1 / 58

Platooning Dynamics and Control on an Intelligent Vehicular Transport System

Platooning Dynamics and Control on an Intelligent Vehicular Transport System. ALEXANDER LEVEDAHL 1 , FROYLAN MORALES 2 , AND GEORGE MOUZAKITIS 1 1 THE COOPER UNION, NEW YORK, NY 2 THE UNIVERSITY OF TEXAS, BROWNSVILLE, TX {levada, mouzak}@cooper.edu, Froylan.Morales48@utb.edu

kiley
Télécharger la présentation

Platooning Dynamics and Control on an Intelligent Vehicular Transport System

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Platooning Dynamics and Control on an Intelligent Vehicular Transport System ALEXANDER LEVEDAHL1, FROYLAN MORALES2, AND GEORGE MOUZAKITIS1 1 THE COOPER UNION, NEW YORK, NY 2 THE UNIVERSITY OF TEXAS, BROWNSVILLE, TX {levada, mouzak}@cooper.edu, Froylan.Morales48@utb.edu August 5, 2010

  2. Motivation • Reduced Oil Consumption • Rise in fuel costs • BP • Reduced drag and congestion • Higher traffic density

  3. Table of Contents • Potential Field Navigation • MAS-Net Platform Test Bed • Nonlinear Control System • Conclusion

  4. Platoon Structure n-vehicle platoon: {Leader, Follower1, Follower2,…, Followern-1} An 8 vehicle platoon of Buick LeSabres [PATH]

  5. Autonomous Navigation • Vector field • Navigational information at every point in space, all contained in a single matrix • Waypoints (attractive force) and obstacles (repulsive force)

  6. Generating the Vector Field • Linear combination

  7. Resultant Vector Field

  8. Vehicle Dynamics • Holonomic dynamics • Update position on display every Δt = 0.5 sec

  9. Vehicle Dynamics • Leader

  10. Vehicle Dynamics • Followers • Inter-vehicle specifications: • Vehicles in platoon must maintain a safe, fixed distance from each other • Only the lead vehicle navigates

  11. Vehicle Dynamics • Physical system implementations • Artificial potential fields • Fluid dynamics • Spring dynamics

  12. Spring Dynamics • Hooke’s Law • Ideal, undamped • Perturbations introduce oscillations  Undesirable aerodynamic performance loss

  13. Spring Dynamics • Critical damping eliminates unwanted oscillations

  14. Inter-Platoon Dynamics

  15. Video • PlatoonMergeSim.wmv • PlatoonMergeSimMASnet.wmv

  16. MAS-net Test Bed

  17. MAS-net Test Bed • MAS-motes • Two-wheel differentially steered chassis • MicaZ programming and communication board from Crossbow • TinyOS, nesC

  18. Pseudo-GPS • 1280x1024 resolution camera • Analyze images using University of Washington’s ARToolKit • Unique markers to determine robot position and orientation

  19. RobotCommander • Developed by CSOIS • Written in C++ • Dispatching application for mobile MAS-motes • Periodic pGPS calibrations

  20. MAS-net Communication Protocol • MAS-net command message • Command header • Command ID • Destination ID • Payload • AM Transceiver transmits message over wireless channel

  21. Existing Platooning Implementation • Waypoints • Orientation error • Increase duty cycle on appropriate motor

  22. MAS-net Platform Adaptation

  23. MAS-net Platform Adaptation • Discrete wheel states • More gently sloping vector field

  24. Video • MAS-net Longitudinal Merging (MoteMerge.wmv)

  25. MAS-Net This image is the global image of the MAS-Net platform. This image is processed on Matlab to get the RGB values of each individual pixels. All of these values are then outputted to a file, which is then read by a C++ program I developed to choose the correct track points.

  26. Matlab code • MatLab code reads the image and determines which of the pixels are considered white. • It arranges and organizes the pixel coordination's into two files read by C++ program. • Benefits • Simplicity of doing it on Matlab • Saves processing time and power . • Makes C++ program less complex.

  27. C++ code • C++ program then loads the points into two vectors separating the X and Y. • These two vectors still have to be refined because the line of the track contains 14px in width.

  28. C++ code continued • This code chooses the outer most white pixel of the track line by scanning the X axis only.

  29. C++ code continued After we have the two new vectors loaded with the outer white pixels a distance algorithm has to be applied for equally distant waypoints.

  30. Code Flowchart

  31. Results

  32. Robotcomander • After applying this code into RobotCommander we get a leader platoon robot following the track. • soloBot.wmv

  33. Platooning behavior • Following robot’s waypoints are triggered when robot in front reaches a specific destination. • platoon.wmv

  34. Platoon merging • 2nd track was added to create a 2nd platoon and make the merging possible .

  35. Platoon after Platoon Merge • First merge that was accomplished was the “platoon after platoon merge” shown in this video. • merge1.wmv

  36. 2nd Merge • Interleaving merge • More control over platoon required • Faster merging • merge2.wmv

  37. Nonlinear Control and Measurement Error • Design of the Control System • Algorithms to handle Real World Scenarios • Measurement Error • Implementation on MASNet Platform

  38. Linear Control System

  39. Nonlinear Control System

  40. Formation Control with Path Constraint

  41. Real World Scenarios • Path Splitting • Lane Changing • Platoon Merging

  42. Path Splitting

  43. Video • Path Splitting (trial1.avi)

  44. Lane Changing

  45. Merging • Attach to End of Platoon • If Platoon within Certain Distance: Merge • Interlace • Similar to Zipper • Platoon Increases Intervehicular Distance

  46. Video • Lane Changing (trialLane.avi)

  47. Measurement Error • Gaussian Error • ISE and ITAE measures of error

  48. Measurement Error

  49. Measurement Error • Integral Square Error = • Integral Time Average Error =

  50. Measurement Error

More Related