Industrial Robots Challenging EnvironmentsMicrosoft PowerPoint - italy.ppt Author: ok Created Date:...
Transcript of Industrial Robots Challenging EnvironmentsMicrosoft PowerPoint - italy.ppt Author: ok Created Date:...
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Robots in Environments Co-habited by Human Beings
Oussama KhatibRobotics Laboratory
Department of Computer ScienceStanford University
r o b o t sa 50 year journey
ICRA 2000, San Francisco
Industrial Robots Challenging Environments
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Service & Assistance Haptic Interaction
Robotically Aided Surgery
The Latest
Honda: P2, P3, & Asimo
The Latest
Sony: SDR-4X
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The Latest
AIST: HRP2
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At Stanford
Stanford Robotic Platforms (1993)
Human Guided MotionAssistance
• Greater Freedom• Branching Structures• Difficult Coordination• Compliance: Multi-Contact• Complex Motion Planning
Exploring Human-Like Structures
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Joint Space Control Joint Space Control
Joint Space Control
F
( )GoalV xF = −∇
( )GoalV x
T FJΓ =
Task-Oriented Control
Γ
F
dynamics( )F F=
x
xΛ F=pµ+ +
Task-Oriented Dynamics Task Dynamics – Posture Space
Λ
Fx
x pµ+ + F=
TJT
J
Task Space: TJ
Posture Space: TN
= Γ
Joint-Space Dynamics
Task-relatedPosture
TN
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Task Dynamics – Posture Space
Fx
Λ x pµ+ + F=
TJT
J
Task Space: TJ
Posture Space: TN
= Γ
Joint-Space Dynamics
Task-relatedPosture
TN
Task Dynamics – Human-Like Structure
x J⇒
1
2
.
m
x
xx
x
=
T TJ N⇒ ⇒
Task & Posture Decomposition
Whole-body Control Task/Posture Control
Task T skT
aJ=Γ F
PostureTask ΓΓΓ +=
0=x⇒
Posture Desired Po uT
st reN −=Γ Γ
Task/Posture Consistency Task and Posture Control
no joint trajectories⇒
Whole-body Control
Task Field
Posture Field
Dynamically Decoupled
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Posture Energy
( )Posture Desired Po t rT
s u eN V −= −∇Γ( )T
Task TaskJ V= −∇Γ
PostureTask ΓΓΓ +=
Posture Energy
Total center-of-mass
Human Natural Motion
Motion Capture Specific Robot MotionsMotion CharacteristicsNatural Energies
Posture EnergyHuman Natural Motion
• Load Distribution• Balance - CoM• Joint Efforts• Joint Limits• Joint Stiffness• Kinetic Energy• Environment Awareness• Kinematic Symmetry• Etc. Motion Capture
HumanMotionCapture
HumanDynamicModel
Learning from Human Motion
Natural Energies Animation of motion capture data
Simulation 79 DOF and 136 MusclesBiometric Data & Bone Geometry (Scott Delp)
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HumanMotionCapture
RobotDynamic
Model
HumanDynamicModel
Learning from Human Motion
Human Energies Robot Energies
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General Motion
Task 1 = joint limits avoidance.
Task 2 = balancing.
Task 3 = left hand positioning.
Task 4 = right hand positioning.
Task 5 = wrist alignment.
Task 6 = body symmetry.
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2
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PriorityTasks
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In motion, humansminimize fatigue & maximize social posture, under physical constraints
Compliant MotionMulti-point Contact
DecentralizedCooperation
Safety
Human-Friendly Robots
• Dependable & Safe• Soft Actuators• Light Structures• Impact-reduction Skin• Low Reflected Inertia• Distributed Sensing & Control• Advanced Capabilities
Towards Human-Friendly Robots
Safety
Performance
Competing?
Requirements
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Why Are Robotic Arms Unsafe?
Equivalent Mass-Spring Model
Robot Collision
Head Injury Criteria (HIC)
Injury Prediction
• Uncontrolled collision biggest danger
• robot characteristics impact on injury
Risk Of Serious Injury!
>20 cm compliant covering
DMM Actuation
Parallel Actuation
Elastic Coupling
Large Base Actuator
Small JointActuator
Distributed Macro Mini Acutation
Safety Through Inertia Reduction
>20 cm compliant covering
Revisited: Risk Of Serious Injury!
DMM: 10x reduction in effective inertia
Human-Friendly Robot ConceptElastic Planning
Real-time collision-free path modification
ConnectingReactive Local AvoidancewithGlobal Motion Planning
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Artificial Potential Field -Video
Free-Space Representation Free-Space Tunnel
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Elastic Strip Task Consistent
Humanoid Elastic Planning - Video
Haptic Interaction
• Physical Interaction with Virtual Environments
• Touch and Manipulation• Interactive Dynamic
Simulation - inter-object interactions
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Virtual PrototypingCAD Assembly/Serviceability
Animation
Many Applications...
Medicine
Education
Robotics
Interfaces for the Impaired
Sculpting
Many Devices...
Exos Exoskeleton
Immersion LaparoscopicImpulse Engine
Virtual Technologies CyperGrasp Glove
CMU MagLev Wrist
Iwata 6dof HapticDisplay
McGill PantographSensAblePhantom
Interactive Haptic Simulation
Contact Resolution
– Handle Multi-point contact
– Handle Multi-body Articulated Systems
– Needs to be interactive
Fast Dynamic AlgorithmsSimulation, Contact Resolution, and Control
• Efficient algorithms for contact dynamics O(n)
• Avoid constraints elimination• Cost-free effective mass at
arbitrary contacts
M
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Contact Space
S
=
7
6
5
4
3
2
1
6
4
1
0100000
0001000
0000001
x
x
x
x
x
x
x
x
x
x
( )X⊕Augmented Contact Space–Full set of contact points
–Non Independent
( )XActive Contact Space– subset of contact points
– independent, but unknown
X XS ⊕=Active Contact Matrix
Contact Mass Properties
• Augmented Space
• Active Contact Space
1 1 TJ A J− −⊕ ⊕ ⊕Λ =
1 1 TS S− −⊕Λ = Λ
:J⊕
where
augmented space jacobian
Contact Mass Properties
• Augmented Space
• Active Contact Space
1 1 TJ A J− −⊕ ⊕ ⊕Λ =
1 1 TS S− −⊕Λ = Λ
:J⊕
where
augmented space jacobian
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SAI Environment
Thank You!