Reinforcement learning for autonomous robot navigation

Author

Armstrong, William W. ; Coghlan, Brant ; Gorodnichy, Dmitry O.

Author_Institution

Dept. of Comput. Sci., Alberta Univ., Edmonton, Alta., Canada

Volume

4

fYear

1999

fDate

1999

Firstpage

2282

Abstract

The goal of the Boticelli project is to show the usefulness of piecewise linear functions (PLFs) in various tasks of autonomous mobile robot navigation. One of the tasks is to deal with the world model where the 3D occupancy function is efficiently represented as a PLF; and the other is to represent the value function during reinforcement learning for the purpose of path planning. The paper overviews the project and demonstrates that the PLF approximation, as a solution to Bellman´s equation, can support robot motion planning

Keywords

function approximation; learning (artificial intelligence); mobile robots; navigation; path planning; piecewise linear techniques; Bellman equation; Boticelli project; autonomous navigation; function approximation; mobile robot; motion planning; occupancy grids; piecewise linear functions; reinforcement learning; Cameras; Image processing; Learning; Motion planning; Path planning; Piecewise linear techniques; Robot sensing systems; Robot vision systems; Software architecture; Sonar navigation;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.833418

Filename

833418