The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation
Open accessAbstract
We present a learning-based mapless motion planner by taking the sparse 10-dimensional range findings and the target position with respect to the mobile robot coordinate frame as input and the continuous steering commands as output. Traditional motion planners for mobile ground robots with a laser range sensor mostly depend on the obstacle map of the navigation environment where both the highly precise laser sensor and the obstacle map building work of the environment are indispensable. We show that, through an asynchronous deep reinforcement learning method, a mapless motion planner can be trained end-to-end without any manually designed features and prior demonstrations. The trained planner can be directly applied in unseen virtual and real environments. The experiments show that the proposed mapless motion planner can navigate the nonholonomic mobile robot to the desired targets without colliding with any obstacles.
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Wang, X., Wang, S., Liang, X., Zhao, D., Huang, J., Xu, X., Dai, B., & Miao, Q. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3207346 · Cited by 972
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A Review of Domain Adaptation without Target Labels
Kouw, W. M., & Loog, M. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2945942 · Cited by 613
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
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Metadata
- Authors
- Tai, L., Paolo, G., & Liu, M.
- Venue
- 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Publisher
- IEEE
- Published
- 2017-09
- Pages
- 31-36
- OpenAlex
- W2963428623
- Cited by
- 834
- References
- 22
- FWCI
- 36.20
- Type
- conference paper
- Language
- en
- Open access
- green
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- Tai, L., Paolo, G., & Liu, M. (2017). Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation. 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). https://doi.org/10.1109/iros.2017.8202134
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