Flexible Manufacturing Line with Multiple Robotic …telerobot.cs.tamu.edu/CMA/slides/Heping...
Transcript of Flexible Manufacturing Line with Multiple Robotic …telerobot.cs.tamu.edu/CMA/slides/Heping...
Robotics Laboratory Ingram School of Engineering
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Flexible Manufacturing Line with Multiple Robotic Cells
Heping Chen [email protected]
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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Trim and Final Assembly Line
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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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Proposed Solution
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Station controller/Computer
Station controller/Computer
Station controller/Computer
Station controller/Computer
Station controller/Computer
Other devices
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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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#1 FMS #2 FMS #n FMS
MTS
SICK
Kinect
Off-L
ine to On-L
ine Teaching
Off-
Line t
o On-L
ine T
each
ing
FMS:Fixed Manufacturing Station MTS:Mobile Teaching Station
Online Transfer Learning Online Transfer Learning
Online
Learning
Online
LearningOnline
Learning
Failure Correction
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Autonomous Robot Teaching
• Reducing the operational cost
• Sensor accuracy problem
• Calibration is difficult
• Noise
Target Hole Transmission
Part
Robot ToolMarker for
Tool Detection
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Autonomous Robot Teaching
Autonomous
Industrial Mobile
Manipulator
Hole
Transmission Part Tool/Peg
ABB IRB 140
Industrial Camera
“Adult”Robot
PC
Controller IRC5 Robot
Controller“Child”IRB140
Workpiece
Tool & Peg
DALSA Industrial
Camera
Gigabit
Ethernet
Ethernet
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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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POMDP Method
• Belief State – 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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Example: Single Stage Assembly
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Assembly Process
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How to tune the Assembly Parameters?
Parameters: search force, search radius, search speed, insertion force
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Example: Multi-Stage Assembly
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Problems
• Design of Experiment (DOE)
• Genetic Algorithm (GA)
• Genetic Algorithm (GA)+ANN
• Model Free
• Offline
• Manufacturing line has to be stopped
• Cannot deal with variations, including part location errors, part geometry errors and environmental errors.
Low Efficiency Low Accuracy
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Single Work Station Optimization
• First Time Through (FTT) rate
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• 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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Multi Work Station Optimization
• FTT rate of the whole system
• Cycle time of the production line
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Performance Optimization
• Performance optimization
– Knowledge transfer
• What can we transfer? How to transfer?
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kyM
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Stage 1 Stage 2 Stage M
Multi-Stage Learning Methodology
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Discussions
• Comments
– Robot teaching
– System optimization
• Interesting topics?
–
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