Deep Reinforcement Learning Robot for Search and Rescue Applications: Exploration in Unknown Cluttered Environments

Rescue robots can be used in urban search and rescue (USAR) applications to perform the important task of exploring unknown cluttered environments. Due to the unpredictable nature of these environments, deep learning techniques can be used to perform these tasks. In this letter, we present the first...

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Bibliographic Details
Published in:IEEE robotics and automation letters Vol. 4; no. 2; pp. 610 - 617
Main Authors: Niroui, Farzad, Kaicheng Zhang, Kashino, Zendai, Nejat, Goldie
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.04.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Autonomous agents
Computer architecture
Deep learning
deep learning in robotics and automation
Exploration
Layout
Layouts
Machine learning
Microprocessors
Navigation
Robot sensing systems
Robots
Search and rescue missions
search and rescue robots
Task analysis
Wideband communications
ISSN:2377-3766, 2377-3766
Online Access:Get full text
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Summary:Rescue robots can be used in urban search and rescue (USAR) applications to perform the important task of exploring unknown cluttered environments. Due to the unpredictable nature of these environments, deep learning techniques can be used to perform these tasks. In this letter, we present the first use of deep learning to address the robot exploration task in USAR applications. In particular, we uniquely combine the traditional approach of frontier-based exploration with deep reinforcement learning to allow a robot to autonomously explore unknown cluttered environments. Experiments conducted with a mobile robot in unknown cluttered environments of varying sizes and layouts showed that the proposed exploration approach can effectively determine appropriate frontier locations to navigate to, while being robust to different environment layouts and sizes. Furthermore, a comparison study with other frontier exploration approaches showed that our learning-based frontier exploration technique was able to explore more of an environment earlier on, allowing for potential identification of a larger number of victims at the beginning of the time-critical exploration task.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2019.2891991