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1688 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 57, NO. 5, MAY 2010

study [22] applied sensor fusion techniques to combine the

ultrasonic sensors, encoders, and gyroscopes with a differential

GPS system to detect and estimate the dimensions of the

parking space.

Fuzzy set theory has been developed for a linguistic de-

scription of complex systems, and it can be utilized to for-

mulate and translate the human experience. This kind of human intelligence is easily represented by the fuzzy logic

control (FLC) structure. Most advanced control algorithms in

mechatronics and autonomous mobile robots can benefit from

FLC [23]–[30]. For CLMR, earlier studies [24], [27], [28]

proposed a fuzzy controller which is based on heuristic knowl-

edge and numerical data of geometric information, where seven

task modules are developed to build a fuzzy parking controller.

An earlier study [30] presented an FPGA-based fuzzy controller

for steering control of a mobile robot to perform a wall-

following task. However, undesired oscillations occurred when

the robot tracked the predefined trajectory. In this paper, by uti-

lizing the concept “stop,” we construct a novel fuzzy rule table

of speed control to avoid collisions and ensure safe driving.

Furthermore, four fuzzy behavior modes have been proposed

to drive the CLMR, and all these four behavior controls have

been fused into the heuristic fuzzy controller.

Considering that the CLMR travels in unknown and dy-

namic environment, the construction of complete and contact-

less sensory coverage of the workspace is essentially difficult.

To overcome this problem, several sensor systems are used,

such as infrared sensors, laser sensors, and ultrasonic sensors

[31]–[36]. Reijniers and Peremans [34] utilized spectral analy-

sis to estimate the distance and bearing of reflectors, where a

realistically complex environment was reconstructed based on

the time–frequency representations of the echoes; however, thecomputational load of the proposed scheme was heavy.

In fact, some literature [37]–[39] only adopted ultrasonic

sensors to figure out the relative positions and postures between

the robots and their surroundings. An earlier study [37] used

a ring of sonar sensors to reduce the risk of collision, but it

caused serious crosstalk noise, owing to one sensor receiving

the sonar waves emitted by the neighborhood onboard sensors.

To reduce the direct crosstalk noise, we designed an ultrasonic

sensor array system in which the firing interval was appropriate

for avoiding the crosstalk effect and obtaining the correct

environment information. Bank [38] developed a multiaural

measurement system with high beam width, overlapped by anumber of neighboring sensors, so that it could easily take the

advantage of multiple cross echoes. However, the overall de-

tecting cycle time is 250 ms, which corresponded to a sampling

rate of 4 Hz, and the robot had to be located in a fixed position

to detect the environment information. In another study [39],

the authors presented a binaural technique that demonstrated

a higher performance speed when compared with a mechani-

cally scanned ultrasonic beam. However, the binaural technique

was ambivalent in recognizing the intersection point of the

equidistant curves when detecting a planar wall. Therefore, a

displacing position method was proposed to determine the type

of reflector. In this paper, we have applied only the ultrasonic

sensors to complete the task of autonomous parking, with anoverall sampling rate of 10 Hz, and have eliminated the influ-

Fig. 1. Arrangement of the ultrasonic sensor array.

ence of crosstalk by applying the binaural method. Moreover,

the type of reflector was determined by the displacing position

method.

II. ULTRASONIC S ENSING S YSTEM FOR THE CLMR

This paper developed an ultrasonic sensor array system withsequential sensor firing intervals, in which a binaural approach

to the CLMR was adopted for providing complete contactless

sensory coverage of the entire workspace. In this paper, we

integrated the sensed data from the ultrasonic sensor array

to explore the information about the parking space and path

profile.

A. Arrangement of the Ultrasonic Sensor Array

Unlike earlier related works, the appearance of our robot in

our study was not circular; thus, the ultrasonic sensors could

not be arranged as a type of a ring. The layout of 14 ultrasonicsensors is shown in Fig. 1, where two ultrasonic sensors FL and

FR are placed at the front end and two ultrasonic sensors RL

and RR are placed at the rear end. Ultrasonic sensors RF, RM,

and RB are placed on the right-hand side of the CLMR, and LF,

LM, and LB are placed on the left-hand side. The DFR and DFL

located between the two front sensors and the DBR and DBL

located between the two rear sensors are the auxiliary sensors

used for positioning in the parking mode.

B. Firing Interval of the Ultrasonic Sensor Array

We arranged the sensors into five firing sets in groups of three or four sensors. The proposed firing sets are given as

the first set: LF–FR–RB–RL; the second set: FL–RF–RR–LB;

the third set: DFR–RM–DBR; the fourth set: DFL–LM–DBL,

and the fifth set: FL–FR–RL–RR–LF–LM–LB–RF–RM–RB.

The sensor data obtained from the first firing set to the fourth

firing set in sequential order can be used to clearly detect

the distance of the reflector without any crosstalk noise. At the

fifth firing set, we utilized the crosstalk data to calculate the

actual position of the reflector. As the distance information

between the reflector and the CLMR was detected without

crosstalk noise at the preceding firing sets, the CLMR may not

confuse the fifth distance information with the other four sensed

data. We utilized the binaural approach to calculate the actualposition of the reflector.

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Fig. 3. Detection by multichannel method.

Fig. 4. Detection judgment using additional displacing method.

2) Displacing Position Method: In this method, the CLMR

is slightly moved ahead and allowed to sense the reflector again.

If the measurement data were similar to the previous data, then

we can conclude that the reflector is a planar wall, as there may

be a minor change in the echo signal, as shown in Fig. 4(a). The

other case is shown in Fig. 4(b), where the reflectors are the two

obstacles. It has been observed that, when the CLMR moves

a little forward, Sensor 1 could not detect any obstacle and

a virtual point is determined in the emitted intersection from

Sensors 2 and 3. This result demonstrates that the reflectors are

not a planar wall. Thus, the type of reflector can be judged by

the side-mounted sensors on the CLMR.

III. FLC DESIGN AND B EHAVIOR M ODES OF THE CLMR

When the CLMR is moving in an unknown and variedenvironment and finding a parking space, the wall-following

Fig. 5. Architecture of the FLC.

mode and obstacle-avoidance mode should be executed through

proper reaction by the sensed information from the environ-

ment. For this purpose, human driving skill is merged to form

reactive behavior patterns for the CLMR motivation. Four kinds

of control design of fuzzy behaviors are proposed to drive the

CLMR, and then, all of these four behavior controls are fused

into autonomous fuzzy control.

A. FWFC Design

The goal of this task is to steer the CLMR so that it can

move smoothly following a wall with a secured distance. Based

on the real-time sensed data, the CLMR must respond to the

correct actions, such as forward moving, backward moving, and

direction of turning. Fig. 5 shows the basic architecture of the

fuzzy wall-following controller (FWFC) design.

The driving motions of the CLMR comprise moving forward

and backward, and the driving habits include right-hand drive

and left-hand drive. Thus, there are four kinds of FWFC for

these situations. If the driver steers the CLMR on the rightside and moves forward, then the CLMR adopts the right-side

FWFC. The definition of measurement variables for CLMR is

shown in Fig. 6, where d1 and d2 are the measured distancesbetween the CLMR and the wall by the right-front (RF) and

the right-back (RB) ultrasonic sensors, respectively; d3 and d4are the measured distances between the CLMR and the wall

by the left-front (LF) and left-back (LB) ultrasonic sensors,

respectively; θ is the angle of the CLMR direction with respectto the wall; φ is the steering angle of the front wheel; and Distis the safety distance.

1) Right-Side FWFC Design: The steering angle control is

the kernel of the FWFC, where the two inputs are the deviationof the CLMR from the safety distance X d and the difference inthe distance to the wall sensed by the RF and RB sensors, i.e.,

X e, and φ is the corresponding output described as follows:

X d =

d1 −Dist, for forward drivingd2 −Dist, for backward driving

(7a)

X e = d1 − d2. (7b)

If X d is positive, then this means that the wheel of the CLMRis located outside of the safety distance, i.e., the CLMR is

outside of the desired path. However, if X d is negative, then

this indicates that the wheel of the CLMR is located inside of the desired path, i.e., the CLMR is near the wall. Furthermore,

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Fig. 6. Definition of measurement variables for CLMR.

Fig. 7. Fuzzy membership functions of steering angle control of the FWFC.(a) Input X d and X e. (b) Output φ.

if X e is a positive number, then this shows that the CLMR isgoing away from the wall; if it is a negative number, then this

indicates that the CLMR is approaching the wall.

Both the input values X d and X e and the output value φ of the FWFC are decomposed into five fuzzy partitions, denoted

by negative big (NB), negative small (NS), zero (ZE), positive

small (PS), and positive big (PB). The partitions and the shapesof the membership functions are shown in Fig. 7.

TABLE IRUL E TABLE OF R IGHT-S ID E FWFC: (a) FORWARD CAS E,

(b) BACKWARD CAS E, A ND (c) SPEED CONTROL

The fuzzy rules for steering the angle control of the right-

side forward FWFC can be expressed in the following linguis-

tic forms:

R1 : if X d is PB and X e is PB, then φ is ZE.R2 : if X d is PB and X e is PS, then φ is NS.

...

R25 : if X d is NB and X e is NB, then φ is ZE.

The steering angle control of the right-side forward and

backward FWFC rule table is summarized in Table I.The design of the left-side FWFC is very similar to that of

the right-side FWFC. The left-side forward FWFC is activated

when the CLMR is on left-hand drive or when the CLMR

detects the obstacle and then turns left to avoid it in the right-

hand drive situation.

2) Speed Control of FWFC: The speed control is also the

kernel of the FWFC. When the CLMR is on a forward FWFC,

the sensor information of FR–FL–DFR–DFL is utilized as the

input value of FLC. The definition of measurement variables

for CLMR is shown in Fig. 6(b). The input variable d_f isdefined as the distance between the CLMR and a front wall

or obstacle, and it can be calculated by FR and FL, based

on the binaural method. The other input variables d_df r andd_df l are the sensor data of DFR and DFL. If d_f is greater,

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Fig. 8. Fuzzy membership functions of speed control of the FWFC. (a) d_f .(b) d_dfl. (c) d_dfr. (d) v.

then it indicates that the CLMR is far away from the front

wall or obstacle; otherwise, if d_f is smaller, then it showsthat the CLMR is approaching gradually close to the front

wall or obstacle. Similarly, if d_df r is greater, then it showsthat the CLMR is keeping a long distance from the left-hand

side object; on the other hand, if d_df r is smaller, then thisindicates that the sensor DFR detects a shorter distance. In

other words, the CLMR may drive in a narrow passage, or it

may have found an obstacle on the left-hand side. If d_df l is amedium number, then this shows that the CLMR is following

the wall at a safe distance; however, when d_df l is a bignumber, it indicates that the CLMR may detect a potential

parking space. On the other hand, if d_df l is a small number,then this indicates that the CLMR is approaching the wall.

Based on these experiences, the three-input–single-output FLC

can be derived to control the speed of the CLMR. The output

of the FLC is speed v. The partitions and the shapes of themembership functions for d_f , d_df l, d_df r, and v are shownin Fig. 8, where d_f is divided into three term sets, denotedby Big (B), Medium (M), and Small (S); d_df l is dividedinto three term sets, presented by Big (B), Medium (M), and

Small (S); d_df r is divided into two term sets, indicated by Big(B) and Small (S); and v is also partitioned into four term-setsingletons, expressed by Fast (F), Normal (NR), Slow (S), and

Stop (ST).The fuzzy rule table of speed control is summarized in

Table I(c). The defuzzification strategy is the weighted average

method.

B. Fuzzy Parallel-Parking Mode

For fuzzy parallel-parking mode (FPPM), there are some

basic constraints that must be first set to ensure that the parking

space has enough space for the CLMR parking

(1.2W < d_rf

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Fig. 10. Flowchart of FPPM.

Fig. 11. Illustration of the four steps for FPPM. (a) Step 1. (b) Case 1 of Step 1. (c) Case 2 of Step 1. (d) Step 2. (e) Step 3. (f) Step 4.

Fig. 12. Garage-parking space scanning.

region beside the potential garage-parking space and checks the

potential parking space by the RF and RM ultrasonic sensors. If

the field is recognized as a garage-parking field, then the CLMR

changes the mode to the garage-parking mode. The parking

process of fuzzy garage-parking mode (FGPM) behavior is

shown in Fig. 13. The process contains five steps, as shown in

Fig. 14.

Step 1) Consider that the RF has conformed to the garage-parking mode, but suddenly, the front ultrasonic

sensors detect an obstacle intruding in the desired

parking path or trying to occupy the garage-parking

space. In this situation, the CLMR could halt for a

while and deal with this unexpected situation, which

could be divided into two cases.

Case 1.1) The obstacle could move away from the

parking path, and the CLMR could con-

tinue in the garage-parking mode.

Case 1.2) The obstacle could try to occupy the park-ing space, and the CLMR could move

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Fig. 13. Flowchart of FGPM.

forward to find the next potential parking

space.

If there is no obstacle intrusion, proceed to

Step 2).

Step 2) The CLMR turns the steering wheel left and moves

forward until the RM sensor can detect if the CLMR

is passing over the parking space already.

Step 3) The CLMR turns the steering wheel right and moves

backward, which is divided into two possible cases.

Case 3.1) The turning angle is not large enough to

cause the rear sensors or the LB sensorto detect if the CLMR is too close to the

wall. Then, proceed to Step 4).

Case 3.2) The value of the LB is greater than that

of RB. Subsequently, the CLMR may

proceed to Step 5).

Step 4) The CLMR turns the steering wheel left and drives

forward a little distance. Then, the CLMR turns the

steering wheel to the right and moves backward until

the side sensors could detect whether the CLMR is at

a safe distance. Subsequently, the CLMR proceeds

to Step 3).

Step 5) The CLMR examines the position to find out if it

is properly staying in the garage, via the sensors.If the position of the CLMR does not meet our

Fig. 14. Illustration of the five steps for FGPM. (a) Step 1. (b) Case 1 of Step 1. (c) Case 2 of Step 1. (d) Step 2. (e) Case 1 of Step 3. (f) Case 2 of Step 3.(g) Step 4. (h) Step 5.

requirement, then the CLMR will move forward and

backward by using the right-side FWFC to correct

its parking position.

D. OAM

The CLMR uses two ultrasonic sensors at the front end to

detect the position of the obstacle by the binaural method. The

CLMR can not only halt and wait for the obstacle to be moved

away but also detour around the obstacle to avoid collisions.

The process of obstacle avoidance mode (OAM) behavior is

shown in Fig. 15. We can divide the process into three steps, as

shown in Fig. 16.

Step 1) Check if there is an obstacle blocking the forward

path. If the front ultrasonic sensors have detected anobstacle, then the CLMR may find a safe path to de-

tour the obstacle. The choice of the path depends on

the relative position of the obstacle and the CLMR,

which are two possible situations.

Case 1.1) If the obstacle is on the left side of the

forward path, then the CLMR will check

the distance between the car body and the

right wall. If this distance is large enough,

then the CLMR will turn right so as to be

near the right wall.

Case 1.2) If the obstacle is on the right side of the

forward path, the CLMR will check the

distance between the car body and the leftwall. If the distance is large enough, the

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Fig. 18. Flowchart of all the modes.

a wider safety distance from the wall and detour around the

obstacle safely. When the ultrasonic sensors detect a potential

parking space, the CLMR would change to parking mode and

drive backward close to the right wall to determine if the space

is a potential parking space. The flowchart of all the modes is

shown in Fig. 18. The relationship between the CLMR and

the environment is important information for the controller.

According to this information, the controller can ascertain theposition of the CLMR and decide on the next suitable command

to control the CLMR.

V. EXPERIMENTAL R ESULTS

To demonstrate that our multifunctional intelligent auton-

omous parking controllers are feasible and effective, a one-

tenth-scale model car was chosen as the platform for parking

and obstacle avoidance field experiments. The basic architec-

ture of the CLMR consists of the following eight modules: the

robot mechanism unit, the NIOS-embedded system unit, the dc

motor unit, the servo motor unit, the ultrasonic range sensorunit, the PDA unit, the wireless module unit, and the power

regulator unit.

The CLMR uses the NIOS development board (EP1s10) as

the platform, which provides a hardware platform for devel-

oping embedded systems based on Altera Stratix devices. As

shown in Fig. 19, the computer-aided design tool is used to

implement the hardware design, including the processor core

configuration, synthesis, place, and route. The processor core

configuration tool is realized by verilog hardware description

language (VHDL) and graphical user interface (GUI). The

assignment of I/O, including the ultrasonic signals and the

motor driving signals, is set in the GUI, and the function of

the hardware is realized by VHDL code. After the process of compiler and programmer, the hardware design is completed.

Fig. 19. Design of NIOS-embedded process system.

The subsequent step is to compile the application program; our

parking program has been compiled in the NIOS II integrated

development environment. After building the C++ program

and downloading the binary program files to the memory

of DE2 board, the NIOS-embedded process system has been

completed.

The experimental field, 600 cm in length and 150 cm in

width, was set up at our lab for performing experiments to eval-

uate the feasibility and effectiveness of the proposed controller.

The test experiments included the parallel-parking mode, the

garage-parking mode, the auto parking space selection mode,

the forward OAM, and the autonomous mode.

Fig. 20(a) shows the experimental setting of the binauralmethod. In this case, an object is put at nine set positions

in turns, and the ultrasonic sensors are allowed to detect the

location of the object. Fig. 20(b) shows the experimental result,

where the bottom two figures show the detected distance and

the middle figure shows the relative position of the object with

ultrasonic sensor. At every position, we detected the object

30 times and calculated the mean of every location, as shown

in the right table. Furthermore, the most likely location is pre-

sented in the middle figure. The mean of the errors of the nine

positions was about 2 cm, and the corresponding standard devi-

ation was about 0.86. Thus, the experimental results confirmed

that the binaural method applied in our system is reliable.A test environment setup is shown in Fig. 21, where the

detected signals of the three right-side-mounted sensors RB,

RM, and RF are shown in the three left columns of Fig. 21(b),

and the fourth column indicates the detection judgment of

the type of reflectors. We wanted to demonstrate whether the

CLMR can determine an object in the right-hand side according

to the three detected signals d_rf , d_rm, and d_rb. The thirdcolumn in Fig. 21(b) shows the detected result of the RF sensor,

and the regions 2–4, 7–9, and 12–14 of the fourth column

indicate that the RF sensor has detected an object. When two

of the three sensors detect an object simultaneously, the CLMR

can calculate its relative location. However, when all the three

sensors detect objects simultaneously, as shown in regions 4,9, and 14 of the fourth column, it means that there may be a

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Fig. 20. Experimental setting and result of the binaural method.

Fig. 21. Test ground setup and experimental results of the multichannel and displacing position methods.

wall. In this situation, the CLMR will drive forward and check

whether it is a wall or not by displacing position method. In

regions 4 and 14, when the CLMR passes by the two bottles,

the fake wall will vanish after a short time. Consequently, the

two bottles will be determined correctly as a nonwall object.

On the contrary, the CLMR may detect another possible wall

and checks it. After verifying, the wall would exist for a longtime, and the CLMR would treat it as a wall. Thus, the exper-

iment demonstrated that the multichannel and the displacing

position methods can identify whether the object is a wall

or not.

The photographs shown in Figs. 22–27 are the sequential

images extracted from the digital video files taken by a hand-

held SONY DV camera. The first test was carried out when the

CLMR entered the parallel-parking field, as shown in Fig. 22.The sequential images show that the CLMR searches for a

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Fig. 22. Sequential images of the CLMR (top left to bottom right) finding andentering the parallel-parking space and, subsequently, parking correctly.

Fig. 23. Sequential images of the CLMR (top left to bottom right) finding andentering the garage-parking space and, subsequently, parking correctly.

Fig. 24. Sequential images of the CLMR (top left to bottom right) in theparallel-parking mode with an obstacle blocking the parking path and clearingout later.

Fig. 25. Sequential images of the CLMR (top left to bottom right) in theparallel-parking mode when the obstacle occupies the parking space.

potential parking space and recognizes it as a parallel-parkingspace. After confirmation, the CLMR initializes the parking

action and finally parks correctly in the space. In Fig. 23, the

CLMR finds a corner just in the right front of the forward

path and moves ahead to check this concaved space. Then,

the CLMR checks the size of this space and identifies it as a

garage-parking space. After scanning to ensure that there are

no obstacles in this space, the CLMR starts the parking action

and finally parks in the space correctly.

If the CLMR detects an obstacle blocking its parking path,

then it will halt for a while until the obstacle is removed and

then continue to follow the associated parking procedures, as

shown in Fig. 24. If there is any obstacle blocking the parking

route or trying to occupy the designated parking space, thenthe CLMR will give up this unsuitable space and move forward

Fig. 26. Sequential images of the CLMR (top left to bottom right) in theparking space selection mode when another vehicle occupies the space found.

Fig. 27. Sequential images of the CLMR (top left to bottom right) in theautonomous mode with a parking space found.

to find another potential-parking space, as shown in Fig. 25.

However, the subsequent test field is more complicated than

the previous ones as shown in Fig. 26, which demonstrates

that the CLMR can select a suitable parking space and switch

to the appropriate behavior mode to perform the associated

parking procedures. Initially, the CLMR remains in the garage-

parking mode and tries to park into the space found. However,

if this space has been occupied by other vehicles in advance, the

CLMR will change to the wall-following mode and move for-

ward to seek any potential-parking space. Finally, after finding a

parallel-parking space, the CLMR would change to the parallel-parking mode and perform the related parking processes.

The sequential images shown in Fig. 27 demonstrate that the

CLMR is in the autonomous driving state. First, the CLMR

goes straight in the wall-following mode and then switches

to the OAM after having detected an obstacle blocking the

forward path. After bypassing the obstacle, the CLMR finds a

potential-parking space and identifies it as a parallel-parking

space. In the meantime, it switches into parallel-parking mode

and performs the associated parking procedures. The results

of the experiment emphasize that, once the CLMR is powered

and allowed to go by itself, it has the capability to maneuver

autonomously in all the test fields mentioned earlier. In other

words, the CLMR can autonomously determine which behaviorshould be executed according to the fusion control.

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VI. CONCLUSION

In this paper, multifunctional intelligent autonomous parking

controllers of the CLMR have been implemented by using

the NIOS-embedded process system. The binaural method is

the key technique for the ultrasonic sensing system, which

can acquire the completed information from a nearby envi-

ronment successfully. An autonomous parking controller thatis capable of effectively parking the CLMR in an appro-

priate parking space, by integrating sensor data capable of

obtaining surrounding data of the robot, has been developed.

The proposed controller can obtain the accurate posture of a

mobile robot in a parking space. In addition, the proposed

controller has the ability to make the CLMR avoid colli-

sions to ensure safe parking. Importantly, a car, which is

equipped with the proposed controller, can recognize the park-

ing space and the obstacle’s position to ensure safe autonomous

parking.

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Tzuu-Hseng S. Li (S’85–M’90) received the B.S.degree in electrical engineering from Tatung Instituteof Technology, Taipei, Taiwan, in 1981, and the M.S.and Ph.D. degrees in electrical engineering fromNational Cheng Kung University (NCKU), Tainan,Taiwan, in 1985 and 1989, respectively.

Since 1985, he has been with the Department of Electrical Engineering, NCKU, where he is currently

a Distinguished Professor. Since 1996, he has beena Researcher in the Engineering and TechnologyPromotionCenter, National Science Council, Tainan.

From 1999 to 2002, he was the Director of the Electrical Laboratories, NCKU.He has served as the Dean of the College of Electrical Engineering and Com-puter Science, National United University, Miaoli, Taiwan. His current researchinterests include fuzzy control, singular perturbation, intelligent-control chipdesign, mechatronics, mobile robots, humanoid robots, and automobile activesuspension systems.

Dr. Li has been the Vice President of the Federation of International Robot-soccer Association since August 1, 2009. He was the President of the ChineseAutomatic Control Society (CACS) in Taiwan from 2008 to 2009. He was aTechnical Editor for the IEEE/ASME TRANSACTIONS ON MECHATRONICSand is currently an Associate Editor for the Asia Journal of Control, the Inter-national Journal of Fuzzy Systems, and the International Journal of Nonlinear Science and Numerical Simulations. He received the Outstanding AutomaticControl Award from CACS in 2006. He was also elected a CACS Fellow

in 2008.

Ying-Chieh Yeh was born in Kaohsiung, Taiwan, in1978. He received the B.S. and M.S. degrees fromthe Department of Electrical Engineering, NationalCheng Kung University, Tainan, Taiwan, in 2000 and2002, respectively, where he is currently workingtoward the Ph.D. degree.

His major research interests include carlike mo-bile robots, intelligent control IC design, computervision, sensor fusion, and fuzzy control.

Jyun-Da Wu was born in Chiayi, Taiwan, in 1981.He received the B.S. and M.S. degrees from the De-partment of Electrical Engineering, National ChengKung University, Tainan, Taiwan, in 2004 and 2006,respectively.

He has been an R&D Engineer with UniversalScientific Industrial Company, Ltd. His research in-terests include carlike mobile robot, fuzzy control,

intelligent control, and Win CE/Mobile systempower design.

Ming-Ying Hsiao (M’09) received the B.S. andM.S. degrees in power mechanical engineering fromNational Tsing Hua University, Hsinchu, Taiwan, in1979 and 1981, respectively, and the Ph.D. degreein electrical engineering from National Cheng KungUniversity, Tainan, Taiwan, in 2008.

He was an Associate Professor/Chairman of theDepartment of Multimedia Design, Fortune Instituteof Technology, Kaohsiung, Taiwan, where he is cur-rently with the Department of Electrical Engineering.His current research interests include fuzzy control,

type-2 fuzzy systems, intelligent control, and robotics.

Chih-Yang Chen received the B.S. degree in elec-trical engineering from Southern Taiwan University,Yung-Kang, Taiwan, in 2001, and the M.S. and Ph.D.degrees in electrical engineering from NationalCheng Kung University, Tainan, Taiwan, in 2003 and2008, respectively.

Since March 2009, he has been an R&D Engineerwith ITRI South, Taiwan. His research interests in-clude machine learning, adaptive fuzzy control, in-telligent control, reinforcement learning, and roboticapplications.

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