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Social Robot Navigation and Comfortable Following: A Novel Knowledge-Based Robot-Pedestrian Interaction Model with Self Learning Strategy

  • Xujun Xu
  • , Xiaofei Gong
  • , Chenyang Cao
  • , Qingchuan Xu
  • , Chengfeng Sun*
  • , Wenzheng Chi*
  • , Lining Sun
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Currently, mobile service robots have been playing an increasingly significant role in daily life. However, many mobile service robots still face challenges in navigation and interaction modes. Current navigation and interaction patterns are often fixed and lack personalization, making it difficult to meet the preferences of different users. Users often need to continually provide specific action instructions to the robot, resulting in a complex and cumbersome interaction process. This paper aims to propose a human-robot interaction framework for robot navigation and pedestrian following based on natural language processing (NLP), which combines a knowledge base and neural network predictions for comfortable following distance to achieve a more intelligent, comfortable, and personalized human-robot interaction experience. To validate the effectiveness of the proposed framework and algorithms, a series of experiments were conducted in different scenarios. The results demonstrate that the NLP-based navigation and following human-robot interaction framework presented in this paper exhibits favorable feasibility and applicability in various working environments. Additionally, the neural network-based algorithm for predicting comfortable following significantly enhances the pedestrian following experience.

Original languageEnglish
Title of host publication2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023
EditorsMehmet Dogar, Bin Fang, Dimitrios Kanoulas, Jia Pan, Alessandra Sciutti, Moju Zhao, Guanjun Bao, Bimbo Joao, Boyle Jordan Hylke, He Chen, Chen Teng, Yunduan Cui, Dagnino Giulio, Wenbo Ding, Liang Du, Farinha Andre, Yuan Gao, Hasegawa Shun, Liang He, Taogang Hou, Zhe Hu, Zhong Huang, Jackson-Mills George, Yunfeng Ji, Jirak Doreen, Feng Ju, Kaddouh Bilal, Kim Wansoo, Takuya Kiyokawa, Haiyuan Li, Peng Li, Shihao Li, Xu Li, Jianfeng Liao, Ling Jie, Chunfang Liu, Quanquan Liu, Liang Lu, Qiuyue Luo, Yudong Luo, Zebing Mao, Martinez-Hernandez Uriel, Matsuno Takahiro, Nguyen Thanh Luan, Nishio Takuzumi, Pasquali Dario, Pierella Camilla, Chao Ren, Ricci Serena, Rossini Luca, Shi Fan, Summa Susanna, Rongchuan Sun, Zhenglong Sun, Vannucci Fabio, Gang Wang, Wei Wang, Xin Wang, Yuquan Wang, Ziya Wang, Qingxiang Wu, Xiaojun Wu, Yuxin Sun, Youcan Yan, Lei Yang, Yanokura Iori, Jingfan Zhang, Shuai Zhang, Tianwei Zhang, Jinglei Zhao, Na Zhao, Chengxu Zhou, Peng Zhou, Haifei Zhu
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350325706
DOIs
StatePublished - 2023
Externally publishedYes
Event2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023 - Koh�Samui, Thailand
Duration: 4 Dec 20239 Dec 2023

Publication series

Name2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

Conference

Conference2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023
Country/TerritoryThailand
CityKoh�Samui
Period4/12/239/12/23

Keywords

  • Following Strategy
  • Natural Language Processing
  • Neural Network
  • Robot Navigation

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