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Sensors and Materials, Vol. 31, No. 3 (2019) 1021–1036
MYU Tokyo
S & M 1832

Obstacle Avoidance of Mobile Robot Based on HyperOmni Vision
Shih-An Li,* Li-Hsiang Chou, Tsung-Han Chang,Chao-Hsu Yang, and Yu-Cheng Chang
Electrical and Computer Engineering, Tamkang University,
151, Ying-Chuan Rd., Tamsui, New Taipei City, Taiwan (R.O.C.)(Received November 23, 2018; accepted February 7, 2019)

Keywords: dynamic window approach, artificial potential field, HyperOmni Vision, mobile robot,
obstacle avoidance

In this paper, we present a method that combines an improved dynamic window approach(IDWA) and an artificial potential field to avoid obstacles for autonomous wheeled robots with
HyperOmni Vision. The experimental environment follows the rule of RoboSot avoidancechallenge in the Federation of International Robot-soccer Association (FIRA) competition.
There are three parts of this study, namely, (1) image processing, (2) an IDWA, and (3) an
improved artificial potential field. In the avoidance challenge in FIRA, the robot must avoid
eight obstacles that are placed randomly in the playing field. The results indicate that the threewheeled robot can avoid obstacles autonomously by using the method proposed in this paper.
Our team won the FIRA avoidance challenge championship this year by the method proposed inthis paper.

1.

Introduction

Designing a robot soccer system involves a combination of many disciplines that requirevarious academic theories and techniques. There are two essential parts in completing the
avoidance challenge, for example, obstacle detection and path planning. It is important toconsider the robot’s path plan to avoid obstacles. Some researchers used an artificial potential
field(1,2) to seek targets and obstacles. The robot regarded a target as an attractive force and an
obstacle as a repulsive force. It then combined all the forces to establish the direction of motion.
This method has been widely used to avoid obstacles and move smoothly. However, owing
to local optimization problems, it easily causes a local minima problem before arriving at its
destination.
The method we proposed in this paper is based on the dynamic window approach (DWA),
which establishes the dynamic window and the predicted trajectories through an OmniHypercamera. After that, the DWA was combined with the artificial potential field to renew thepredicted trajectories and finish the obstacle avoidance.
This study is carried out to increase the stability of the robot and ensure that the robot avoids
collision with obstacles so that it can move smoothly. The experimental environment is an*

Corresponding author: e-mail: lishyhan@gmail.com
https://doi.org/10.18494/SAM.2019.2226
ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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avoidance challenge field in the RoboSot competition. In this study, the avoidance challenge
field is used as the experimental environment, and a three-wheeled robot is used as the testing
platform. This test platform is equipped with sensors such as an omnidirectional camera,an inertial measurement unit (IMU), and a motor encoder. The omnidirectional camera willcapture the environmental image of the robot. The IMU detects the angle of rotation of therobot and the acceleration of the movement. The motor encoder is used to detect the moving
distance and speed of the robot. The robot can calculate the better obstacle avoidance paththrough these parameters.

2.

Background Discussion

In this section, our robot and experimental environment are described. This section isdivided into the following two parts: (1) image processing system and (2) path planning. These
parts are briefly described below.
2.1

Image processing system

In this study, an omnidirectional vision system is used to capture an image. Thisomnidirectional vision system includes a hyperbolic mirror and a high-speed camera, as shownin Fig. 1. The high-speed camera, Prosilica GC655, is made by Allied Vision. Moreover, thefeatures are shown in Table 1. Prosilica GC655 is a high-performance vision camera with a
GigE Vision compliant gigabit ethernet interface. This camera performs up to 90 FPS (frame
per second) in the full resolution mode. The vision system is located at the center of the sixthgeneration robots designed in our laboratory, as shown in Fig. 2. The computer on the robot canobtain a 360° circular image using the camera, as shown in Fig. 3.
After obtaining the image, the soccer and goal are identified by using color models, such
as RGB, HSV, and CMYK color models. The RGB color model exhibits a nonlinear change
in color saturation. Moreover, all colors are composed of the values of the three components
in RGB, thus resulting in a high correlation between the three components. When the

Table 1Model no.
Prosilica GC 655
Resolution
659 (H) × 493 (V)Sensor type
CCD Progressive
Max. frame rate
at full resolution

Fig. 1.

(Color online) Omnidirectional vision system.

Interface
SensorSensor size

IEEE 802.3
Sony ICX414
Type 1/2
90 FPSSensors and Materials, Vol. 31, No. 3 (2019)1023

contrast, brightness, or color saturation in the image is changed, the effects among these threecomponents must be considered. The RGB color model is easily affected by shadows or light,
thus reducing the rate of color recognition. In contrast, the HSV color model(3) is more resistantto the aforementioned effects. In this study, the HSV color model shown in Fig. 4 is used. The
HSV color model consists of the following three components: hue (H), saturation (S), and value (V).
Image noise is a common issue in image processing. Therefore, we binarized the image tofilter the noise. In this study, the HSV color model is used to separate obstacles. However, theshadows of obstacles have negative effects on binarization, misleading image noise between
different color models. Therefore, breadth-first search(4) is used to filter image noise.
The breadth-first search is an uninformed search, so we need to use queues to record theorder of visits. We assume that the gray area is an obstacle. In the breadth-first search, we
initially set queue-containing vertex 1 and search for its surrounding adjacent vertices. We
then dequeue vertex 1 and enqueue vertices 2, 3, and 4. The queue now contains vertices 2, 3,
and 4, as shown in Fig. 5(a) Assume that we first visit vertex 2 and search for its surrounding
adjacent vertices and then dequeue vertex 2 and enqueue vertices 5 and 6, as shown in Fig. 5(b)Moreover, the breadth-first search terminates until the queue is empty.

Fig. 2.

(Color online) Sixth-generation robot.

Fig. 3.
vision.

(Color online) Image of an omnidirectional(a)Fig. 4.

(Color online) HSV color model.

(b)Fig. 5. (Color online) (a) Breadth-first search. (b)Breadth-first search.

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In this study, we defined a specific size of the black area in this image. Objects under thesize of the definition will be considered as image noise; otherwise, objects will be considered as
obstacles (Fig. 6).
2.2

Path planning

2.2.1 Dynamic window approach
Path planning is the ability of an autonomous robot to move to a destination. The DWA wasproposed by Fox et al. in 1997.(5) It is a collision avoidance algorithm that is efficient and robust.(6,7)
The DWA is an algorithm that takes a variety of velocity samples from the speed space.
These velocity samples are used to simulate the trajectories of the robot to the target within acertain time. Pick the best trajectory from these trajectories. There are two parts in the DWA
algorithm, namely, (1) search space and (2) optimization, which will be described below.
In the search space, there are three parts, namely, (1) circular trajectories, (2) admissible
velocities, and (3) dynamic window. In the circular trajectory section, there are linear moving
speed and angular velocity (v, ω) in the velocity space. In the next n time interval, the velocity
sample is selected from the velocity space as the moving path of the robot to the target point.
In the admissible velocity section, when obstacles are placed around the robot, the translation
and rotation speeds of the robot are limited. Assume the sample speed is (v, ω) and dist(v, ω) is
the length of the curve trajectory generated by the movement that will collide with the nearest
obstacle based on the sample speed. The length can be obtained from the radius “r” of thecurved path and the angle “γ” between the collision point and the robot, so the arc length can beobtained from “r”, as shown in Fig. 7. Let vb and ω b be accelerations for breakage. According
to the above conditions, the suitable speed sample can be defined as ^ ω ≤ 2 ⋅ dist (v,ω ) ⋅ ω b }.
Va = {(v,ω ) | v ≤ 2 ⋅ dist (v,ω ) ⋅ vb

(1)

In the dynamic window section, according to the acceleration limit of the motor, the entire
speed space can be reduced to the speed space that can be reached in the next moment. The
reduced speed space is the dynamic window. Suppose t is the acceleration v and angular(a)Fig. 6.

(b)(Color online) (a) Binarized image and (b) breadth-first search.

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Fig. 7.

1025(Color online) Calculation of “dist(v, ω)”.

acceleration ω that the motor can provide at each moment, and (va, ω) is the actual speed. The
velocity space Vd of the dynamic window can be defined as=
Vd {(v,ω ) | v ∈ [Va − v ⋅ t ,Va + v ⋅ t ] ∧ ω ∈ [ω a − ω ⋅ t ,ω a + ω ⋅ t ]}.

(2)

The dynamic window is a speed space centered on the actual speed. In the next moment, all thecurved tracks are expected not to exceed this speed space and not collide with the obstacles inthe space. According to the above three parts, Vr denotes the velocity sample contained in the
final search space and Vs denotes the speed space that the robot can provide, as shown in Fig. 8,
and Vr can be defined as
Vr =Vs ∩Va ∩Vd .

(3)

After determining the resulting search space Vr, a velocity is selected from Vr. The algorithm
scores the curve trajectory generated by the velocity sample through an evaluation function andselects the curve trajectory with the highest score as the path of the next robot movement. The
evaluation function is
G (v,ω=) σ (α g ⋅ heading (v,ω ) + β g ⋅ dist (v,ω ) + γ g ⋅ velocity (v,ω )) .

(4)

The target heading (v, ω) measures the alignment of the robot with the target direction. It is
given by 180 − θ, where θ is the angle of the target point relative to the robot’s heading direction,
as shown in Fig. 9.

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Fig. 8.

Dynamic window.

Fig. 9.

Illustration of angle θ.

2.2.2 Artificial potential field
The artificial potential field algorithm is a robot path planning algorithm proposed by
Khatib in 1986 at Stanford University.(8) It regards the target and obstacle as objects that are
gravitational and repulsive to the robot, and the robot moves along the combined force of gravity
and repulsive force, as shown in Fig. 10.
The artificial potential field has the advantages of simple mathematical description, low
computation speed, high response speed, and smooth trajectory planning. In recent years, theartificial potential field has been widely used as a method in the robot path planning,(9,10) Thismethod has become one of the most mature and effective traditional calculation methods.
There are problems associated with the characteristic of the artificial potential field.
According to Eqs. (5.1)–(5.3), we learned that Fatt decreases when the robot is close to the target
and Frep increases rapidly when the robot is close to the obstacle.(11) Therefore, the artificial
potential field is a non-optimal path solution and has a local minima problem,(12) as shown in
Figs. 11(a) and 11(b).
The equations used in the artificial potential field method are as follows:
Fatt = α × v,

(5.1)

2
 β × ( d s − d ob ) × v, d ob ≤ d s
Frep = d ob > d s
0,


(5.2)

F = Fatt + Frep,(5.3)

where Fatt is the attractive force, Frep is the repulsive force, F is the composition of forces, α isthe parameter of the attractive force, β is the parameter of the repulsive force, v is the vector of
the robot velocity, dob is the distance between the robot and the closest obstacle, and ds is thesafe distance between the robot and the obstacle, as shown in Fig. 12.

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

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(a)

(b)
Fig. 10. (Color on li ne) Path pla n n i ng th roug hArtificial Potential Field.

Fig. 11. (a) Non-optimal path. (b) Local minima
problem.

Fig. 12. (Color online) Artificial potential field.

In the omnidirectional mobile platform, the equations for the concept of the artificial
potential field are as follows:
Fatt= α × vnew ,(6)

Frep = ( ( d ob − d s ) cos θ R + ( d ob − d s ) sin θ R ) × β × v ,(7)

F = Fatt + Frep ,

(8)

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where vnew is the new velocity vector of the robot calculated by DWA, and v is the vector of
the robot velocity, which is set by the user interface. The schematic diagram of the artificial
potential field method of the omnidirectional mobile platform is shown in Fig. 13.

3.

Research Methods

The flow chart of the wheeled robot obstacle avoidance method proposed in this paper isshown in Fig. 14. The industrial personal computer placed on the robot first takes the image
captured by the camera for image processing. Then, the improved DWA method proposed inthis chapter is used to determine the most suitable path of motion.(13) Subsequently, the path
is recorrected by the artificial potential field algorithm to avoid nearby obstacles. Finally, a
motion vector is produced to make the robot move.
Take the movement of differential robots as an example. The velocity space in the DWA
contains v and ω. The robot determines whether the trajectory will hit the obstacle, and thealgorithm searches for a velocity space over time and calculates the new robot’s trajectory, as
shown in Fig. 15.
Compared with the differential mobile platform, the omnidirectional wheeled platformmoves in a 360 degree linear motion.(14) Therefore, the study only considersand v when moving,
and then θ and v are respectively converted into an X vector and a Y vector, as shown in Fig.
16. We set the x-axis for the front direction. We calculated the angle using the front direction.
Moreover, we considered that counterclockwise is positive and clockwise is negative. The
equations are as follows:
x = | v | cos θ ,Fig. 13. (Color online) APF of the omnidirectional mobile platform.

Fig. 14. Flow chart of obstacle avoidance.

(9.1)

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Fig. 15. (Color online) Trajectory of the differential
robot.

Fig. 16. (Color online) Vector conversion.

y = | v | cos θ .

(9.2)

Since the experimental environment in this study follows the rule of the Federation ofInternational Robot-soccer Association (FIRA)(15) obstacle avoidance challenge, the scan line
of image recognition was used to determine obstacles. The robot determines the free areaaccording to the position of the obstacle, as shown in Fig. 17.
The robot prioritizes the active areas, and the area closer to the target point takes precedence.
Therefore, the robot first determines that area 2 is the priority path, then it is area 1, and thefinal is area 3. After completing the above steps, the robot will calculate the width of area 2
and determine if the width can accommodate it to ensure that it does not hit obstacles. Take
two obstacles as the identification of three scan lines. The distance between the robot andthe obstacle is calculated using the distance information obtained from the known scan line.
The robot uses R1 as the baseline to operate with all the scan lines L1, L2, and L3 of anotherobstacle. Then, it can calculate the distances D1, D2, and D3 as θ1, θ2, and θ3, respectively,
as shown in Fig. 18. The calculation of R2 and R3 is the same as that of R1. There will be i*j
distance information in total, and finally, the shortest distance is found to be smaller than thefuselage. The equations are as follows:=
V {Dk∣
Dk : IR 2 },W= {min Dk∣
Dk=i=, j , k 1, 2, 3, …, n,

Ri 2 + L j 2 − 2 Ri L j cos θ k },∀Dk ∈V .

(10)
(11)

The moving speed vector vnew is first obtained by Eqs. (10) and (11). Then Eq. (12) can
obtain the minimum angle between the robot and the obstacle. From this angle, the obstacle
distance DR can be calculated as shown in Fig. 19.

θ min = θVnew − θ R

(12)

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Fig. 17. Active area of the robot.

Fig. 18. (Color online) Calculation of the distance
between two obstacles.

Fig. 19. (Color online) DWA to avoid the trajectoryof obstacles.

Fig. 20. (Color online) Improved dynamic window
approach.

In addition to changing the original differential mobile platform into an omnidirectional
mobile platform, two layers of dynamic windows called dynamic windows I and II are used,which is considered an improved DWA method, as shown in Fig. 20.
When the robot moves, it uses V to predict the robot’s trajectory. In addition, it plans the
trajectory of obstacle avoidance when there are obstacles in the track. The equations are as
follows.
 −1  X ⋅T 
(13)
θV = cos  X T Sensors and Materials, Vol. 31, No. 3 (2019)

1031



−1  r =θVnew X − tan  , r > DR ⋅ tan (θ R )
 DR 
=r ≤ DR ⋅ tan (θ R )θVnew θV ,(14)


Here, T is a target vector and θmin is the angle of the obstacle closest to the target.
The detection range of dynamic window I is expanded so that the robot can detect relatively
far obstacles to select a better path and plan the trajectory, as shown in Fig. 21(a). Then, therobot sets the target in Dynamic Window II and detects relatively close obstacles to replan the
trajectory, as shown in Fig. 21(b). In addition, the artificial potential field is used to ensure thatthe robot will not touch the obstacles. In this manner, the robot filters out the better path, asshown in Fig. 21(c).

4.

Research Results

The method proposed in this paper was used to combine and improve the DWA andartificial potential field methods, and the authors named it space-perception approach (SPA).
In this study, the experimental results are divided into two parts, namely, simulation andimplementation.
In the simulation part, the advantages and disadvantages of the three algorithms, namely,
DWA, artificial potential field, and SPA, are compared. The simulation results in the MATLAB
program are shown in Fig. 22–25 below.
It can be seen from the results that, when the DWA encounters obstacles around it, it reduces
its moving speed and leads to a narrower measurement range. In the artificial potential field,
when there are more than two obstacles around the robot, the movement trajectory causes
oscillation, making the robot hit the obstacles. In the SPA method, the robot’s movement path is
the smoothest and fastest, as shown in Fig. 24.

(a)

(b)

(c)

Fig. 21. (a) Path planning of dynamic window I. (b) Path planning of dynamic window II. (c) Path planning of
improved DWA.

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Fig. 22. (Color online) Dynamic window approach.

Fig. 23. (Color online) Artificial potential field.

Fig. 24. (Color online) Space-perception approach.

Fig. 25. (Color online) Three different methods ofrobot trajectory.

In the implementation part, the proposed method is compared with the other three methods
and verified in the FIRA avoidance challenge, as shown in Fig. 25. The avoidance challenge
rule sets the yellow goal as the destination. A robot must move from a blue goal to a yellow
goal without touching any obstacles. The referee randomly places eight obstacles on the path.
The size of each obstacle is similar to that of a black box with a length of 45 mm, a width of 45mm, and a height of 70 mm. The distance between the obstacles is at least 60 cm. The robot
is 45 cm in length and width, so it has an error range of only 15 cm when crossing an obstacle.
The speed of the wheeled robot is set to 25 cm/s. The distance between the starting point andthe destination is 4.2 m. We tested each method 20 times and took the average data, as shownin Table 2. The method proposed in this paper has the highest success rate and is also thefastest.
A piece of pink paper was stuck on top of the robot and the movement trajectory of the robot
is recorded according to the fisheye set on the ceiling. The trajectory is drawn after image
processing, as shown in Fig. 26.
The first method uses the SPA as the obstacle avoidance algorithm. This method not only
makes the trajectory smooth but also plans a better path. When the robot moves to the midfield,
owing to the dynamic window I, it senses an obstacle farther to the right and passes through awider path on the left side. In this manner, path planning is more efficient and more stable, as
shown in Fig. 27.

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Table 2Comparison of four different methods.
Fig. 27
Fig. 28
Time (s)19
21
Distance (m)
4.75
5.25
Stability
ExcellentGreat
Efficiency
ExcellentGreat
Success rate
95%
90%

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Fig. 29
24
6.00
Bad
Good
40%

Fig. 30
Fail
Fail
Worst
Worst
0%

Fig. 26. (Color online) Comparison of four different
methods.

Fig. 27. (Color online) Space-perception approach.

Fig. 28. (Color online) Original DWA with artificial
potential field.

Fig. 29. (Color online) Artificial potential field
without DWA.

The second method uses the original dynamic window approach with the artificial potential
field as obstacle avoidance protection. Its trajectory is also smooth, but the path selection ismuch worse than the SPA. When the robot moves to half of the field, since the detection rangeof the DWA window is small, it selects a poor path on the right side. Although the stability is
excellent, the path planning efficiency is worse, as shown in Fig. 28.
The third method only uses the artificial potential field. The path selection is uninformed
and very inefficient. This is because the presence of repulsive forces makes the trajectory
produce many shocks. In this approach, the stability is not sufficient, probably producing local
minima problems. Moreover, the robot touches the obstacles easily when the distance between
the obstacles is decreased, as shown in Fig. 29.

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Fig. 30. (Color online) DWA algorithm only.

The fourth method uses only the DWA without the artificial potential field. The result is theworst; the robot can only plan the path. The robot touches the obstacles to produce numerous
collisions without the artificial potential field when it goes through the half field. Thus, the testshows us the importance of the artificial potential field as the obstacle avoidance protection, as
shown in Fig. 30.

5.

Conclusions

In this study, an effective obstacle avoidance method is proposed to achieve anomnidirectional mobile platform with an omnidirectional vision system. Moreover, winningthe championship in the 2017 FIRA avoidance challenge proves that the method is robust. In
future developments, as long as obstacles can be identified through an imaging system, themethod can be applied to various experimental environments. The method that we proposed inthis paper improves these two algorithms and combines them. It uses the breadth-first search
to filter image noise, which makes the image recognition more stable and accurate. Moreover,the success rate applied in the competition approaches 100%. Therefore, this not only avoids acollision to reduce the loss of the robot equipment but also has a higher efficiency to accomplish
the mission.

Acknowledgments
This research was supported in part by the Ministry of Science and Technology (MOST) of
the Republic of China under contract MOST 106-2221-E-032-007.

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About the Authors
Shih-An Li received his B.S. degree in Electrical Engineering from National
Taipei University of Technology, Taipei, Taiwan, in 1997. He received his
M.S. and Ph.D. degrees in Electrical Engineering from Tamkang University,
Taiwan, in 2004 and 2008, respectively. He joined the Electrical Engineering
Department of Tamkang University in 2010 and is now an assistant professor.
His research interests include a fuzzy system, intelligent control, SOPC
design, FPGA design, and genetic algorithms.
Li-Hsiang Chou received his B.S. and M.S. degrees in Electrical Engineeringfrom Tamkang University, Taipei, in 2014 and 2016, respectively. He iscurrently a Ph.D. student in the Department of Electrical and Computer
Engineering, Tamkang University. His primary research interests include
FPGA design, intelligent control, and robot system.

Tsung-Han Chang received his B.S. and M.S. degrees in ElectricalEngineering from Tamkang University, Taipei, in 2014 and 2016, respectively.
He is currently a Ph.D. student in the Department of Electrical and Computer
Engineering, Tamkang University. His primary research interests include
image processing, system integration, and robot system.

Chao-Hsu Yang received his B.S degree in Electrical Engineering fromTamkang University, Taipei, in 2018. He is currently an M.S. student in the
Department of Electrical Engineering, National Taiwan University of Scienceand Technology. His primary research interests include robot system anddeep learning.

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Yu-Cheng Chang received his B.S degree in Electrical Engineering fromTamkang University, Taipei, in 2018. He is currently an M.S. student in the
Department of Mechanical and Electromechanical Engineering, National Sun
Yat-sen University. His primary research interests include system integrationand self-driving cars.