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Formation Control
for Platoons of
Autonomous
Vehicles
Student : Chouaieb Nemri
Supervisor : Prof. Cédric Pradalier
OUTLINE
Introduction
State of the Art
Problem statement
Suggested control solution
Simulation Results
Conclusion
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INTRODUCTION
Safety Highway Capacity
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Source : http://www.toyota-global.com/innovation/intelligent_transport_systems/images/The_Future_of_Mobility.pdf
INTRODUCTION
Eco-friendly Less workforce
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WHERE ARE WE NOW?Source : IHS
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STATE OF THE ART
Control law based
on local/Global
platoon data?
Vehicle commands
computed on
central computer?
Platooning
Decentralized
Global Local
Centralized
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Platooning approaches
STATE OF THE ART
Accurate tracking.
Depends on whole
convoy data.
Communication delays
– Packet Loss – Data
processing and
transmission time
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Centralized & Global decentralized architectures
STATE OF THE ART
• No communication
needed
• Based exclusively on
proprioceptive and
exteroceptive sensors
• Not as accurate as
Global or Centralized
approaches
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Local decentralized architectures
PROBLEM STATEMENT
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Lateral error propagation – proportional controller (video)
Lateral error getslarger as we
consider laterRobots in the convoy
SUGGESTED CONTROL SOLUTION
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© Jano Yazbeck, Alexis Scheuer and François CharpilletUniversité de Lorraine, LORIAInria, MAIA team - FRANCE
SUGGESTED CONTROL SOLUTION
Control Law
Longitudinal
SSC Proportional
Lateral
MemoLAT
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Decoupling control components
SUGGESTED CONTROL SOLUTION
A typical control loop
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The Big Picture
Compute
Longitudinal
Control
Choose target and
Compute Later
Control
Apply
control
laws
Rebelotte
Aquire and save
predecessor’s
position
12
3
4
SUGGESTED CONTROL SOLUTION
Middleware : ROS indigo
Simulation software : V-REP
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Used simulation environment
SUGGESTED CONTROL SOLUTION
IndivMarkersNoKinect
• Detects the alvar
markers and yields
their coordinates in the
camera frame
Tracker
• Adding new
points to the path
• Coordinate transform
from previous frame to
current frame
everytime we update
odometry
• Delete old points
Controller
Computes
and applies
contol laws
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SUGGESTED CONTROL SOLUTION
Tracker
•Sync_odo_cb : (left/right wheel encoders)
- Odometry
- Path coordinate transform
- delete old points
•Ar_cb : (AlvarMarkers)
- Coordinate transform from camera frame to robot frame
- add new point to path
Controller
•List_cb: (custom pointlists msg)
- Computes current distance from predecessor.
- Selects target point for MemoLAT
•control_cb : (Timer 10 ms)
- Longitudinal control : proportional law
- Lateral control : Modified MemoLAT (Video)
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SUGGESTED CONTROL SOLUTION
Limited camera’s angle of view.
Robots get lost when miss the AlvarMarkers
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Solution : Combining 2 behaviours. MemoLAT & keeping the markers in the visible zone. (cost : less accuracy)
w = alpha * keep_visible + (1-alpha) * MemoLAT
SIMULATION RESULTS
Improved accuracy (Videos)
Angular error as a function of time
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SIMULATION RESULTS
Small d_l
• Small lateral
deviation.
• BUT creates
oscillation around
small curvature
paths
Large d_l
• Large lateral
deviation for high
curvature paths
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Effect of establishment distance
We have no analytic relation in order to dynamically set a suitable value for
CONCLUSION
Platooning is awesome !
Lateral and longitudinal controls can be decoupled
We suggested a high level controller. It is applicable to all kind of mobile robots
Experimantal trials, setting dynamically the establishment distance…
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