Experimental Study on Geo-Pointing Control System for ...Experimental Study on Geo-Pointing Control System for Quadcopter . Do yoon Kim. 1, Sung Kyung Hong * 1. Future Aerospace Technology

Experimental Study on Geo-Pointing Control System

for Quadcopter

Do yoon Kim1 , Sung Kyung Hong*

1

Future Aerospace Technology Team, Korea Aerospace Research Institute(KARI), Daejeon,

Korea ([email protected])

*Department of Aerospace Engineering, Sejong University, Seoul, Korea

([email protected]), Corresponding Author

Abstract. The This paper presents a novel Geo-Pointing algorithm consists of

three phases; 1) estimating coordinates with line of sight(LOS) from the

quadcopter to the target objects, and then 2) tracking the stationary objects for

orienting the line of sight toward the object (Point of Interest, POI), and 3) the

quadcopter automatically around an object (Circle of Interest, COI). With

application of these functions that enables the desired objects always on screen,

all that is required is to fly above the object and mark it in flight via Ground

Control System (GCS). The performance and its usefulness are verified through

flight experiments.

Keywords: Quadcopter, Camera Line of Sight (LOS), Geo-Pointing, Geo-

Location, Circle of Interest (COI), Point of Interest (POI),

1 Introduction

Following the appearance of inertial measurement units with MEMS technologies and

high efficiency small batteries included along with the striking development of

avionics-related technologies for unmanned aerial vehicles (UAV), UAVs have been

rapidly miniaturized and popularized. Among them, quadcopter platforms have

recently been in the limelight, because they are structurally simple yet highly

stable.[1-3] In the present study, geo-pointing algorithms that combine a quadcopter

with a gimbal system to link missions such as reconnaissance, surveillance, and

searches with autonomous flight are proposed. The proposed algorithm consists of

three stages: 1) estimating the target coordinates through the line of sight (LOS)

pointing angle from the quadcopter to the target object(geo-location); 2) tracking the

stationary target by updating that the LOS pointing angle always points at the

target(POI); and 3) perform automatic flight along the circle of interest (COI)

centered on the target. That is, when operating a quadcopter, the controller is not

required to control camera firsthand to obtain continuous and stereoscopic image

information for the designated target but is just required to set the target

Advanced Science and Technology Letters Vol.118 (Electrical and Electronic Engineering 2015), pp.14-20

http://dx.doi.org/10.14257/astl.2015.118.04

ISSN: 2287-1233 ASTL Copyright © 2015 SERSC

Fig. 1. Configuration of Experimental Setup

2 Configuration of flight system

For experimental verification of the quadcopter geo-pointing algorithm, not only a

quadcopter platform but also a controller to drive a gimbal for pointing at the POI and

linking the gimbal with autonomous flight is necessary. A conceptual diagram of the

entire system configured as such is shown in Figure 1, and the details of individual

components are as follows.

2.1 Quadcopter platform

The quadcopter platform used in the present study was a Pelican model from

Ascending Technology Co. in Germany, made for research [4]. The main processor

for flight control was composed of two processors, a low-level processor (LLP) and a

high-level processor (HLP). Meanwhile, to design the outer control loops (geo-

pointing control in the present study) to be installed on the HLP, a mathematical

model that integrates the platform as such and the inner control loops of the LLP is

required. The inner control loops of individual axes are composed of roll ( ), pitch

( ), and yaw angular velocity ( ) control loops. To identify the mathematics for these

control loops, a linear black box model was assumed, and a prediction error method

algorithm [5] was used.

2.2 Camera gimbal system

The camera gimbal installed on the quadcopter was a direct BLDC motor-driven

gimbal with two small axes (roll/pitch angles), installed with a. It is embedded with

its own IMU to implement a basic image-stabilizing algorithm that compensates for

flight vehicles’ dynamic states, and it is driven by roll and pitch angle commands

from the HLP of the quadcopter, installed with a geo-pointing algorithm to move the

LOS vector to target

Advanced Science and Technology Letters Vol.118 (Electrical and Electronic Engineering 2015)

Copyright © 2015 SERSC 15

Figure. 2. Relative vector of target with respect to quadcopter in ENU frame coordinates

3 Geo-Pointing control system of Quadcopter

3.1 Estimation of the target coordinate(geo-location)

The estimation of the target coordinate (geo-location) corresponds to the first stage

in the geo-pointing system. As can be seen in Figure 2, the target position vector ( ) in the ENU coordinate system can be defined as in equation (1) using the quadcopter

position vector ( ) received from the GPS and its relative vector ( )

= + (1)

Here, the relative vector ( ) is produced as follows through the geometric

relationship between the quadcopter height ( ) and the LOS pointing angle

(quadcopter yaw angle : , gimbal pitch angle: ) toward the target

Here, the relative distance (R) is defined as =

3.2 LOS pointing angle control for POI mode

The POI mode is aimed to continuously point at the fixed target [equation (2)]

extracted independently from the movement of the flight vehicle, as described in

section 3.1 of this chapter. To this end, the LOS pointing angles (quadcopter yaw

angle : , gimbal pitch angle: ) intended to position the LOS vector point at the

fixed target based on the moving quadcopter positions are automatically generated

according to the sampling period, and the system is controlled to follow these LOS

pointing angles

= [ 𝑠𝑖𝑛 𝑐𝑜𝑠 ] (2)


16 Copyright © 2015 SERSC

1) LOS pointing angle update

To update the LOS pointing angles to those that compensate for quadcopter

maneuvering, the relative position coordinate ( 𝑖) of the fixed target ( ), whose

origin point is the changed position ( 𝑖) of the quadcopter obtained from GPS

information according to the sampling period, should be renewed first. That is, the

coordinate ( ) of the target obtained at the time of lock-on, based on equation (1), is

fixed as . At this time, the new relative position coordinate ( 𝑖) is updated

through equation (3).

𝑖 = − 𝑖 (3)

Through the component ( 𝐸𝑖 , 𝑁𝑖 , 𝑈𝑖) of the renewed relative position vector, the

LOS pointing angle is renewed to the compensated LOS pointing angle (quadcopter

yaw angle : , gimbal pitch angle: ) as follows.

= 𝑎𝑡𝑎𝑛2 𝐸𝑖 , 𝑁𝑖 , = 𝑎𝑡𝑎𝑛2 𝑈𝑖 , 𝑅𝑖

(4)

Where, 𝑅𝑖 = √ 𝐸𝑖2 + 𝑁𝑖

2 .

2) LOS pointing angle control loop

Since the direction axes of the Pelican platform from Ascending Technology Co. used

in the present study are composed only of the LLP angular velocity control loops it

was necessary to design an yaw angle control loop to follow the quadcopter yaw

angle ( ) of the LOS pointing angles updated, as mentioned above. A proportion-

differential (PD) controller that used the yaw angle angular velocity control loop

derived earlier as an inner loop was implemented and installed on the HLP,

3.3 COI control mode

The circle of interest control mode refers to the automatic flight along a circular

trajectory around the target with a certain radius ( ), maintaining a certain speed ( )

In this case, the LOS pointing angle control for the POI mode described in section 3.2

needed to be conducted in combination. The controller maintaining the radius (R) of

the circular trajectory was configured as multiple loops that created pitch commands

( ) using the X axis (forward) velocity ) control loop as an inner loop, and the

circular trajectory circling speed was set to be controlled by roll commands ( )

generated through the speed ( ) control loop in the Y axis direction on the body frame

3.4 Simulation

In the present study, simulations of the quadcopter geo-pointing control system

presented above were implemented. The noise characteristics of various sensors

actually used and GPS error characteristics were measured and added to the



simulation environment. The performance of geo-location and of the POI control loop

was verified together by implementing the POI mode, reverse estimating the position

pointed to by the LOS pointing angle ( , ) generated through the foregoing, and

comparing the initially set target and the estimated target position. (Figure 3)

Fig. 3. Geo-location Simulation Fig. 4. COI mode Simulation

4 Flight Test

4.1 POI mode test

To verify the LOS pointing angle control for the POI, flight tests were conducted

under the following scenario. First, camera images obtained during flight were

monitored in real time on the ground while locking on a certain target, and the

position and height of the quadcopter were manually changed arbitrarily

Figure. 5. POI mode Flight Test Figure. 6. Geo-location Flight Test

4.2 COI mode Test

The flight scenario used to verify the COI mode is as follows. First, a certain POI was

set and the POI mode was implemented first to maintain the LOS pointing angle.

Thereafter, the radius ( ) and speed ( ) input values at the center of the POI were

-20 -15 -10 -5 0 5 10-20

-15

-10

-5

0

5

10

COI(Circle of interest) Test

E (meter)

N (

mete

r)

Quadcopter Path

Target



applied to implement the COI mode. The initial radius ( ) and speed ( ) were 10 m

and 0 m/s, respectively, and the COI mode was applied at around 15 sec to follow

commands.

Fig. 7. Radius Response in COI mode

Fig. 8. Velocity Response in COI mode Fig. 9. Flight Path in COI mode

5 Conclusion

The present study proposed geo-pointing algorithms to estimate target position (geo-

location) and tracking the target coordinate(POI) through LOS pointing anglethat

point at the target from the quadcopter and perform automatic flight along the

circle(COI). To design controllers for the proposed algorithms, modeling was

conducted based on flight data, and model parameters were estimated through the

prediction error method. Based on the model, the effects of diverse disturbance

conditions, including position error elements on the designed geo-pointing algorithms,

were analyzed. The usefulness of the algorithms was demonstrated through flight tests

applied to actual hardware, and performance indicators were derived.

References

1. Luukkonen, Teppo. "Modelling and control of Quadcopter." Independent research project

in applied mathematics, Espoo, 2011



2. Bouabdallah, Samir, Pierpaolo Murrieri, and Roland Siegwart. "Design and control of an

indoor micro quadrotor." Robotics and Automation, 2004. Proceedings. ICRA'04. 2004

IEEE International Conference on. Vol. 5. IEEE, 2004.

3. Gurdan, D., Stumpf, J., Achtelik, M.,Doth, K.M., Hirzinger, G. , Rus, D.,“Energy-efficient

autonomous four-rotor flying robot controlled at 1 kHz” , IEEE International Conference

on Robotics and Automation (ICRA), pp. 361–.366. Roma, Italy (2007)

4. AscTec Hummingbird with AutoPilot User’s Manual, Ascending Technologies GmbH

5. Abed-Meraim, Karim, Eric Moulines, and Philippe Loubaton. "Prediction error method for

second-order blind identification." Signal Processing, IEEE Transactions on 45.3 (1997):

694-705.



Documents

Experimental Study on Geo-Pointing Control System for ...Experimental Study on Geo-Pointing Control System for Quadcopter . Do yoon Kim. 1, Sung Kyung Hong * 1. Future Aerospace Technology