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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.
REFERENCES
[1] D. Gorinevsky, A. Kapitanovsky, and A. Goldenberg, “Neural net-work architecture for trajectory generation and control of automated carparking,” IEEE Trans. Control Syst. Technol., vol. 4, no. 1, pp. 50–56,Jan. 1996.
[2] S. Lee, M. Kim, Y. Youm, and W. Chung, “Control of a car-like mobilerobot for parking problem,” in Proc. IEEE Int. Conf. Robot. Autom.,Detroit, MI, 1999, pp. 1–6.
[3] T.-H. S. Li and S.-J. Chang, “Autonomous fuzzy parking control of a car-like mobile robot,” IEEE Trans. Syst., Man, Cybern. A, Syst.,
Humans, vol. 33, no. 4, pp. 451–465, Jul. 2003.[4] T.-H. S. Li, S.-J. Chang, and Y. X. Chen, “Implementation of human-like
driving skills by autonomous fuzzy behavior control on an FPGA-basedcar-like mobile robot,” IEEE Trans. Ind. Electron., vol. 50, no. 5, pp. 867–880, Oct. 2003.
[5] K.-Z. Liu, M. Q. Dao, and T. Inoue, “An exponentially ε-convergentcontrol algorithm for chained systems and its application to automaticparking systems,” IEEE Trans. Control Syst. Technol., vol. 14, no. 6,pp. 1113–1126, Nov. 2006.
[6] T. Hiroshi, U. Daisuke, K. Kazuhiko, and H. Kaoru, “A study on predict-ing hazard factors for safe driving,” IEEE Trans. Ind. Electron., vol. 54,no. 4, pp. 781–789, Apr. 2007.
[7] C.-L. Hwang, L.-J. Chang, and Y.-S. Yu, “Network-based fuzzy decentral-ized sliding-mode control for car-like mobile robots,” IEEE Trans. Ind.
Electron., vol. 54, no. 1, pp. 574–585, Feb. 2007.[8] B. Müller, J. Deutscher, and S. Grodde, “Continuous curvature trajectory
design and feedforward control for parking a car,” IEEE Trans. ControlSyst. Technol., vol. 15, no. 3, pp. 541–553, May 2007.
[9] C.-L. Hwang and C.-Y. Shih, “A distributed active-vision network-spaceapproach for the navigation of a car-like wheeled robot,” IEEE Trans. Ind.
Electron., vol. 56, no. 3, pp. 846–855, Mar. 2009.[10] Y. Yamaguchi and T. Murakami, “Adaptive control for virtual steering
characteristics on electric vehicle using steer-by-wire system,” IEEE Trans. Ind. Electron., vol. 56, no. 5, pp. 1585–1594, May 2009.
[11] H. An, T. Yoshino, D. Kashimoto, M. Okubo, Y. Sakai, and T. Hamamoto,“Improvement of convergence to goal for wheeled mobile robot usingparking motion,” in Proc. IEEE Int. Conf. Intell. Robot. Syst., 1999,pp. 1693–1698.
[12] T. C. Lee, C. Y. Tsai, and K. T. Song, “Fast parking control of mo-bile robots: A motion planning approach with experimental valida-tion,” IEEE Trans. Control Syst. Technol., vol. 12, no. 5, pp. 661–676,Sep. 2004.
[13] R. M. Murray and S. S. Sastry, “Nonholonomic motion planning: Steeringusing sinusoids,” IEEE Trans. Autom. Control, vol. 38, no. 5,pp. 700–716,May 1993.
[14] J. P. Laumond, P. E. Jacobs, M. Taix, andR. M. Murray, “A motion plannerfor nonholonomic mobile robots,” IEEE Trans. Robot. Autom., vol. 10,no. 5, pp. 577–593, Oct. 1994.
[15] D. Leitch and P. J. Probert, “New techniques for genetic development of aclass of fuzzy controllers,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev.,vol. 28, no. 1, pp. 112–123, Feb. 1998.
[16] L. Dorst, “Analyzing the behaviors of a car: A study in abstraction of goal-directed motions,” IEEE Trans. Syst., Man, Cybern. A, Syst., Humans,vol. 28, no. 6, pp. 811–822, Nov. 1998.
[17] A. R. Willms and S. X. Yang, “An efficient dynamic system for real-time robot path-planning,” IEEE Trans. Syst., Man, Cybern. B, Cybern. ,vol. 36, no. 4, pp. 755–766, Aug. 2006.
[18] S. Kloder and S. Hutchinson, “Path planning for permutation-invariantmultirobot formations,” IEEE Trans. Robot., vol. 22, no. 4, pp. 650–665,
Aug. 2006.[19] A. Piazzi, C. G. Lo Bianco, and M. Romano, “η3-splines for the smoothpath generation of wheeled mobile robots,” IEEE Trans. Robot., vol. 23,no. 5, pp. 1089–1095, Oct. 2007.
[20] K. Y. Lian, C. S. Chin, and T. S. Chiang, “Parallel parking a car-like robotusing fuzzy gain scheduling,” in Proc. IEEE Int. Conf. Control Appl.,1999, vol. 2, pp. 1686–1691.
[21] K. Jiang and L. D. Seneviratne, “A sensor guided autonomous parkingsystem for nonholonomic mobile robots,” in Proc. IEEE Int. Conf. Robot.
Autom., Detroit, MI, 1999, pp. 311–316.[22] K. Jiang, “A sensor guided parallel parking system for non-
holonomic vehicles,” in Proc. IEEE Conf. Intell. Trans. Syst., Oct. 2000,pp. 270–275.
[23] Y.-S. Kung, R.-F. Fung, and T.-Y. Tai, “Realization of a motion controlIC for X-Y table based on novel FPGA technology,” IEEE Trans. Ind.
Electron., vol. 56, no. 1, pp. 43–53, Jan. 2009.[24] I. Baturone, F. J. Moreno-Velo, S. Sanchez-Solano, and A. Ollero, “Auto-
matic design of fuzzy controllers for car-like autonomous robots,” IEEE Trans. Fuzzy Syst., vol. 12, no. 4, pp. 447–465, Aug. 2004.
[25] K. Tanaka, K. Yamauchi, H. Ohtake, and H. O. Wang, “Sensor reductionfor backing-up control of a vehicle with triple trailers,” IEEE Trans. Ind.
Electron., vol. 56, no. 2, pp. 497–509, Feb. 2009.[26] G. Antonelli, S. Chiaverini, and G. Fusco, “A fuzzy-logic-based approach
for mobile robot path tracking,” IEEE Trans. Fuzzy Syst., vol. 15, no. 2,pp. 211–221, Apr. 2007.
[27] S. Sánchez-Solano, A. J. Cabrera, I. Baturone, F. J. Moreno-Velo, andM. Brox, “FPGA implementation of embedded fuzzy controllersfor robotic applications,” IEEE Trans. Ind. Electron., vol. 54, no. 4,pp. 1937–1945, Aug. 2007.
[28] I. Baturone, F. J. Moreno-Velo, V. Blanco, and J. Ferruz, “Designof embedded DSP-based fuzzy controllers for autonomous mobilerobots,” IEEE Trans. Ind. Electron., vol. 55, no. 2, pp. 928–936,Feb. 2008.
[29] C.-F. Juang, C.-M. Lu, C. Lo, and C.-Y. Wang, “Ant colony optimiza-tion algorithm for fuzzy controller design and its FPGA implemen-tation,” IEEE Trans. Ind. Electron., vol. 55, no. 3, pp. 1453–1462,Mar. 2008.
[30] W. Tsui, M. S. Masmoudi, F. Karray, I. Song, and M. Masmoudi, “Soft-computing-based embedded design of an intelligent wall/lane-followingvehicle,” IEEE/ASME Trans. Mechatronics, vol. 13, no. 1, pp. 125–135,Feb. 2008.
[31] P. Krammer and H. Schweinzer, “Localization of object edges in arbitraryspatial positions based on ultrasonic data,” IEEE Sensors J., vol. 6, no. 1,pp. 203–210, Feb. 2006.
[32] E. U. Acar, H. Choset, and J. Y. Lee, “Sensor-based coverage with ex-tended range detectors,” IEEE Trans. Robot., vol. 22, no. 1, pp. 189–198,Feb. 2006.
[33] N. Petrellis, N. Konofaos, and G. P. Alexiou, “Target localization utilizingthe success rate in infrared pattern recognition,” IEEE Sensors J., vol. 6,no. 5, pp. 203–210, Oct. 2006.
[34] J. Reijniers and H. Peremans, “Biomimetic sonar system perform-ing spectrum-based localization,” IEEE Trans. Robot., vol. 23, no. 6,pp. 1151–1159, Dec. 2007.
[35] D. Bank and T. Kämpke, “High-resolution ultrasonic environmentimaging,” IEEE Trans. Robot., vol. 23, no. 2, pp. 370–381, Apr. 2007.
[36] L. Vachhani and K. Sridharan, “Hardware efficient prediction correc-tion based generalized voronoi diagram construction and FPGA imple-mentation,” IEEE Trans. Ind. Electron., vol. 55, no. 4, pp. 1558–1569,Apr. 2008.
[37] W. L. Xu and S. K. Tso, “Sensor-based fuzzy reactive navigation of amobile robot through local target switching,” IEEE Trans. Syst., Man,Cybern. C, Appl. Rev., vol. 29, no. 3, pp. 451–459, Aug. 1999.
[38] D. Bank, “A novel ultrasonic sensing system for autonomous mobilesystem,” IEEE Sensors J., vol. 2, no. 6, pp. 597–605, Dec. 2002.
[39] R. Kazys and L. Mazeika, “Determination of spatial position of multipletargets by ultrasonic binaural method,” Ultrasonics, vol. 40, no. 1–8,
pp. 397–402, May 2002.[40] R. Kuc, “Biomimetic sonar recognizes objects using binaural informa-tion,” J. Acoust. Soc. Amer., vol. 102, no. 2, pp. 689–696, Aug. 1997.
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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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