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Crowd-Comfort Robot Navigation Among Dynamic Environment Based on Social-Stressed Deep Reinforcement Learning

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Abstract

Robot navigation in a dynamic environment is a challenging task because not only the safety but also the comfort of surrounding pedestrians shall be necessarily considered. This paper proposes the concept of social stress based on tension space of robot and human, which is an important part of Human-Robot interaction. Especially, the proposed approach develops crowd-comfort navigation by combining social stress indexes with a deep reinforcement learning framework and the value network. A set of typical simulation experiments show that our method improves the comfort of surrounding pedestrians effectively during the process of robot navigation. In addition, the fine-tuned technology proposed in this paper has also been proven to be suitable for different scenarios.

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Notes

  1. https://sites.google.com/view/ssdrl2020.

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Acknowledgements

The authors would like to thank Associate Prof. Dr. Xian Guo from our institute for discussing.

Funding

This work is supported in part by National Key Research and Development Project under Grant 2019YFB1310604, in part by National Natural Science Foundation of China under Grant 91848108.

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Authors and Affiliations

  1. Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tanjin, China

    Zhengxi Hu, Yingli Zhao, Lei Zhou & Jingtai Liu

  2. Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tanjin, China

    Zhengxi Hu, Yingli Zhao, Lei Zhou & Jingtai Liu

  3. School of Mechanical Engineering, Tianjin University of Technology, Tanjin, China

    Sen Zhang

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  1. Zhengxi Hu
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  2. Yingli Zhao
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  3. Sen Zhang
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  4. Lei Zhou
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Correspondence to Jingtai Liu.

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Hu, Z., Zhao, Y., Zhang, S. et al. Crowd-Comfort Robot Navigation Among Dynamic Environment Based on Social-Stressed Deep Reinforcement Learning. Int J of Soc Robotics 14, 913–929 (2022). https://doi.org/10.1007/s12369-021-00838-x

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