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Sensors and Materials, Vol. 31, No. 3 (2019) 889–903
MYU Tokyo
S & M 1823

Path Planning for Unmanned Surface Vessels
with Multisensor Fusion Design
Yung Hsiang Chen,1 Yung Yue Chen,2* and Joe Ming Yang2
1

Department of Mechanical Engineering, National Pingtung University of Science and Technology,
No. 1, Xuefu Rd., Neipu Township, Pingtung County 91201, Taiwan R.O.C.
2
Department of Systems and Naval Mechatronic Engineering, National Cheng Kung University,
No. 1, Daxue Rd., East Dist., Tainan City 70101, Taiwan R.O.C.
(Received October 15, 2018; accepted January 8, 2019)

Keywords: path planning, aerial photography, image resolution, quadrotor, real-time image

To achieve a successful sea voyage, an effective path planning algorithm plays an importantrole in avoiding vessel collisions in an autonomous surface vessel design . This algorithm
depends on real-time global images of explored environments. Currently, satellite images
used for the path planning algorithm always have rough resolutions, which increase the risk ofvessel collision. To solve this problem, an alternative solution of integrating the aerial images
captured by the image sensor of a flying quadrotor and the global positioning system (GPS),
electrical compass, and height sensor data is proposed to provide real-time images in the global
coordinate instead of satellite images. Naturally, aerial images possess higher resolution pixels
than the satellite images and allow real-time modifications of geographic information. Images
captured by the image sensor installed on the quadrotor are essentially represented in local
coordinates; hence, in this investigation, a coordinate transformation for converting the local
coordinate of the aerial images into the global coordinate is developed and named the distance–
resolution transformation. With the fusion of these sensors and designs, the global coordinateof the high-resolution image can be calculated directly, and the real-time path planning using
this aerial image can be applied to plan a collision-free path to navigate the unmanned surface
vessel in any unknown ocean environment.

1.

Introduction

Designs of unmanned vehicles have been an emerging research topic in recent years, since
they can be used for military purposes, cargo transport, and environment monitoring.(1,2)
Unmanned vehicles are highly useful for missions dangerous to humans. Recently, Amazon
launched unmanned aerial vehicles to deliver parcels to clients. The US Navy launches
unmanned surface vehicles (USVs) and underwater vehicles to scout and secure harbors. All
these applications demonstrate that USVs are becoming popular solutions in different fields.
Guiding a USV through complicated ocean environments is highly challenging, and effective
path planning algorithms play an important role in overcoming this challenge. Research on*

Corresponding author: e-mail: yungyuchen@mail.ncku.edu.twhttps://doi.org/10.18494/SAM.2019.2161
ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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path planning design for vehicles has progressed over many years. Many robotic applications
such as a robotic arm and an unmanned aerial vehicle(3) require path planning algorithms,
which have been widely used in game design.(4–6) Dijkstra proposed a solution called
Dijkstra algorithm for the shortest path problem.(7) Hart et al. first described the Astar (A*)
algorithm, which is an extension of Dijkstra’s algorithm(8) for the path planning design. The
A* algorithm uses heuristic search that reduces computing time; therefore, the A* algorithm ismore efficient than the Dijkstra algorithm when planning the shortest path. In the year 2007,
the Any Time Weighted Astar Algorithm(9) based on an error bound concept that encompasses
the differences between the upper and lower bounds to limit the weight of the weighted A*(WA*) algorithm was proposed by Hansen and Zhou. This method can yield the optimal pathfaster than the A* algorithm. These algorithms are based on gridded maps, and their solutions
cause the planned paths to be constrained by the branching factor. The potential headings ofthe branching factor are restricted to 45 degrees or its multiple, so the paths found by A*-based
methods are not the shortest. In order to ameliorate this shortcoming, Botea et al. developed apost-smooth method named A* Post-Smoothed (A*PS).(10) This method removes the redundantturns after the paths are found by the A* method. Utilizing A*PS, the shorter paths can befound, being more precise than the purely A* method, but the derived paths are still not the
shortest. The Finite Angle A* (FAA*) proposed by Yang and co-workers(11,12) minimizes the
problem of restricted turning angles by raising the branching factor unlimitedly to allow users’
adjustments for the shortest path. In existing studies, path planning algorithms always use
satellite images as the map. However, satellite images cannot provide real-time information formonitoring the ocean envronment, and the stored image resolution is worse than that of aerial
images. Otherwise, in reported studies, the ships’ dynamics were not considered in the path
planning for USVs.(11) When the ship’s dynamics are considered, the ship’s initial heading
and its turning ability should be taken into account as well. If the ship’s initial heading is
ignored, the path planning results will fail to navigate the ship to the right direction from the
beginning. In this investigation, a constraint condition of the initial status of USVs is added
into the path planning algorithm to obtain a feasible path. However, the real-time identificationof this constraint condition needs clear images. For the above desired reason, previous path
planning designs of USVs used satellite images as maps,(11,12) but the image from a satellite is
not a real-time one and has a low quality resolution. Recently, the aerial images obtained witha quadrotor have replaced the satellite images and are used as the map for precisely controlling
mobile robots.(13,14) Thus, to improve the quality of images, the aerial image obtained witha quadrotor will be adopted for path planning designs of USVs. Inherently, aerial images
are represented in local coordinates; hence, a coordinate transformation should be developed
to convert the local coordinate of aerial images into a global coordinate. This coordinatetransformation can be achieved by global positioning system (GPS), electrical compass, andaltimeter sensor fusion. Then, a new path planning algorithm that integrates aerial images, the
FAA* method, and a distance–resolution transformation is proposed for the navigation of USVs.
With the help of distance–resolution transformation, the global coordinates of the aerial image
can be immediately calculated from the resolution of images, the height of the quadrotor, andthe positions measured by the GPS and used in the path planning design of USVs. This paper isorganized as follows: the real-time image mapping and path planning algorithm are introduced

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in Sect. 2, the practical implementation and tests of the proposed design are described in Sect. 3,
and finally, the conclusions for this proposed design are summarized in Sect. 4.

2.

Real-time Image Mapping and Path Planning Algorithm

To achieve a path planning design of a USV, two main functions, (1) real-time image
mapping and (2) a path planning algorithm with a low-calculation-power structure, should beconstructed. The first objective is to determine the correlation between the photographing
distance and the image resolution of the camera; the method for this is named “distance–
resolution transformation”. The photographing distance is the distance between the camera
and the photographed object. The image resolution is defined as the real-world length (m) of
one pixel in the image. Distance–resolution transformation is important for the coordinatingtransformation between images taken with the quadrotor and the navigation coordinates. The
image resolutions vary when the quadrotor takes pictures at different heights; therefore, it isessential to determine the distance–resolution correlation to quickly and accurately determine
the image resolution for computing the navigation coordinate when the quadrotor flies atdifferent heights.
2.1

Real-time image mapping

During the design stage, the camera of a smartphone is used to take a series of pictures
for sample papers attached to a wall. Once the real size and pixel length of the sample papers
are known, the image resolution can be computed by dividing the paper size by the pixel
length (cm/pixel). The image processing techniques are utilized to determine the pixel lengthof the sample paper in the image. In addition, for each picture, the photographing distances arerecorded; hence, the correlation between the photographing distance and the image resolution
can be formulated using a regression model. The image resolution can be quickly computedby obtaining the flying height of the quadrotor with a barometric altimeter; then the distance–
resolution transformation can be determined. Figure 1 shows the experimental setup, the redrectangle being the sample paper of known size. The following two sizes of sample paper are
used: 30 and 100 cm2. These sample papers are attached to the wall in this setup. The yellow

Fig. 1.

(Color online) Schematic of the experiment for finding the distance–resolution transformation.

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rectangle is the camera of a smart phone (Sony Xperia) used in this study. The pink rectangle isthe tripod for the camera.
The procedure of the experiment is detailed below.
(1) To simulate real conditions of a quadrotor taking pictures from the air, the camera should beparallel to the wall to allow vertical alignment between the camera and its targets (e.g., L1
parallel to L2 and L3 parallel to L4).
(2) Since all edges of the sample papers are required to determine the image resolution, thesquare sample papers should be located in the middle of the image to guarantee that all edges
are captured in the pictures. Moreover, to make the experiment consistent, the middle of thesample papers is aligned with the camera lens, and the height of the camera lens is 1.35 m inthis experiment.
Repeat steps 1 and 2 while gradually increasing the photographing distance (D). In thisexperiment, D is increased 2 m for each repetition when the distance is less than 30 and 10 m
for each step from 30 to 70 m.
The original size of the picture is 3264 × 2448 pixels, as shown on the left of Fig. 2(a), and
it is composed of 3 color layers. To boost edge detection, the original color picture is binarized
and shown in Fig. 2(a) on the right. The color threshold is used to transform the color picture
to a binarized picture, and then the sample paper can be differentiated from the background. In
this investigation, the canny edge detection method is then utilized to search for the edges of thedetected sample paper in the binarized images. Once the edges are found, the pixel length of
the sample paper can be calculated. To minimize the error of the calculation, the pixel lengthsof the four edges are averaged. Since the real edge length of the paper is known, the image
resolution can be calculated as the real length divided by the average pixel length (cm/pixel).
Besides, the 100 cm2 paper is too large to be imaged with the camera when the photographing
distance is shorter than 8 m. Hence, the 30 cm2 paper is used for a short distance from 2 to 8 m.
Figure 2(a) shows the original picture photographed at 2 m and its binarized result. Figure 2(b)shows the coordinates of the corners of the white square, the lengths of each edge of the whitesquare, and the resulting image resolution of the pictures.
By repeating the same process, practical tests can be done for 4, 6, …, 70 m. Data pairs of
the true distances and the corresponding image resolutions are listed Table 1.

(a)

(b)

Fig. 2. (Color online) (a) Original and binary images (photographing distance: 2 m). (b) Result of distanceresolution (photographing distance: 2 m).

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Table 1
Data pairs of the true distances and the corresponding image resolutions.
Distance d (m)
2
4
6
8
10
12
14
16
18
20

2.2

Resolution Φ (cm/pixel)
0.06567
0.131290.19775
0.266190.32499
0.391080.455160.52083
0.588230.65573

Distance d (m)
22
24
26
28
30
40
50
60
70

Resolution Φ (cm/pixel)
0.72463
0.79365
0.86206
0.93721.003
1.36051.731
2.10522.5

Regression model for distance–resolution relationshipIn the previous stage, a series of photographing distances and image resolutions wererecorded and are listed in Table 1. The data pairs shown in Table 1 can be treated as training
data. On the basis of the training data, the distance–resolution relationship can be formulated
as the regression model.

Φ = αd2 + βd +φ ,(1)Here, Φ is the estimated image resolution (cm/pixel), d is the photographing distance (m), and α,β, and Φ are identifiable parameters that can be optimally obtained by the least-squares method(LS method). The LS method has been widely used in research to determine the parameters
for the regression model(15–17) and can minimize the square sum of the errors between
the measurement data and approximated results of Eq. (1). In this study, Matlab is used to
implement the LS method, and the identified parameters are α = 6 × 10−5, β = 0.0315, and Φ =0.0044. The resulting equation of the regression model is Eq. (2), and Fig. 3 shows the fitting
curve.

()

Φ=
6 × 10−5 d 2 + 0.0315d + 0.0044(2)

From Fig. 3, it is easy to see that the image resolution can be quickly estimated by using thephotographing distance d in Eq. (2).
The accuracy of this identified regression model will be verified using out sample data pairs.
A series of images are taken with different photographing distances. The distance ranges
are 3–21 and 4.5–22.5 m. Note that these images are different from pictures of training data
pairs. The out sample data pairs are used to examine the accuracy of the model against theactual image resolution. Tables 2 and 3 show the estimated and real image resolutions. The
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Fig. 3.

(Color online) Fitting curve of distance-resolution relationship.

Table 2
Sample data pairs for different testing distances (3–21 m).
Distance (m)
3
5
7
9
11
13
15
17
19
21

Real (cm)0.09868
0.16574
0.237150.289220.357140.42060.486
0.57570.61255
0.67796

Estimated (cm)0.09944
0.16340.22784
0.29276
0.358160.42404
0.49040.557240.62456
0.69236

Error
0.007702−0.01412−0.03926
0.012240.002856
0.008179
0.009053−0.03207
0.019607
0.02124

Table 3Sample data pairs for different testing distances (4.5–22.5 m).
Distance (m)
4.5
6.5
8.5
10.5
12.5
14.5
16.5
18.5
20.5
22.5

Real (cm)0.14888
0.21505
0.27770.341
0.40733
0.469480.53050.59520.65789
0.72332

Estimated (cm)0.147365
0.211685
0.276485
0.341765
0.407525
0.473765
0.540485
0.607685
0.675365
0.743525

Error−0.01028−0.0159−0.00439
0.002238
0.000478
0.009045
0.018474
0.020545
0.025875
0.027175e=(Φ − R ) ,R(3)

where e is the percentage error, Φ is the estimated image resolution, and R is the real image
resolution.

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895

Figure 4 reveals the distribution of errors, the mean of errors, and the standard deviation oferrors in Tables 2 and 3. In Fig. 4, the average error is only 0.31%, and the standard deviation oferrors is only 1.84%. This means that 95% of all errors fall within −3.37–3.99% (two standard
deviations). Furthermore, the histogram shows the distribution to be narrow. The error of theregression model in Eq. (2) causes no more than 1 cm error, which is much smaller than thatof the navigation system (GPS). Thus, we can conclude that the regression model in Eq. (2) isaccurate and acceptable for use.
2.3

FAA* path planning algorithm

The FAA* algorithm is adopted as the path planning algorithm in this investigation because
it possesses the obstacle-free property. A branch factor of 16, whereby each signal potential
heading only corresponds to one path length, is directly used. Because of the large branching
factor, the FAA* method can effectively enhance the resulting paths on grids(11,12) andredundant expansions are eliminated. With this arrangement, the concept of line of sight isalso used in FAA* to find a safer path. The detailed description of the proposed path planningalgorithm is presented below.
2.4

Branching factors of FAA*In Fig. 5, a branch factor of 16 is illustrated, and the corresponding lengths are listed in Table 4.
In this list, 16 headings and the relative paths will be saved for generating an obstacle-free path.

Fig. 4. (Color online) Distribution and mean valueof errors.

Fig. 5. (Color online) Illustration of the branching
factor of 16.

Table 4
Corresponding lengths (squared magnitudes) for the branching factor of 16.
Angle
0
π/8
π/4

Length
1
5
2

Angle
3π/8
π/2

Length51

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As seen in Fig. 5, all potential headings are clearly symmetrical about the central grid. Becauseof this symmetrical property, only the potential headings in the first quadrant are recorded tosave memory. The corresponding lengths of these potential headings are shown in Table 4. For
calculation convenience, the squared magnitude of the length is adopted.
The dimension of the branching factor of 16 is a matrix with a size of 5 × 5 as in Fig. 6. In
this figure, the expanding lengths are set as 2 for the left, right, up, and down directions. The
obstacle is indicated by the brown color.
2.5

Procedure of expansions in FAA*

The procedure of FAA* is very similar to that of A*. There are two additional conditions thatare considered in the expansions of FAA*. One is that the visible neighbors of the expanding
vertex are generated using branching factors, and the other is that the lines-of-sight (LOSs)
between visible neighbors and the expanding vertex are examined. For example, in Fig. 6, thecentral grid C3 is selected to be expanded. There are 16 visible neighbors that were generated
on the basis of the list of branching factors. However, five black paths were blocked by an
obstacle, which means that the LOSs do not exist between vertex C3 and vertices B2, A2, B3,
A4, and B4. Ultimately, the resulting number of paths successfully generated is 11. Algorithm
1 in Fig. 7 is the pseudocode of FAA*.
There is a scenario in which three expansions are introduced to detail the design procedureof FAA* with the branching factor of 16 in the following. Figures 8–10 illustrate the scenario
for describing the manipulations of FAA* referring to the expansion scheme of FAA* withthe branching factor of 16 in Fig. 6. In each vertex, two values G and H will be marked. The
definitions of G and H values are in Remark 1. An effective expanded path is defined as “Thesafe distance along which the arrow points towards the current position must be larger than2 / 2 unit lengths”.(11)Fig. 6. (Color online) Scheme of the expansion of
FAA* with the branching factor of 16.

Fig. 7.

Algorithm 1: pseudocode of FAA*.

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Fig. 8. (Color online) Vertex expansion 1 of FAA*
for a start point in B1.

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Fig. 9.
exist.

(Color online) The LOS (B1,C3) does not

Fig. 10. (Color online) Results of expansion 1 (green arrows) and expansion 2 (orange arrows).

Remark 1:

The G(s) value reveals the optimal length from the starting point to vertex “s”.
The H(s) value is the estimated distance from vertex s to the goal vertex and canbe calculated using the heuristic function.
			 The manipulations of FAA* for a scenario with three expansions are given as
follows.
Expansion 1: In Fig. 8, FAA* expands vertex B1 (Start) on the basis of the branching factor of
16 displayed in Fig. 6. This expansion generates the following seven vertices:
A1, A2, A3, B2, C1, C2, and C3. Because there is an obstacle in C2, the vertex
C2 should be eliminated in this process to avoid a collision.
			 In addition, the vertex C3 is also a meaningless point because, as seen in
Fig. 9, the LOS between vertex B1 and vertex C3 cannot be obtained, i.e., theeffective distance between the origin at grid C3 and the line length of B1C3 isless than2 / 2 unit lengths because of blocking by the brown obstacle in C2.

In expansion 1, five candidate vertices (A1, A2, A3, B2, and C1) are finallyselected and stored in the open list. The initial vertex “Start” will move to theclosed list from the open list after expansion 1 is complete. Furthermore, one ofthese five candidate vertices, which is with the minimal value, will be selected
as the next one to be expanded, i.e., vertex B2.

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Expansion 2: On the basis of the result of expansion 1, vertex B2 will be expanded
continuously by FAA*. Vertex B2 as shown in Fig. 10 is the new initial point of
this expansion, and vertices A4, B3, and C4 are new for this expansion; hence,
according to FAA*, they can be directly stored in the open list. In this process,vertices A1, A2, and A3 are selected and well verified.
			 Because the new G values of the generated vertices A1, A2, and A3 aregreater than the original G values, it is unnecessary to update the stored G
values; for example, when the initial point B2 is expanded, then a new G valueof 2.41 can be obtained for vertex A3 and the old G value of 2.24 is smaller than
this new G value. Hence, the G value (2.24) of vertex A3 does not need to be
updated. Because there is no LOS for vertices (B2, C3) and vertices (B2, C1),
vertex C4 is selected as a candidate point to be expanded next and will be stored
in the closed list after the expansion of vertex B2 is completed.
Expansion 3: FAA* will be terminated because the goal vertex C4 is reserved in the closed
list. By extracting the initial point B1 from vertex C4, one optimal path without
obstacles, C4→B2→B1, is then obtained.

3.

Practical Implementation and Tests of the Proposed Design

Figure 11 shows a quadrotor 0.45 m in height and width, 1.5 kg in weight, and powered by a
14.8 V–4.8 Ah LiPo battery. The camera of an on-shelf smartphone is used in this investigation.
The smartphone, Sony Xperia, works on the Android operating system. Android has become
one of the most popular mobile operating systems in the world and is a free open-source system.
A smartphone-based camera is utilized in this study for its several advantages.
(1) The smartphone is multisensor-integrated, having a GPS module, gyroscope, accelerometer,
electrical compass, global system for mobile (GSM) communication, wireless fidelity(WiFi) communication, and a high-resolution camera. It also has a computing ability and iscompact in size.
(2) The smartphone can be easily maintained and replaced. If the camera is broken or ahigher resolution camera is required, it can be easily exchanged or upgraded to a fancier
smartphone.

Fig. 11. (Color online) Quadrotor installed with a smartphone camera.

Sensors and Materials, Vol. 31, No. 3 (2019)899(3) The camera on the smartphone has antiblur, autofocus, and auto-white balance capabilities
The procedure of the android system developed in this research is described below. The
USV continuously transmits its GPS coordinate to the quadrotor through the local network, asshown in Fig. 12. The quadrotor calculates the distance between the quadrotor itself and the
USV, which serves to ensure that the camera on the quadrotor can capture the image of the
USV. The communication protocol in this study is the 2.4 G WiFi signal, and the quadrotor and
USV are connected to a local network by adopting the TCP/IP protocol.
The platform in the USV is a computer (Lenovo-i7-3610QM CPU@2.3 GHz, 8 GB of RAM),and the development environment is Labview. The developed user interface of the Android
app is shown and described in Fig. 13. In this app-based user interface, the locations of thecontrolled USV and quadrotor are displayed in real time.
The aerial image taken by the controlled quadrotor will be used as the map for guiding
USVs. The path planning algorithm, FAA*, is implemented on the captured aerial image.
However, the captured aerial image represents the local coordinate, and the transformation of
coordinates for resulting paths in the aerial image is essential, i.e., the coordinates in the aerial
image should be transformed into GPS coordinates for the navigation of a USV. In this section,
the procedures for coordinate transformation are detailed below, and the procedures are shownin Fig. 14.
(1) Picture taken by the quadrotor should be oriented to the Geographic North Pole. The
electric compass built into the smartphone installed on the quadrotor is utilized to obtainthe magnetic north, and then, the calibration for magnetic declination is implemented.
NOAA provides the magnetic declination using the GPS location.(18) Once the magnetic
declination is obtained, the picture can be oriented to the Geographic North Pole shown in
Fig. 15.
(2) Determine the image resolution using distance–resolution transformation. A barometricaltimeter was installed on the quadrotor, the altitude of the photograph can be transmitted
to the USV through the barometric altimeter, and then the image resolution for that altitude
can be determined.

Fig. 12. (Color online) Scheme of USV communication with the quadrotor.

Fig. 13. (Color online) Screen shot of the app of thecamera system.

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Fig. 15. (Color onli ne) Rot ate aer ial i mage toGeographic North Pole.

Fig. 14. Flow chart of the coordinate transformation.

Fig. 16. (Color online) Portion of the path planning
result.

(3) Use the smartphone to transmit the GPS location of the center of the image to the USV.
Given the GPS location, image resolution, and oriented images, the coordinates of every
pixel in the aerial image can be transformed into TWD97 coordinates. Detailed informationof TWD97 can be obtained from Refs. 11, 19, and 20. Finally, the TWD97 coordinates canbe transformed into WGS84 coordinates for navigation purposes.
By the above designed procedure, the path planning algorithm on the aerial image canbe demonstrated. Figure 16 shows a portion of path planning results. The resulting pathis composed of a set of nodes and arrows. Every node points to its next node connected byarrows. Each arrow denotes the direction of motion of the USV shown in yellow circles in the
figure. Two points are selected randomly as the start and end points for path planning on theaerial image shown in Figs. 17 and 18. These two path planning takes are performed using two
different safety margins, namely, 1.12 and 2.77 m on the same image. Both results show that theplanning paths are collision-free and maintain the safety margins near obstacles.
To validate the coordinate transformation of the aerial image to the navigation coordinate,
the resulting paths are printed on the satellite image, which is a global and well-oriented image,shown in Fig. 19. It is found that the calculated paths are consistent with the aerial images,
although the resolution of the satellite image is worse. However, it shows that the coordinatetransformation of our proposed method is correct and useful. Note that the blue path (2.77 m
safety margin) is farther from the dock than the red path, which is consistent with the results on
the aerial image.

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Fig. 17. (Color online) Path planning on aerial image(safety margin: 1.12 m).

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Fig. 18. (Color online) Path planning on aerial image(safety margin: 2.77 m)

Fig. 19. (Color online) Printed resulting paths on the satellite image with respect to different safety margins: 1.12 (redline) and 2.77 m (blue line).

4.

Conclusions

In this research, the high-accuracy path planning problem of USVs was successfully solved
with the help of a multisensor fusion design. By integrating the FAA* method, distance–
resolution transformation, and coordinate transformation of real-time aerial images, a newpath planning algorithm with the collision-free property was proposed for the navigation of
USVs. The FAA* method, which yields a smoother and more pragmatic path than previously
used methods, was enhanced considering the USV’s initial conditions. The distance resolution
and coordinate transformation were developed and implemented in practice for the real-timetransformation of the aerial images. With the distance–resolution transformation, a highly
accurate transformation performance was obtained, i.e., the average error was only 0.31% andthe standard deviation of errors was only 1.84%. This proposed method is verified by real USVs

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in the harbor. From comparisons of the path planning results with respect to aerial and satellite
images, this proposed method was found to be promising for the path planning of USVs, andhas the potential to be applied in the path planning designs of surface moving vehicles becauseof its high accuracy and real-time image updating property. In the near future, this proposed
method will be practically applied to the guidance law design of unmanned surface vessels inmessy ocean environments.

Acknowledgments
This work was financially supported by the National Science Council, Taiwan, R.O.C. (Grant
No. MOST 104-2628-E-006-012-MY3).

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Sensors and Materials, Vol. 31, No. 3 (2019)903

About the Authors
Yung Hsiang Chen received his M.S. degree from Da Yeh University,
Taiwan, in 1998 and his Ph.D. degree from the Department of ElectricalEngineering at National Cheng Kung University, Taiwan, in 2013. Currently,
he is an assistant professor at National Pingtung University of Science andTechnology, Taiwan. His research interests are in nonlinear control andsensors. (yhchen@mail.npust.edu.tw)
Yung Yue Chen was born in Pingtong, Taiwan, in 1972. He received his
Ph.D. degree from the Department of Electrical Engineering, National Tsing
Hua University, Hsinchu, Taiwan, in 2003. From 2004 to 2010, he served as adepartment manager and a product line leader in the Smart Microsystems
Technology Center, Industrial Technology Research Institute. He is currently
an associate professor at National Cheng Kung University, Taiwan, and theleader of the Information Team in the Engineering and Technology Promotion
Center, NSC, Taiwan. He has published more than 50 scientific publications
in refereed journals and conference proceeding and is the owner of 20 patents.
His research interests include noise reduction design of cellular phones,
micro- and nanocontrol design, MEMS-based inertial navigation systemdesign, intelligent system design, system estimation, and control design.
(yungyuchen@mail.ncku.edu.tw)
Joe Ming Yang received his B.S. degree from National Taiwan Ocean
University, Taiwan, and his M.S. and Ph.D. degrees from the University ofCalifornia-Berkeley, USA. Currently, he is an associate professor in the
Department of Systems and Naval Mechatronic Engineering of National
Cheng Kung University, Taiwan. His research interests are in novel ship
design, path planning of vehicles, vibration system analysis, and sensors.
(z7908036@mail.ncku.edu.tw)