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Robot navigation using deep reinforcement learning (DRL) has shown great potential in improving the performance of mobile robots.
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S. Yao, G. Chen, Q. Qiu, J. Ma, X. Chen, and J. Ji, “Crowd-aware robot navigation for pedestrians with multiple collision avoidance strategies via map-based deep reinforcement learning,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 8144–8150
2021
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2018
Cited alongside, same era.
A. Faust, K. Oslund, O. Ramirez, A. Francis, L. Tapia, M. Fiser, and J. Davidson, “Prm-rl: Long-range robotic navigation tasks by combining reinforcement learning and sampling-based planning,” in 2018 IEEE international conference on robotics and automation (ICRA) . IEEE, 2018, pp. 5113–5120
2018
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2019
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H. Ma, Y. Wang, L. Tang, S. Kodagoda, and R. Xiong, “Towards navigation without precise localization: Weakly supervised learning of goal-directed navigation cost map,” June 2019
2019
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G. Chen, S. Yao, J. Ma, L. Pan, Y. Chen, P. Xu, J. Ji, and X. Chen, “Distributed non-communicating multi-robot collision avoidance via map-based deep reinforcement learning,” Sensors , vol. 20, no. 17, p. 4836, 2020
2020
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2022
Later among the works it cites.
2022
Later among the works it cites.
2022
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Y. Duan, J. Peng, Y. Zhang, J. Ji, and Y. Zhang, “Pfilter: Building persistent maps through feature filtering for fast and accurate lidar-based slam,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 11 087–11 093
2022
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2023
Closest in time.
Z. Xie and P. Dames, “Drl-vo: Learning to navigate through crowded dynamic scenes using velocity obstacles,” IEEE Transactions on Robotics , pp. 1–20, 2023
2023
Closest in time.