Collision Safety for Physical Human-Robot Collaboration

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1 Collision Safety for Physical Human-Robot Collaboration IROS 2015 Workshop Physical Human-Robot Collaboration Jae-Bok Song School of Mechanical Engineering Korea University Seoul, Korea

2 Outline 2 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 Summary

Collision Safety 3 Physical Human-Robot Interaction

the same workspace Collaborative robots Safety

collision Avoidance Detection Absorption Vision

3 Collision Safety 3 Physical Human-Robot Interaction Frequent contacts between humans and robots Sharing the same workspace Collaborative robots Safety Strategies Before collision Collision After collision Avoidance Detection Absorption Vision Torque sensing Spring Human-robot collision Need for collision safety

4 Safe Physical Human-Robot Interaction (phri) 4 3-Step Safety Strategy Step 1 Prediction Step 2 Active Safety Step 3 Passive Safety Approach to Human Collision Prediction Non-contact sensors Path Regeneration Path planning Safe Motion Collision avoidance Fail Collision Detection Sensor: JTS, skin Sensorless: current monitoring Collision Reaction Fail Collision Absorption Safe Joint Mechanism Limit switch Collision Reaction Emergence stop Reflex motion Success Success Success Collision Safety!!

5 Step 1: Collision Prediction and Avoidance 5 Collision Prediction Based on noncontact sensors - Vision sensors, Kinect sensors - Ultrasonic sensors Kinect Camera Collision prediction and avoidance No Capacitive sensor Ultrasonic sensor Human approach Danger? Original path Yes Trajectory generation New path <Collision avoidance using Kinect>

Use of Ultrasonic Sensors Multiple sensors needed d < 0.

6 Step 1: Collision Prediction and Avoidance 6 Problems with Vision System Occlusion Multiple cameras Sensitive to lighting conditions Use of Ultrasonic Sensors Multiple sensors needed d < 0.3m Warning d< 0.1m Emergence Stop <Collision avoidance using ultrasonic sensors>

7 Step 2: Active Safety 7 Collision Detection & KU Detection: Disturbance observer + JTS (Joint Torque Sensor) Reaction: Different reaction modes <Collision detection with styrofoam> <Collision detection & reaction with chest>

8 Collision Detection using Disturbance Observer (DOB) 8 Principle of collision detection Human-robot collision External force applied to a robot External torque generated at each joint Normal operation * τ j : joint torque, τ ext : external torque Human-robot collision τ = M ( q) q + C( q, q ) q g( q) j + τ M ( q) q + C( q, q ) q g( q) j τ ext = + Collision can be detected by monitoring external torque.

Collision Detection using DOB 9 External torque estimation External torque: τ = τ { M ( q) q + C( q, q ) q g( q)} ext j + - Sensor based solution - Measurement of acceleration <Joint torque sensor

9 Collision Detection using DOB 9 External torque estimation External torque: τ = τ { M ( q) q + C( q, q ) q g( q)} ext j + - Sensor based solution - Measurement of acceleration <Joint torque sensor (JTS)> - Sensorless solution <Joint module> Use of additional sensors Impractical solution - Computation of acceleration Numerical differentiation of encoder signal Noise due to differentiation External torque estimation <Motor current & Friction model> for collision detection

Disturbance observer (DOB) Basic disturbance

ˆ s D + + = ) ( ) ( ) ( ) ( ) ( 1 ) ( 1 ) ( )

Fault Detection & Isolation Adaptive control

10 Disturbance observer (DOB) Basic disturbance observer Collision Detection using DOB 10 ) ( ˆ s D + + = ) ( ) ( ) ( ) ( ) ( 1 ) ( 1 ) ( ) ( ) ( ) ( ˆ s D s G s G s N s G s U s G s G s Q s D n n n ) ( ) ( ) ( ˆ s D s Q s D Human-Robot Collision Detection Robust control Fault Detection & Isolation Adaptive control Applications (if G(s)/G n (s) 1, N(s) 0 ) Disturbance observer

Collision Detection using DOB 11 External Torque Estimator

(robot arm joint) External torque estimator Input Joint torque q

s) External torque estimate: ˆ K τ ext( s) = Q( s) τ ext( s) ˆ τ

11 Collision Detection using DOB 11 External Torque Estimator External torque estimator based on disturbance observer System (robot arm joint) External torque estimator Input Joint torque q (s) Output Joint velocity Disturbance External torque ˆ τ ext ( s) External torque estimate: ˆ K τ ext( s) = Q( s) τ ext( s) ˆ τ ext ( s) = τ ext ( s) s + K D ˆ ( s) = Q( s) D( s) (Q(s): Low pass filter)

12 Collision Detection using DOB Collision detection based on external torque External torque estimate in time domain K ˆ τ ext ( s) = τ ext ( s) ˆ τext = K s + K [ τ j { M ( q) q + C( q, q ) q + g( q)} ˆ τext( dl)] dl Generalized momentum: p = M ( q) q (De Luca, 2003) T ˆ τ ext = K [ ( τ j + C ( q, q ) q g( q) ˆ τ e) dt p] External torque estimation without the acceleration information 12 τˆext ˆ τext τth? τˆext <Collision detection algorithm> <Example of typical case>

13 Collision Detection 13 Demonstrations 7 DOF manipulator Specifications Weight 15 kg TCP speed 1 m/s Payload 7 kg Acc. 5 m/s 2 Reach 780 mm DOFs 7 RTOS TwinCAT Control period 1 ms

Step 3: Passive Safety 14 Safe Joint Mechanism (SJM) Passive joint mechanism consisting of springs

Low stiffness for collision safety Small & Lightweight Automatic return to home position Operation

spring Unsafe region Low stiffness spring Certain collision force Safe region Emergency (large

14 Step 3: Passive Safety 14 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 Dangerous High stiffness spring Unsafe region Low stiffness spring Certain collision force Safe region Emergency (large impact) soft arm shock absorption 10 Working region Inaccurate Displacement (mm) positioning 40

15 Passive Safety: Demo 15 <Balloon & can> Static collision <Shoulder collision> <Industrial robot with SJMs>

16 16 Advanced Collision Detection 1. Sensorless Collision Detection 2. Collision Dection Index (CDI) Frequency-based CDI Projection-based CDI

Sensorless Collision Detection 17 Drawbacks of Sensor-based Collision

industrial manipulators Need for collision detection without the use of

using the motor current and friction model Estimation of joint torques

17 Sensorless Collision Detection 17 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 Estimation of joint torques without sensors Sensorless <Joint torque sensor> <Motor current> <Friction model>

18 Sensorless Collision Detection 18 Estimation of joint torque Power transmission τ m : Motor torque α : Torque constant i : Motor input current τ m = α i, τ j = nτ m τ f n : Speed reduction ratio ( M ( q) q + C( q, q ) q g( q) ) τ ext + τ f = nα i + Friction torque model τ f Friction torque τ c sgn( τ h), if q < ε and = τ s sgn( τ h), if q < ε and τ c sgn( q ) + τ v( q ), if q ε q q d d = 0 0 Identification of unknown parameters IROS 2015, S.D. Lee, M.C. Kim, J.B. Song Sensorless Collision Detection for Safe Human-Robot Collaboration

19 19 Sensorless Collision Detection Estimation of joint torque Friction torque identification using least-squares technique Friction torque observer Analysis on friction torque q (s) rˆ ( s ) Friction model Identification τ f τ c sgn( τh), = τ s sgn( τh), τ c sgn( q ) + τv( q ), if if if q < ε and q < ε and q ε q q d d = 0 0 Regressor = Data acquisition LS technique = Data set =

20 Sensorless Collision Detection 20 Demonstrations 7 DOF robot arm Human-robot collision Specifications Weight 15 kg TCP speed 1 m/s Payload 7 kg Acc. 5 m/s 2 Reach 780 mm DOFs 7 RTOS TwinCAT Control period 1 ms Collision detection without the use of any extra sensors

21 Sensorless Collision Detection 21 Demonstrations 6 DOF industrial manipulator 5 DOF collaborative robot arm Specifications Weight 33 kg TCP speed 1 m/s Payload 6 kg Acc. 5 m/s 2 Reach 1044 mm DOFs 6 Specifications Weight 125kg TCP speed 1.15 m/s Payload 15 kg Acc. 5 m/s 2 Reach 2105 mm DOFs 5

22 Collision Detection for Human-Robot Collaboration 22 Motivation Various tasks of collaborative robots SAFE Human-robot cooperation τ ext Contact task Handling of payload Physical interaction DANGER Unexpected collision τ ext Generation of external torque collision? Need for New Collision Detection algorithm

23 Frequency-based Collision Detection Index 23 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 Torque (Nm)

5 Nm Intended contact - Maximum Residual: 0.

24 Frequency-based Collision Detection Index 24 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 Intended contact Collision

frequency Examples: Collisions in low velocity, clamping No clear frequency threshold to distinguish

25 Frequency-based Collision Detection Index 25 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 assembly Measured contact force F x F y F z Need for more accurate but practical solution

Projection-based Collision Detection Index 26 Subspace Projection based Approach Types of tasks for human-robot collaboration Cases 1. 2. 3.

26 Projection-based Collision Detection Index 26 Subspace Projection based Approach Types of tasks for human-robot collaboration Cases Payload F i F p F cb F ce F cb F ce F g F cb F ce F e Source of τext Collision w/o collision w/ collision (EE or Body) none τp τe case 1-1 τce case 1-2 τcb case 2-1 τp + τce case 2-2 τp + τcb case 3-1 τe +τce case 3-2 τe +τcb Applications Position control (painting, welding) Position control with payload (pick-and-place, material handling) Force control (grinding, hand guiding),

Projection-based Collision Detection Index 27 Subspace Projection based approach Collision detection strategy for human-robot collaboration Detectable Cases Collision detection index

27 Projection-based Collision Detection Index 27 Subspace Projection based approach Collision detection strategy for human-robot collaboration Detectable Cases Collision detection index collision 1. F cb τext Available arms Any robot arms 2. F ce F cb Payload F i F ce ( I J J + ) τ p p 6~7 DOF robot arms ext 3. F p F g F cb F ce ( I T J T + J ( ) ) τ ext 7 DOF robot arms F e

Projection-based Collision Detection Index 28 Projection based Approach Main idea of proposed collision detection method Example of subspace projection (in Cartesian space) - F p = (0, 1, 1) in the

28 Projection-based Collision Detection Index 28 Projection based Approach Main idea of proposed collision detection method Example of subspace projection (in Cartesian space) - F p = (0, 1, 1) in the yz plane ( only payload) - F ext = (1, 1, 1) in the xyz space ( col. Included) - Projection of F ext into the x axis (orthogonal to the yz plane) - Collision force F c = (1, 0, 0) F = F + ext c F p If F ext = F p F ext F ext CDI : zero vector If F ext F p CDI : not zero vector

29 CDI : decoupled with τ p & sensitive to τ c m n S p =

Detection Index Collision Detection for Handling a

29 29 CDI : decoupled with τ p & sensitive to τ c m n S p = dim( ) c p p p p p c p p ext p p J J I J J I J J I J J I CDI τ τ τ 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 p c ext τ τ τ + =

30 Projection-based Collision Detection Index 30 Experimental results Collision detection for various payloads (w/o payload 1kg 2kg) kg 2kg CDI (Nm) The developed CDI can detect a collision for unknown payloads.

31 CDI : decoupled with τ e & sensitive to τ c c T T e T T c T T ext T T J J I J J

Detection for Human-Robot Collaboration Collision Detection for Human-Robot

based on force applied to its end-effector External force on the end-effector

31 31 CDI : decoupled with τ e & sensitive to τ c c T T e T T c T T ext T T J J I J J I J J I J J I CDI τ τ τ τ ) ) ( ( ) ) ( ( ) ) ( ( ) ) ( ( = Collision Detection for Human-Robot Collaboration Collision 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 e c ext τ τ τ + =

force/position control Collision detection - Intended interaction force for

32 Collision Detection for Human-Robot Collaboration 32 Experimental results Collision detection during hybrid force/position control ) Hybrid force/position control Collision detection - Intended interaction force for impedance control in the x direction - Collision between human and manipulator < Written letters: IRL >

Projection-based Collision Detection Index 33

Case 2: Handling of payload Case 3: Physical

33 Projection-based Collision Detection Index 33 Scenario for human-robot collaboration Human-robot collaboration in car assembly line Human-robot collaboration Case 1: Approaching Case 2: Handling of payload Case 3: Physical interaction (Position control) (Pick and place) (Hand guiding)

Collision Detection for Human-Robot Collaboration 34 Collision detection

interaction Normal operation: τ ext Collision: τ = = 0 ext τ c τ Normal

CDI CDI CDI ext ( I J J +) τ p p ext Collision: τ = τ τ = τ + τ ext ( I J (

34 Collision Detection for Human-Robot Collaboration 34 Collision detection strategy Case 1: Approaching Case 2: Handling of payload Case 3: Physical interaction Normal operation: τ ext Collision: τ = = 0 ext τ c τ Normal operation: Collision: τ = τ + τ ext c p Normal operation: τ ext = τ p ext e CDI CDI CDI ext ( I J J +) τ p p ext Collision: τ = τ τ = τ + τ ext ( I J ( c T J T ) + e ) τ ext Detectable collision Detectable collision Detectable collision

35 Collision Analysis & Simulation 35

36 Various Safety Criteria 36 Safety criteria for safety evaluation ISO Collaborative operation with humans - v TCP <0.25m/s, F TCP <150N, P max <80W Human pain tolerance [Yamada, 1996] - Static collision (v<0.6m/s) - F<50N Too restrictive criteria Limitation of performance Too generous for a robot arm - Low collision speed Head Injury Criterion (HIC) - HIC saturation with increasing mass - Automobile crash test No robots become dangerous at - HIC<650 prob(ais 3)<0.05 2m/s. [Haddadin, 2008] - Used to be the most popular index

37 Safety Evaluation 37 Safety evaluation of human-robot collision Real impact test Real impact test & evaluation Using a crash-test dummy Simulation S/W Collision analysis DLR Haddadin Features + Most realistic data available - Considerable cost and time for tests - Need to construct a robot

38 Safety Evaluation 38 Safety evaluation of human-robot collision Real impact test Collision simulation Using simulation S/W Simulation S/W Collision analysis MADYMO S/W Features + Relatively reliable results + No need to construct a robot - Expensive S/W

Safety Evaluation 39 Safety evaluation of

analysis Morita 00 Features + No need to

39 Safety Evaluation 39 Safety evaluation of human-robot collision Real impact test Collision analysis and evaluation Analytic method Bicchi 04 Simulation S/W Collision analysis Morita 00 Features + No need to construct a robot + Low cost and easy application - Less reliable data

40 Various Safety Criteria 40 Injury tolerance of body parts Cranial bone [SAEJ885, 1980] Frontal Temporal Occipital Facial bone [Nahum, 1972 & 1976] Mandible (C) Mandible (L) Zygomatic Maxilla Nasal Chest Compression criterion [Lau, 1983] Viscous criterion [Lau, 1986] Abdominal [Miller, 1989] Liver Lower abdomen Fracture tolerance 4.0 kn 3.12 kn 6.41 kn Fracture tolerance 1.89 kn 0.82 kn 0.85 kkn 0.62 kn kn Injury tolerance 22mm 0.5m/s Injury tolerance 310kPa 3.76kN No injury[haddadin, 09] Neck (indirect impact) Shear [Mertz, 1993] Tension [Mertz, 1993] Compression [Mertz, 1993] Extension [Mertz, 1967] Flexion [Mertz, 1967] Bending angle [Gadd, 1971] Neck (direct impact) Thyroid and cricoid [Melvin, 1973] Lower extremities [Devore, 1999] Femur Tibia Upper extremities [Begeman, 1999] Humerus Elbow Forearm Injury tolerance 0msec 25-35msec 45msec 0msec 35msec 60msec 0msec 30msec 57Nm 87.8Nm Extension: 80 Lateral: 60 Injury tolerance kn Injury tolerance 3.8kN 5.4kN Injury tolerance 1.96kN 1.75kN 1.37kN

$bone of head - Fracture force : 342 N Comminuted fracture Neck injury Thyroid and$

41 Safety Criteria 41 Safety criterion for service robots (blunt impact) Safety criteria (Collision force) Head injury Nasal bone - Protrusion of head - Weakest bone of head - Fracture force : 342 N Comminuted fracture Neck injury Thyroid and cricoid cartilages - Upper end of airway passage - Fracture force : 337 N Obstruction of airflow

42 HuRoCol: Model Parameters 42 HuRoCol (Human-Robot Collision Analysis) Parameters of collision model Human (Hybrid III 50 th percentile male) Weight: 4.5kg(head), 1.5kg(neck), 71kg(body) Neck stiffness: 0.44Nm/deg Robot arm Hybrid III Robot arm model

43 HuRoCol: Collision model 43 Head-Neck Model (3 DOF) Head: Revolute joint (OC), Neck stiffness Neck: Revolute joint (C7), Neck stiffness Body: Prismatic joint Collision model Human model

HuRoCol: Collision model 44 Chest Model Lobdell [17]): 2 DOF Lumped-mass model of anteroposterior thoracic impact To obtain uncoupled inertia matrix Dummy mass is added between k ve and c ve k

44 HuRoCol: Collision model 44 Chest Model Lobdell [17]): 2 DOF Lumped-mass model of anteroposterior thoracic impact To obtain uncoupled inertia matrix Dummy mass is added between k ve and c ve k r c b y x k ve c ve x 5 x 6 x 7 - k r : rib cage and directly coupled viscera - c b : air in lungs and blood in the vessels - k ve and c ve : viscoelastic tissue such as thoracic muscle tissue

45 HuRoCol : Collision model 45 Various collision cases Unconstrained human Impact to head Impact to neck Collision model Constrained human Partially constrained human Wall Wall z x z x Impact to head Impact to neck Impact to head Impact to neck

46 HuRoCo : Solution Method 46 Solution: q = M ( q) 1 ( F C( q, q ) K( q) G( q) D( q )) Matlab/Simulink -4 th and 5 th -order Runge-Kutta method 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

$fracture 0.4 0.6 0.8 1.$ (deg) Displacement (cm)

47 HuRoCol : Analysis Results Collision with unconstrained human -Impacttotheneckismore dangerous than impact to the head. ( airway obstruction) Impact to head Robot link Impact to neck z x Body O.C. C7 Collision force (N) N 342 N Nasal bone fracture Time (s) Angle (deg) Displacement (cm) 80 O.C.+C7 60 C O.C. Collision Time (s) Collision Body Time (s) 47

48 HuRoCol : Analysis Results Collision with partially constrained human - Impact to the neck is more dangerous than impact to the head. Impact to head z x Wall Collision force (N) Angle (deg) Impact to neck z x Wall Collision force (N) Angle (deg) 48

49 HuRoCol : Analysis Results Collision with constrained human - Impact to the neck is more dangerous than impact to the head. Impact to head Wall z x N 342 N Nasal bone fracture Time (s) Impact to neck 49

50 HuRoCol : Design of Safe Robot Arm Design of safe robot arm - Design of the robot arm can be modified according to analysis results. - Mass (inertia), length, velocity Impact to neck N 337 N 210 N Thyroid & cricoid fracture (337N) 2.5 kg 2.3 kg 2.1 kg Time (s) Inertia of robot link (1.5m/s) N 344 N 289 N Thyroid & cricoid fracture (337N) 1.5 m/s 1.2 m/s 1.0 m/s Time (s) Velocity of robot (2.5kg)

51 HuRoCol : Design of Safe Robot Arm The robot arm with SJM can provide much higher safety. Design of the robot arm can be modified according to analysis results. Impact to head Robot link z x O.C. C7 Body Impact to neck

52 HuRoCol : Verification 1 Analysis versus Dummy crash-test KUKA KR6 (inertia: 67 kg) Unconstrained human Close agreement with dummy crash-test data Impact to head Haddadin, ICRA 09 Robot link O.C. C7 z x Body Collision force (N)

53 HuRoCol : Verification 2 Analysis versus Dummy crash-test KUKA KR500 (Refl. inertia: 1870 kg) Unconstrained human Close agreement with dummy crash-test data Impact to head Haddadin, ICRA 09 Robot link z x O.C. C7 Body

54 Summary 54 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

55 Q & A Thank you!

Safe Joint Mechanism using Inclined Link with Springs for Collision Safety and Positioning Accuracy of a Robot Arm

1 IEEE International Conference on Robotics and Automation Anchorage Convention District May 3-8, 1, Anchorage, Alaska, USA Safe Joint Mechanism using Inclined Link with Springs for Collision Safety and