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Deep Learning for Exploration and Recovery of Uncharted and Dynamic Targets from UAV-like Vision

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publication2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018)
Publisher or commissioning bodyInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Electronic)9781538680940
ISBN (Print)9781538680957
DateAccepted/In press - 29 Jul 2018
DateE-pub ahead of print - 7 Jan 2019
DatePublished (current) - Jan 2019

Publication series

ISSN (Print)2153-0858


This paper discusses deep learning for solving static and dynamic search and recovery tasks – such as the retrieval of all instances of actively moving targets – based on partial-view Unmanned Aerial Vehicle (UAV)-like sensing. In particular, we demonstrate that abstracted tactic and strategic explorational agency can be implemented effectively via a single deep network that optimises in unity: the mapping of sensory inputs and positional history towards navigational actions. We propose a dual-stream classification paradigm that integrates one Convolutional Neural Network (CNN) for sensory processing with a second one for interpreting an evolving long-term map memory. In order to learn effective search behaviours given agent location and agent-centric sensory inputs, we train this design against 400k+ optimal navigational decision samples from each set of static and dynamic evolutions for different multi-target behaviour classes. We quantify recovery performance across an extensive range of scenarios; including probabilistic placement and dynamics, as well as fully random target walks and herd-inspired behaviours. Detailed results comparisons show that our design can outperform naïve, independent stream and off-the-shelf DRQN solutions. We conclude that the proposed dual-stream architecture can provide a unified, rationally motivated and effective architecture for solving online search tasks in dynamic, multi-target environments. With this paper we publish 3 3 Source code available at: and key source code and associated models.

    Research areas

  • Navigation, robot sensing systems, task analysis, history, visualisation, vehicle dynamics, Reinforcement Learning

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via IEEE at Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 2.98 MB, PDF document


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