This article was downloaded by: [Tulane University]On: 10 October 2014, At: 19:24Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954Registered office: Mortimer House, 37-41 Mortimer Street, London W1T3JH, UK

Advanced RoboticsPublication details, including instructions forauthors and subscription information:http://www.tandfonline.com/loi/tadr20

Study of a self-diagnosissystem for an autonomousmobile robotShinnosuke Okina , Kuniaki Kawabata , TeruoFujii , Yasuharu Kunii , Hajime Asama & Isao EndoPublished online: 02 Apr 2012.

To cite this article: Shinnosuke Okina , Kuniaki Kawabata , Teruo Fujii , YasuharuKunii , Hajime Asama & Isao Endo (2000) Study of a self-diagnosis systemfor an autonomous mobile robot , Advanced Robotics, 14:5, 339-341, DOI:10.1163/156855300741889

To link to this article: http://dx.doi.org/10.1163/156855300741889

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of allthe information (the “Content”) contained in the publications on ourplatform. However, Taylor & Francis, our agents, and our licensorsmake no representations or warranties whatsoever as to the accuracy,completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views ofthe authors, and are not the views of or endorsed by Taylor & Francis.The accuracy of the Content should not be relied upon and should beindependently verified with primary sources of information. Taylor andFrancis shall not be liable for any losses, actions, claims, proceedings,demands, costs, expenses, damages, and other liabilities whatsoever

or howsoever caused arising directly or indirectly in connection with, inrelation to or arising out of the use of the Content.

This article may be used for research, teaching, and private studypurposes. Any substantial or systematic reproduction, redistribution,reselling, loan, sub-licensing, systematic supply, or distribution in any formto anyone is expressly forbidden. Terms & Conditions of access and usecan be found at http://www.tandfonline.com/page/terms-and-conditions

Dow

nloa

ded

by [

Tul

ane

Uni

vers

ity]

at 1

9:24

10

Oct

ober

201

4

Advanced Robotics, Vol. 14, No. 5, pp. 339–341 (2000)Ó VSP and Robotics Society of Japan 2000.

Study of a self-diagnosis system for an autonomous mobilerobot

SHINNOSUKE OKINA 1;¤, KUNIAKI KAWABATA 2, TERUO FUJII 3,YASUHARU KUNII 1, HAJIME ASAMA 4 and ISAO ENDO 2

1 Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan2 Biochemical Systems Laboratory, The Institute of Physical and Chemical Research (RIKEN),

2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan3 Institute of Industrial Science, University of Tokyo, 7-22-1 Roppongi, Minato-ku,

Tokyo 106-8558, Japan4 Advanced Engineering Center, The Institute of Physical and Chemical Research (RIKEN),

2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan

Keywords: Self-diagnosis; fault diagnosis; fault detection; autonomous mobile robot.

Most research related to intelligent robots are discussed in case when the systemworks ideally and does not consider how to cope with the fault situations. However,it is an important topic for robots working in real environments. Therefore, weare developing a self-diagnosis system for an autonomous robot. In this report, webuild a self-diagnosis system of an omni-directional mobile robot (ZEN) which wasdeveloped in RIKEN. Figure 1 shows the con� guration of the self-diagnosis systemon ZEN. The diagnosis system needs to observe the detailed status of the systemto maintain reliability. We consider that the system consists of plural modules andequip new sensors to the mobile robot system for detailed inspection. When thedifference between a realizable and required function is over its allowable limit, thesystem falls into fault. Here, realizable function means the ability that the systemcan work and required function means the ability that the system should work. Fordetecting detailed fault parts, the system must know what kind of in� uence eachmodule’s fault gives to the whole system. Here, we compose a status table whichindicates the robot’s condition corresponding to each module’s fault (Fig. 2). Forexample, when it occurs that the motor or the wheel does not rotate, although thebattery and the motor driver work normally, the system can identify which part has

¤E-mail: okina@cel.riken.go.jp

Dow

nloa

ded

by [

Tul

ane

Uni

vers

ity]

at 1

9:24

10

Oct

ober

201

4

340 S. Okina et al.

Figure 1. Overview of system.

Figure 2. Table of fault detection.

an error by referring to this table. We also examine a self-diagnosis algorithm withthis status table (Fig. 3). The system the compares input and output value of thesection, which consists of the modules. If the system � nds the section is faulty afterthis inspection, the system inspects each module in this section in detail. Usingthis algorithm, the system classi� es the fault into ‘sensor fault’, ‘system fault’ and‘combined fault’. Also, the diagnosis system can detect error parts in the systemto repeat the inspections automatically. To con� rm the validity of our proposedmethod, we have performed a fault detection experiment with a real mobile robot.The objective of this experimentation is to detect the motor’s fault (system fault) andthe inside-encoder’s fault (sensor fault). Figure 4 shows the experimental result. In

Dow

nloa

ded

by [

Tul

ane

Uni

vers

ity]

at 1

9:24

10

Oct

ober

201

4

Self-diagnosis system for autonomous robots 341

Figure 3. Algorithm of fault detection.

Figure 4. Experimental result.

this experiment we produce the troubles arti� cially while the robot goes straightahead. Using an output value of the outside-encoder (magnetic sensor), whichdirectly measures the wheel rotation, the diagnosis system divides the fault into theinside-encoder’s fault, motor’s fault and combined fault. As the result, our proposedmethod detects the fault parts in the system (Fig. 4). In our future work, we will tryto examine an adaptive behavior generation method when the faults occur.

Dow

nloa

ded

by [

Tul

ane

Uni

vers

ity]

at 1

9:24

10

Oct

ober

201

4