Illustration - IRCCyN - Ecole Centrale de Nantes
Illustration - IRCCyN - Ecole Centrale de Nantes
Illustration - IRCCyN - Ecole Centrale de Nantes
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ECN: Filière Transport<br />
IRCCYN, <strong>Ecole</strong> <strong>Centrale</strong> <strong>de</strong> <strong>Nantes</strong><br />
Professor Philippe Martinet<br />
IRCCYN Laboratory<br />
ECN<br />
Philippe.Martinet@irccyn.ec-nantes.fr<br />
http://www.irccyn.ec-nantes.fr/~martinet<br />
Philippe Martinet<br />
1 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Intelligent Transportation System<br />
PART V<br />
<strong>Illustration</strong>s<br />
Professor Philippe Martinet<br />
ECN – IRCCYN<br />
<strong>Nantes</strong>, France<br />
Philippe.Martinet@irccyn.ec-nantes.fr<br />
http://www.irccyn.ec-nantes.fr/~martinet<br />
Philippe Martinet<br />
2 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction to ITS<br />
• Definition<br />
• ITS examples<br />
• GPS and GIS<br />
• Navigation strategies<br />
Sensor Based Control<br />
Basic Concept<br />
Examples<br />
<strong>Illustration</strong>s<br />
Road, Urban, Offroad, Indoor<br />
Philippe Martinet<br />
Content<br />
Motion and Mission planning<br />
Problem <strong>de</strong>finition<br />
Definitions<br />
Motion Planning<br />
RoadMap Approaches<br />
Cell Decomposition methods<br />
Bug algorithm<br />
Autonomous navigation<br />
Introduction<br />
Locomotion<br />
Mo<strong>de</strong>ling<br />
Localization<br />
Control<br />
3 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Navigation strategies<br />
Navigation scheme<br />
End user level<br />
FDIR<br />
monitoring<br />
Execution level<br />
Planning<br />
Mission, route<br />
Obstacle<br />
avoidance<br />
Localization<br />
Single, platoon mo<strong>de</strong><br />
Scheduling<br />
Reference<br />
trajectory<br />
Modified reference<br />
trajectory<br />
Control<br />
Single, platoon mo<strong>de</strong><br />
Philippe Martinet<br />
4 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Navigation strategies<br />
Navigation scheme<br />
End user level<br />
FDIR<br />
monitoring<br />
Execution level<br />
Planning<br />
Mission, route<br />
Localization<br />
Single, platoon mo<strong>de</strong><br />
Obstacle<br />
avoidance<br />
Scheduling<br />
Reference<br />
trajectory<br />
Control<br />
Single, platoon mo<strong>de</strong><br />
Philippe Martinet<br />
5 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Navigation strategies<br />
3D Map based navigation (absolute navigation)<br />
Exteroceptive<br />
sensors<br />
Proprioceptive<br />
sensors<br />
Itinerary<br />
Selection<br />
& Execution<br />
Localization<br />
GIS<br />
3D map based<br />
Reference trajectory<br />
Global<br />
Lateral <strong>de</strong>viation<br />
Angular <strong>de</strong>viation<br />
Curvature<br />
Curvilinear abscissa<br />
Online step<br />
Philippe Martinet<br />
6 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Navigation strategies<br />
Memory based navigation (relative navigation)<br />
Exteroceptive<br />
sensors<br />
Proprioceptive<br />
sensors<br />
Itinerary<br />
Selection<br />
& Execution<br />
Topological<br />
In<strong>de</strong>xation<br />
Extraction<br />
of features<br />
GIS<br />
Augmented GIS<br />
Topological<br />
Representation<br />
sensory memory<br />
Learning step<br />
Philippe Martinet<br />
7 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Navigation strategies<br />
Memory based navigation (relative navigation)<br />
Exteroceptive<br />
sensors<br />
Proprioceptive<br />
sensors<br />
Itinerary<br />
Selection<br />
& Execution<br />
Localization<br />
Augmented GIS<br />
Topological<br />
Representation<br />
Reference trajectory<br />
LOCAL<br />
Lateral <strong>de</strong>viation<br />
Angular <strong>de</strong>viation<br />
Curvature<br />
Curvilinear abscissa<br />
Online step<br />
Philippe Martinet<br />
8 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Examples : Localization<br />
Philippe Martinet<br />
9 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Examples : Localization &Tracking<br />
Philippe Martinet<br />
10 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Examples : SLAM RADAR<br />
Philippe Martinet<br />
11 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Examples : SLAM Laser<br />
Philippe Martinet<br />
12 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Examples : SLAM VISION<br />
Philippe Martinet<br />
13 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Debain [96]<br />
2D : Automatic Gui<strong>de</strong>d Vehicles<br />
Control laws for agricultural machines<br />
Combine-harvester<br />
Harvesting work<br />
Philippe Martinet<br />
14 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Debain [96]<br />
Perception<br />
2D : Automatic Gui<strong>de</strong>d Vehicles<br />
• non supervised segmentation (Derras93)<br />
• supervised segmentation (Château99)<br />
Iterative Process<br />
Philippe Martinet<br />
15 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Debain [96]<br />
Perception<br />
2D : Automatic Gui<strong>de</strong>d Vehicles<br />
• non supervised segmentation (Derras93)<br />
• supervised segmentation (Château99)<br />
Measure<br />
Reference<br />
S<br />
=<br />
( θ , ρ)<br />
Philippe Martinet<br />
16 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Debain [96]<br />
2D : Automatic Gui<strong>de</strong>d Vehicles<br />
Flat Terrain<br />
• CRV (Debain96)<br />
• Pole assignment (Khadraoui96)<br />
• Neural networks (Rouveure96)<br />
Sloping Terrain<br />
• Adaptive CRV (Debain96)<br />
• Neural networks (Rouveure R.)<br />
Philippe Martinet<br />
17 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Debain [96]<br />
2D : Automatic Gui<strong>de</strong>d Vehicles<br />
Guiding on a flat ground x & = V ψ.<br />
V<br />
ψ & = − .δ<br />
L<br />
Bicycle mo<strong>de</strong>l<br />
CRV<br />
Pole assignment<br />
Neural networks<br />
Philippe Martinet<br />
18 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Debain [96]<br />
2D : Automatic Gui<strong>de</strong>d Vehicles<br />
Guiding on a flat ground x & = V ψ.<br />
V<br />
ψ & = − .δ<br />
L<br />
Bicycle mo<strong>de</strong>l<br />
CRV<br />
Pole assignment<br />
Neural networks<br />
( S(<br />
t)<br />
− *)<br />
T +<br />
T = −λ. L . B.<br />
S<br />
S<br />
L L<br />
δ = − . Ωy<br />
= − . ψ&<br />
V V<br />
Philippe Martinet<br />
19 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Debain [96]<br />
2D : Automatic Gui<strong>de</strong>d Vehicles<br />
Guiding on a flat ground x & = V ψ.<br />
V<br />
ψ & = − .δ<br />
L<br />
Bicycle mo<strong>de</strong>l<br />
CRV<br />
Pole assignment<br />
Neural networks<br />
S & = A. S + B.δ<br />
δ = −<br />
( g g ).<br />
S<br />
1<br />
2<br />
Philippe Martinet<br />
20 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Debain [96]<br />
2D : Automatic Gui<strong>de</strong>d Vehicles<br />
Guiding on a flat ground x & = V ψ.<br />
V<br />
ψ & = − .δ<br />
L<br />
Bicycle mo<strong>de</strong>l<br />
CRV<br />
Pole assignment<br />
Neural networks<br />
ρ<br />
θ<br />
δ<br />
Philippe Martinet<br />
21 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Debain [96]<br />
Guiding on a flat ground<br />
2D : Automatic Gui<strong>de</strong>d Vehicles<br />
Philippe Martinet<br />
22 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Debain [96]<br />
2D : Automatic Gui<strong>de</strong>d Vehicles<br />
Guiding on a sloping ground<br />
Lateral offset<br />
Driving si<strong>de</strong>ways<br />
Philippe Martinet<br />
23 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Debain [96]<br />
Guiding on a sloping ground<br />
2D : Automatic Gui<strong>de</strong>d Vehicles<br />
P.I.<br />
Adaptive reference<br />
Adaptive<br />
Module<br />
( ) ρ,θ<br />
Image<br />
Processing<br />
( ρ adapt,θ<br />
)<br />
( ρ*,θ *)<br />
-<br />
+<br />
Control law PI<br />
δ<br />
Philippe Martinet<br />
24 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Debain [96]<br />
2D : Automatic Gui<strong>de</strong>d Vehicles<br />
Guiding on a sloping ground<br />
Philippe Martinet<br />
25 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Khadraoui [96]<br />
2D : Automatic Gui<strong>de</strong>d Vehicles<br />
Visual servoing applied to car-like vehicles<br />
Car-like vehicle<br />
Philippe Martinet<br />
26 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Khadraoui [96]<br />
Bicycle mo<strong>de</strong>l<br />
2D : Automatic Gui<strong>de</strong>d Vehicles<br />
x&<br />
= −V ψ.<br />
V<br />
ψ & = .δ<br />
L<br />
Scene mo<strong>de</strong>lling<br />
Vehicle + Camera + Road<br />
2D line in image space<br />
Dynamic mo<strong>de</strong>lling<br />
S<br />
=<br />
( θ , ρ)<br />
( a b)<br />
S = ,<br />
S & = A. S + B.δ<br />
Philippe Martinet<br />
27 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Khadraoui [96]<br />
Objective<br />
Positioning tasks<br />
2D : Automatic Gui<strong>de</strong>d Vehicles<br />
Robustness with regard to RTH variations<br />
Pole assignment<br />
S & = A. S + B.δ<br />
δ = −<br />
( g g ).<br />
S<br />
1<br />
2<br />
With and without<br />
integrator<br />
Philippe Martinet<br />
28 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Khadraoui [96]<br />
Objective<br />
Positioning tasks<br />
2D : Automatic Gui<strong>de</strong>d Vehicles<br />
Robustness with regard to RTH variations<br />
Robust control<br />
Fa<br />
Fb<br />
a 1<br />
= = −<br />
δ ξ1 . L.<br />
p<br />
b p.<br />
ξ ξ<br />
δ ξ1.<br />
L.<br />
ξ 3 p<br />
( p) 2<br />
1 + 2<br />
( p) = =<br />
2<br />
Ca<br />
Cb<br />
( p)<br />
( p)<br />
ξ1.<br />
L.<br />
p<br />
= −<br />
τ.<br />
2 + τ.<br />
p<br />
ξ1.<br />
L.<br />
ξ 3 p<br />
=<br />
τ.<br />
( )<br />
( p.<br />
ξ1<br />
+ ξ 2)<br />
Philippe Martinet<br />
29 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Khadraoui [96]<br />
Results<br />
Experimental Context<br />
2D : Automatic Gui<strong>de</strong>d Vehicles<br />
G<br />
Y<br />
Image<br />
Processing<br />
Camera<br />
Road<br />
Y*<br />
k<br />
+<br />
-<br />
δ<br />
Vehicle<br />
x&<br />
ψ&<br />
Cartesian<br />
Robot<br />
Philippe Martinet<br />
30 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Khadraoui [96]<br />
Results<br />
parameter a<br />
0<br />
-0.1<br />
-0.2<br />
-0.3<br />
-0.4<br />
-0.5<br />
CROB<br />
2D : Automatic Gui<strong>de</strong>d Vehicles<br />
PPI: Pole assignment<br />
CROB: Robust control<br />
-8°< α
Introduction<br />
to ITS<br />
Clady [02]<br />
Fixed focal<br />
length camera<br />
Wi<strong>de</strong> angle<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Our Vision<br />
sensor<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Automatic Gui<strong>de</strong>d<br />
Vehicles :<br />
target tracking<br />
Sensor<br />
Decision Module<br />
Actuators<br />
PTZ<br />
Controlled<br />
Camera<br />
Philippe Martinet<br />
33 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Clady [02]<br />
Motion and<br />
Mission planning<br />
The tracked feature is <strong>de</strong>fined by :<br />
- one bounding ellipsis<br />
- one pattern vector I<br />
Sensor Based<br />
Control<br />
- one parameters vector E<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Automatic Gui<strong>de</strong>d<br />
Vehicles :<br />
target tracking<br />
Philippe Martinet<br />
34 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Clady [02]<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
The principle is to link :<br />
- one displacement ∆E of one feature with<br />
- the difference ∆I in image space of this feature<br />
through an interaction matrix A :<br />
∆E t = A × ∆I<br />
<strong>Illustration</strong>s<br />
Automatic Gui<strong>de</strong>d<br />
Vehicles :<br />
target tracking<br />
+<br />
-<br />
∆E t = A × ∆I<br />
∆E<br />
Ellipsis centered<br />
on the feature<br />
object displacement<br />
∆I<br />
Referenced feature Iref<br />
Philippe Martinet<br />
35 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Clady [02]<br />
<br />
Motion and<br />
Mission planning<br />
Demonstrator VELAC<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Automatic Gui<strong>de</strong>d Vehicles : target tracking<br />
<br />
Experimental context:<br />
– Embed<strong>de</strong>d workstation: Silicon Graphics Indy.<br />
– Parameters: non optimal (gain, calibration).<br />
– Focalization and initialization by hand.<br />
– Limitations to some particular vehicle (truck, camping-car).<br />
Philippe Martinet<br />
36 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Clady [02]<br />
Automatic<br />
Gui<strong>de</strong>d<br />
Vehicles :<br />
Target<br />
tracking<br />
Philippe Martinet<br />
37 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Applications and projects: MOBIVIP<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
AGV using RTK-GPS<br />
Mobivip : Clermont-Fd 2004<br />
Navigation using RTK-GPS<br />
Clermont-Ferrand : LASMEA-GRAVIR<br />
Philippe Martinet<br />
38 Intelligent<br />
38<br />
Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Applications and projects: MOBIVIP<br />
AGV using vision only<br />
urban context<br />
[Royer04]<br />
Visual memory<br />
Reference<br />
Trajectory<br />
and<br />
Image features<br />
(3D points)<br />
Experimental data<br />
Compiegne city center<br />
(BODEGA/MOBIVIP)<br />
Philippe Martinet<br />
39 Intelligent<br />
39<br />
Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Perception<br />
Automatic Gui<strong>de</strong>d Vehicles : urban context<br />
Localization using monocular camera<br />
[Royer04]<br />
Reference<br />
Trajectory<br />
Key images<br />
And<br />
features<br />
and<br />
localization<br />
Current<br />
images<br />
and<br />
features<br />
Philippe Martinet<br />
40 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Applications and projects: BODEGA<br />
AGV using vision only<br />
BODEGA : Clermont-Fd 2005<br />
Navigation using vision only<br />
Clermont-Ferrand : LASMEA-GRAVIR<br />
Philippe Martinet<br />
41 Intelligent<br />
41<br />
Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Perception<br />
And<br />
Control<br />
Automatic Gui<strong>de</strong>d Vehicles : urban context<br />
Localization using monocular camera<br />
[Royer06]<br />
Philippe Martinet<br />
42 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Applications and projects: MOBIVIP<br />
AGV using vision only<br />
MOBIVIP : Clermont-Fd 2006<br />
Navigation using vision only<br />
Clermont-Ferrand : LASMEA-GRAVIR<br />
Philippe Martinet<br />
43 Intelligent<br />
43<br />
Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Navigation strategies<br />
Using a topological <strong>de</strong>scription of the environment<br />
Topological representation<br />
of the environment<br />
Performing navigation<br />
as a visual route to follow<br />
Philippe Martinet<br />
44 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Blanc [04] Ait A<strong>de</strong>r[04]<br />
Automatic Gui<strong>de</strong>d Vehicles<br />
Navigation using visual memory<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Philippe Martinet<br />
45 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Previous Work Blanc [04] Ait A<strong>de</strong>r[04]<br />
Navigation using visual memory<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Philippe Martinet<br />
46 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Previous Work H. Hadj Ab<strong>de</strong>lka<strong>de</strong>r [05]<br />
Navigation with omnidirectional visual<br />
memory : [ICRA07]<br />
Philippe Martinet<br />
47 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Other application fields<br />
J. Courbon [08]<br />
Navigation with fisheye visual memory :<br />
[ICARCV08]<br />
Ic<br />
Ig<br />
Philippe Martinet<br />
48 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
J. Courbon [07] Automatic Gui<strong>de</strong>d Vehicles<br />
robot control using a fisheye lens<br />
Autonomous navigation using a visual memory<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Philippe Martinet<br />
49 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Applications and projects: CITYVIP<br />
General framework :<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
AGV using visual memory<br />
Vision based memory<br />
navigation strategy :<br />
3 steps :<br />
1. Visual memory building<br />
2. Localization into the visual memory<br />
3. Navigation into the visual memory<br />
Philippe Martinet<br />
50 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Applications and projects: CITYVIP<br />
Graph based representation<br />
Vision based memory<br />
navigation strategy :<br />
Visual memory building<br />
a-Topological <strong>de</strong>scription<br />
b-Learning the environment<br />
c-Extraction of interesting<br />
features<br />
Can be<br />
•Harris points<br />
•Sift features<br />
•…<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
AGV using visual memory<br />
Oriented graphs<br />
containing<br />
the key images<br />
Philippe Martinet<br />
51 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Applications and projects: CITYVIP<br />
Localization<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
AGV using visual memory<br />
Vision based memory<br />
navigation strategy :<br />
Localization into the visual memory<br />
Current image<br />
Desired image<br />
a- Global localization [ICRA08]<br />
b- Selection of key images<br />
c- Relative pose computation<br />
Frame F c Frame F i+1<br />
(R,t)<br />
Philippe Martinet<br />
52 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Applications and projects: CITYVIP<br />
Localization<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
AGV using visual memory<br />
Vision based memory<br />
navigation strategy :<br />
Localization into the visual memory<br />
a- Global localization [ICRA08]<br />
b- Selection of key images<br />
c- Relative pose computation<br />
(y, θ) evaluation<br />
Philippe Martinet<br />
53 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Applications and projects: CITYVIP AGV using visual memory<br />
Unified mo<strong>de</strong>l Use of unified camera mo<strong>de</strong>l for fisheye camera<br />
[IROS07]<br />
[GEYER00]<br />
X m = 1 ρ<br />
⎡<br />
⎣ X Y<br />
Z<br />
Motion and<br />
Mission planning<br />
⎤<br />
⎦<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
Image plane<br />
m p<br />
<strong>Illustration</strong>s<br />
K M<br />
ρ = X = √ X 2 + Y 2 + Z 2<br />
m n = [x T β] T = ⎢<br />
⎣<br />
m p = K M m n<br />
⎡<br />
X<br />
Z + ξρ<br />
Y<br />
Z + ξρ<br />
β<br />
⎤<br />
⎥<br />
⎦<br />
ξ<br />
F m<br />
F c<br />
m n X 3D (X, Y, Z)<br />
X m<br />
Mirror : unitary sphere<br />
Philippe Martinet<br />
54 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
ϕ - 2 ξ<br />
Introduction<br />
ξ<br />
to ITS<br />
Special cases :<br />
ǫ = 1 and ξ = 0<br />
ǫ = 0 and ξ = 1<br />
Motion and<br />
Mission planning<br />
Image plane<br />
F m<br />
F c<br />
mirror<br />
Sensor Based<br />
Control<br />
Applications and projects: CITYVIP<br />
Unified mo<strong>de</strong>l<br />
m p<br />
M =<br />
X m<br />
X 3D (X, Y, Z)<br />
perspective projection<br />
spherical projection<br />
Single-viewpoint systems<br />
Case of points ϕ and ξ : Mirror parameters<br />
K =<br />
f(X 3D ) =<br />
Autonomous<br />
navigation<br />
AGV using visual memory<br />
⎡<br />
⎣ ϕ − ξ 0 0<br />
0 ϕ − ξ 0<br />
0 0 1<br />
⎡<br />
⎣ f fs u 0<br />
0 fr v 0<br />
0 0 1<br />
⎡<br />
⎢<br />
⎣<br />
⎤<br />
⎦<br />
X<br />
ǫZ+ξ √ X 2 +Y 2 +Z 2<br />
Y<br />
ǫZ+ξ √ X 2 +Y 2 +Z 2<br />
1<br />
Generic Projection function<br />
m p = K<br />
<br />
M<br />
f(X 3D)<br />
K M<br />
<strong>Illustration</strong>s<br />
⎤<br />
⎥<br />
⎦<br />
⎤<br />
⎦<br />
Philippe Martinet<br />
55 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Applications and projects: CITYVIP<br />
Localization<br />
Scaled euclidian reconstruction<br />
Sensor Based<br />
Control<br />
m p<br />
Autonomous<br />
navigation<br />
m ∗ p<br />
X ∗ m<br />
<strong>Illustration</strong>s<br />
AGV using visual memory<br />
X m<br />
Epipolar constraint can be expressed by :<br />
For pinhole mo<strong>de</strong>l<br />
[Nister04]<br />
From five couples of points<br />
E can be estimated<br />
Outliers are rejected by<br />
using RANSAC<br />
Philippe Martinet<br />
56 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Applications and projects: CITYVIP<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
AGV using visual memory<br />
State mo<strong>de</strong>l of the mobile robot<br />
(s, y, θ)<br />
⋆ F i = (O i , X i , Y i , Z i )<br />
⋆ F i+1 = (O i+1 , X i+1 , Y i+1 , Z i+1 ) the frames attached to the robot when<br />
I i<br />
Kinematic mo<strong>de</strong>l of the mobile robot<br />
Chained<br />
System<br />
theory<br />
[IAV04]<br />
Philippe Martinet<br />
57 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Applications and projects: CITYVIP<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
AGV using visual memory<br />
Philippe Martinet<br />
58 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Applications and projects: CITYVIP<br />
Testbed<br />
Experimental robot is a Robucab from Robosoft<br />
Algorithms are implemented in C ++ language<br />
on a laptop using RTAI-Linux OS with a<br />
2GHz Centrino processor<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
AGV using visual memory<br />
Fujinon fisheye lens, mounted onto a<br />
Marlin F131B camera<br />
Field-of-view of 185 <strong>de</strong>g<br />
Image resolution in the experiments was 800 × 600 pixels<br />
Frame rate of 15fps<br />
Longitudinal velocity V has been fixed to 1 ms −1<br />
K p and K d are tuned regarding a double pole located at value 0.3<br />
Philippe Martinet<br />
59 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Applications and projects: CITYVIP<br />
Testbed : SOVIN Architecture<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
AGV using visual memory<br />
Philippe Martinet<br />
60 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Applications and projects: CITYVIP<br />
Experiment<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
AGV using visual memory<br />
Loop 1200 m – 35 edges<br />
Navigation 1700m (26 minutes, 1400 keys images, 54 edges)<br />
Philippe Martinet<br />
61 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Applications and projects: CITYVIP<br />
Experiment<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
AGV using visual memory<br />
Lateral error:<br />
- mean: 23 cm<br />
-standard <strong>de</strong>viation: 30 cm.<br />
Navigation stops after<br />
1700m for security reasons<br />
(small number of matched<br />
points)<br />
Philippe Martinet<br />
62 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Applications and projects: CITYVIP<br />
Experiment<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
AGV using visual memory<br />
Errpoints:Mean distance between an image point<br />
and its position in <strong>de</strong>sired image<br />
Philippe Martinet<br />
63 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Applications and projects: CITYVIP<br />
Experiment<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
AGV using visual memory<br />
Philippe Martinet<br />
64 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Applications and projects: CITYVIP<br />
Vi<strong>de</strong>o<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
AGV using visual memory<br />
Topological<br />
navigation<br />
using<br />
visual memory<br />
Cityvip<br />
Clermont-Fd 2008<br />
Using vision only<br />
Clermont-Ferrand<br />
LASMEA-GRAVIR<br />
Philippe Martinet<br />
65 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Mo<strong>de</strong>lling: platoon<br />
⋆ d i : curvilinear distance<br />
Philippe Martinet<br />
66 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Mo<strong>de</strong>lling: State mo<strong>de</strong>l The vector (s i , y i , ˜θ i ) <strong>de</strong>scribes the state of the i th<br />
vehicle<br />
Mo<strong>de</strong>lling is <strong>de</strong>rived un<strong>de</strong>r non-slipping assumptions (tricyle mo<strong>de</strong>l)<br />
[Samson95, Daviet95, Thuilot04] → relies on a kinematic mo<strong>de</strong>l<br />
→ <strong>de</strong>signed with respect to the reference path<br />
⎧<br />
⎪⎨<br />
⎪⎩<br />
ṡ i = v i<br />
cos ˜θ i<br />
1−y i c(s i )<br />
ẏ i = v i sin ˜θ i<br />
<br />
˙˜θ i = v tan δi<br />
i l<br />
Longitudinal Mo<strong>de</strong>lling<br />
− c(s i) cos ˜θ i<br />
1−y i c(s i )<br />
Syst Ia<br />
<br />
Control objectives<br />
y i+1 and ˜θ i+1 to 0<br />
δ i+1<br />
⎧<br />
⎨<br />
⎩<br />
d i = s i − s i+1<br />
d˙<br />
i = ṡ i − ṡ i+1<br />
˙ d i = v i<br />
cos ˜θ i<br />
1−y i c(s i ) − v i+1<br />
Syst Ib<br />
cos ˜θ i+1<br />
1−y i+1 c(s i+1 )<br />
(s i − s i+1 ) to d<br />
v i+1<br />
Philippe Martinet<br />
67 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Control: Longitudinal control in curved path following<br />
Kinematic mo<strong>de</strong>l d i = s i − s i+1 e i = d i − d<br />
[Bom05]<br />
Syst Ib<br />
Exact linearization<br />
Syst IIb<br />
ė i = v i<br />
cos ˜θ i<br />
1 − y i c(s i ) − v cos ˜θ i+1<br />
i+1<br />
1 − y i+1 c(s i+1 )<br />
v i+1 = 1 − y i+1 c(s i+1 )<br />
cos ˜θ i+1<br />
u i+1 = ė i<br />
<br />
auxiliary control law<br />
v i cos ˜θ i<br />
1 − y i c(s i ) − u i+1<br />
<br />
Proportional control law<br />
u i+1 = −k e i v i+1 = 1 − y i+1 c(s i+1 )<br />
k > 0<br />
ė i = −k e i<br />
d i → d<br />
cos ˜θ i+1<br />
<br />
v i cos ˜θ i<br />
1 − y i c(s i ) + ke i<br />
<br />
Standard longitudinal control mo<strong>de</strong><br />
Philippe Martinet<br />
68 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Control: Longitudinal control in curved path following<br />
[Bom05]<br />
MCS : Mixte Control Strategy<br />
LCS<br />
Lea<strong>de</strong>r<br />
LCS<br />
x i+1 = e i i+1<br />
e i i+1 =s i −s i+1 −d<br />
GCS<br />
GCS<br />
x i+1 = e 1 i+1<br />
e 1 i+1 = s 1 − s i+1 − i.d<br />
MCS<br />
Lea<strong>de</strong>r<br />
Lea<strong>de</strong>r<br />
MCS<br />
x i+1 = σ i+1 e 1 i+1 + (1 − σ i+1)e i i+1<br />
Philippe Martinet<br />
69 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Applications and projects : testbed<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Thales Navigation 'Aquarius 5002'<br />
Position (2cm), velocity, at 10Hz<br />
Network<br />
RTK-GPS<br />
RS-232<br />
High level<br />
Computer<br />
Cycab Computer<br />
CAN<br />
IEEE-1394<br />
Cycab from Robosoft<br />
18 km.h -1<br />
WiFi<br />
UHF<br />
Wireless<br />
Communication<br />
(i th Cycab<br />
State vector)<br />
WiFi<br />
UHF<br />
IEEE-1394<br />
RTK-GPS<br />
RS-232<br />
High level<br />
Computer<br />
Cycab Computer<br />
CAN<br />
Network<br />
MPC MPC MPC MPC<br />
Actuators<br />
Cycab i<br />
Actuators<br />
Cycab i+1<br />
Philippe Martinet<br />
70<br />
70 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Applications and projects : MOBIVIP<br />
Autonomous<br />
navigation<br />
Platooning<br />
<strong>Illustration</strong>s<br />
Mobivip<br />
Clermont-Fd 2006<br />
Using RTK-GPS only<br />
Clermont-Ferrand<br />
LASMEA-GRAVIR<br />
Philippe Martinet<br />
71<br />
71 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Applications and projects : MOBIVIP<br />
Autonomous<br />
navigation<br />
Platooning<br />
<strong>Illustration</strong>s<br />
Mobivip : Clermont-Fd 2006<br />
Mobivip : Clermont-Fd 2006<br />
Vehicle to be inserted in platoon at<br />
(3 rd position)<br />
Reference<br />
trajectory<br />
Platoon with 5 vehicles<br />
direction<br />
At Beginning<br />
Stop<br />
Platoon with 5 vehicles<br />
Reference<br />
trajectory<br />
Direction<br />
Platoon using RTK-GPS<br />
Insertion<br />
Clermont-Ferrand : LASMEA-GRAVIR<br />
Philippe Martinet<br />
Platoon using RTK-GPS<br />
Joining<br />
Clermont-Ferrand LASMEA-GRAVIR<br />
72<br />
72 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Applications and projects : MOBIVIP<br />
Autonomous<br />
navigation<br />
Platooning<br />
<strong>Illustration</strong>s<br />
Mobivip : Clermont-Fd 2006<br />
Mobivip : Clermont-Fd 2006<br />
Platoon using RTK-GPS<br />
Insertion<br />
Clermont-Ferrand : LASMEA-GRAVIR<br />
Philippe Martinet<br />
Platoon using RTK-GPS<br />
Joining<br />
Clermont-Ferrand LASMEA-GRAVIR<br />
73<br />
73 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Applications and projects : CRISTAL<br />
Autonomous<br />
navigation<br />
Platooning<br />
<strong>Illustration</strong>s<br />
Cristal : Montbelliard 2008<br />
Cristal : Clermont-Fd PAVIN 2009<br />
Platoon using RTK-GPS<br />
Manually driven lea<strong>de</strong>r<br />
Clermont-Ferrand : LASMEA-GRAVIR<br />
Philippe Martinet<br />
Platoon using RTK-GPS<br />
Manually driven lea<strong>de</strong>r<br />
Clermont-Ferrand LASMEA-GRAVIR<br />
74<br />
74 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Applications and projects : CRISTAL<br />
Platooning<br />
Cristal : Clermont-Fd 2008<br />
Platoon using vision/ranger fin<strong>de</strong>r<br />
Clermont-Ferrand LASMEA-GRAVIR<br />
Philippe Martinet<br />
75<br />
75 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Flying robot<br />
Philippe Martinet<br />
76 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
[IROS09]<br />
Flying robot<br />
Automatic Gui<strong>de</strong>d Vehicles<br />
Visual<br />
Route Ψ<br />
ground<br />
Philippe Martinet<br />
77 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
[IROS09]<br />
Flying robot<br />
Automatic Gui<strong>de</strong>d Vehicles<br />
Topological<br />
navigation<br />
using<br />
visual memory<br />
CEA 2009<br />
Fontenay-aux-Roses<br />
Using vision only<br />
Clermont-Ferrand<br />
LASMEA-GRAVIR<br />
Philippe Martinet<br />
78 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Visual servoing of landing<br />
[Bourquar<strong>de</strong>z07]<br />
IROS07<br />
Philippe Martinet<br />
79 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong><br />
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
Examples : Vulog<br />
Philippe Martinet<br />
80 Intelligent Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
Introduction<br />
to ITS<br />
Motion and<br />
Mission planning<br />
Sensor Based<br />
Control<br />
Autonomous<br />
navigation<br />
<strong>Illustration</strong>s<br />
GRAVIR : experimental site<br />
● Available: PAVIN urban area<br />
Urban area : 2500 m 2 – 320 m of streets / rural area : 1900 m 2 – 260 m of tracks<br />
● Future: PAVIN in natural environment<br />
Tree area<br />
Peripheral track<br />
Ground track<br />
Stabilized track<br />
length 1 km, width 10 m<br />
Sinus profil<br />
Philippe Martinet<br />
Crossing ramp<br />
Negative obstacle<br />
area<br />
Inclined<br />
profil<br />
Unstable area<br />
81 Intelligent<br />
81<br />
Transportation System - ECN, Transportation cursus, <strong>Nantes</strong>
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