Sampling-based optimal kinodynamic planning with motion primitives

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Metadaten
Titel
Deep learning of structured environments for robot search
verfasst von
Jeffrey A. Caley
Nicholas R. J. Lawrance
Geoffrey A. Hollinger
Publikationsdatum
14.01.2019
Verlag
Springer US
Erschienen in
Autonomous Robots / Ausgabe 7/2019
Print ISSN: 0929-5593
Elektronische ISSN: 1573-7527
DOI
https://doi.org/10.1007/s10514-018-09821-4

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