The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning
Open accessAbstract
Autonomous exploration is an important application of multi-vehicle systems, where a team of networked robots are coordinated to explore an unknown environment collaboratively. This technique has earned significant research interest due to its usefulness in search and rescue, fault detection and monitoring, localization and mapping, etc. In this paper, a novel cooperative exploration strategy is proposed for multiple mobile robots, which reduces the overall task completion time and energy costs compared to conventional methods. To efficiently navigate the networked robots during the collaborative tasks, a hierarchical control architecture is designed which contains a high-level decision making layer and a low-level target tracking layer. The proposed cooperative exploration approach is developed using dynamic Voronoi partitions, which minimizes duplicated exploration areas by assigning different target locations to individual robots. To deal with sudden obstacles in the unknown environment, an integrated deep reinforcement learning based collision avoidance algorithm is then proposed, which enables the control policy to learn from human demonstration data and thus improve the learning speed and performance. Finally, simulation and experimental results are provided to demonstrate the effectiveness of the proposed scheme.
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Cites
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782
Design and use paradigms for gazebo, an open-source multi-robot simulator
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Coordinated multi-robot exploration
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A Probabilistic Approach to Collaborative Multi-Robot Localization
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Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning
Long, P., Fanl, T., Liao, X., Liu, W., Zhang, H., & Pan, J. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8461113 · Cited by 538
Mobile robot path planning using membrane evolutionary artificial potential field
Orozco-Rosas, U., Montiel, O., & Sepúlveda, R. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.01.036 · Cited by 426
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Cited by
Tan, C. S., Mohd-Mokhtar, R., & Arshad, M. R. (2021) · IEEE Access · doi:10.1109/access.2021.3108177 · Cited by 346
Bio-Inspired Collision Avoidance in Swarm Systems via Deep Reinforcement Learning
Na, S., Niu, H., Lennox, B., & Arvin, F. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3145346 · Cited by 64
Self-citationBarth, A., & Ma, O. (2024) · Acta Astronautica · doi:10.1016/j.actaastro.2023.11.014 · Cited by 15
Metadata
- Authors
- Hu, J., Niu, H., Carrasco, J., Lennox, B., & Arvin, F.
- Venue
- IEEE Transactions on Vehicular Technology
- Published
- 2020-12
- Volume
- 69
- Issue
- 12
- Pages
- 14413-14423
- ISSN
- 0018-9545 (ISSN-L)
- 1939-9359
- OpenAlex
- W3095315965
- Cited by
- 438
- References
- 29
- FWCI
- 20.74
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Hu, J., Niu, H., Carrasco, J., Lennox, B., & Arvin, F. (2020). Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/tvt.2020.3034800
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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