A global decentralized control strategy for urban vehicle platooning relying solely on monocular vision

Transcription

1 IROS09 A global decentralized control strategy for urban vehicle platooning relying solely on monocular vision Avanzini Pierre, Thuilot Benoit, Eric Royer, LASMEA, UMR 6602 du CNRS Clermont-Ferrand, France Cityvip Project.@lasmea.univ-bpclermont.fr

2 Outline of the presentation Introduction Motivation Previous contribution and control Modelling Control Observer design Simulation Experimental setup Real Experiment 2

3 and Control Motivation : Inner-cities congestion 3

4 and Control Motivation: Urban Transportation Systems Concept Their attractiveness depends mainly on their flexibility "they have to answer to various public needs" Praxitèle Lisélec Car sharing concept CARLINK appears as a suitable answer in specific areas (inner-cities pedestrian zones, airport terminals,...) Such systems have been developed since the mid-90's : Praxitèle in France, CarLink in USA, Crayon in Japan 4 Honda Singapour Crayon

5 and Control Motivation: Autonomous navigation Car sharing concept demands for automatic guidance capabilities To transport passengers in an entire automatic way Autonomous vehicle station #3 station #2 station #1 To deliver free vehicles for customer use To bring back empty vehicles to stations for refilling and reuse Platoon capability 5

6 PLATOON AGV Introduction and Control Previous Work AGV & Platoon Autonomous Leader 6 Manually driven Leader

7 Introduction and Control Previous Work AGV Automatic Guided Vehicles Mobivip : Clermont-Fd 2004 BODEGA : Clermont-Fd 2005 Navigation using RTK-GPS Clermont-Ferrand : LASMEA-GRAVIR 7 Navigation using vision only Clermont-Ferrand : LASMEA-GRAVIR

8 Introduction and Control Previous Work AGV Automatic Guided Vehicles Platooning Mobivip : Clermont-Fd 2006 Mobivip : Clermont-Fd 2006 Platoon using RTK-GPS Insertion Clermont-Ferrand : LASMEA-GRAVIR 8 Platoon using RTK-GPS Joining Clermont-Ferrand LASMEA-GRAVIR

9 Introduction and Control Previous Work Cristal project Platooning Cristal : Montbelliard 2008 Cristal : Clermont-Fd PAVIN 2009 Platoon using RTK-GPS Manually driven leader Clermont-Ferrand : LASMEA-GRAVIR 9 Platoon using RTK-GPS Manually driven leader Clermont-Ferrand LASMEA-GRAVIR

10 Introduction and Control Previous Work Cristal project Platooning Cristal : Clermont-Fd 2008 Platoon using vision/ranger finder Clermont-Ferrand LASMEA-GRAVIR 10

11 and Control Present Work Platoon using vision only Use of a 3D visual memory approach WIFI inter vehicle communication Work concerns one line scale factor 11

12 and Control Modelling: Notations M Y A v y Reference path tangent O M C X A 12

13 and Control Modelling: platoon 13

14 and Control Modelling: State model Modelling is derived under non-slipping assumptions (tricyle model) [Samson95, Daviet95, Thuilot04] relies on a kinematic model designed with respect to the reference path Syst Ia Control objectives Longitudinal Modelling Syst Ib 14

15 and Control Control: Lateral control in curved path following [Thuilot04] Chained state vector Chained Control vector Chained System [Samson95] Exact linearization Chained model driven by s Syst Ia Syst IIa PD control law linear system structure full lateral / longitudinal decoupling (k d, k p ) tuning specifies a settling distance Vehicle trajectories are velocity independent 15

16 and Control Control: Longitudinal control in curved path following [Bom05] Kinematic model Syst Ib Exact linearization Syst IIb auxiliary control law Proportional control law k > 0 16 Standard longitudinal control mode

17 MCS GCS LCS Introduction and Control Control: Longitudinal control in curved path following MCS : Mixte Control Strategy Leader LCS GCS Leader MCS Leader 17

18 and Control 3steps strategy: human supervised Learning 3D Visual memory building localization and control 18

19 and Control AGV using vision only urban context [Royer04] Vis ual me mo r y Reference Trajectory and Image features (3D points) Experimental data Compiegne city center (BODEGA/MOBIVIP) 19

20 and Control 3D visual memory Virtual trajectory (vision) Ground truth trajectory (RTK-GPS) Error of arc length distance along the trajectory (115m length) Local distorsion of the 2 world RTK-GPS vs vision Max:7.48m End: 1.72m 20

21 and Control Repetitive test 3D visual memory Virtual trajectory (vision) Scale factor evolution on 2m length segment) along the trajectory (115m length) 21

22 and control Observer design Notations 22

23 and control Observer design Hypothesis : distorsion are locally homogeneous (both direction in the plane ) 23

24 Introduction and control Observer design Duality observer control Observation model 24

25 and control Observer design Duality observer control 25

26 Introduction and control Observer design Curvilinear abscissa 26

27 Simulations Single vehicle is Simulated: V = 1m/s -1 Standard deviation = 0.015m/s -1 Local segment Factors (same like real one) Visual data at 15Hz And with 2 standard deviations v=0m and v=0.02m Observer gain K=2 and control Convergence within 3m Average error < 2.4 mm (17.6) < 3cm (7) 27

28 and control Experimental setup : pavin site 28

29 and control Experimental setup : vehicles Cycab vehicle Robosoft company 18 km.h -1 lead-acid batteries 4 DC motors Thales Navigation Zmax RTK GPS receiver 29 position velocity supplied at 10 Hz, with a 2cm accuracy

30 and control Experimental setup : platoon architecture Network RTK-GPS RS-232 High level Computer Cycab Computer CAN IEEE-1394 Cycab from Robosoft 18 km.h -1 WiFi UHF Wireless Communication (i th Cycab State vector) WiFi UHF IEEE-1394 RTK-GPS RS-232 High level Computer Cycab Computer CAN Network MPC MPC MPC MPC Actuators Cycab i 30 Actuators Cycab i+1

31 and control Real experiment One line scale factor 31

32 and control Real experiment Vehicle inter distance error with corrected vision data refered to the RTK GPS ground truth Veh1/Veh2: error < 14 cm Veh1/Veh3: error < 17 cm 32

33 and control Real experiment Vehicle inter distance error with the raw vision data refered to the RTK GPS ground truth Veh1/Veh2: error < 1 m Veh1/Veh3: error < 1.5 m 33

34 and control, control, scale factor, modeling and control approach On-line of the scale factor Vision only based localization step Validation Use of the PAVIN site Real experimentation with 3 vehicles Perspectives Manually driven platoon Other scale factor approach 34

35 and control Thanks for your attention Any questions 35