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A Knowledge-based Full-time Non-homotopy Path Optimization Method Through Online Environmental Learning

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

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

Abstract

Recently, many researchers propose different methods to solve the motion planning problem in the human-robot co-existing environments. However, when encountering crowds that suddenly appear, conventional motion planning algorithms often generate similar homotopy paths with slight local variations, which may not generate new feasible paths in time and thus lead to congestion for the robot. In order to address this problem, this paper proposes a knowledge-based full-time non-homotopy path optimization method through online environmental learning, which generates heuristic paths to guide motion planning by combing the pedestrian knowledge and the environmental structure. Firstly, a pedestrian matrix based knowledge base is proposed to record the pedestrian flow pattern with respect to different locations, and the corresponding pedestrian matrix is extracted according to the perception during the robot navigation process. Secondly, the environmental perception is performed surrounding the key obstacles that appears around the crowds to extract the feature points. Then, the extracted feature points are reused as prior knowledge in subsequent navigation. When crowds suddenly appear in the global path, the algorithm will prioritize searching a non-homotopy path to avoid crowds. The experimental results show that the method proposed in this paper can significantly reduce the time required for the robot to navigate and improve the success rate of navigation in crowded environments.

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

  • Heuristic Path
  • Knowledge Base
  • Non-Homotopy Path
  • Obstacle Perception

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