AN EMBEDDED SYSTEM FOR SMALL-SCALED AUTONOMOUS

VEHICLES

David Vissi`ere and Nicolas Petit

D´el´egation G´en´erale pour l’Armement, France

Ecole Nationale Sup´erieure des Mines de Paris, France

Keywords:

Embedded systems, autonomous vehicles, UAVs.

Abstract:

We consider the problem of designing a modular real-time embedded system for control applications with

unmanned vehicles. We propose a simple and low-cost solution. Its computational power can be easily

improved, depending on application requirements. To illustrate its performance, we report the implementation

of a 75 Hz Extended Kalman Filter used for state estimation on a small-scaled helicopter.

1 INTRODUCTION

In this paper, we present some of our research effortin

a current program aiming at proposing control strate-

gies and a control architecture for a group of hetero-

geneous autonomous vehicles.

To conduct this research, we designed a versatile

and simple real time embedded system, which can be

easily used as real time guidance and navigation sys-

tem on various platforms. Our work focuses on het-

erogenous vehicles, including small-scaled (typically

less than 2 m wide) Vertical Take-Off and Landing

aerial vehicles (VTOL as in (Castillo et al., 2005)) or

ﬁxed wing aircraft, and ground vehicles with tank like

dynamics (as in (Morin and Samson, 2006; Vissi`ere

et al., 2007)). In the future, these vehicles will be

asked to act cooperatively on the battleﬁeld as pic-

tured in Figure 1 (see also (Kaminer et al., 2004;

Olfati-Saber, 2006) for other scenarios).

In practice, the aerial vehicles represent the most

challenging applications in terms of navigation and

guidance. The main reason for this, is that these ve-

hicles can not easily go into any safe mode, as op-

posed to the ground vehicles which are, in compar-

ison, slower and simpler. While it was proven that,

with lowered performance expectations, it is possi-

ble to stabilize a ﬁxed-wing Unmanned Air Vehicles

(UAV) by directly closing the loop with signals from

well-chosen sensors (e.g. in (Lee et al., 2003), the

authors propose a solution to automatically control a

ﬁxed-wing UAV using only a single-antenna GPS re-

ceiver), it is considered by the vast majority of the

UAV community that navigation systems require data

fusion (Cheng et al., 2006). In facts, each sensor

technology has its own ﬂaws (among which are drift,

noises, and possibly low resolution or low update fre-

quency). Yet, large factors of accuracy can be gained

by reconciliating their data.

Example of on-board data fusion applications are

ubiquitousamong autonomous vehicle control experi-

ments. Reconciliating GPS and Inertial Measurement

Unit (IMU) measurements is a classic case-study.

In (Xiaokui and Jianping, 2002), results of data fusion

from a BeeLine GPS receiver from Novatel

and a

miniaturized IMU are presented. In (Cremean et al.,

2005), high-speed data fusion systems have been de-

veloped in view of the DARPA Grand Challenge.

In this later experiment, several technological

breakthroughs are presented using a high-end and

powerful computer architecture. Software compo-

nents communicate in a machine-independentfashion

through a module management system.

Our experiments can not use such a high end

setup, because the typical payload of our aerial ve-

hicles does not exceed 5 kg.

Much smaller and lower-weighting systems can

be considered though. In (Jung and Tsiotras, 2007),

an embedded system is proposed which does not

incorporate any powerful calculation board. A

simple Rabbit Semiconductor RCM-3400

micro-

controller is used to perform complementary ﬁltering

data fusion using a limited computational power. In

the same spirit, in (Jung et al., 2005), a low-cost test-

bed for UAVs is presented. It is reported that the

main advantage of designing such an autopilot from

157

Vissière D. and Petit N. (2008).

AN EMBEDDED SYSTEM FOR SMALL-SCALED AUTONOMOUS VEHICLES.

In Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - RA, pages 157-164

DOI: 10.5220/0001497701570164

 SciTePress

Figure 1: Cooperative autonomous vehicles in a future bat-

tleﬁeld.

scratch is that, by contrast to commercially available

products (Cloud Cap Technologies, 2004; Micro Pi-

lot, 2004), it provides full access to the internal con-

trol structures. We totally agree with this point of

view.

In this paper, we present a solution lying in the

middle of the two previously mentioned categories.

Our system uses two processors. One processor is

used to gather data from the sensors and to control

the actuators. The other processor is used to perform

the data fusion calculations (and possibly the control

algorithms). The advantages of this structure are as

follows: i) task scheduling is easily programmed, be-

cause only one of the two processors is in charge of

handling the numerous devices and I/O; ii) the com-

putations are performedas one single thread on a ded-

icated board (PC type); iii) depending on the com-

putational requirements, the computation board can

be easily upgraded without requiring any software

changes or rising any concern about task scheduling;

iv) ﬁnally, the overall system is quite low-cost, since

it relies on off-the-shelf components and can be easily

maintained.

The paper is organized as follows. In Section 2,

we present our system architecture. We detail our

hardware components and comment on their choices.

In Section 3, we present as a test-case the embedding

of our system into a small-scaled helicopter. Numer-

ous details of implementation are provided. Finally,

we conclude and give directions of future work.

2 SYSTEM ARCHITECTURE

Our primary goalwas to develop an embedded system

to test algorithms of various complexity on-board var-

ious small-scaled platforms. Early in the design pro-

cess, one ﬁrst constraint which appeared to us was the

payload limitations of the considered ﬂying machines.

This lead us to focus on designing a low weight em-

bedded system.

A second issue that was also raised early in the

design stage was that the real-time requirements of a

control system for such small UAVs are very strong.

This is mostly due to the short time horizons instabil-

ities. Yet, in the context of embedded systems, real-

time scheduling of a number of sensing and compu-

tation tasks is known to be a difﬁcult problem. More

precisely, as exposed in (Caccamo et al., 2005), the

problem of determining the feasibility of a periodic

sequence of prioritized tasks is often (NP)-hard. Suf-

ﬁcient, but not necessary tests are pessimistic. Pop-

ular strategies such as the Rate-Monotonic policy

(see again (Caccamo et al., 2005)), which consists of

putting the highest priority on the shortest task can be

proven to be unfeasible is the CPU load is too large

While being troublesome on ground vehicles, such in-

feasibilities (and the induced inconsistencies in the

embedded calculations) would represent a cause of

potential major failure for our aerial platforms.

Keeping these two considerations in mind, we de-

cided to develop a robust two-processors embedded

system, running two distinct softwares and communi-

cating through a simple two-ways protocol. The sys-

tem speciﬁcations are as follows.

1. It performs the sensing and calculation tasks sep-

arately.

2. It is fast enough to run a typical 15 to 30 dimen-

sional states EKF algorithm with a low latency

(to eventually produce satisfactory closed-loop re-

sults).

3. Itis easy to upgrade.

4. It is versatile enough to handle various type of

sensors and communication protocols.

As exposed in Figure 2 and Figure 3, this (mod-

ular) embedded system is composed of a micro-

controller,which is in chargeof gathering information

from all the sensors, and a calculation board. These

two elements are connected by a serial interface. The

micro-controller also has a downlink to a ground sta-

tion. We now present the details of the hardware com-

ponents of our system.

2.1 Sensors

Considering both ground and aerial vehicles control

applications, we listed a series of useful sensors that

needed to be incorporated into our embedded system.

Among these are: an IMU, a GPS receiver, a pres-

sure sensor, an anemometer, magnetometers, and var-

ious switches. Other possibilities include odometers,

the upper limit on admissible load is 69%, approxi-

mately.

ICINCO 2008 - International Conference on Informatics in Control, Automation and Robotics

158

LADARs (as used in (Cremean et al., 2005)), and

sonars (as used in (Vissi`ere et al., 2007)), or cameras

(as used in (Hamel and Mahony, 2007)). In the con-

text of our study, we only considered low-cost sen-

sors. We now detail these. In each case, we specify

the weight (in g), the cost (in USD), the dimensions

(in mm), the update rate (f in Hz), and the protocol of

communication (Comm.).

• Inertial Measurement Unit (IMU). Our IMU

is a 3DM-GX1 from Microstrain

. It contains

three angular rate gyroscopes, three orthogonal

single-axis magnetometers, and three single-axis

accelerometers, along with 16 bits A/D convert-

ers and a micro-controller. This IMU can deliver

different messages, ranging from raw-data, to rec-

onciliated measurements. In our setup, we ask

the IMU to deliver only calibrated sensors data at

a 75Hz rate.

Weight Cost Size f Comm.

30 1450 39,54,18 75 RS232

• Global Positioning System (GPS). Our GPS is

a TIM-LS from µblox

. Through a proprietary

binary protocol, it provides position and velocity

information at a 4Hz rate. Position error is 2.5 m

(Circular Error Probability CEP) and velocity er-

ror is 2 m CEP.

Weight Cost Size f Comm.

23 100 32,47,9.5 4 RS232

The GPS receiver is not very tolerant against

power supply voltage ripples. These can be kept

below the 50 mV requirements thanks to a dedi-

cated power supply regulator from TRACO

• Barometer. Our barometer is the MS-5534 from

Intersema

. Using a SPI-type protocol, it gives

calibrated digital pressure and temperature infor-

mation. This device requires a 3 V power supply

which is obtained through a fast response diode

from the main 5 V power supply of the micro-

controller.

Weight Cost Size f Comm.

2 14 5,4,2 20 SPI

• Anemometer. A PXLA sensor from ASensTec

delivers a differential pressure analog signal,

which can be read through a 10 bit A/D converter.

Weight Cost Size f Comm.

10 25 11,8,12 75 Analog

• Magnetometer. We use a HMR2300 three axis

magnetometer from Honeywell

. Its range is ±

2 gauss and it has a 70 µgauss resolution.

Weight Cost Size f Comm.

28 230 75,30,20 154 RS232

• Take-off and Landing Detector. Being able to

detect take-off and landing instants is necessary to

properly initialize data fusion algorithms. In de-

tails, detection of the corresponding switches in

the dynamics deﬁnes when the controls actually

have an effect on the system. This is not the case

when an UAV is on the ground. This detection

is performed with on-off switches which can be

located, e.g., on the landing gear. They deliver a

logic signal which can be readily interpreted. To

prevent electric arcs which might cause trouble to

the connected micro-controller, we added a spe-

ciﬁc interfacing circuit. This switches can also be

replaced by active switches which can be used to

activate various devices such as digital cameras,

or parachutes.

Weight Cost Size f Comm.

10 6 25,10,5 75 Boolean

Our system is data-driven by the IMU. The main

reason behind this choice is that the IMU is consid-

ered as a critical sensor.

2.2 MPC555 Micro-controller

The micro-controller which serves as an interface

for the sensors and actuators is a MPC555 Power

PC from Motorola

. It runs a speciﬁc software

we developed using the Phytec

development kit.

The reason for choosing this micro-controller are as

follows. This device provides a double precision

ﬂoating-pointunit (64 bit) which is convenientfor po-

tential embedded algorithms (even if we do not use

this possibility here since all computations are per-

formed on the calculation board), it has a relatively

fast 40 MHz clock, it has 32 bit architecture and

448Kbyte of Flash memory and 4 MBytes of RAM.

Most importantly, among the family of 32 bits kits,

the MPC555 has substantial computational capabili-

ties and a large numberof versatile and programmable

Input/Output ports. In particular, we make an exten-

sive use of its TPUs (Time Process Units), UARTs,

A/D converters, SPIs, MPIOs (Modular I/O system)

(see (Motorola, 2000)). Finally, it is small (credit card

format) and has a low weight.

Weight Cost Size f Comm.

25 450 72,57,8 all all

No operating system is used on the micro-

controller. Rather the MPC555 runs a speciﬁc

interrupts-driven software written in C. Information

AN EMBEDDED SYSTEM FOR SMALL-SCALED AUTONOMOUS VEHICLES

159

Sensors

Ground

station

Remote

Control

Power PC

Vehicle

actuators

Computation

Board

Figure 2: Sensors and computation board connections to the

central micro-controller.

TPU

SCI2 MPWM

QSPI

IMU

QADC

GPS

BAROMETER

PITOT

COMPASS

Ground

Station

DATALINK

Remote

Control

Receiver

SWITCH

actuators

MDASM

QSCI1

Computation Board

TPU

T/L interrupt

MIOS

VEHICLE

+3V

+5V

+12V

main.c

Figure 3: Embedded system internal connections.

from each sensors is transferred using a dedicated in-

terrupt handler routine. Each external source or group

of sources has its own interrupt level which avoids po-

tential conﬂicts. Each data link is associated with a

checksum to validate reception.

The data acquisition software running on the

micro-controlleris event-drivenby the IMU messages

which periodically sends 31 bytes of data. Once the

message of the IMU is received and validated by the

micro-controller, others sensors information are ei-

ther directly read or picked in data buffers which are

constantly fed with serial messages from the sensors

through hardware interrupts. Information is gathered

in a 116 bytes message containing all the onboard

measurements. This message is sent to the calculation

board through a high-speed serial port. Once the mes-

sage is received and validated, the calculation board

carries out one navigation loop consisting of a predic-

tion equation and an estimation equation of a Kalman

ﬁlter.

2.3 PC Computation Board

The computing board is a PC running the Knop-

pix 3.8.1 Linux distribution. The PC board was se-

lected among numerous models (mostly mini-ITX,

and PC104) based on computational power, energy

consumption, toughness, and price. A fan-less board

was considered as the most relevant choice, due to the

often observed failures of fans in mechanically dis-

turbed environments.

The chosen fan-less calculation board has a stan-

dard mini-ITX PC architecture. Its processor is a

1.2Ghz C7-M from VIA

designed for embedded

applications. It can perform 1500 MIPS and has clas-

sic PC Input/Output ports such as a UART serial port

(used as main data link with the micro-controller), an

ethernet board (not used here), a VGA screen output

(which can be used to monitor the system during de-

bugging phases of the software and hardware devel-

opment), a keyboard, and 4 USB ports (which can be

considered for plugging future devices such has con-

trollable cameras).

Experimental preliminary tests have shown us that

multi-threading(one threadfor messagedecodingand

one thread for the main algorithm) presents two major

drawbacks: some data can be lost, and the calculation

cycle may end unexpectedly (slightly) late. For this

reason, we decided to write our own UART driver us-

ing an interrupt handler in the kernel space. Further,

we de-activated all hardware interruptions associated

to unnecessary devices. As a result, only interrupts

from the UART are enabled. Finally, we used a single

computation thread.

The operating system is installed on a

bootable 1 Gbyte Disk-On-Chip system which

prevents all possible mechanical failure associated

to hard-drives. This ﬂash memory device is directly

connected to the IDE port of the mother-board. The

board is powered by a pico-PSU

power supply

which provides various voltages ranging from 5V to

18V. The computation software are written in C and

can be either be updated directly on the board via a

ssh

connection, or transferred, in a compiled form,

from a remote PC. Custom scripts for compiling and

distributing our executable code and conﬁguration

ﬁles are an efﬁcient way to upgrade the software

during development and testing.

Weight Cost Size f Comm.

800 350 170,180,40 1.5e6 RS232

3 FIELD EXPERIMENT

We now report some ﬁeld experiment using our em-

bedded system. To perform model identiﬁcation

tasks and design a real-time state observer in view of

closed-loop control, we decided to load our embed-

ded system into a small-scaled helicopter. In this sec-

tion, we expose this work, and give numerous details

about solutions to speciﬁc interfacing and vibrational

issues.

ICINCO 2008 - International Conference on Informatics in Control, Automation and Robotics

160

3.1 Experimental Setup

We conducted all the experiments with the help of a

human pilot which could, at any time remotely con-

nect the inputs of the actuators of the helicopter to the

outputs of our embedded system or directly to the out-

puts of the radio receiver (and thus take direct control

of the helicopter). This is achieved thanks to a “re-

motely controlled switch board” which we designed.

This switch is actuated from our Graupner

MC24

remote controller using one of the 12 available radio

channels. This system provides a rapid transition be-

tween manual and autonomous ﬂight.

During all ﬂights, the pilot could see images from

an on-board camera and read measurements from the

embedded system received for the down datalink. A

convenient Head-Up display system on the ground

station was designed for that purpose. Our data link

is a MaxStream

module operating at 2.4 GHz. It

provides a RS232 serial data link at 9600 baud send-

ing the information from the embedded system to the

ground station.

Weight Cost Size f Comm.

250 230 40,68,9 30 RS232

3.2 Small-scaled Helicopter

We use a Benzin acrobatic helicopter from Vario

with Graupner

electronics (C4441 servomotors

which have high speed and high torque (8.5 Nm), a 16

channels receiver, and a yaw hobby gyroscope). It is a

very reliable helicopter platform (we never observed

any mechanical issue over mode than 100 ﬂights). Its

payload capacity (4Kg) is large enough for numerous

reconnaissance applications. Its rotor is 1.9 m wide.

3.3 Interfacing and Vibrational Issues

Wiring the embedded system to the existing heli-

copter circuitry was achieved using some speciﬁc ad-

ditional boards and connectors. To measure the pilot’s

orders in real-time, we used a 6 channels voltage fol-

lower circuit. Numerous LEDs were added to check

the status of our system.

A central problem observed on-board helicopters

is the 25 Hz vibrations induced by the main rotor

blades. These vibrations generate a large amount of

noise on the inertial sensors. In practice, these noises

totally overwhelm the useful signals. Fortunately, it

is possible to solve this issue by using well-chosen

noise dampers. On our helicopter, we decided that

the micro-controller and the sensors would all be lo-

cated on a board which would be physically con-

nected to the frame of the helicopter through four

Figure 4: Our embedded system ﬁtted into the (custom-

built) landing gear of a small-scaled Vario Benzin heli-

copter. Springs and dampers are used to ﬁlter out vibrations

from the main rotor blades.

spring-damper systems (see Figure 4). Experiments

conducted on a vibrating table have shown that it was

advantageous to keep the embedded system as com-

pact and as rigid as possible. The total weight of the

subsystem is about 600 g. We decided to attach some

of the batteries to it to bring the weightclose to 1.8 kg.

This enabled us to use off-the-shelf dampers yield-

ing appropriate cut-off frequencies. MV801-5CC

dampers from Paulstra

were chosen for their abil-

ity to work with low masses vibrating at low frequen-

cies. With these, we obtained a satisfactory vibration

damping, with a cut-off frequency around 9 Hz. This

is represented in Figure 5. Further, resonant frequen-

cies due to the engine frequency (around 160 Hz),

the tail rotor frequency (around 115 Hz), and the tail

boom were removed using a digital notch ﬁlter. The

presented solution attenuates high frequency vibra-

tion inputs down to negligible levels.

3.4 Experimental Identiﬁcation

Preliminary model identiﬁcation experiments need

to be conducted before state estimation can be per-

formed.

In particular, using our embedded system, we

studied the actuators transfer functions and the yaw

rate gyroscope.

In some works (see (Mettler, 2003) or (Mettler

et al., 2000)), actuators (servomotors) are considered

as ﬁrst order systems with dead band. We identiﬁed

such transfer functions for various Graupner

and

Futaba

servomotors. Results of various tip-in and

tip-out in reference signals were recorded to compute

the time constant of the ﬁrst order model.

On board our helicopter, a hobby gyroscope from

AN EMBEDDED SYSTEM FOR SMALL-SCALED AUTONOMOUS VEHICLES

161

Figure 5: Bode diagram showing the resonance peak and the cut-off frequency of the mechanical structure equiped with the

sensors, the micro-controller, and the spring-dampers suspension. The various plots are obtained on varying locations on the

vibrating structure and show a good spatial uniformity of the vibration damping.

Graupner

is used to help the human pilot keeping

the yaw rate as small as possible. Pilot orders are

transferred from the R/C receiver to the tail actuator

through this gyroscope.

To validate simple models of this transfer, we put

our IMU under this gyroscope to measure the angu-

lar velocity. Simultaneously, we connected the gyro-

scope and recorded the gyroscope signals sent to the

tail actuator. Surprisingly, it was discovered that the

gyroscope feedback behaves as a 2 Hz low-pass ﬁlter

on the pilot orders, and directly transmits the opposite

of the received angular rate, without ﬁltering it. This

can be summarized under the form

gyro

= N

pilot

1+ τ

gyro

3.5 State Estimation

The helicopter is a 6 degrees of freedom mechanical

system with high bandwidth dynamic. Reconstruct-

ing the full state of this system from low-cost sensors

only is quite a challenge. We use an ExtendedKalman

Filter (EKF, see e.g. (Simon, 2005)) to estimate the

state of this system. In practice, the state of our EKF

is composed of 23 variables including conﬁguration

states (13 variables using quaternions instead of Euler

angles), and the aerodynamic and external forces and

torques (10 variables including the harmonic expan-

sion of theﬂapping phenomenaas exposed in (Mettler

et al., 2001)). We used equally valued tuning parame-

ters for the 3 axis. These are chosen to capture fast

dynamics (statistics data are σ

acceleration

= 8 m.s

−2

State EstimationReceive data

Send Data

Figure 6: Succession of steps of data transfer and computa-

tion.

and σ

torque

= 4 rad.s

−2

, respectively). Classically,

discrete-time updates are implemented. As already

discussed, updates are synchronized with the 75Hz

measurements from the IMU.

The succession of tasks performed by the compu-

tation board of our embedded system is described in

Figure 6. As can be seen, it is necessary, due to a re-

ception time being superior to the available time be-

tween two calculation cycles to simultaneously read

or send data and perform computations. Some ex-

perimental results are presented in Figure 7. Position

estimates around hovering are reported. In practice,

they appear to be in great accordance with recorded

videos.

4 CONCLUSIONS

Designing an embedded system which can qualify

as a control system for various small-scaled air and

ground vehicles is the subject of the research project

presented in this paper. The embedded system we

propose here has some interesting features. It is sim-

ple, low cost and, most of all, easy to upgrade. Its two

processors architecture can incorporate various new

ICINCO 2008 - International Conference on Informatics in Control, Automation and Robotics

162

−2

−4

−2

−1

x(m)

y(m)

z(m)

Figure 7: Position estimates during a hovering ﬂight.

processors and sensors.

In our laboratory, this embedded system has been

successfully used on a ﬁxed wing aircraft (Ras-

cal 110), a small-scaled helicopter (Vario

Benzin

trainer), and ground vehicles (Pioneer 4 from Mobile

Robots

Further developments focus on giving more au-

tonomy, including path planning algorithm, follow-

ing (Kogan and Murray, 2006; Murray et al., 2003), to

respond to high-levels orders from a remote user. We

will surely need more computational power, which

can be obtained by simply upgrading the computa-

tional board. In parallel, we develop a new aerial ve-

hicle for urban area applications. For this last project

we consider using other sensors. In particular, for

obstacle avoidance and navigation, we will connect

LADARS, and ultrasonic sensors to our embedded

system.

ACKNOWLEDGEMENTS

The authors are indebted to the numerous students,

technicians and engineers who have been collaborat-

ing to the development of the presented technology

and have brought their support during the conducted

experiments.

REFERENCES

Caccamo, M., Baker, T., Burns, A., Buttazzo, G., and Sha,

L. (2005). Real-time scheduling for embedded sys-

tems. In Hristu-Varsakelis, D. and Levine, W. S., ed-

itors, Handbook of networked and embedded control

systems. Birkh¨auser.

Castillo, P., Lozano, R., and Dzul, A. E. (2005). Modelling

and control of mini-ﬂying machines. Advances in in-

dustrial control. Springer.

Cheng, J., Lu, Y., Thomas, E. R., and Farrell, J. A. (2006).

Data fusion via Kalman ﬁlter: GPS and INS. In Ge,

S. S. and Lewis, F. L., editors, Autonomous mobile

robots, Control engineering series. Taylor and Francis.

Cloud Cap Technologies (2004). http://

www.cloudcaptech.com. Hood River, OR, USA.

Cremean, L. B., Foote, T. B., Gillula, J. H., Hines, G. H.,

Kogan, D., Kriechbaum, K. L., Lamb, J. C., Leibs, J.,

Lindzey, L., Rasmussen, C. E., Stewart, A. D., Bur-

dick, J. W., and Murray, R. M. (2005). Alice: An

information-rich autonomous vehicle for high-speed

desert navigation. Journal of Field Robotics, (9).

Hamel, T. and Mahony, R. (2007). Image based visual

servo-control for a class of aerial robotic systems.

43(11):1975–1983.

Jung, D., Levy, E. J., Zhou, D., Fink, R., Moshe, J., Earl,

A., and Tsiotras, P. (2005). Design and development

of a low-cost test-bed for undergraduate education in

UAVs. In Proc. of the 44th IEEE Conf. on Decision

and Control, and the European Control Conference

2005.

Jung, D. and Tsiotras, P. (2007). Inertial attitude and posi-

tion reference system development for a small UAV.

In AIAA Infotech at Aerospace.

Kaminer, I., Yakimenko, O., Dobrokhodov, V., Lizaraga,

M., and Pascoal, A. (2004). Cooperative control of

small uavs for naval applications. In Proc. of the 43rd

IEEE Conf. on Decision and Control.

Kogan, D. and Murray, R. M. (2006). Optimization-based

navigation for the DARPA Grand Challenge. In Proc.

of the 45th IEEE Conf. on Decision and Control.

Lee, S., Lee, T., Park, S., and Kee, C. (2003). Flight test re-

sults of UAV automatic control using a single-antenna

GPS receiver. In AIAA Guidance, Navigation, and

Control Conference and Exhibit.

Mettler, B. (2003). Identiﬁcation Modeling and Character-

istics of Miniature Rotorcraft. Boston : Kluwer Aca-

demic Publishers.

Mettler, B., Tischler, M. B., and Kanade, T. (2000). System

identiﬁcation of a model-scale helicopter. Technical

Report CMU-RI-TR-00-03, Carnegie Mellon Univer-

sity.

Mettler, B., Tischler, M. B., and Kanade, T. (2001). System

identiﬁcation modeling of a small-scale unmanned he-

licopter. Journal of the American Helicopter Society.

Micro Pilot (2004). http://www.micropilot.com. Stony

Mountain, Canada.

Morin, P. and Samson, C. (2006). Trajectory tracking

for nonholonomic vehicles. In Kozlowski, K., ed-

itor, Robot Motion Control: Recent Developments.

Springer.

Motorola (2000). MPC555 / MPC556 user’s manual. User’s

manual, Motorola.

Murray, R. M., Hauser, J., Jadbabaie, A., Milam, M. B., Pe-

tit, N., Dunbar, W. B., and Franz, R. (2003). Online

control customization via optimization-based control.

In Samad, T. and Balas, G., editors, Software-Enabled

Control, Information technology for dynamical sys-

tems, pages 149–174. Wiley-Interscience.

AN EMBEDDED SYSTEM FOR SMALL-SCALED AUTONOMOUS VEHICLES

163

Olfati-Saber, R. (2006). Flocking for multi-agent dynamic

systems: Algorithms and theory. IEEE Trans. Au-

tomat. Control, 51(3):401–420.

Simon, D. (2005). Optimal state estimation. Wiley.

Vissi`ere, D., Chang, D. E., and Petit, N. (2007). Experi-

ments of trajectory generation and obstacle avoidance

for a UGV. In Proc. of the 2007 American Control

Conference.

Xiaokui, Y. and Jianping, Y. (2002). Study on low-

cost GPS/DMU integrated navigation system. In

AIAA/AAS Astrodynamics Specialist Conference and

Exhibit.

ICINCO 2008 - International Conference on Informatics in Control, Automation and Robotics

164