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Author:

Xu, Lu (Xu, Lu.) | Zhang, Li-Guo (Zhang, Li-Guo.) (Scholars:张利国) | Chen, De-Gang (Chen, De-Gang.) | Chen, Yagn-Zhou (Chen, Yagn-Zhou.)

Indexed by:

CPCI-S

Abstract:

Mobile robots often operate in dynamic environment, such as an exploratory robot or one that must Move to a Goal location without the benefit of a floorplan or terrain map. In this case, the robot needs to be able to replan quickly as the knowledge of the environment changes. The process of robot navigation in dynamic environment mainly includes three steps: first translate the configuration space that the robot and obstacles exist in into 2D Cartesian Grid by Quadtree method; second design the sub-goals given the global map, start and Goal points by A* Alaorithm; last replan the path between two adjacent sub-goals by improved D* Lite Algorithm when the environment changes. It is able to replan faster than planning from scratch since it modifies its previous search results locally and enables robots adapt to the dynamic environment. The simulation results that are reported show that the mobile robot navigation method is efficient and feasible.

Keyword:

replanning dynamic window path planning mobile robot navigation

Author Community:

  • [ 1 ] [Xu, Lu]Beijing Univ Technol, Autonomous Navigat Ctr, Beijing 100022, Peoples R China
  • [ 2 ] [Zhang, Li-Guo]Beijing Univ Technol, Autonomous Navigat Ctr, Beijing 100022, Peoples R China
  • [ 3 ] [Chen, Yagn-Zhou]Beijing Univ Technol, Autonomous Navigat Ctr, Beijing 100022, Peoples R China
  • [ 4 ] [Chen, De-Gang]North China Elect Power Univ, Dept Comp, Beijing 100022, Peoples R China

Reprint Author's Address:

  • [Xu, Lu]Beijing Univ Technol, Autonomous Navigat Ctr, Beijing 100022, Peoples R China

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Source :

PROCEEDINGS OF 2007 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7

Year: 2007

Page: 566-,

Language: English

Cited Count:

WoS CC Cited Count: 1

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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