IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV - 8

Monocular Visual Autonomous Landing System for Quadcopter Drones Using Software in the Loop
commands and to let the inner controllers ofthe flight controller
unit (FCU) handle the thrust.
We use a reference vector for the controllers Sp ¼
2 ; Ih
½Iw
2 ; Ih
2 ; 0T,where Iw
2 represent the center of the current
image frame and zero corresponds to the desired
angle between the aircraft and template measured with
respect to the x-axis of the image. These controllers command
the x; y velocities of the rotorcraft, denoted as
x_a; y_a,
to center the vehicle with respect to the template detected in
the current image frame. The third controller modifies the
yaw rate
c_ of the aircraft in order to align it with the landing
padinthe x-axis. The error vector et 2 R3 ofthe controllers
at time t is given by the following equation:
et ¼ SptXði¼1:3Þ
t
:
(16)
Figure 5 is a simplified representation of the three PIDbased
controllers and an altitude controller with an ON/OFF
strategy to control the descent ofthe UAV. The error vector e
is used to feed the first three controllers and to produce a control
effort Ut 2 R3, which is delivered to the cascade controllers
of the FCU. The output of the three PID controllers is
provided by the vector in (17). Each PID controller was discretized
using trapezoidal integration and derivation
Ut ¼½ _xa; y_a; c_T:
(17)
The ON/OFF altitude controller in Figure 5 starts to land
the aircraft whenever the difference between the height
and width of the template estimates Xði¼4:5Þ
(18). uzt
t
tends to zero
represents the output of the altitude controller,
Zp is the current height in meters of the vehicle, and Zf is
a descent constant. This descent condition guarantees that
the aircraft will land only if the template dimensions form
a square, which is the actual shape of the landing platform
uzt
¼
ZpZf;
Zp;
if jOwOhj < 5
otherwise:
(18)
Acquiring feedback from the vision-based module
closes the visual servoing control loop and allows for the
implementation ofan on-board end-to-end control strategy
for a UAV. Algorithm 2 shows pseudocode for the controller
pipeline for the rotorcraft.
SIMULATION RESULTS
This section describes the experiments carried out to
assess the different modules of the autonomous landing
system using a Gazebo-based simulation. We provide an
open-source implementation ofour system in Github.2
2https://github.com/MikeS96/autonomous_landing_uav
8
Algorithm 2. Landing Controller
1: Inputs:
X 2 R10 State vector
Iw image Width
Ih image Height
2: Initialization:PIDxa 2 R3 PID parameters for x_a
PIDya 2 R3 PID parameters for y_a
PIDc 2 R3 PID parameters for c_
Sp 2 R3 x; y; and u setpoints
Zp initial height of the vehicle
Zf descent factor
3: Outputs:
U 2 R3 PID control efforts
uz ON/OFF altitude controller output
4: for each state vector X do
5:
6:
et SptXði¼1:3Þ
errorSize absðXði¼4Þ
https://github.com/MikeS96/autonomous_landing_uav
IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV

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