Transcript Vision System in real-time tracking

Vision System in real-time tracking
2002. 8. 12
MAI LAB
Ryu Mi Wyun
Real-time vision-based tracking control of an
unmanned vehicle
Kok-Meng LEE, Zhi Zhou, Robert Blenis and Erik Blasch*
*The George W. Woodruff School of Mechanical Engineering,
Georgia Institute of Technology, Atlanta, GA 30332-0405, USA
Mechatronics Vol.5, No. 8 (1995) 973 - 997
Contents
 Introduction
 Integrated vision-based control
 Dynamic modeling and control strategy
 Integrated vision-based tracking control system
 Experimental investigation
 Conclusions
Introduction (1/2)
Advances In Manufacturing Automation
Increased Demand for Autonomous Vehicles
Unattended
Factories
Material
Handling
Warehouse
Operation
Hazardous
Environment
Requires real-time position and/or velocity feedback
Using Machine Vision to provide the feedback info.
Introduction (2/2)
 Major difficulties
 Slow and/or expensive visual feedback
 Complicated dynamics and control with constraints in ‘target space’
Target Space
Vision system
Drive Space
• where the vehicle is located and oriented • where the vehicle is controlled,
with an implementation of a path
planning mechanism
 The development of a practical method
for implementing real-time vision based control of autonomous vehicles.
Integrated vision-based control - Idea
 Vision-based vehicle control systems
 Spaced-fixed vision systems
: range of motion is restricted/ more than one vision system is required
 On-the-Vehicle (On-board) vision systems
• Takes pictures periodically while moving
• Captures the patterns within its fields of view
• Stores and analyzes the images
• Returns the vehicle’s locations
Integrated vision-based control – Fiducial patterns
 Landmarks to determine the vehicle’s location
 using a feature set
 made of retro-reflective material to eliminate noise
 An illustrative example

,
,
-Two distinct distances between two elements :
- Pattern orientation :
Integrated vision-based control – Fiducial patterns
Integrated vision-based control – Pattern recognition
• Real world Coordinate
• Image Coordinate
Dynamic modeling and control strategy
Obtain the complete set of dynamic models
Propose a path planning mechanism
Outline the control strategy development
• Three-wheeled vehicle
• Vehicle is modeled as a rectangular plate of dimensions
• The axes of the driving motor shafts are perfectly aligned /
are located directly below the transversal axis of the vehicle
Dynamic modeling and control strategy
 Vehicle Dynamics : using Lagrangian formulation
position
orientation
of the center of gravity of the vehicle,
of the axis .. Equations of motion for the vehicle are..
• Masses of the wheel and the vehicle
• The torques applied at the wheels
• The radius of the rear wheels
Dynamic modeling and control strategy
 Path Planning : Implementation of simplified controller design
Dynamic modeling and control strategy*
• Utilizing the visual feedback and modern digital control technology
• Control functions will be implemented using DSP and/or computers
- design the controllers in the discrete-time domain
 Digital tracking controller
: Discretize state-space representation of the motor dynamics
Dynamic modeling and control strategy*
• Utilizing the visual feedback and modern digital control technology
• Control functions will be implemented using DSP and/or computers
- design the controllers in the discrete-time domain
 Digital tracking controller
: Discretize state-space representation of the motor dynamics
 Regulator with reduced-order observer
: Obtain the specified time response of the system
Dynamic modeling and control strategy*
• Utilizing the visual feedback and modern digital control technology
• Control functions will be implemented using DSP and/or computers
- design the controllers in the discrete-time domain
 Digital tracking controller
: Discretize state-space representation of the motor dynamics
 Regulator with reduced-order observer
: Obtain the specified time response of the system
 Digital tracking filter
: To follow an arbitrary desired time-varying trajectory
Integrated vision-based tracking control system
 Laboratory prototype system
• RS170-based systems
: Require pixel data to be stored in a video buffer
: Increased time and require video buffer size
• FIVS – Providing the feedback information
: integrates imaging sensors, control, illumination, signal processing and data communication
: By directly transferring the A/D converter output to the DSP
Integrated vision-based tracking control system
• ViTra (Vision-based Tracking system)
: Three wheel configuration with two rear driving wheels & ball-joint-like front wheel
: Rear wheels are geared to two D.C motors
• Using the two motors
: reduces the backlash, friction and inertia..
• Ball-joint-like front wheel
: to maneuver in all directions without slip
 ViTra integrates the three-wheeled autonomous vehicle,
an on-board FIVS, a ceiling fiducial board,
a DSP board with A/D and D/A converters,
and an Intel 486 as central computer
Experimental investigation
• 30 Patterns are placed 9.75 ft above the CCD lens
• For different vehicle positions and orientations around a predetermined trajectory.
Experimental investigation
• Fig.11-12 give the corresponding plots.
• Slightly off from the known positions.
- average position deviation from the true position is 2.92 and 2.63 in.
• Due to the level of the CCD lens and the lighting conditions etc.
Experimental investigation
• Desired circular path with radius of 20 in.
• A number of tests / Two typical paths are recorded.
• Overall real paths follow the desired ones.
• Some data points far from desired paths
- mismatch of the FIVS update rate & moving speed
- can be reduced by optimizing the pattern recognition
 Reducing the trajectory discrepancy
- unmodeled friction
- ceiling board not perfectly leveled
- approximation in path planning
Conclusions
 ViTra uses a DSP-based flexible integrated vision system(FIVS)
: low cost, computational efficiency, flexibility..
 Using fiducial patterns and corresponding pattern recognition
 Can use the digital controllers and observer to enhance the performance
 Sample circular trajectory tracking experiments illustrated the possibility
 Optimizing the fiducial patterns,
Conducting various trajectory tracking tests