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Technologies for Mobile Manufacturing
Sanjiv Singh/Reid SimmonsRobotics InstituteCarnegie Mellon University
February 2008
OutlineOutline
Motivation Objectives A Simple Example
Autonomous Assembly Tele-operated Operation “Sliding” Autonomy
More complicated examples Key Technologies
Relative Position Estimation Coordinated Control of Mobile Manipulation Task Control Architecture
Terrestrial ConstructionTerrestrial Construction
• Many different tasks• Complimentary entities• Big plan that is constantly refined
ObjectivesObjectives
Enable heterogeneous multiple robots to coordinate complex assembly tasks
Emphasis on tasks that can not be done by single robots
Enable flexible human-robot interaction during assembly Deal with unanticipated contingencies Reduce need for operator
Candidate Tasks: Assemble multi-element, compliant structure Brace structure for strength
Cable structure
Previous Work: Distributed Previous Work: Distributed ArchitecturesArchitectures
ExecutiveExecutive
BehavioralControl
PlanningPlanning
ExecutiveExecutive
BehavioralControl
ExecutiveExecutive
BehavioralControl
PlanningPlanning PlanningPlanning
Previous Work: Multi-Robot Previous Work: Multi-Robot SynchronizationSynchronization
Enable agents to allocate and synchronize tasks; detect and handle each others exceptions
Robot 1
Robot 2
Robot 3
Execute Sequentially
Execute Concurrently
Execute Sequentially
Task A
Task C
Task B
Coordinated AssemblyCoordinated Assembly
Three heterogeneous robots
Crane has large workspace, high weight capacity
Manipulator has fine control
Roving eye provides high degree of resolution
Independent robot operation without accurate inter-robot calibration
Multi-Robot TestbedMulti-Robot Testbed
A Simple Example: Fully A Simple Example: Fully AutonomousAutonomous
Dock a single beam into two upright connectors with mm tolerance
QuickTime™ and aCinepak decompressor
are needed to see this picture.
Combined State-Machine for Dual-End Combined State-Machine for Dual-End DockDock
Lower beam
Align beamover far
stanchion
Turn far endinto view
Align beamover near stanchion
Lower beaminto far
stanchion
Watch crane
WatchMM
Move to far stanchion
Watchdock
Watch MM
Watchcrane
Watchpush
Move awayFrom MM
Align MM at near
stanchion
Dock beam innear stanchion
Grasp beam
Dock beam
Turn 180˚Align MM atfar stanchion
Push beam overfar stanchion
Contact beam
Push beam
Stow armStow arm
CR
AN
EM
OB
ILE
M
AN
IPU
LAT
OR
RO
VIN
G E
YE
*Sequential connections for watch tasks not shown for clarity.
Dual End Dock - Percent Completed
0%
20%
40%
60%
80%
100%
Startup failure.
Bad turn.
Beam caught on grove.
Startup failure.Near end failure.Near end failure.Near end failure.Beam misaligned.Beam misaligned.Base misaligned.Base misaligned.
Successful.
Near end failure.Fiducial blocked.
Successful.
Near end failure.Beam misaligned.
Bad turn.
Startup failure.
Beam caught on grove.
Base misaligned.Base misaligned.Xavier problem.Xavier problem.Fiducial blocked.Base misaligned.
MM problem.MM problem.Successful.
Fiducial blocked.Base misaligned.Xavier problem.
Base misaligned.
Beam caught on grove.
Base misaligned.Fiducial blocked.Base misaligned.Fiducial blocked.
MM problem.Xavier problem.
Bad turn.Bad turn.
MM problem.MM problem.
Fiducial blocked.Fiducial blocked.Fiducial blocked.Fiducial blocked.
1 2 3 4 5 6 7 8 9 1011 121314151617181920212223242526272829303132333435363738394041424344454647484950
Trials
Near End Dock
Swap Ends
Far End Dock
First ResultsFirst Results
FailuresFailures
First dock succeeds 70% of the time Complete second dock succeed only 6% of the time 20% of the time, initial conditions are not set
Common errors: Mobile Manipulator might over or underturn Beam gets caught on groove Arm gets caught on beam Fiducials are blocked Actuator deadband causes infinite loop Visual servoing fails Crane actuator slip causes offset error
Mature Autonomous SystemMature Autonomous System
Setup Completely autonomous 50 trials
Typical Failures Electrical failure on MM Software crash Near collision due to errors
in visual perception Obscured fiducial MM lost grip on beam Assembled portion broke
apart Speed
µ=9.9min, =1.6min
Teleoperated SystemTeleoperated System Setup
50 trials (total) with four robot-experienced users
Several robot-specific GUIs Teleoperation using visual
feedback from Roving Eye
Typical Failures Visual feedback created
“tunnel” vision Stereo vision did not provide
users with good depth perception
Experienced one network and one electrical failure
Speed µ=12.5min, =4.0min
Sliding Autonomy: Adding an Sliding Autonomy: Adding an OperatorOperator
Fully autonomous operation has many failure modes Not enough bang for the buck to automate some
operations Would like seamless method to switch between
operator and system Operator should be able to take over either control or
monitoring of task Three modes of human interaction:
Pre-assigned task Intervention Exception Handling
Sliding TasksSliding Tasks
Mobile Manipulator First dock Turn Second dock*
Roving Eye Visual search Turn
Crane Second dock*
Results w. Sliding AutonomyResults w. Sliding Autonomy
Setup Several task-specific GUIs Limited adjustable tasks Feedback available from
any autonomous task 50 trials
Results Discretionary-Intervention
Successes Mandatory-Intervention
Successes Failures due to damaged
hardware & network failure
Successes
Completion Times
Mean Std-dev
Fully Autonomous 64% 9.9m 1.6m
Tele-operated 96% 12.5m 4.0m
Sliding Autonomy 94% 9.9m 1.9m
Discretionary Only 68% 9.5m 1.5m
Mandatory Only 26% 11.1m 2.6m
OutlineOutline
Motivation Objectives A Simple Example
Autonomous Assembly Tele-operated Operation “Sliding” Autonomy
More complicated examples Key Technologies
Relative Position Estimation Coordinated Control of Mobile Manipulation Task Control Architecture
More complicated example #1More complicated example #1
ModesModes
Teleoperated: User controls each robot in turn through keyboard and mouse
Autonomous: Hit start and step back System Initiative: System asks for help when needed Mixed Initiative: System initiative + Operator intervention
Completion Time
Success Rate TLX
Workload
Teleop
Autonomous
System Initiative
Mixed Initiative
ModesModes
Teleoperated: User controls each robot in turn through keyboard and mouse
Autonomous: Hit start and step back System Initiative: System asks for help when needed Mixed Initiative: System initiative + Operator intervention
Completion Time [Std]
Success Rate (Total Exp)
TLX
Workload [Std]
Teleop 732 [227] sec 94% (16) 52 [16]
Autonomous 516 [125] sec 89% (35) 0
System Initiative 500 [182] sec 100% (16) 27 [21]
Mixed Initiative 529 [148] sec 94% (16) 29 [13]
Tele-Op
Mixed
System Initiative
Autonomous
516[125]
89% (35)
0
500[182]
100% (16)
27 [21]
529 [148]
94% (16)
29 [13]
732 [227]
94% (16)
52 [16]
More complicated example # 2More complicated example # 2
QuickTime™ and aAnimation decompressor
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Extended scenario involves planning because
• constraints make it difficult to script a plan
• Recovery from failure might require many steps
University of Maryland Space Systems LabUniversity of Maryland Space Systems LabNeutral Buoyancy TankNeutral Buoyancy Tank
EASE Truss Assembly
Ranger
Space Shuttle Cargo Bay
“Roving Eye”
“Crane”
“Mobile Manipulator”
Trestle - U Maryland SSL Trestle - U Maryland SSL CooperationCooperation
Operator at CMU
Ranger Arms at U Maryland
internet
OutlineOutline
Motivation Objectives A Simple Example
Autonomous Assembly Tele-operated Operation “Sliding” Autonomy
More complicated examples Key Technologies
Relative Position Estimation Coordinated Control of Mobile Manipulation Task Control Architecture
Sensing LocationSensing Location
Need to localize parts wrt to robot during operation so robot can plan motion and adapt to any variations in starting conditions & performance
Complicated because the robot base is not stationary Method 1: No global reference frame. Relative position
(between parts & between robot and part) is determined via fiducials
Advantages: flexible, low infrastructure Disadvantages: accuracy can be low unless high fiducials are sensed
with high resolution, computationally expensive
Method 2: Establish global reference frame. Parts and Robots are all registered in common frame.
Advantages: high accuracy, low computation requirements Disadvantages: high infrastructure costs, must guarantee line of sight
from fixed infrastructure
Sensing Relative PositionSensing Relative Position
Visual Fiducial allows determination of ID & 6 DOF displacement between camera and fiducial.
Fiducials have some redundancy, can work even if the fiducial is partly obscured.
Main computational expense is in detecting fiducial in the scene.
Accuracy increases as camera gets closer to fiducial
Tracking Fiducials (with occlusion) Tracking Fiducials (with occlusion)
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Other kinds of FiducialsOther kinds of Fiducials
Active Fiducials can be used.
QuickTime™ and aMotion JPEG OpenDML decompressor
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Sensing in a Global Reference Sensing in a Global Reference FrameFrame
Transmitters fixed to infrastructure Receivers on items that move Requires synchronization between receivers
QuickTime™ and aTIFF (Uncompressed) decompressor
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http://www.indoorgps.com/
Mobile ManipulationMobile Manipulation
Want to place the robot end effector accurately in a large workspace. Could do this by coupling manipulator & mobile base.
Coordination of base and arm motions of Mobile manipulators is complicated because of redundant degrees of freedom degrees of freedom.
Further considerations: Want to keep the arm from getting close to singularities. Want to control end-effector but want to ensure that the
base meets the constraints. Arm and base have very different response
Mobile ManipulationMobile Manipulation
Resolved motion control withArm motion only-- SMALLER WORKSPACE
Resolved motion control withCoordinated Arm and Base-- LARGER WORKSPACE
QuickTime™ and aCinepak decompressor
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Implementation on CMU MMImplementation on CMU MM
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Projecting into the Null Space Projecting into the Null Space (Example1)(Example1)
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Projecting into the Null Space Projecting into the Null Space (Example2)(Example2)
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Offline Planning to decouple Base & Offline Planning to decouple Base & Arm MotionArm Motion
Each grid cell gets a score based on how much of the path and how well the it can be covered with the base at that point.
Seam Following Seam Following
Motion sped up by 4x
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