collision safety for physical human-robot collaboration
TRANSCRIPT
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Jae-Bok Song
School of Mechanical EngineeringKorea University
Seoul, Korea
IROS 2015 Workshop “Physical Human-Robot Collaboration”
Collision Safety for
Physical Human-Robot Collaboration
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OutlineOutline
Human-Robot Interaction
3-Step Safety Strategy
• Collision Prediction & Avoidance
• Collision Detection & Reaction : Active Safety
• Collision Absorption : Passive Safety
Advanced Collision Detection
• Sensorless Collision Detection
• Collision Detection Index (CDI): Frequency-based
• Collision Detection Index (CDI): Projection-based
Collision Analysis & Simulation
2
Summary
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Physical Human-Robot Interaction • Frequent contacts between humans and robots
• Sharing the same workspace Collaborative robots
Human-robot collisionHuman-robot collision Need for collision safetyNeed for collision safety
Safety Strategies
3
Before collision VisionAvoidance
Collision Torque sensingDetection
After collision SpringAbsorption
Collision Safety
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3-Step Safety Strategy
4Safe Physical Human-Robot Interaction (pHRI)
Step 1Prediction
Step 2 Active Safety
Step 3 Passive Safety
Collision Safety !!
Collision PredictionNon-contact sensors
Path RegenerationPath planning
Safe MotionCollision avoidance
Collision DetectionSensor: JTS, skin
Collision Reaction
Sensorless: current monitoring
Collision AbsorptionSafe Joint Mechanism
Limit switch
Collision ReactionEmergence stopReflex motion
Approachto Human
Fail
Success Success Success
Fail
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Collision Prediction
• Based on noncontact sensors
- Vision sensors, Kinect sensors
- Ultrasonic sensors
Step 1: Collision Prediction and Avoidance 5
<Collision avoidance using Kinect>
Kinect
Capacitive sensor
Camera
Ultrasonic sensor
Collision prediction and avoidance
No
Yes
Trajectorygeneration
Original path
Danger?Human
approach
New path
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6Step 1: Collision Prediction and Avoidance
Use of Ultrasonic Sensors
• Multiple sensors needed
• d < 0.3m → Warning
• d < 0.1m → Emergence Stop
<Collision avoidance using ultrasonic sensors>
• Occlusion Multiple cameras
• Sensitive to lighting conditions
Problems with Vision System
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Collision Detection & Reaction @ KU
• Detection: Disturbance observer + JTS(Joint Torque Sensor)
• Reaction: Different reaction modes
Step 2: Active Safety 7
<Collision detection with styrofoam> <Collision detection & reaction with chest>
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Principle of collision detection
• Human-robot collision
External force applied to a robot
External torque generated at each joint
8
Normal operation
Collision Detection using Disturbance Observer (DOB)
Human-robot collision
Collision can be detected by monitoring external torque.
)(),()( qgqqqCqqMj ++= τ )(),()( qgqqqCqqMextj ++=− ττ
* τj : joint torque, τext : external torque
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External torque estimation
• External torque:
9Collision Detection using DOB
External torque estimation
for collision detection
)}(),()({ qgqqqCqqMjext ++−= ττ
<Joint torque sensor (JTS)> <Joint module>
<Motor current & Friction model>
- Measurement of acceleration
Use of additional sensors
Impractical solution
- Computation of acceleration Numerical differentiation of
encoder signal
Noise due to differentiation
- Sensor based solution
- Sensorless solution
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Disturbance observer (DOB)• Basic disturbance observer
Collision Detection using DOB 10
)(ˆ sD
++
−= )(
)()()(
)(1)(1
)()()()(ˆ sD
sGsGsN
sGsU
sGsGsQsD
nnn
)()()(ˆ sDsQsD ≈
Human-Robot Collision Detection
Robust control
Fault Detection & Isolation
Adaptive control
Applications
(if G(s)/Gn(s) ≈ 1, N(s) ≈ 0 )
• Disturbance observer
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External Torque Estimator
• External torque estimator based on disturbance observer
11
)(ˆ sextτ
)(sq
)()()(ˆ ssQs extext ττ = )()(ˆ sKs
Ks extext ττ+
=
Collision Detection using DOB
Input Joint torque
• External torque estimate:
System (robot arm joint)
Output Joint velocity
Disturbance External torque
External torque estimator
(Q(s): Low pass filter))()()(ˆ sDsQsD =
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Collision detection based on external torque• External torque estimate in time domain
• Generalized momentum: (De Luca, 2003)
12Collision Detection using DOB
<Example of typical case><Collision detection algorithm>
)()(ˆ sKs
Ks extext ττ+
= −++−= dldlqgqqqCqqMK extjext )](ˆ)}(),()({[ˆ τττ
])ˆ)(),(([ˆ pdtqgqqqCK eT
jext −−−+= τττ
External torque estimation without the acceleration information
qqMp )(=
extτ̂?|ˆ| thext ττ ≥
ext
τ̂
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13
Demonstrations
Collision DetectionCollision Detection
7 DOF manipulator
Specifications
Weight 15 kg TCP speed 1 m/s
Payload 7 kg Acc. 5 m/s2
Reach 780 mm DOFs 7
RTOS TwinCAT Control period 1 ms
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Safe Joint Mechanism (SJM)• Passive joint mechanism consisting of
springs and cam-cam follower mechanisms
• Nonlinear spring system
• High stiffness for positioning accuracy
• Low stiffness for collision safety
• Small & Lightweight
• Automatic return to home position
Operation of SJM• Normal operation
stiff arm accurate positioning
• Emergency (large impact)
soft arm shock absorption
Step 3: Passive SafetyStep 3: Passive Safety
0
10
20
30
40
50
0 10 20 30Displacement (mm)
40
Working region
Safe region
Unsafe region60
High stiffness spring
Inaccurate positioning
Dangerous
Low stiffness spring
Certain collision force
14
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Static collision
Passive Safety: Demo 15
<Industrial robot with SJMs>
<Balloon & can> <Shoulder collision>
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Advanced Collision Detection
16
1. Sensorless Collision Detection
2. Collision Dection Index (CDI)
• Frequency-based CDI
• Projection-based CDI
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Drawbacks of Sensor-based Collision Detection
• Costly solution due to the use of sensors
• Not applicable to industrial manipulators
Need for collision detection without the use of extra sensors
Sensorless Collision Detection
• Estimation of joint torques using the motor current and friction model
17Sensorless Collision DetectionSensorless Collision Detection
Estimation of joint torques without sensors
SensorlessSensorless
<Joint torque sensor> <Motor current> <Friction model>
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Estimation of joint torque
18
Power transmissionPower transmission
Friction torque
,im ατ = fmj n τττ −=
τm : Motor torque
α : Torque constant
i : Motor input current
n : Speed reduction ratio
( ))(),()( qgqqqCqqMinfext ++−=+ αττ
Sensorless Collision DetectionSensorless Collision Detection
Friction torque model
≥+≠<=<
=εττεττεττ
τ||if),()sgn(
0and||if),sgn(0and||if),sgn(c
qqqqqqq
vc
dhs
dh
f
Identification of
unknown parameters
IROS 2015, S.D. Lee, M.C. Kim, J.B. Song “Sensorless Collision Detection for Safe Human-Robot Collaboration”
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Friction torque identification using least-squares technique
Friction torque observerFriction torque observer
Friction modelFriction model Identification Identification == ̅ ′ =
Data setData setRegressor
LS technique
Data
acquisition
Analysis on friction torqueAnalysis on friction torque
)(ˆ sr
)(sq
Estimation of joint torque
Sensorless Collision DetectionSensorless Collision Detection 19
≥+≠<=<
=εττεττεττ
τ||if),()sgn(
0and||if),sgn(0and||if),sgn(
qqqqqqq
vc
dhs
dhc
f
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20
Demonstrations
7 DOF robot arm
Specifications
Weight 15 kg TCP speed 1 m/s
Payload 7 kg Acc. 5 m/s2
Reach 780 mm DOFs 7
RTOS TwinCAT Control period 1 ms
Collision detection without the use
of any extra sensors
Human-robot collisionHuman-robot collision
Sensorless Collision DetectionSensorless Collision Detection
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Demonstrations
21Sensorless Collision DetectionSensorless Collision Detection
6 DOF industrial manipulator
Specifications
Weight 33 kg TCP speed 1 m/s
Payload 6 kg Acc. 5 m/s2
Reach 1044 mm DOFs 6
5 DOF collaborative robot arm
Specifications
Weight 125kg TCP speed 1.15 m/s
Payload 15 kg Acc. 5 m/s2
Reach 2105 mm DOFs 5
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22
Human-robot cooperation
Collision Detection for Human-Robot CollaborationCollision Detection for Human-Robot Collaboration
Contact task
Unexpected collision
SAFE
DAN
GER
τext
τext
Need for New Collision Detection algorithm
Motivation
Handling of payload Physical interaction
Various tasks of collaborative robots
Generation of external torque collision ?
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23
Torq
ue (N
m)
Frequency-based Approach
• Rate of change of external force: Frequency-based Collision Detection Index
- Safe Intended Contact : Relatively slow rate of change
- Dangerous Unexpected Collision : relatively fast rate of change
Need for an observer that detects only the fast-changing external torque
Add a high-pass filter to the conventional collision detector
Frequency-based Collision Detection IndexFrequency-based Collision Detection Index
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Collision detection of unexpected collision
• Threshold: ±0.5 Nm
• Intended contact
- Maximum Residual: 0.2 Nm < threshold
• Unexpected collision
- Maximum Residual: 2.2 Nm > threshold
24
Intended contact Collision
Frequency-based Collision Detection IndexFrequency-based Collision Detection Index
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25Frequency-based Collision Detection IndexFrequency-based Collision Detection Index
Limitations of Frequency-based Approach
• No guarantee that intended contact force is always low frequency
• No guarantee that unexpected collision force is always high frequency
Examples: Collisions in low velocity, clamping
• No clear frequency threshold to distinguish collision from external torque
Box assemblyBox assembly Measured contact forceMeasured contact force
FxFyFz
Need for more accurate but practical solution
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26
Cases Source of τext Collision
(EE or Body)Applications
w/o collision w/ collision
Fce
Fcb1.
none
case 1-1
Position control (painting, welding)
τce case 1-2
τcb
Fi
Fp Fg
PayloadFce
Fcb2.
τp
case 2-1
Position control with payload
(pick-and-place, material handling)
τp + τce case 2-2
τp + τcb
Fe
Fce
Fcb3.
τe
case 3-1
Force control (grinding, hand
guiding),
τe +τce case 3-2
τe +τcb
Projection-based Collision Detection Index Projection-based Collision Detection Index
Subspace Projection based Approach
• Types of tasks for human-robot collaboration
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27
Cases Collision detection index Detectable collision
Available arms
Fce
Fcb1.
τext Any robot arms
Fi
Fp Fg
PayloadFce
Fcb2.
extpp JJI τ)( +−
6~7 DOF robot arms
Fe
Fce
Fcb3.
extTT JJI τ))(( +−
7 DOF robot arms
Projection-based Collision Detection Index Projection-based Collision Detection Index
Subspace Projection based approach
• Collision detection strategy for human-robot collaboration
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28
extFextF• If Fext = Fp
CDI : zero vector
- Fp = (0, 1, 1) in the yz plane ( only payload)
- Fext = (1, 1, 1) in the xyz space ( col. Included)
- Projection of Fext into the x axis (orthogonal to
the yz plane)
- Collision force Fc = (1, 0, 0)
pcext FFF +=
• If Fext ≠ Fp
CDI : not zero vector
Projection-based Collision Detection Index Projection-based Collision Detection Index
Projection based Approach
• Main idea of proposed collision detection method
Example of subspace projection (in Cartesian space)Example of subspace projection (in Cartesian space)
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29
CDI : decoupled with τp & sensitive to τc
mnSp −=⊥ )dim(
cpp
pppcpp
extpp
JJI
JJIJJI
JJICDI
τ
ττ
τ
)(
)()(
)(
+
++
+
−≈
−+−≈
−=
Projection-based Collision Detection Index Projection-based Collision Detection Index
Collision Detection for Handling a Payload (Case 2)
• Available for 6 – 7 DOF robot arms pcext τττ +=
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30Projection-based Collision Detection Index Projection-based Collision Detection Index
Experimental results
• Collision detection for various payloads (w/o payload 1kg 2kg)
CDI
(Nm
)
-20-10
01020
0 2 4 6 8 10 121kg 2kg
The developed CDI can detect a collision for unknown payloads.
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31
CDI : decoupled with τe & sensitive to τc
cTT
eTT
cTT
extTT
JJI
JJIJJI
JJICDI
τ
ττ
τ
))((
))(())((
))((
+
++
+
−≈
−+−≈
−=
Collision Detection for Human-Robot CollaborationCollision Detection for Human-Robot Collaboration
Collision detection for Contact Task (Case 3)
• Physical interaction based on force applied to its end-effector
• External force on the end-effector intended interaction force
• External force on the body unexpected collision force
ecext τττ +=
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Experimental results
• Collision detection during hybrid force/position control
•) Hybrid force/position control
32Collision Detection for Human-Robot CollaborationCollision Detection for Human-Robot Collaboration
- Intended interaction force for
impedance control in the x direction
- Collision between human and
manipulator< Written letters: IRL >
Collision detection
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Human-robot collaboration in car assembly line
Scenario for human-robot collaboration
Case 1: Approaching Case 3: Physical interaction Case 2: Handling of payload
33Projection-based Collision Detection Index Projection-based Collision Detection Index
Human-robot collaboration
(Pick and place) (Hand guiding)(Position control)
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34Collision Detection for Human-Robot CollaborationCollision Detection for Human-Robot Collaboration
Collision detection strategy
Case 1: Approaching Case 3: Physical interaction Case 2: Handling of payload
Normal operation:
Collision:
0=extτ
cext ττ =
Normal operation:
Collision:
pcext τττ +=
Normal operation:
Collision:
ecext τττ +=
pext ττ = eext ττ =
CDI CDI CDIextτ extpp JJI τ)( +− ext
TT JJI τ))(( +−
Detectable collision Detectable collision Detectable collision
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Collision Analysis & Simulation
35
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Safety criteria for safety evaluation
Various Safety CriteriaVarious Safety Criteria 36
• ISO 10218-1
- Collaborative operation with humans
- vTCP<0.25m/s, FTCP<150N, Pmax<80W
• Human pain tolerance [Yamada, 1996]
- Static collision (v<0.6m/s)
- F<50N
• Head Injury Criterion (HIC)
- Automobile crash test
- HIC<650 prob(AIS≥3)<0.05
- Used to be the most popular index
• Too restrictive criteria Limitation of performance
• Too generous for a robot arm- Low collision speed- HIC saturation with increasing mass No robots become dangerous at 2m/s. [Haddadin, 2008]
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Safety evaluation of human-robot collision
Real impact test
Simulation S/W
Collision analysis
• Real impact test & evaluation• Using a crash-test dummy
• Features
+ Most realistic data available
- Considerable cost and time for tests
- Need to construct a robot
Safety EvaluationSafety Evaluation 37
DLR – Haddadin
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Safety evaluation of human-robot collision
Real impact test
Simulation S/W
Collision analysis
• Collision simulation• Using simulation S/W
• Features
+ Relatively reliable results
+ No need to construct a robot
- Expensive S/W
38Safety EvaluationSafety Evaluation
MADYMO S/W
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Safety evaluation of human-robot collision
Real impact test
Simulation S/W
Collision analysis
39Safety EvaluationSafety Evaluation
Bicchi ‘04
Morita ‘00
• Collision analysis and evaluation• Analytic method
• Features
+ No need to construct a robot
+ Low cost and easy application
- Less reliable data
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Injury tolerance of body parts
40Various Safety CriteriaVarious Safety Criteria
Cranial bone [SAEJ885, 1980] Fracture tolerance
Frontal 4.0 kNTemporal 3.12 kNOccipital 6.41 kN
Facial bone[Nahum, 1972 & 1976] Fracture tolerance
Mandible (C) 1.89 kNMandible (L) 0.82 kNZygomatic 0.85 kkN
Maxilla 0.62 kNNasal 0.342 kN
Chest Injury toleranceCompression criterion
[Lau, 1983] 22mm
Viscous criterion[Lau, 1986] 0.5m/s
Abdominal[Miller, 1989] Injury tolerance
Liver 310kPaLower abdomen 3.76kN
Neck (indirect impact) Injury tolerance
Shear[Mertz, 1993]
3.1kN @ 0msec1.5kN @ 25-35msec1.1kN @ 45msec
Tension[Mertz, 1993]
3.3kN @ 0msec2.9kN @ 35msec1.1kN @ 60msec
Compression[Mertz, 1993]
4kN @ 0msec1.1kN @ 30msec
Extension[Mertz, 1967] 57Nm
Flexion[Mertz, 1967] 87.8Nm
Bending angle[Gadd, 1971]
Extension: 80°Lateral: 60°
Neck (direct impact) Injury toleranceThyroid and cricoid
[Melvin, 1973] 0.337 kN
Lower extremities[Devore, 1999] Injury tolerance
Femur 3.8kNTibia 5.4kN
Upper extremities[Begeman, 1999] Injury tolerance
Humerus 1.96kNElbow 1.75kN
Forearm 1.37kN- KR6@2m/s No injury[Haddadin, ‘09]
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• Neck injury
• Thyroid and cricoid cartilages
- Upper end of airway passage
- Fracture force : 337 N
Obstruction of airflow
• Head injury
• Nasal bone
- Protrusion of head
- Weakest bone of head
- Fracture force : 342 N
Comminuted fracture
Safety criteria (Collision force)
Safety criterion for service robots (blunt impact)
Safety CriteriaSafety Criteria 41
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Hybrid III
HuRoCol (Human-Robot Collision Analysis)
Parameters of collision model
• Human (Hybrid III 50th percentile male)
• Weight: 4.5kg(head), 1.5kg(neck), 71kg(body)
• Neck stiffness: 0.44Nm/deg
• Robot arm
42HuRoCol: Model ParametersHuRoCol: Model Parameters
Robot arm model
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Human model
Head-Neck Model (3 DOF)
• Head: Revolute joint (OC), Neck stiffness
• Neck: Revolute joint (C7), Neck stiffness
• Body: Prismatic joint
43HuRoCol: Collision modelHuRoCol: Collision model
Collision model
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Chest Model• Lobdell [17]): 2 DOF
• Lumped-mass model of anteroposterior thoracic impact
• To obtain uncoupled inertia matrix
Dummy mass is added between kve and cve
- kr : rib cage and directly coupled viscera
- cb : air in lungs and blood in the vessels
- kve and cve : viscoelastic tissue such as thoracic muscle tissue
44HuRoCol: Collision modelHuRoCol: Collision model
x5
xy
x6
kr
cb
x7
kve cve
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Various collision cases
45HuRoCol : Collision modelHuRoCol : Collision model
Unconstrained human
Partially constrained human
Impact to head Impact to neck Collision model
xz
Wall
xz
Wall
Impact to head Impact to neck
Constrained human
Impact to head Impact to neck
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Solution:
• Matlab/Simulink
- 4th and 5th-order Runge-Kutta method
46HuRoCo : Solution MethodHuRoCo : Solution Method
( ))()()(),()( 1 qDqGqKqqCFqMq −−−−= −
Robotica 2015, J.J. Park, J.B. Song, S. Haddadin, “Collision analysis and safety evaluation using a collision model for a frontal robot-human impact”
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Collision with unconstrained human- Impact to the neck is more dangerous than impact to the head.
( airway obstruction)
47
HuRoCol : Analysis ResultsHuRoCol : Analysis Results
Impact to head
Impact to neck
xz
Robot link
O.C.
C7
Body 0
100
200
300
400
500
0.4 0.6 0.8 1.0 1.2Time (s)
Col
lisio
n fo
rce
(N) 407 N
342 N
Nasal bone fracture
Ang
le (d
eg)
Disp
lace
men
t (cm
)
Time (s)
Time (s)
O.C.+C7
Collision
C7O.C.
020406080
0 0.5 1.0 1.5 2.0
00.20.40.6
0 0.5 1.0 1.5 2.0
CollisionBody
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Collision with partially constrained human- Impact to the neck is more dangerous than impact to the head.
48
HuRoCol : Analysis ResultsHuRoCol : Analysis Results
Impact to head
Impact to neck
xz
Wall
xz
Wall
Col
lisio
n fo
rce
(N)
Ang
le (d
eg)
Col
lisio
n fo
rce
(N)
Ang
le (d
eg)
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49
HuRoCol : Analysis ResultsHuRoCol : Analysis Results
Collision with constrained human- Impact to the neck is more dangerous than impact to the head.
Impact to head
Impact to neck
xz
Wall
0.4 0.6 0.8 1.0 1.2Time (s)
Nasal bone fracture
342 N
425 N
0
100
200
300
400
500
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Design of safe robot arm- Design of the robot arm can be modified according to analysis results.- Mass (inertia), length, velocity…
HuRoCol : Design of Safe Robot ArmHuRoCol : Design of Safe Robot Arm
Impact to neck
Inertia of robot link (1.5m/s)
Velocity of robot(2.5kg)
0.4 0.6 0.8 1.0 1.2Time (s)
344 N
423 N
0
100
200
300
400
500Thyroid & cricoid fracture (337N)
1.5 m/s1.2 m/s
1.0 m/s
289 N
0.4 0.6 0.8 1.0 1.2Time (s)
337 N
423 N
0
100
200
300
400
500
2.5 kg
2.3 kg
2.1 kg210 N
Thyroid & cricoid fracture (337N)
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• The robot arm with SJM can provide much higher safety. • Design of the robot arm can be modified according to analysis results.
HuRoCol : Design of Safe Robot ArmHuRoCol : Design of Safe Robot Arm
Impact to head
Impact to neck
xz
Robot link
O.C.
C7
Body
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Analysis versus Dummy crash-test• KUKA KR6 (inertia: 67 kg)• Unconstrained human Close agreement with dummy crash-test data
Impact to head
HuRoCol : Verification 1HuRoCol : Verification 1
xz
Robot link
O.C.
C7
Body
Haddadin, ICRA ‘09
Col
lisio
n fo
rce
(N)
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Impact to head
HuRoCol : Verification 2HuRoCol : Verification 2
xz
Robot link
O.C.
C7
Body
Haddadin, ICRA ‘09
Analysis versus Dummy crash-test• KUKA KR500 (Refl. inertia: 1870 kg)• Unconstrained human Close agreement with dummy crash-test data
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• Safe Joint Mechanism: passive approach, infinite bandwidth
• Frequency-based Collision Detection
Intended contact: low frequency & Collision: high frequency
• Projection-based Collision Detection Index
Any collision regardless of frequency and magnitude of collision
• Safety Criterion: fracture force of thyroid & cricoid cartilages for neck injury
- The most appropriate safety indicator for a service robot
• Proposed collision model and analysis
• Accurate model
- More reliable analysis results for human-robot collisions
• Evaluation in the robot design phase
- Can save time and cost associated with collision tests
54Summary
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Thank you!
Q & A