The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
A Deep Reinforcement Learning Method for Mobile Robot Collision Avoidance based on Double DQN
Abstract
We propose a deep reinforcement learning method based on Double Q-learning Network(DDQN) to enable mobile robots to learn collision avoidance and navigation capabilities autonomously. Information such as target position, obstacle size and position is taken as input, and the direction of movement of the robot is taken as an output. Traditional mobile robots usually requires real-time accurate and fast Simultaneous Localization And Mapping(SLAM) technology for global navigation. We aim at the scenario that after an initial globally feasible path is established, the path could be split into finite segments of sub-goals, and the proposed method focuses on using deep reinforcement learning to control the robots reaching the subgoals in sequence. Experiments show that the proposed method can navigate the mobile robots to desired target position without colliding with any obstacle and other moving robots, and the method is successfully implied on a physical robot platform. In addition, the method is a non-global path planning method, which greatly reduces the computational cost.
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“Therefore, Q -network is used for approximating the control strategy matrix and solves the problem of curse of dimensionality [23].”
From Deep reinforcement learning-based adaptive fuzzy control for electro-hydraulic servo system (2025) · cites this paper as [23]
Citations
Cites
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Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
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
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471
Deep Reinforcement Learning with Double Q-Learning
Van Hasselt, H., Guez, A., & Silver, D. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10295 · Cited by 3,318
Reciprocal n-Body Collision Avoidance
van den Berg, J., Guy, S. J., Lin, M. C., & Manocha, D. (2011) · Springer tracts in advanced robotics · doi:10.1007/978-3-642-19457-3_1 · Cited by 1,868
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Socially aware motion planning with deep reinforcement learning
Chen, Y. F., Everett, M., Liu, M., & How, J. P. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202312 · Cited by 743
Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning
Chen, Y. F., Liu, M., Everett, M., & How, J. P. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989037 · Cited by 689
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Research works printed but not linked: 2
- yu, 2011, Mobile robot path planning based on improved artificial potential field method, Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology, 43, 50
- mnih, 2016, Asynchronous methods for deep reinforcement learning, International Conference on Machine Learning, 1928
Cited by
Sorted by citations
Deep reinforcement learning-based adaptive fuzzy control for electro-hydraulic servo system
Khater, A. A., Fekry, M., El-Bardini, M., & El-Nagar, A. M. (2025) · Neural Computing and Applications · doi:10.1007/s00521-024-10741-x · Cited by 17
Metadata
- Authors
- Xue, X., Li, Z., Zhang, D., & Yan, Y.
- Affiliations
- Harbin Institute of Technology (CN)
- Venue
- 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 2131-2136
- OpenAlex
- W2965554769
- Cited by
- 48
- References
- 19
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 14 · 2023: 10 · 2022: 11 · 2021: 6 · 2020: 2
- FWCI
- 1.82
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Symposium on Industrial Electronics (ISIE) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Control and Systems Engineering › Robotic Path Planning Algorithms
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Keywords
- double deep Q-network, deep reinforcement learning, robot navigation, obstacle avoidance, autonomous robot control, computational complexity reduction
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- Xue, X., Li, Z., Zhang, D., & Yan, Y. (2019). A Deep Reinforcement Learning Method for Mobile Robot Collision Avoidance based on Double DQN. 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE). https://doi.org/10.1109/isie.2019.8781522
