Flexible Manufacturing Line
with Multiple Robotic Cells
Heping Chen
hc15@txstate.edu
Robotics Laboratory
Ingram School of Engineering
Texas State University
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Robotics Laboratory
Ingram School of Engineering
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OUTLINE
• Introduction
• Autonomous Robot Teaching
– POMDP based learning algorithm
• Performance Optimization
– Single cell optimization
– Multi cell Optimization
• Discussion
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Robotics Laboratory
Ingram School of Engineering
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Trim and Final Assembly Line
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Robotics Laboratory
Ingram School of Engineering
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Problems in Production Line
• When one work station fails, the production line
has to be stopped
– Low efficiency
– High cost
• For complex manufacturing processes, is it
possible to optimize the system performance?
– Single work station optimization?
– Multi work station optimization?
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Ingram School of Engineering
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Proposed Solution
Station
controller/
Computer
Station
controller/
Computer
Station
controller/
Computer
Other
devices
Station
controller/
Computer
Station
controller/
Computer
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Robotics Laboratory
Ingram School of Engineering
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Problems
• Big part location errors
– Can happen after a new batch is launched
– Parts could come from different suppliers
• Typically manual teaching methods are used to adjust
the parameters
– Production line has to be stopped
• Installing additional sensors generates new problems
– System upgrading cost
– Maintenance issues
– Sensor failure
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Ingram School of Engineering
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Failure Correction
Online Transfer Learning
Online Transfer Learning
Online
Learning
Online
Learning
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FMS
#2
Online
Learning
FMS
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FMS:Fixed Manufacturing Station
MTS:Mobile Teaching Station
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Robotics Laboratory
Ingram School of Engineering
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Autonomous Robot Teaching
•
•
•
•
Reducing the operational cost
Sensor accuracy problem
Calibration is difficult
Noise
Target Hole
Marker for
Tool Detection
Transmission
Part
Robot Tool
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Ingram School of Engineering
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Autonomous Robot Teaching
Autonomous
Industrial Mobile
Manipulator
ABB IRB 140
Industrial Camera
Hole
Transmission Part
“Adult”
Robot
PC
Controller
Tool/Peg
Gigabit
Ethernet
Ethernet
DALSA Industrial
Camera
IRC5 Robot
Controller
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Workpiece
Tool & Peg
“Child”IRB140
Robotics Laboratory
Ingram School of Engineering
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POMDP
• Partial Observable Markov Decision Process
– State Transition is Uncertain
– Observation is uncertain
– State is partial observable
• Why POMDP?
– Using POMDP to estimate the underlying errors
through executing actions and receiving observations
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Ingram School of Engineering
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POMDP Method
• Belief State
–
–
–
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Real state is unknown
How to make decision?
Assign a belief b over state S
Update the belief state in each step
– Defining Value Functions
– Approximating Value Functions
• Alpha Vector
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Robotics Laboratory
Ingram School of Engineering
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OUTLINE
• Introduction
• Autonomous Robot Teaching
– MDP based learning algorithm
– POMDP based learning algorithm
• Performance Optimization
– Single work station optimization
– Multi work station optimization
• Discussion
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Ingram School of Engineering
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Example: Single Stage Assembly
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Ingram School of Engineering
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Assembly Process
Parameters:
search force, search radius, search speed, insertion force
How to tune the Assembly Parameters?
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Ingram School of Engineering
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Example: Multi-Stage Assembly
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Ingram School of Engineering
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Problems
• Design of Experiment (DOE)
• Genetic Algorithm (GA)
• Genetic Algorithm (GA)+ANN
Low Efficiency
Low Accuracy
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• Model Free
• Offline
• Manufacturing line
has to be stopped
• Cannot deal with
variations, including
part location errors,
part geometry errors
and environmental
errors.
Robotics Laboratory
Ingram School of Engineering
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Single Work Station Optimization
• First Time Through (FTT) rate
• How to balance FTT rate and cycle time?
–
–
–
–
FTT rate is calculated statistically.
Cycle time is recorded each assembly
Modeling?
Optimization methods?
• FTT prediction? Cycle time estimation?
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Ingram School of Engineering
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Multi Work Station Optimization
• FTT rate of the whole system
• Cycle time of the production line
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Ingram School of Engineering
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Performance Optimization
• Performance optimization
– Knowledge transfer
• What can we transfer? How to transfer?
n1k
n 2k
n kM
e1k
e 2k
e kM
y1k
y 2k
y kM
x1k
x 2k
x kM
u1k
u 2k
u kM
Stage 1
Stage 2
Stage M
Multi-Stage Learning Methodology
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Ingram School of Engineering
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Discussions
• Comments
– Robot teaching
– System optimization
• Interesting topics?
–
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Robotics Laboratory
Ingram School of Engineering
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