Locomotion selection of Multi-Locomotion Robot based on Falling Risk and moving efficiency

Author

Kobayashi, Taisuke ; Aoyama, Tadayoshi ; Sekiyama, Kosuke ; Lu, Zhiguo ; Hasegawa, Yasuhisa ; Fukuda, Toshio

Author_Institution

Dept. of Mech. Eng., Nagoya Univ., Nagoya, Japan

fYear

2012

fDate

7-12 Oct. 2012

Firstpage

2869

Lastpage

2874

Abstract

This paper deals with a method of locomotion selection based on Falling Risk and moving efficiency. The robot estimates information from sensors by solving state equation. The robot evaluates the Falling Risk as an indicator of uncertainty. Falling Risk is derived from measured information by using Bayesian Network. Locomotion selection during walking is modeled as a Semi-Markov Decision Process and the most appropriate locomotion is selected by using the greedy algorithm. As a result, the robot can move in the environment that is difficult to travel by single locomotion mode, maintaining the maximum moving efficiency.

Keywords

Bayes methods; Markov processes; decision theory; greedy algorithms; mobile robots; multi-robot systems; Bayesian network; falling risk; greedy algorithm; locomotion selection; moving efficiency; multilocomotion robot; semiMarkov decision process; state equation; Bayesian methods; Legged locomotion; Measurement by laser beam; Robot sensing systems; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on

Conference_Location

Vilamoura

ISSN

2153-0858

Print_ISBN

978-1-4673-1737-5

Type

conf

DOI

10.1109/IROS.2012.6385774

Filename

6385774