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Dynamics Identification of human-like systems Dr. Gentiane...
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Dynamics Identification of human-like systems Dr. Gentiane Venture The University of Tokyo - Department of Mechano-Informatics - Nakamura & Yamane Lab. info: [email protected] Global Identification Methodology Application to Humanoid Robots Application to Human Application to Human to Support the Diagnosis of Neuromuscular Diseases 10 12 14 16 18 10 12 14 16 18 Rigth Shoulder Stiffness Nm/rad Left Shoulder Stiffness Nm/rad Patient 1 Patient 2 Patient 3 Patient 4 Patient 5 Candidate 2 Candidate 1 Right Left σ k% = 1 σ k% = 5 σ k% =10 normal condition global stiffness stiffness of the right side stiffness of the left side Y Z Y X Ny[Nm] 0 5 10 15 20 2.5 3 3.5 4 4.5 5 5.5 Time[s] 0 5 10 15 20 -3 -2 -1 0 1 2 3 Fx[N] Time[s] 0 5 10 15 20 -3 -2 -1 0 1 2 3 Fy[N] Time[s] 0 5 10 15 20 60 65 70 75 80 85 Fz[N] Time[s] 0 5 10 15 20 -3 -2 -1 0 1 2 3 Nx[Nm] Time[s] 0 5 10 15 20 -0.3 -0.2 -0.1 0 0.1 0.2 Nz[Nm] Time[s] Cross-validation with a bending motion Patients discrimination according to estimated shoulders stiffness WHY? Neurological disorders : slowly progressive, degenerative diseases. Affected persons experience various symptoms such as trembling, muscle rigidity, slowed motion, difficulty in walking, problems with body balance and coordination. Outcome assessments of neurological disorders partly relies on the qualitative clinical rating of symptoms : bradykinesia, tremor, and rigidity using rating scales... Objective quantification tools highly required, apparatus proposed so far too expensive, too complex and required too much time to be used in a clinical setting. Clinical diagnosis based on patient medical history, observations of symptoms, neurologic examination : - passive tests : patient not moving (no contraction of muscles) but movements induced by the PT. - semi-active tests: patient asked to perform lower-body motions resulting in upper-body passive motions. - active tests: patient performs full body active motion. constraint-free movements passive painless suitable for identification Movements of the human limbs time time time (a) (b) (c) Test P4 Test P4’ Test P5 Passive tests used for identification during clinical diagnosis of neuromuscular diseases Type of motion required to identify joint dynamics for medical applications Modelling of joint passive visco-elastic properties −10 −5 0 5 10 Patient 5 left shoulder joint torque (Nm) 35 −10 −5 0 5 10 time (s) joint torque (Nm) preparation walk volte face walk walk volte face walk volte face Patient 5 right shoulder Validation for the shoulders 0 2 4 6 8 10 12 14 16 -1.5 -1 -0.5 0 0.5 1 1.5 Nz[Nm] Time[s] 0 2 4 6 8 10 12 14 16 2 3 4 5 6 7 8 Ny[Nm] Time[s] 0 2 4 6 8 10 12 14 16 -2 -1 0 1 2 Nx[Nm] Time[s] 0 2 4 6 8 10 12 14 16 -15 -10 -5 0 5 10 Fx[N] Time[s] 0 2 4 6 8 10 12 14 16 -15 -10 -5 0 5 10 15 Fy[N] Time[s] 0 2 4 6 8 10 12 14 16 60 70 80 90 Fz[N] Time[s] Cross-validation with a left-fronted squat Dynamic model and Identification model of humanoid robots Identifiability of the vector of base parameter with the upper system only has been proven WHY? Knowledge of the robot inertial parameters crucial data to do simulation and control of robot dynamics. Robot manufacturers and designers often give inertial parameter values obtained by CAD software : don’t take into account the whole actuators and sensors information Identification of the system dynamics thus expressly required. Conventionnal methods based on knowledge of all joints informations : joint angle, joint torque... Humanoid robot not always equipped with torque sensors. But have contact force-sensors. HOW? Proposed method based on contact force measurement: - Using the property of the dynamic model by using the base-link equations only. - Required data are limited to joint angles, external forces, and generalized coordinates of the base-link. WHY? Identification also a solution to measure dynamics in-vivo. Knowledge of dynamics of human mandatory for simulations of body motions, support of medical diagnosis, monitoring of body physical condition... HOW? Based on similar methodology as for humanoid robots, extended to humans. Instead of sensors, joint angles provided by motion cature data and computation of inverse kinematics making use of a skeletal model. Motion capturing advantages: convenient, noninvasive, painless way to measure motion, assures interaction-free measurement between subject and measuring system, allow to perform all kind of motions. Skeletal model of human body HOW? Proposed Constraint-free method : Capturing motions during the clinical diagnosis and identifying joints parameters with constraint-free passive or semi-active motions. Features and advantages : - Use of a very detailed rigid-body model to allow to use all kind of motions. - Roll-out easy, positioning of markers over anatomical points robust and fast. - Capture performed during the normal clinical diagnosis allowing the PT to perform simultaneously his visual rating. - Non-invasive, non-harrowing and painless. - Simultaneous multi-joint estimation. 1. MODELLING THE DYNAMICS Modelling based on the fundamental equations of dynamics obtained for multi-body systems. τ = Γ + Q = H(q,q,q,I P ) + τ vef - τ vector of joint torques - Γ vector of joint forces and torque due to actuation - Q vector of generalized efforts (projection of external efforts) - H vector of inertial, Coriolis, centrifugal, gravity forces - q vector of joint angles - I P vector of inertial parameters: mass, inertia, first moment of inertia - τ vef vector of visco-elasticity and friction forces of joints 2. IDENTIFICATION MODEL Dynamic model linear in inertial parameters and viscoelastic properties. τ = Y(q,q,q) φ - Y regressor matrix or observation matrix - φ vector parameters to estimate After sampling along a trajectory, identification model solved by linear least-squares method: - fast, reliable - allow to compute indicators to interpret the results: Condition number of the regressor matrix and standard deviation for each parameter estimated BIBLIOGRAPHY - G. Venture, K. Yamane, and Y. Nakamura, “In-vivo estimation of the human elbow joint dynamics during passive movements based on the musculo-skeletal kinematics computation,” in IEEE/Int. ICRA, Orlando, USA, May 2006, pp. 2960–2965. - M. Khalil, W. Gautier and J.F. Kleinfinger, “Automatic generation of identification models of robots,” Int. Jour. of Robotics and Automation, vol. 1, no. 1, pp. 2–6, 1986. - J.M. Winters and L. Stark, “Analysis of fundamental human movement patterns through the use of in-depth antagonistic muscle model,” IEEE Trans. on Biomedical Engineering, vol. 32, no. 10, pp. 826–839, 1985. - Khalil W. and Dombre E., “Modeling, identification and control of robots”, Hermès Penton, London-U.K, 2002. - M. Gautier and W. Khalil, “Exciting trajectories for the identification of base inertial parameters of robots,” Int. Jour. of Robotic Research, vol. 11, no. 4, pp. 363–375, 1992. - Y. Nakamura, K. Yamane, I. Suzuki, and Y. Fujita, “Dynamic computation of musculo-skeletal human model based on efficient algorithm for closed kinematic chains,” in Proc. of the 2nd Int. Symp. on Adaptive Motion of Animals and Machines, Kyoto, Japan, March 2003. - Y. Nakamura, K. Yamane, Y. Fujita, and I. Suzuki, “Somatosensory computation for man-machine interface from motion- capture data and musculoskeletal human model,” IEEE Transactions on Robotics, vol. 21, no. 1, pp. 58–66, Feb. 2005. - Pressé C. and Gautier M., “New criteria of exciting trajectories for robot identification,” in Proc. IEEE Int. Conf. on Robotics and Automation, Atlanta, 1993, pp. 907–912. - J. Swevers, C. Ganseman, D. Bilgin, J. De Schutter, and H. Van Brussel, “Optimal robot excitation and identification,” IEEE Trans. on Robotics and Automation, vol. 13, no. 5, pp. 730–740, 1997. - T. Sugihara and Y. Nakamura, “Balanced micro/macro contact model for forward dynamics of rigid multibody,” in Proc. of the IEEE Int. Conf on Robotics and Automation, May 2006, pp. 1880–1885. 3. EXCITING TRAJECTORIES A good estimation relies on: (1) an appropriate model to describe the system, (2) accurate measurements, (3) properly chosen movements that can excite the dynamics to identify, named persistently exciting trajectories 4. BASE PARAMETERS Only minimal set of parameter describing the model can be identify: Base parameters Can be computed symbolically: essential base parameters, dependant only on kinematic structure of the system. Can be computed numerically: in addition to essential base parametersparameters not exited by the motion are grouped or suppressed. 5. VALIDATIONS A-priori data often not known. Validations consist in comparing joint torques or efforts measured to the computed one using the identification model and the estimated parameters. Direct validation : motion in the data set used for identification. Cross-validation : motion not used for identification Cross-validation with a side walk motion 200 400 600 800 −20 −15 −10 −5 0 5 10 15 20 25 Nx[Nm] Samples 200 400 600 800 −100 −80 −60 −40 −20 0 20 40 60 80 100 Fy [N] Samples 200 400 600 800 −15 −10 −5 0 5 10 Samples Ny[Nm] 200 400 600 800 −12 −10 −8 −6 −4 −2 0 2 4 Samples Nz[Nm] 200 400 600 800 −30 −20 −10 0 10 20 30 Samples Fx [N] 200 400 600 800 500 550 600 650 700 Samples Fz [N] MOTIONS USED FOR IDENTIFICATION 128 parameters to estimate for the whole body. Exciting trajectories must be natural and painless. Several motions recorded to excite all body parts: walk, side walk, body bending, movements of the arms, movements of the trunk... Most exciting motions chosen for identification (according to the condition number of the regressor). Remaining motions used for cross-validations. EXPERIMENTAL SET-UP Candidate equipped with 35 optical markers at define atanomical points Execute motions in the motion capture studio, on top of force-plates to measure the 6 components of the contact forces. Motion capturing Camera and force-``plates
Transcript of Dynamics Identification of human-like systems Dr. Gentiane...
Dynamics Identification of human-like systems Dr. Gentiane Venture
The University of Tokyo - Department of Mechano-Informatics - Nakamura & Yamane Lab. info: [email protected]
Global Identification Methodology
Application to Humanoid Robots
Application to Human
Application to Human to Support the Diagnosis of Neuromuscular Diseases
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Rigth Shoulder Stiffness Nm/rad
Left
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Patient 1 Patient 2
Patient 3
Patient 4
Patient 5Candidate 2
Candidate 1
Right Left
σk% = 1σk% = 5
σk% =10normal condition
globalstiffness
stiffness ofthe right side
stiffness ofthe left side
Y
Z
Y
X
Ny[Nm]
0 5 10 15 202.53
3.54
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0 5 10 15 20-3-2-10123
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0 5 10 15 20-3-2-10123Fy[N]
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0 5 10 15 20606570758085Fz[N]
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0 5 10 15 20-3-2-10123
Nx[Nm]
Time[s]
0 5 10 15 20-0.3-0.2-0.1
00.10.2Nz[Nm]
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Cross-validation with a bending motion
Patients discrimination accordingto estimated shoulders stiffness
WHY?Neurological disorders : slowly progressive,degenerative diseases. Affected personsexperience various symptoms such as trembling,muscle rigidity, slowed motion, difficulty inwalking, problems with body balance andcoordination.Outcome assessments of neurological disorderspartly relies on the qualitative clinical rating ofsymptoms : bradykinesia, tremor, and rigidity usingrating scales... Objective quantification tools highlyrequired, apparatus proposed so far too expensive,too complex and required too much time to be usedin a clinical setting.
Clinical diagnosis based on patient medical history,observations of symptoms, neurologic examination :- passive tests : patient not moving (no contractionof muscles) but movements induced by the PT.- semi-active tests: patient asked to perform lower-bodymotions resulting in upper-body passive motions.- active tests: patient performs full body active motion.
constraint-freemovements
passive
painless
suitable for identification
Movements ofthe human limbs time
time
time(a)
(b)
(c)
Test P4
Test P4’
Test P5
Passive tests used for identification duringclinical diagnosis of neuromuscular diseases
Type of motion required toidentify joint dynamics formedical applications
Modelling of joint passive visco-elastic properties
−10
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5
10 Patient 5 left shoulder
join
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preparation walk volteface walkwalk volte
face walk volteface
Patient 5 right shoulder
Validation for the shoulders
0 2 4 6 8 10 12 14 16-1.5-1
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0 2 4 6 8 10 12 14 162345678Ny[Nm]
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0 2 4 6 8 10 12 14 16-15-10-5051015Fy[N]
Time[s]
0 2 4 6 8 10 12 14 1660
70
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90Fz[N]
Time[s]
Cross-validation with a left-fronted squatDynamic model and Identification modelof humanoid robots
Identifiability of the vector of base parameter with the uppersystem only has been proven
WHY?Knowledge of the robot inertial parameters crucial data todo simulation and control of robot dynamics.Robot manufacturers and designers often give inertialparameter values obtained by CAD software : don’t takeinto account the whole actuators and sensors information Identification of the system dynamics thus expressly required.
Conventionnal methods based on knowledge of all jointsinformations : joint angle, joint torque... Humanoid robot not always equipped with torque sensors.But have contact force-sensors.
HOW?Proposed method based on contact force measurement:- Using the property of the dynamic model by using thebase-link equations only.- Required data are limited to joint angles, external forces,and generalized coordinates of the base-link.
WHY?Identification also a solution to measure dynamics in-vivo.Knowledge of dynamics of human mandatory for simulations of bodymotions, support of medical diagnosis, monitoring of body physicalcondition...
HOW?Based on similar methodology as for humanoid robots, extended tohumans.Instead of sensors, joint angles provided by motion cature data and computation of inverse kinematics making use of a skeletal model.Motion capturing advantages:convenient, noninvasive, painless way to measure motion, assures interaction-free measurement between subject and measuring system,allow to perform all kind of motions.
Skeletal model of human body
HOW?Proposed Constraint-free method :Capturing motions during the clinical diagnosisand identifying joints parameters with constraint-freepassive or semi-active motions.
Features and advantages :- Use of a very detailed rigid-body model to allowto use all kind of motions.- Roll-out easy, positioning of markers over anatomicalpoints robust and fast.- Capture performed during the normal clinical diagnosisallowing the PT to perform simultaneously his visual rating. - Non-invasive, non-harrowing and painless. - Simultaneous multi-joint estimation.
1. MODELLING THE DYNAMICSModelling based on the fundamental equations of dynamicsobtained for multi-body systems.
τ = Γ + Q = H(q,q,q,IP) + τvef
- τ vector of joint torques
- Γ vector of joint forces and torque due to actuation
- Q vector of generalized efforts (projection of external efforts)
- H vector of inertial, Coriolis, centrifugal, gravity forces
- q vector of joint angles
- IP vector of inertial parameters: mass, inertia, first moment of inertia
- τvef vector of visco-elasticity and friction forces of joints
2. IDENTIFICATION MODELDynamic model linear in inertial parameters andviscoelastic properties.
τ = Y(q,q,q) φ
- Y regressor matrix or observation matrix
- φ vector parameters to estimate
After sampling along a trajectory, identification modelsolved by linear least-squares method:- fast, reliable- allow to compute indicators to interpret the results:Condition number of the regressor matrix and standarddeviation for each parameter estimated
BIBLIOGRAPHY- G. Venture, K. Yamane, and Y. Nakamura, “In-vivo estimation of the human elbow joint dynamics during passive movementsbased on the musculo-skeletal kinematics computation,” in IEEE/Int. ICRA, Orlando, USA, May 2006, pp. 2960–2965.- M. Khalil, W. Gautier and J.F. Kleinfinger, “Automatic generation of identification models of robots,” Int. Jour. of Robotics andAutomation, vol. 1, no. 1, pp. 2–6, 1986.- J.M. Winters and L. Stark, “Analysis of fundamental human movement patterns through the use of in-depth antagonisticmuscle model,” IEEE Trans. on Biomedical Engineering, vol. 32, no. 10, pp. 826–839, 1985.- Khalil W. and Dombre E., “Modeling, identification and control of robots”, Hermès Penton, London-U.K, 2002.- M. Gautier and W. Khalil, “Exciting trajectories for the identification of base inertial parameters of robots,” Int. Jour. of Robotic Research, vol. 11, no. 4, pp. 363–375, 1992.- Y. Nakamura, K. Yamane, I. Suzuki, and Y. Fujita, “Dynamic computation of musculo-skeletal human model based on efficientalgorithm for closed kinematic chains,” in Proc. of the 2nd Int. Symp. on Adaptive Motion of Animals and Machines, Kyoto, Japan, March 2003.- Y. Nakamura, K. Yamane, Y. Fujita, and I. Suzuki, “Somatosensory computation for man-machine interface from motion-capture data and musculoskeletal human model,” IEEE Transactions on Robotics, vol. 21, no. 1, pp. 58–66, Feb. 2005.- Pressé C. and Gautier M., “New criteria of exciting trajectories for robot identification,” in Proc. IEEE Int. Conf. on Roboticsand Automation, Atlanta, 1993, pp. 907–912.- J. Swevers, C. Ganseman, D. Bilgin, J. De Schutter, and H. Van Brussel, “Optimal robot excitation and identification,” IEEE Trans.on Robotics and Automation, vol. 13, no. 5, pp. 730–740, 1997.- T. Sugihara and Y. Nakamura, “Balanced micro/macro contact model for forward dynamics of rigid multibody,” in Proc. ofthe IEEE Int. Conf on Robotics and Automation, May 2006, pp. 1880–1885.
3. EXCITING TRAJECTORIESA good estimation relies on:(1) an appropriate model to describe the system,(2) accurate measurements,(3) properly chosen movements that can excite the dynamics to identify, named persistently exciting trajectories
4. BASE PARAMETERSOnly minimal set of parameter describing the model can beidentify: Base parametersCan be computed symbolically: essential base parameters,dependant only on kinematic structure of the system.Can be computed numerically: in addition to essential baseparametersparameters not exited by the motion are groupedor suppressed.
5. VALIDATIONSA-priori data often not known. Validations consist in comparingjoint torques or efforts measured to the computed one using the identification model and the estimated parameters.Direct validation : motion in the data set used for identification.Cross-validation : motion not used for identification
Cross-validation with a side walk motion
200 400 600 800−20−15−10−5
05
10152025Nx[Nm]
Samples
200 400 600 800−100−80−60−40−20
020406080
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Ny[Nm]
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Nz[Nm]
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Fx [N]
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Fz [N]MOTIONS USED FOR IDENTIFICATION128 parameters to estimate for the whole body.Exciting trajectories must be natural and painless.Several motions recorded to excite all body parts:walk, side walk, body bending, movements of the arms, movementsof the trunk...Most exciting motions chosen for identification (according to the condition number of the regressor).Remaining motions used for cross-validations.
EXPERIMENTAL SET-UPCandidate equipped with 35 opticalmarkers at define atanomical pointsExecute motions in the motion capturestudio, on top of force-plates to measurethe 6 components of the contact forces.
Motion capturing
Camera and force-``plates
