The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Deep Reinforcement Learning Robot for Search and Rescue Applications: Exploration in Unknown Cluttered Environments
Abstract
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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Cites
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Asynchronous Methods for Deep Reinforcement Learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., Silver, D., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1602.01783 · Cited by 7,132
Reinforcement learning in robotics: A survey
Kober, J., Bagnell, J. A., & Peters, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913495721 · Cited by 3,160
A frontier-based approach for autonomous exploration
Yamauchi, B. (2002) · Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation' · doi:10.1109/cira.1997.613851 · Cited by 1,695
Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
Garg, R., B.G., V. K., Carneiro, G., & Reid, I. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_45 · Cited by 1,632
Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
Garg, R., BG, V. K., Carneiro, G., & Reid, I. (2016) · arXiv · doi:10.48550/arxiv.1603.04992 · Cited by 1,632
Deepvo: towards end-to-end visual odometry with deep recurrent convolutional neural networks
Wang, S., Clark, R., Wen, H., & Trigoni, N. (2017) · 2017 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2017.7989236 · Cited by 970 · cited 1×
“Traditional machine learning and deep learning techniques have been applied to image classification [13]–[16], target object detection [17], [18], robot-team work division [18], and robot visual odometry [19], [20] problems.”
MethodROBOT ARM PERCEPTIVE EXPLORATION BASED SIGNIFICANT SLAM IN SEARCH AND RESCUE ENVIRONMENT
Wang, H., Zhang, C., Song, Y., & Pang, B. (2018) · International Journal of Robotics and Automation · doi:10.2316/journal.206.2018.4.206-5122 · Cited by 2
Cited by
Hu, J., Niu, H., Carrasco, J., Lennox, B., & Arvin, F. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.3034800 · Cited by 438
Tan, C. S., Mohd-Mokhtar, R., & Arshad, M. R. (2021) · IEEE Access · doi:10.1109/access.2021.3108177 · Cited by 346
Lygouras, E., Santavas, N., Taitzoglou, A., Tarchanidis, K., Mitropoulos, A., & Gasteratos, A. (2019) · Sensors · doi:10.3390/s19163542 · Cited by 253
UAV-Based Real-Time Survivor Detection System in Post-Disaster Search and Rescue Operations
Dong, J., Ota, K., & Dong, M. (2021) · IEEE Journal on Miniaturization for Air and Space Systems · doi:10.1109/jmass.2021.3083659 · Cited by 174
Motion Planning for Mobile Robots—Focusing on Deep Reinforcement Learning: A Systematic Review
Sun, H., Zhang, W., Yu, R., & Zhang, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3076530 · Cited by 143
Metadata
- Authors
- Niroui, F., Zhang, K., Kashino, Z., & Nejat, G.
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2019-04
- Volume
- 4
- Issue
- 2
- Pages
- 610-617
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W2910054127
- Cited by
- 385
- References
- 8
- FWCI
- 18.42
- Type
- journal article
- Language
- en
- Open access
- closed
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