Ts 2 b topic

SPECIAL SECTION ON SEQUENTIAL DATA MODELING AND ITS EMERGING APPLICATIONS
Received April 14, 2018, accepted July 2, 2018, date of publication July 25, 2018, date of current version August 20, 2018.
Digital Object Identifier 10.1109/ACCESS.2018.2859754
A Human-Tracking Robot Using Ultra
Wideband Technology
TAO FENG 1, YAO YU1, LIN WU2, YANRU BAI3, ZIANG XIAO1, AND ZHEN LU4
1School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
3School of Advanced Engineering, University of Science and Technology Beijing, Beijing 100083, China
4China Soft Information Systems Engineering Co., Ltd, Beijing 100000, China
Corresponding author: Yao Yu (yuyao@ustb.edu.cn)
This work was supported by the National Science Foundation of China under Grants 61520106009, 61533008, and 61473324.
ABSTRACT In this paper, a target person tracking method based on the ultra-wideband (UWB) technology is
proposed for the implementation of the human-tracking robots. For such robots, the detection and calculation
of the position and orientation of the target person play an essential role in the implementation of the human-
following function. A modified hyperbolic positioning algorithm is devised to overcome the challenge of
the measurement errors. In addition, a modified virtual spring model is implemented in the robot to track
the target person. One important advantage of such a UWB-based method compared with the computer
vision-based methods lies in its robustness against the environment condition. In fact, the computer vision
methods can be very sensitive to lighting conditions which make them suffer from the unconstrained outdoor
environment. The robust performance of our methods is illustrated by the experimental results on a real-world
robot.
INDEX TERMS Human robot interaction, mobile robots, ultra wideband technology.
I. INTRODUCTION
In tradition, robots are only used for industrial fields in the
restricted areas such as factory assembly lines. However,
the significant advances in robotic technology make it pos-
sible for robots to operate in a complex environment while
sharing their workspaces with humans to perform tasks such
as health care, home automation, construction, etc [1]. As
a result, Human Robot Interaction(HRI) has been drawing
much attention from all over the world in recent years. HRI
involves the human dynamics, behaviors, and various of
sensors and algorithms required to perform quality and safe
cooperation. While traditionally, HRI systems interact with
users by passive methods such as voice command, gesture
control and other Human-Computer Interfaces, people are
trying to make a robot interact with the users more actively
recently [2].
As a specific aspect of HRI, automatic human-tracking is
an essential and fundamental function for these robots inter-
acting with humans effectively. The technology of automatic
tracking makes mobile robots keep a certain distance from
the target person continuously, which therefore makes the
robots capable of working together with humans safely and
effectively.
A typical application of automatic human-tracking pre-
sented in [3] is that the robot can help people carry the
baggages and follow them automatically so that people are
able to walk outside without the need to carry the baggages
themselves, especially when they are in the airport or going
shopping. Another application is to allow a smart wheelchair
to be led by a person toward a place, as described in [4]. In that
system, driver assistance is used to keep the wheelchair from
stairs falling and obstacle collision when the user is driv-
ing the wheelchair in complex environments with unknown
obstacles and human following is used to guarantee the
wheelchair to follow the user with a suitable distance when
the user wants to walk along by himself. In [5], a robot is
used to help the customs carry the products they are intended
to buy and follow them to the counter in a retail store.
Besides, a mobile robot keeping itself facing humans and
acting as an assistant robot is presented in [6]. A friendly
small size robot assistant presented in [7] is equipped with
a variety of different sensors for human-tracking and has
a storage box to help people delivery the books or note-
books. In [8], a construction robot is presented which
can autonomously follow the human operator to the work
position.
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2169-3536
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42541

T. Feng et al.: Human-Tracking Robot Using UWB Technology
The detection and calculation of the position and ori-
entation of the target person play an essential role in the
implementation of the human-following function [6]. Various
methods regarding the detecting and tracking of the target
person in the complex environment have been presented
recently. In early years, LED lights were carried by the per-
son being detected and tracked by a camera [9]. An sensor
network comprising of several cameras was used to help the
robot track a human in [10]. The laser range finder is also used
to acquire the distance information and in turn to improve
the detection of target human in the background [11]–[13].
While the laser range finder has the ability to obtain the
distance accurately, it is difficult for it to identify the target
person [7]. As a result, some research combined several
sensors to improve the accuracy of human detection and
tracking [2], [14], [15]. Hu et al. [16] have proposed a sensing
system based on a novel 3-D meanshift algorithm on RGBD
camera. They also derived an anticipatory human walking
model based on relevant research about human walking with
which the robot can predict the target person’s movement and
take actions in advance, thus enhancing the human-tracking
performance. Sound signal was used in [17] to locate and
track humans.
The vision-based algorithms have dominated all these
approaches because of the low cost of cameras and the recent
improvement in machine vision algorithms [1], [18], [19].
Nevertheless, the vision-based human-tracking approaches
have some fundamental disadvantages due to the effects such
as camera calibration errors, variation in illumination, field
of view (FOV) constraints, partial or full occlusion, etc. Such
algorithms are also confronted with the challenges like cam-
era motion [20], [21], in-plane pose variation and pose change
due to out-of-plane rotation [22] and localization errors
resulting from inaccurate robot odometry [23] and nonholo-
nomic constraints [1]. Moreover, human-tracking approaches
based on vision demand higher computer power and hence
higher hardware cost, limiting their commercial use further.
In this paper, a method is proposed using UWB tech-
nology to detect and track the human location. The UWB
technology features a variety of advantages, including the low
power, the anti-multipath interference ability, the high secu-
rity, the low system complexity, the especially high precision
in location. It has the potential to become a mainstream tech-
nology of indoor wireless location system. In this sense, this
paper is an effort in exploring the performance of such a UWB
based method for human body tracking in an indoor envi-
ronment. Besides, unlike the computer vision based methods
that are sensitive to lighting conditions, the UWB based
technology is expected to perform well in the outdoor envi-
ronment. This feature is regarded as an important advantage
over the computer vision based approaches, no matter depth
information is used or not.
Except the technology of target person detection and loca-
tion, the human-tracking algorithm also plays an important
role for the robot to follow the target person. The basic
human-tracking method is to move the robot toward the target
person until the robot is close enough to the person and then
cease moving. Afterward the robot will keep tracking the
target person and follow him/her continuously whenever the
person moves [16]. In this paper, a modified virtual spring
model which connects the human and the robot is proposed
to control the robot to track the person smoothly afterward.
This paper is organized as follows: the system architec-
ture including hardware and software components of the
mobile robot is described in Section II. The process of the
UWB data calibration and the tag position calculation using
a modified hyperbolic positioning algorithm is presented in
Section III. The moving-average filter is also discribed in
this section. The dynamic model of the robot is derived
in Section IV. The human tracking algorithm is presented
in Section V. The experimental results and discussion are
presented in Section VI. Finally this paper is concluded in
Section VII.
II. SYSTEM ARTCHITECTURE
A. ROBOT HARDWARE ARCHITECTURE
The robot presented in this paper is designed and constructed
by ourselves. It includes an embedded control board, and
is equipped with four ultrasound sensors and three UWB
anchors. The hardware architecture and components are
shown in Figure 1 and Figure 2. Figure 3 is the control board
of the system.
FIGURE 1. The hardware architect of our robot. Specially the wheel robot
is driven by the embedded controller. Two motors are installed for moving
and turning around. Accordingly there are two wheels on two sides with
an additional wheel for directional movement. The sensors include the
UWB anchors and ultrasound ones. There is a control board connecting
all the sensors and compute the real-time motion signals to control the
movement of the two wheels by the motors. The whole robot including
the hardware, software and mechanics is all implemented by the authors.
In this system, two high performance controllers are used,
namely the A33 SOC and the STM32F103 MCU. The
A33 SOC features quad CortexTM -A7 CPU cores to enable
the best balance between high performance and power effi-
42542 VOLUME 6, 2018

FIGURE 2. The hardware component of the robot. Different sensor and
communication modules are involved including the UWB, ultrasound, and
Bluetooth. The component is designed and integrated by the authors.
FIGURE 3. The control board of the robot. The A33 SOC, STM32F103 MCU
and other components are all included on the board. The control board is
designed by the authors.
ciency. It comes with a wide range of connectivity which
reduces system complexity and speeds up development pro-
cess significantly. The UWB data reception, calibration, fil-
tering and human tracking algorithm are executed in the
A33 SOC.
The Cortex-M3 core and a variety of peripherals
such as UART, SPI, I2C and so on are included in
the STM32F103 devices with a maximum CPU speed
of 72 MHz. In this system the STM32F103 MCU is used
as the lower controller, which is connected to the Bluetooth
module for data communication; to the motor drives for
movement; and to the ultrasound modules for distance esti-
mation which can be further used for the obstacle avoidance.
The A33 SOC is connected with the STM32F103 MCU
through the UART. The sensor data from three UWB anchors
will be first input to the A33 SOC to process the raw data.
Because of the existence of the significant errors within
the distance data, it is necessary to calibrate them for the
further calculation. After that, the tag position and orienta-
tion will be calculated by a modified hyperbolic positioning
algorithm which overcomes the difficulty of calculating the
actual position of the tag due to the inevitable measurement
errors when the typical hyperbolic positioning algorithm is
used. Then the position of the tag is filtered by the moving-
average filter algorithm which reduces the position drifting
due to the random measurement errors efficiently. After the
average-moving filter, the position and orientation of the
target person have been obtained with minimum errors. Next
these data are used to execute the path planning and human
tracking algorithm. A modified virtual spring is supposed
to be connected within the target person and the robot. The
desired velocities of the left and right wheel will be obtained
through the human tracking algorithm and be transmitted to
the STM32F103 MCU through the UART between them.
The close-loop control algorithm is implemented within
the STM32F103 MCU to control the two wheels to rotate
in the desired velocities instructed by the A33 SOC. In this
way, the robot tries to move toward the target person con-
tinuously until it is within a desired distance between itself
and the person. The motor drives are H-bridges which are
composed of two half bridges.
The Bluetooth module which is connected with the
STM32F103 MCU through a UART can receive the instruc-
tions from the user by a smart phone which include the
desired distance, start/stop command etc. Four ultrasound
modules for distance estimation and obstacle avoidance are
also connected to the STM32F103 MCU through another
UART.
Other hardware components include a switching power
module and several LDO linear regulators which convert
the battery power into the desired voltages to power the
A33 SOC, the STM32 MCU and other sensors and modules
within the system. A debug port is also included for the
data communication and monitoring during the debugging
process. A JTAG port is used to download the firmware into
the STM32F103 MCU, and some spare ports are reserved
for further functions and modules such as GPS, GPRS, gyro-
scope module, etc. Besides, there is a type-A USB host port
with its corresponding power management circuit used for
powering the UWB anchors and receiving the distance data
from them.
B. THE FLOW CHART OF THE SYSTEM
In this paper, the UWB technology is used to detect and
track the target person. Because of the measurement errors
and additional noises caused by the real-world environments,
the UWB data must be calibrated and filtered before position
and orientation calculation.
Specially, a modified hyperbolic positioning algorithm
(see more detailed description in Section III.C) is used in this
paper to obtain the position and orientation of the target per-
son, which overcomes the difficulty in the position calcula-
tion when using traditional hyperbolic positioning algorithm.
Moreover, the moving-average filter is used and implemented
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for data filtering. Then if the robot is within ±45◦ toward the
target person, the control algorithm is implemented to track
and follow the target person. Otherwise, the robot stops mov-
ing forward and keeps rotating until the target person is within
±45◦ in front of the robot again. In this way, the robot can
track and follow the target person continuously and smoothly.
The flow chart is shown in Figure 4.
FIGURE 4. The flowchart of the robot system. The robot continuously
samples the UWB data by calibration and filtering, in order to estimate
the position and orientation of the target person. Then it takes a policy
that if the robot is in a close direction to the target, then it follows the
target by autonomous moving. Otherwise, it starts to rotate to search for
the lost target until it appears again.
III. UWB DATA PROCESS
A. THE LAYOUT OF THE UWB ANCHORS
There are three UWB anchors in our system, namely A0,
A1, A2. As illustrated in Figure 5, the UWB anchors are
mounted on top of the robot, and the UWB tag is carried by
the target person. When the target person is moving, the UWB
anchors will get the distance from themselves to the tag, and
then the A33 chip will calculate the position and orientation
of the target person after data calibration and filtering. The
path planning and tracking algorithm is then executed to
FIGURE 5. The location layout of the UWB anchors which forms a triangle
shape. The three anchors are installed on the robot to localize the tag on
the tracked person.
control the robot to move toward the target person and keep
a constant distance with the person.
B. THE CALIBRATION OF THE UWB DATA
There are great errors within the distances between UWB
anchors and tag measured by the modules due to the multipath
effect and other reasons. As a result, it is necessary to calibrate
the distance data from the UWB anchors. To calibrate the
data, the tag is put on a tripod and located on some fixed
points from 0.3m to 2m away from anchor A0, with a gap
of 10cm between these points, as shown in Figure 6.
FIGURE 6. The location layout of the UWB anchors which forms a triangle
shape. The three anchors are installed on the robot to localize the tag on
the tracked person.
Then the distance data from the anchors and the tag are
obtained. The actual distances are also measured by a ruler,
and the data from UWB anchors are calibrated according
to the actual distances measured by the ruler, as shown
in Figure 7 to Figure 9.
FIGURE 7. Calibration result of the anchor A0. The calibrated data can
well obey the linear relation to ground truth measured by the ruler.
C. THE CALCULATION OF THE TAG POSITION
Typically, under an ideal error-free condition, the tag position
can be effectively located by hyperbolic positioning algo-
rithm [24], [25], as illustrated in Figure 10. The anchors
are regarded as located in the centers of three circles whose
42544 VOLUME 6, 2018

FIGURE 10. Typical hyperbolic positioning algorithm. After obtaining the
distance between the tag and A0/A1/A2 three anchors, the circles are
drawn whose centers are the three anchors, and the radius are the
distance between the three anchors and tag. Ideally if there is no
measurement error, the three circles will be intersected on a single point,
which is in fact the location of the tag.
radiuses are the distance from the anchor to the tag. Theo-
retically, the intersection of the circles is the location of the
mobile tag.
However, due to the inevitable measurement errors of the
distances from anchors to the tag, the three circles would
probably not intersect at one point, which could certainly
results into a great challenge to get the correct location of the
mobile tag. In this system, the typical algorithm is modiﬁed
by removing one circle around A2 and searching for the two
intersection points of the remaining two circles, explained as
Figure 11.
FIGURE 11. Modified hyperbolic positioning algorithm. The circles are
drawn whose centers are A0/A1 anchors. The two circles will be
intersected whereby the tag is located. To determine the exacted tag
location from the two candidates T and T , one need to calculate the
distance between the candidate locations and the anchor A2, namely TA2
and T A2. By comparing TA2 and T A2 with the actual distance between
A2 and the tag, the closer one will be selected and the tag location can be
the corresponding candidate.
Brieﬂy speaking, because there are only two circles whose
centers are located at A0 and A1 respectively, there must be
two intersection points of these two circles, and the mobile
tag must be located on one of the two point, namely T and T .
To determine which one is the point where the tag is actually
located, the distances from A2 to T and T are calculated
and compared with the distance data measured from the
anchor A2. The smaller one is the point that is being looked
for. The details are explained as follow.
As illustrated in Figure 12, the x axis is along the line
of A1 and A0, and the y axis is perpendicular to x axis and
corresponds to the forward direction of the robot. The origin
is located in the middle of A0 and A1. While the length from
A1 to A0 has been measured in advance, the distances from
T to A0 and A1 are obtained from anchors in real time. As a
result, the angle φ can be obtained using cosine theorem as
follow:
cos φ =
TA2
0 + A0A2
1 − TA2
1
2TA0 ∗ A0A1
(1)
The equation above yields:
sin φ = ± 1 − cos2φ (2)
VOLUME 6, 2018 42545

FIGURE 12. The calculation of the tag’s position based on the three
anchors A0, A1, A2. Two candidate location T and T are first found then
the final location can be determined from these two candidates.
Thus, the coordinates of the two intersection points T and
T can be calculated by the following formula:
[xT yT ]T
= [xA0 − TA0cosφ TA0sinφ]T
= [xA0 − TA0cosφ TA0 1 − cos2φ]T
(3)
[xT yT ]T
= [xA0 − TA0cosφ TA0sinφ]T
= [xA0 − TA0cosφ − TA0 1 − cos2φ]T
(4)
Then the distances from these two point to anchor A2 are:
DT = (xA0−TA0cosφ−xA2)2+(TA0 1−cos2φ−yA2)2
(5)
DT = (xA0−TA0cosφ−xA2)2+(−TA0 1−cos2φ−yA2)2
(6)
Then DT and DT are compared with the distance from
A2 to T measured by the anchor. The smaller one is the correct
point where the mobile tag is located.
D. THE MOVING-AVERAGE FILTER OF THE UWB DATA
When calculating the location of the mobile tag, it is notice-
able that the coordinate of the tag changes a lot even if the
tag keeps stationary. When the tag is being moved around the
robot while the robot keeps still, the calculated path of the
moving person is illustrated in Figure 13.
As shown in Figure 13, the path is not smooth and changes
dramatically in some portion, which does not match the actual
path. That is because of the measurement error of the dis-
tances obtained from the UWB anchors. To make such change
less, the moving-average filter shown in Figure 14 is adopted
in this system. The data from UWB modules are feed into
this filter continuously and filtered data are outputted for the
control algorithm afterward.
After the data have been filtered, the path becomes much
smoother without a lot of dramatically changes. An example
is shown in Figure 15.
FIGURE 13. The moving path before filtering is performed ed. The path
curves are cluttered with many fluctuations and noises.
FIGURE 14. The moving path before filtering is performed. The path
curves are cluttered with many fluctuations and noises.
FIGURE 15. The moving path after filtering is performed by
moving-average smoothing. The curves become more regular.
IV. DYNAMIC MODEL OF THE ROBOT
To develop the tracking algorithm, it is necessary to develop
the dynamic model of the system that connects the system’s
behavior to its inputs. The construction of the robot is shown
42546 VOLUME 6, 2018

FIGURE 16. The schematic figure of the Hilare-type robot. The robot is
driven by two independent wheels.
in Figure 16. Such type of robot, namely hilare-type robot,
has two wheels which are driven by two motors indepen-
dently. As illustrated in Figure 16, the position of the robot
in the coordinate system x1 − x2 is globally represented by
the coordinates of point (x1, x2), while the orientation of the
robot is defined by the angle θ between the positive x1 axis
and the line that is perpendicular to the axis connecting the
two wheels. In the meanwhile, it is assumed that the body
coordinate system x − y of the robot is fixed on the middle
point of the axis of the two wheels. The center of gravity
of the robot is considered to be on the point illustrated in
Figure 16 [26]. The distance between the two wheels is T.
FIGURE 17. The free body diagrams of the left wheel and right wheel.
Figure 17 shows the free body diagrams of the two wheels
respectively. From the construction of the robot and the free
body diagrams shown in Figure 16 and 17, the dynamic
equations of motion are derived as follow:
mr + 2mw +
2
r2
Mw2 ÿ − mr c ˙θ2
=
1
r
τr + τl)
(7)
Mr + 2Mw + mw
T2
2
+ Mw2
T2
2r2
+ mr c ¨θ =
T
2r
(τr − τl)
(8)
where:
τr and τl: the driving torques of the right and the left wheel.
mr : the mass of the body of the robot.
mw: the mass of the wheels.
Mr : the moment of inertia of the rotating masses of the
robot body.
Mw: the moment of inertia of the rotating masses of the
wheels.
The system’s behavior is described by the state space
variables. In this system, the following variables have been
chosen [26]:
q =






q1
q2
q3
q4
q5






=






x1
x2
θ
˙y
˙θ






(9)
From (7) and (8) the equations of motion of the robot can be
written in the first-order form as follow [26]:
˙q =






˙q1
˙q2
˙q3
˙q4
˙q5






=







q4 cos q3
q4 sin q3
q5
1
m
1
r (τr + τl) + mr cq2
5
T
2M
1
r (τr − τl)







(10)
where
m = mr + 2 mw +
1
r2
Mw2 , (11)
M = Mr + 2Mw + mw +
1
r2
Mw2
T2
2
+ mr c. (12)
V. THE HUMAN TRACKING ALGORITHM
After the average-moving filter algorithm, the target person
position and orientation have been obtained with minimum
errors. Next these data are used to perform path planning
of the robot and the following and tracking function. In this
paper, a modified virtual spring model is proposed as follow.
A virtual spring is assumed to be connected between the
robot and the target person, as shown in Figure 18.
FIGURE 18. The virtual spring model. There is a virtual spring connecting
between the target person and the robot. The control is derived from two
factors. The first is along the direction of the spring, and when the
distance between person and robot is longer there will be a stronger force
to pull back. The second is the angle between the robot and the person,
which drives the robot to close this angle gap using feedback control.
The input (e, α) of the virtual spring model are obtained
from the average-moving filter algorithm, where e represents
the error between the detected actual distance dact which is
from the robot to the target person and the reference distance
dref , and α is the angle between the orientation of the robot
VOLUME 6, 2018 42547

and the virtual spring. vl and ω are the desired linear and
rotating velocity respectively which are defined as follows:
vl = kl
pe + kl
i edt + kl
d e (13)
ω = kr
pα + kr
i αdt + kr
d α (14)
vl and ω are then decoupled into the rotating velocities of
the two wheels ωl and ωr as follow:
ωr
ωl
=
1
r
T
2r
1
r − T
2r
vl
ω
(15)
The whole diagram of the tracking algorithm is illustrated
in Figure 19.
FIGURE 19. The diagram of the tracking algorithm.
When the modified virtual spring model is implemented,
there are some problems specific to the differential driven
mobile robot which should be taken into consideration.
According to Brockett’s theorem, it is impossible to control
a nonholonomic robot to move to one point of the state space
asymptotically using smooth state feedback [27]. Besides,
when the virtual spring is along the x axis, it is not possible
for the robot to move according to the control law above.
As a result, in our implementation, when the absolute value
of angle α is within 45◦ to 90◦, the control law stops working
and then the robot stops moving ahead and begins rotating
toward the target person until the angle α is less than 45◦.
After that, the control law takes effect again and the human
tracking and following is fulfilled.
VI. EXPERIMENTAL RESULTS AND DISCUSSION
Some experimental data has been acquired and illustrated
in Figure 20, Figure 21 and Figure 22, which present three
moving routes of the target person and the tracking paths of
the robot.
In Figure 20, the target person keeps still and the robot
gets the coordinates of the person, and moves forward to
the person until it gets close enough to him and stops there,
during which the distance between them keeps decreasing as
shown in the figure. The initial coordinates of the person are
(1289, 2955)(mm), while the end coordinates of the person
are (−22, 273)(mm).
In Figure 21, the target person keeps walking forward
before the robot, and the robot is trying to follow the person
continuously. The curve in Figure 21 shows how the coor-
dinates of the target person change in the whole following
process. In the beginning, the person is located at the point A
with the coordinates (47, 1047)(mm). When the person starts
to walk the robot starts to move as well and tries to keep itself
within the desired distance from the target person. From point
A to B, the target person is accelerating, and the distance
from the person increases, and the robot begins to accelerate
as well. From point B to C, the robot moves faster than the
person, and as a result, the distance between the robot and
the target person decreases continuously. Finally, the person
stops at the point C with the coordinates of (188, 1267)(mm),
and the robot stops moving, too.
In Figure 22, the target person walks around in a square
route. The person starts walking forward at the point A with
the coordinates of (−115, 818)(mm) and the robot starts to
42548 VOLUME 6, 2018

follow the person immediately. At point B, the person turns
left to the point C, and the robot starts to turn left as well until
the person is located at the point D. Then the person turns
left again until he goes to the point E, and the robot also turn
left and continues to follow the person until he arrives at the
point F and stops there.
As shown in the figures, the robot starts moving when it
detects the target person carrying the tag and moves toward
the person until the distance decreaces below the desired
value, which illustrates our control law.
VII. CONCLUSION AND FURTHER DISCUSSION
In this paper, a method is proposed for real-time tracking and
following of the human with UWB technology. Illustrated
by the experimental results, it can obtain the distance of the
target person accurately by using the UWB anchors and the
tag. The challenge of the measurement errors is overcome by
the modified hyperbolic positioning algorithm and moving-
average filter, and the modified virtual spring model improves
the control performance of human-tracking, which is also
shown by the experimental data.
In the future work, there are two immediate directions
for further research. The first is to improve the autonomous
movement controlling algorithms. In fact, robot’s physical
tracking capability enabled by the underlying movement con-
trol model is closely related to the tracking capability. This is
because in many cases the robots in the beginning can localize
well the target’s position, however due to the limitation of its
movement ability it may lose track of the target and the target
is out the scope of the sensors. We note a relevant controlling
algorithm in [28] where movement in both straight lines and
curves can be performed by automatic control. Second, vision
based techniques could be infused to our current system to
improve its robustness, especially the deep network based
models for motion estimation [29] and object detection [30].
Meanwhile, those less heavy and more efficient object iden-
tity agnostic models e.g. the saliency methods [31] will also
be studied.
REFERENCES
[1] M. Gupta, S. Kumar, L. Behera, and V. K. Subramanian, ‘‘A novel
vision-based tracking algorithm for a human-following mobile robot,’’
IEEE Trans. Syst., Man, Cybern., Syst., vol. 47, no. 7, pp. 1415–1427,
Jul. 2017.
[2] X. Ma, C. Hu, X. Dai, and K. Qian, ‘‘Sensor integration for person tracking
and following with mobile robot,’’ in Proc. IEEE/RSJ Int. Conf. Intell.
Robots Syst., Sep. 2008, pp. 3254–3259.
[3] N. Hirose, R. Tajima, and K. Sukigara, ‘‘Personal robot assisting trans-
portation to support active human life—Human-following method based
on model predictive control for adjacency without collision,’’ in Proc. IEEE
Int. Conf. Mechatronics, Sep./Oct. 2015, pp. 76–81.
[4] J. Zhang, J. Wang, and W. Chen, ‘‘A control system of driver assistance and
human following for smart wheelchair,’’ in Proc. IEEE Int. Conf. Robot.
Biomimetics, Dec. 2015, pp. 1927–1932.
[5] N. Kejriwal, S. Garg, and S. Kumar, ‘‘Product counting using images with
application to robot-based retail stock assessment,’’ in Proc. IEEE Int.
Conf. Technol. Pract. Robot Appl., May 2015, pp. 1–6.
[6] S.-O. Lee, M. Hwang-Bo, B.-J. You, S.-R. Oh, and Y.-J. Cho, ‘‘Vision
based mobile robot control for target tracking,’’ IFAC Proc. Vol., vol. 34,
no. 4, pp. 75–80, 2001.
[7] R. C. Luo, N.-W. Chang, S.-C. Lin, and S.-C. Wu, ‘‘Human tracking
and following using sensor fusion approach for mobile assistive com-
panion robot,’’ in Proc. Conf. IEEE Ind. Electron. (IECON), Nov. 2010,
pp. 2235–2240.
[8] S. Y. Lee, K. Y. Lee, S. H. Lee, J. W. Kim, and C. S. Han, ‘‘Human-robot
cooperation control for installing heavy construction materials,’’ Auto.
Robots, vol. 22, no. 3, pp. 305–319, 2007.
[9] Y. Nagumo and A. Ohya, ‘‘Human following behavior of an autonomous
mobile robot using light-emitting device,’’ in Proc. IEEE Int. Workshop
Robot Human Interact. Commun., Sep. 2001, pp. 225–230.
[10] K. Morioka, J.-H. Lee, and H. Hashimoto, ‘‘Human-following mobile
robot in a distributed intelligent sensor network,’’ IEEE Trans. Ind. Elec-
tron., vol. 51, no. 1, pp. 229–237, Feb. 2004.
[11] F. Hoshino and K. Morioka, ‘‘Human following robot based on control of
particle distribution with integrated range sensors,’’ in Proc. IEEE/SICE
Int. Symp. Syst. Integr., Dec. 2011, pp. 212–217.
[12] K. Morioka, Y. Oinaga, and Y. Nakamura, ‘‘Control of human-following
robot based on cooperative positioning with an intelligent space,’’ Electron.
Commun. Japan, vol. 95, no. 1, pp. 20–30, 2015.
[13] A. Fod, A. Howard, and M. A. J. Mataric, ‘‘A laser-based people
tracker,’’ in Proc. IEEE Int. Conf. Robot. Automat. (ICRA), May 2002,
pp. 3024–3029.
[14] W. Dai, A. Cuhadar, and P. X. Liu, ‘‘Robot tracking using vision and
laser sensors,’’ in Proc. IEEE Int. Conf. Automat. Sci. Eng., Aug. 2008,
pp. 169–174.
[15] A. Scheidig, S. Mueller, C. Martin, and H.-M. Gross, ‘‘Generating persons
movement trajectories on a mobile robot,’’ in Proc. IEEE Int. Symp. Robot
Human Interact. Commun., Sep. 2006, pp. 747–752.
[16] J.-S. Hu, J.-J. Wang, and D. M. Ho, ‘‘Design of sensing system and
anticipative behavior for human following of mobile robots,’’ IEEE Trans.
Ind. Electron., vol. 61, no. 4, pp. 1916–1927, Apr. 2014.
[17] J. Han and T. Jin, ‘‘Sound source based mobile robot control for human
following in a networked intelligent space,’’ Int. J. Control Automat., vol. 8,
no. 7, pp. 67–74, 2015.
[18] N. Dalal and B. Triggs, ‘‘Histograms of oriented gradients for human detec-
tion,’’ in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit.,
Jun. 2005, pp. 886–893.
[19] A. A. Mekonnen, C. Briand, F. Lerasle, and A. Herbulot, ‘‘Fast HOG based
person detection devoted to a mobile robot with a spherical camera,’’ in
Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., Nov. 2013, pp. 631–637.
[20] W. E. Dixon, D. M. Dawson, E. Zergeroglu, and A. Behal, ‘‘Adaptive
tracking control of a wheeled mobile robot via an uncalibrated camera
system,’’ IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 31, no. 3,
pp. 341–352, Jun. 2001.
[21] J.-H. Jean and F.-L. Lian, ‘‘Robust visual servo control of a mobile robot
for object tracking using shape parameters,’’ IEEE Trans. Control Syst.
Technol., vol. 20, no. 6, pp. 1461–1472, Nov. 2012.
[22] Y. Fang, X. Liu, and X. Zhang, ‘‘Adaptive active visual servoing of
nonholonomic mobile robots,’’ IEEE Trans. Ind. Electron., vol. 59, no. 1,
pp. 486–497, Jan. 2012.
[23] K. Wang, Y. Liu, and L. Li, ‘‘Visual servoing trajectory tracking of
nonholonomic mobile robots without direct position measurement,’’ IEEE
Trans. Robot., vol. 30, no. 4, pp. 1026–1035, Aug. 2014.
[24] L. Huan and R. Bo, ‘‘Wireless location for indoor based on UWB,’’ in Proc.
34th Chin. Control Conf., Jul. 2015, pp. 6430–6433.
[25] G. Feng, C. Shen, C. Long, and F. Dong, ‘‘GDOP index in UWB indoor
location system experiment,’’ in Proc. Sensors, Nov. 2015, pp. 1–4.
[26] F. Fahimi, Autonomous Robots: Modeling, Path Planning, and Control.
New York, NY, USA: Springer, 2009.
[27] Y. Nakamura, ‘‘Nonholonomic robot system part 2: Motion planning under
kinematic nonholonomic constraints,’’ J. Robot. Soc. Jpn., vol. 11, no. 5,
pp. 655–662, 1993.
[28] J. Yan, ‘‘Improving mobile robot localization: Grid-based approach,’’ Opt.
Eng., vol. 51, no. 2, p. 4401, 2012.
[29] Z. Ren, J. Yan, B. Ni, B. Liu, X. Yang, and H. Zha, ‘‘Unsupervised deep
learning for optical flow estimation,’’ in Proc. AAAI Conf. Artif. Intell.,
2017, pp. 1495–1501.
[30] R. Girshick, J. Donahue, T. Darrell, and J. Malik, ‘‘Rich feature hierarchies
for accurate object detection and semantic segmentation,’’ in Proc. IEEE
Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2014, pp. 580–587.
[31] J. Yan, M. Zhu, H. Liu, and Y. Liu, ‘‘Visual saliency detection via spar-
sity pursuit,’’ IEEE Signal Process. Lett., vol. 17, no. 8, pp. 739–742,
Aug. 2010.
VOLUME 6, 2018 42549

TAO FENG received the M.S. degree in sys-
tem engineering from the Information Engineer-
ing School, University of Science and Technology
Beijing, Beijing, China, in 2007, where he is cur-
rently pursuing the Ph.D. degree with the School of
Automation and Electrical Engineering. He is also
a mid-level Engineer with the School of Automa-
tion and Electrical Engineering, University of Sci-
ence and Technology Beijing. His current research
interests include mobile robot control. His team
received the Second Place of the Third National Robot Contest (ROBOCON)
held by CCTV in China in 2004. He was awarded the Excellent Young
Engineers of Beijing by the Beijing Government in 2012.
YAO YU received the B.Eng. degree from the
Department of Control Science and Engineering,
Huazhong University of Science and Technology,
in 2004, and the M.S. and Ph.D. degrees from the
Department of Automation, Tsinghua University,
in 2010. She held a post-doctoral position with
Tsinghua University. She is currently an Associate
Professor with the School of Automation and
Electrical Engineering, University of Science and
Technology Beijing. Her current research interests
include nonlinear control, robust control, and time-delay systems.
LIN WU received the master’s degree from
Beihang University in 2008. He is currently pur-
suing the Ph.D. degree with the University of
Science and Technology Beijing. His current
research interests include motor driver and control
simulation.
YANRU BAI received the master’s degree in
control theory and control engineering from the
University of Science and Technology Beijing,
Beijing, China, in 2008. She is currently a mid-
level Engineer with the School of Advanced Engi-
neering, University of Science and Technology
Beijing. Her current research interests include
integrated navigation, neural networks-based con-
trol, wireless sensor networks, and their industrial
applications.
ZIANG XIAO is currently pursuing the bachelor’s
degree with the Department of Instrument Sci-
ence and Technology, University of Science and
Technology Beijing. He is an enthusiastic Young
Scholar. He possesses an intensive interest in the
domains of electronics engineering and robotics,
and determines to pursue his academic career in
relevant professions.
ZHEN LU received the master’s degree from the
University of Science and Technology Beijing
in 2008. He is a Senior Engineer with CS&S
Information System Engineering Co., Ltd. His cur-
rent research interests include information sys-
tem, modeling and simulation, and the Internet of
things.
42550 VOLUME 6, 2018

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