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: https://data.bris.ac.uk/data and https://github.com/CWOA/GTRF key source code and associated models.
|Title of host publication||2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||8|
|Publication status||Published - Jan 2019|
|Name||IEEE International Conference on Intelligent Robots and Systems|
- robot sensing systems
- task analysis
- vehicle dynamics
- Reinforcement Learning
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7 May 2019
Student thesis: Doctoral Thesis › Doctor of Philosophy (PhD)File