2509978-1-7281-1312-8/18/$31.00 ©2018 IEEE
Magnetic Locating AGV Navigation Based on Kalman Filter and PID Control
BoYang Xu
Qingdao University
The college of Automation and Electrical
Engineering
Qingdao, China
772406555@qq.com
Dongqing Wang
Qingdao University
The college of Automation and Electrical
Engineering
Qingdao, China
dqwang64@163.com
Abstract—In order to achieve precise navigation of AGV
(Automatic Guided Vehicle), In this paper, we proposes a AGV
navigation with magnetic nail positioning method based on
Kalman filtering (KF) and PID (Proportion-integral-derivative)
control. The magnetic nail is placed in the area where the AGV
travels, and the current pose information of the AGV is obtained
according to the magnetic sensor, and compared with the
predetermined path to obtain the current heading deviation. As
the input of the PID controller, the position adjustment amount
of the AGV is taken as the output. At the same time, the Kalman
filtering (KF) algorithm is used to estimate the state with noise
interference, which further improves the positioning accuracy of
AGV, and simulation is carried out by Matlab software. The
simulation results show that the proposed Kalman filtering and
PID control are effective for magnetic nail positioning AGV
navigation. This research has broad application prospects.
Keywords— Kalman filter; magnetic nail positioning; AGV
navigation; PID
I. INTRODUCTION
With the advancement of industrial logistics
technology, AGV has become more and more widely used
in industrial manufacturing and warehousing scenarios. In
order to make the AGV adapt to different working scenarios,
the research of AGV navigation is particularly important. In
order to solve the problem of positioning accuracy of the
automatic guided vehicle [2, 3], Quan Yuan uses fuzzy
controller for direction control, enabling the vehicle to run
stably and react quickly [4]. Kun Fan uses fuzzy PID control
to correct the position deviation and direction deviation [5].
Almir describes optical sensors and image processing
techniques and uses PID controllers to increase the
flexibility of the car [6]. To solve this problem of
positioning, mapping and navigation of cars in modern
warehouses, Yunxia Chen proposed an extended Kalman
filter (EKF) to enable the car to automatically locate itself
and accurately track the path during driving [7]. In response
to the precise problem of transporting cargo containers by
unmanned vehicles in the harbor environment, H S Kim
proposes a method of extending the Kalman filter [8]. Based
on this, this paper proposes a magnetic nail positioning
AGV navigation method based on Kalman filtering and PID
control. The method is to install a DC motor on each side of
the wheel, the incremental encoder is installed at the end of
the two DC motors, and the controller controls the rotation
of the electric wheel through the feedback of the encoder
and realize the speed and position of the trolley. In addition,
through the Kalman filter (KF) to reduce the noise
interference generated by the environment, improve the
accuracy and accuracy of positioning. By using PID control,
the AGV system can quickly simulate the errors that occur
during driving and travel precisely according to the planned
path. This study is of great significance for the path tracking
of AGV.
II. K
INEMATIC ANALYSIS OF AGV
The magnetic navigation AGV motion model is shown in
Figure 1, there are two universal wheels in the front of the
model and two driving wheels at the rear. Through two drive
wheel Drive the vehicles.
X
y
o
L
V
R
V
Fig. 1. Magnetic navigation AGV motion model
In Figure 1, consider the center of the two drive wheels
on the axle as the center of the vehicle in the world
coordinate system,
L
V
and
R
V
are the linear speeds of the left
and right wheels,
T
is the heading angle of the vehicle [8],
and D is the driving wheel diameter, L is the distance
between the centers of the two drive wheels. According to
the motion mechanism of the AGV, the instantaneous linear
velocity
V
of the vehicle is calculated by the average value
of
L
V
and
R
V
[9, 10], which is
()/2LRVVV
˄1˅
The angular velocity is
Z
and the distance between the
centers of the two drive wheels is
[9][10]
=)/LRVV L
Z
˄
˄2˅
The heading angle is
0
0
=+
t
t
dt
TT Z
³
˄3˅
Let the speeds in the
x
and
y
directions be
Vx
and
Vy
,
cosxVV
T
,
sin
y
VV
T
. Then at time
t
, the
location of the vehicle is
(, )
tt
xy
, it can be calculated by
0
0
0
0
t
tx
t
ty
xx vdt
yy vdt
°
®
°
¯
³
³
˄4˅
Let the sampling period be T and the displacement
increment be
^
cos
sin
xvT
yvT
T
T
'
'
˄5˅
The vehicle state at time K is defined as
as
T
kkkkxxy
T
ªº
¬¼
, and at
(1)k
time,
>@
1-1 1 1
T
kkkk
xxy
T
.
k
x
can be calculated using