Skip to content

A hybrid path planning method for mobile robot based on artificial potential field method

Research output: Chapter in Book/Report/Conference proceedingConference contribution

This paper proposes a hybrid path planning method based on artificial potential field method (APF) for mobile robot, which combines wall following method (WFM) and obstacles connecting method (OCM) for dealing with local minimum. The environment information is took into consideration to decide the escape direction of WFM. To ensure the success of escaping from local minimum, more reliable switching conditions are designed. OCM is applied to reduce the difficulty of path planning for complex workspace with concave obstacles. Simulation studies have been carried out to verify the validity of the proposed method.
Original languageEnglish
Title of host publicationIntelligent Robotics and Applications
Subtitle of host publication12th International Conference, ICIRA 2019, Shenyang, China, August 8–11, 2019, Proceedings, Part VI
EditorsHaibin Yu, Jinguo Liu, Lianqing Liu, Zhaojie Ju, Yuwang Liu, Dalin Zhou
Number of pages7
ISBN (Electronic)978-3-030-27529-7
ISBN (Print)978-3-030-27528-0
Publication statusPublished - 6 Aug 2019
Event12th International Conference on Intelligent Robotics and Applications - Shenyang, China
Duration: 8 Aug 201911 Aug 2019

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameLecture Notes in Artificial Intelligence
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th International Conference on Intelligent Robotics and Applications
Abbreviated titleICIRA 2019


  • Kong_A Hybrid Path Planning_ICIRA2019_042_final_v3

    Rights statement: This is a post-peer-review, pre-copyedit version of an article published in Intelligent Robotics and Applications. ICIRA 2019. The final authenticated version is available online at:

    Accepted author manuscript (Post-print), 395 KB, PDF document

Related information

Relations Get citation (various referencing formats)

ID: 15488424