walking and stair climbing stabilization for position-controlled ...hrp-4humanoidrobotinshort....
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Walking and stair climbing stabilization forposition-controlled biped robots.
Stéphane CaronMarch 19, 2019
Presentation given at Wandercraft, Paris
final demonstrator.
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stair climbing part.
https://www.youtube.com/watch?v=vFCFKAunsYM
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hrp-4 humanoid robot in short.
Stiff position control on all joints Mechanical flexibility at the ankles
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1. Kenji Kaneko, Fumio Kanehiro, Mitsuharu Morisawa, Kazuhiko Akachi, Gou Miyamori, AtsushiHayashi et Noriyuki Kanehira. « Humanoid robot HRP-4 - Humanoid Robotics Platform with Light-weight and Slim Body ». In : IEEE/RSJ International Conf. on Intelligent Robots and Systems. 2011.
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system overview.
WalkingPattern
Generation
Whole-bodyAdmittanceControl
Whole-bodyKinematicControl
DCMControl
DCMObserver
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walking pattern generation.
FootstepLocations Walking
PatternGeneration
Whole-bodyAdmittanceControl
Whole-bodyKinematicControl
DCMControl
DCMObserver
DesiredKinematicTargets
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linear inverted pendulum mode.
• Equation of motion :
Mq+ N = STτ + JTf
• Floating base dynamics :
c = 1m∑
i fiLc =
∑i(pi − c)× fi
• Angular momentum Lc = 0 :
c = ω2(c− z)
with ω2 = g/h and z the ZMP
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2. Shuuji Kajita, Fumio Kanehiro, Kenji Kaneko, Kazuhito Yokoi et Hirohisa Hirukawa. « The 3DLinear Inverted Pendulum Mode : A simple modeling for a biped walking pattern generation ». In :IEEE/RSJ International Conference on Intelligent Robots and Systems. 2001.
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divergent component of motion.
• LIPM equation of motion :
c = ω2(c− z)
• Divergent Component of Motion :
ξ = c+ cω
• Unstable dynamics :
ξ = ω(ξ − z)
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3. Johannes Englsberger, Christian Ott, Maximo Roa, Alin Albu-Schäffer, Gerhard Hirzinger etal. « Bipedal walking control based on capture point dynamics ». In : IEEE/RSJ International Confe-rence on Intelligent Robots and Systems. 2011.
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walking pattern generation.
Generate a CoM-ZMP trajectory that is :
Consistent
∀t > 0, c(t) = ω2(c(t)− z(t))
Feasible
• ZMP belongs to support area• Contact force within friction cone
ViableNot falling. For this system, same asbounded : ∃M > 0, ∀t > 0, ∥c(t)∥ ≤ M
Figure adapted from [Gri+17].
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walking pattern generation.
So far we have tested three methods :
• Linear Model Predictive Control [Wie06]• Foot-guided Agile Control through ZMP Manipulation [SY17]• Capturability of Variable-Height Inverted Pendulum [Car+18]
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linear model predictive control.
Formulate preview control [Kaj+03] as a Quadratic Program (QP) :
Cost function
• Track desired ZMP reference• Track desired CoM velocity• Minimize CoM jerk
Constraints
• Consistency : state equation• Feasibility : ZMP in support area• Viability : terminal DCM
Allows a number of extensions, including :
• Variable CoM height : [Bra+15]• Variable step timings : [BW17]
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4. Pierre-Brice Wieber. « Trajectory free linear model predictive control for stable walking in thepresence of strong perturbations ». In : IEEE-RAS International Conference on Humanoid Robots.2006.
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foot-guided agile control through zmp manipulation.
Predictive control with ZMP as input
minimizez(t)
∫ T
0
(z(t)− zd)2dt ⇒ Feasibility (best effort)
subject to c = ω2(c− z) ⇒ Consistency
c(T) + c(T)ω
= ξd ⇒ Viability
• Finite horizon, continuous time dynamics• Analytical solution :
z∗(0) = zd + 2(ξ(0)− zd)− (ξd − zd)e−ωT
1− e−2ωT
• Call many times to adapt step timings
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5. Tomomichi Sugihara et Takanobu Yamamoto. « Foot-guided Agile Control of a Biped Robotthrough ZMP Manipulation ». In : IEEE/RSJ International Conference on Intelligent Robots and Sys-tems. 2017.
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variable-height inverted pendulum.
• New input λ > 0 for height variations :
c = λ(c− z) + g
• Viability⇒ boundedness condition :
ξ(0) =
∫ ∞
0
(λ(t)r(t)− g)e−Ω(t)dt
• Solve : tailored optimization (30-50 µs)• Call many times to adapt step timings
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6. Stéphane Caron, Adrien Escande, Leonardo Lanari et Bastien Mallein. « Capturability-basedAnalysis, Optimization and Control of 3D Bipedal Walking ». 2018. url : https://hal.archives-ouvertes.fr/hal-01689331/document.
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visualization.
Visualization of stair climbing pattern in mc_rtc
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walking stabilization.
FootstepLocations Walking
PatternGeneration
Whole-bodyAdmittanceControl
Whole-bodyKinematicControl
DCMControl
DCMObserver
DesiredKinematicTargets
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role of stabilization.
Actuated joints converge but unactuated floating base diverges :
In walking pattern By robot without stabilization
Figure adapted from [Tak+09].
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floating base facts.
Let's review the facts :• Floating base translation is unactuated• Its dynamics are reduced to :
c = ω2(c− z)
• Only way to control it is via indirectforce control of the ZMP z
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indirect force control.
... but our robot is position-controlled ?
Split control into two components :
Admittance controlAllow position changes to improve forcetracking
Floating-base controlAssuming force control, select reactionforce to control the floating base
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visualization.
Standing stabilization under external forces
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admittance control.
FootstepLocations Walking
PatternGeneration
Whole-bodyAdmittanceControl
CommandedJoint AnglesWhole-body
KinematicControl
DCMControl
DCMObserver
DesiredKinematicTargets
DistributedFoot Wrenches
CommandedKinematicTargets
MeasuredFoot Wrenches
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strategies.
Different strategies for differentcomponents of the net contact wrench :• CoP at each contact [Kaj+01b]• Pressure distribution [Kaj+10]• CoM admittance control [Nag99]
This stabilizer implements :• Ankle strategy : yes• Hip strategy : translation, no rotation• Stepping strategy : no
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center-of-pressure control.
• Rotate end-effector to move its CoP• Assumes compliance at contact :
τ = Ke(θ − θe)
• Apply damping control :
θ = Acop(τd − τ)
• Closed-loop behavior has τ → τd Figure adapted from [Kaj+01b]
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7. Shuuji Kajita, Kazuhito Yokoi, Muneharu Saigo et Kazuo Tanie. « Balancing a Humanoid RobotUsing Backdrive Concerned Torque Control and Direct Angular Momentum Feedback ». In : IEEEInternational Conference on Robotics and Automation. 2001.
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pressure distribution control.
• Net vertical force compensatesgravity⇒ only need to control :
∆fz = fRz − fLz
• Push down with foot that needs morepressure, lift the other one
• Apply damping control :
zctrl = Az(∆fzd −∆fz)
ctrlz*d
Rzp*dLzp
RzfLzf
Figure adapted from [Kaj+10]
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8. Shuuji Kajita, Mitsuharu Morisawa, Kanako Miura, Shin'ichiro Nakaoka, Kensuke Harada, KenjiKaneko, Fumio Kanehiro et Kazuhito Yokoi. « Biped walking stabilization based on linear invertedpendulum tracking ». In : IEEE/RSJ International Conference on Intelligent Robots and Systems. 2010.
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com admittance control.
• Recall that c = ω2(c− z)• Accelerate CoM against ZMP error :
c = Ac(z− zd)
• Closed-loop behavior : analysis isonly possible with delay ordisturbance observer 9
measured desired
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9. Discussions with Pr T. Sugihara.10. Ken'ichiro Nagasaka. « Whole-body Motion Generation for a Humanoid Robot by Dynamics
Filters ». In : PhD thesis (1999). The University of Tokyo, in Japanese.
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choice of strategies.
Which ones to choose ?
End-effector strategies
• CoP at each contact [Kaj+01b]• Pressure distribution [Kaj+10]
... are sufficient to control the net wrench,yet :
CoM admittance control [Nag99]
• uses other joints, e.g. hips• helps recover from ZMP saturation 11
11. The effect is similar to Model ZMP Control [Tak+09].
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com admittance in stair climbing.
Sagi
ttal
Coo
rdin
ate
(m)
Time (s)
Measured ZMP
Measured DCM
Desired DCM
Desized ZMP
Maximum ZMP
Minimum ZMP
-0.1
0.0
0.1
0.2
0.3
-0.1
0.0
0.1
0.2
0.3
Figure 1 : Top : no CoM admittance control. Bottom : with Ac = 20 [Hz2].
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dcm control.
FootstepLocations Walking
PatternGeneration
Whole-bodyAdmittanceControl
CommandedJoint Angles
Measured Joint Angles
Whole-bodyKinematicControl
DCMControl
DCMObserver
EstimatedDCM
Measured IMU Orientation
Desired DCM
Desired CoM & Contacts
DesiredKinematicTargets
DistributedFoot Wrenches
CommandedKinematicTargets
MeasuredFoot Wrenches
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net wrench.
• Assume control of z in c = ω2(c− z)• Controlling only the DCM ξ = ω(ξ − z) :requires less control input [Tak+09]yields best CoM-ZMP regulator [Sug09]
• Apply feedback control to it :
ξ = ξd + kp(ξd − ξ)
z = zd −[1 +
kpω
](ξd − ξ)
This gives us the net wrench.
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12. Tomomichi Sugihara. « Standing stabilizability and stepping maneuver in planar bipedalismbased on the best COM-ZMP regulator ». In : IEEE International Conference on Robotics and Auto-mation. 2009.
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damping and integral terms.
• Add damping term, add integral term to eliminate steady-state error :
ξ = ξd + kp(ξd − ξ) + ki∫
(ξd − ξ) + kd(ξd − ξ)
z = zd − (1 +kpω)(ξd − ξ)− ki
ω
∫(ξd − ξ) +
kzω(zd − z)
• Select gains kp, ki, kz (1) by hand or (2) by pole placement• In practice : how to manage the link with admittance control gains ?
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13. Mitsuharu Morisawa, Shuuji Kajita, Fumio Kanehiro, Kenji Kaneko, Kanako Miura et KazuhiroYokoi. « Balance control based on capture point error compensation for biped walking on uneventerrain ». In : IEEE-RAS International Conference on Humanoid Robots. 2012.
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distribute net wrench to contacts.
Wrench distribution by QP :
Cost function
• Realize net wrench• Minimize ankle torques• Desired pressure distribution
Constraints
• Frictional wrench cones• Minimum pressure on each contact
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complete system.
FootstepLocations Walking
PatternGeneration
Whole-bodyAdmittanceControl
CommandedJoint Angles
Measured Joint Angles
Whole-bodyKinematicControl
DCMControl
DCMObserver
EstimatedDCM
Measured IMU Orientation
Desired DCM
Desired CoM & Contacts
DesiredKinematicTargets
DistributedFoot Wrenches
CommandedKinematicTargets
MeasuredFoot Wrenches
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experimental results.
https://www.youtube.com/watch?v=vFCFKAunsYM
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coda : an ode to two tools.
mc_rtc control framework by p. gergondet.
Controller with mc_rtc GUI alongside a Choreonoid dynamic simulation
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choreonoid simulator by s. nakaoka.
Failure case on real robot Reproduction in Choreonoid
http://choreonoid.org/en/
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thanks !.
Thank you for your attention !
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references i.
[Bra+15] Camille Brasseur, Alexander Sherikov, Cyrille Collette, Dimitar Dimitrov etPierre-Brice Wieber. « A robust linear MPC approach to online generation of 3Dbiped walking motion ». In : IEEE-RAS International Conference on HumanoidRobots. 2015.
[BW17] Nestor Bohorquez et Pierre-Brice Wieber. « Adaptive step duration in bipedwalking : a robust approach to nonlinear constraints ». In : IEEE-RAS InternationalConference on Humanoid Robots. 2017.
[Car+18] Stéphane Caron, Adrien Escande, Leonardo Lanari et Bastien Mallein.« Capturability-based Analysis, Optimization and Control of 3D Bipedal Walking ».2018. url : https://hal.archives-ouvertes.fr/hal-01689331/document.
[Eng+11] Johannes Englsberger, Christian Ott, Maximo Roa, Alin Albu-Schäffer,Gerhard Hirzinger et al. « Bipedal walking control based on capture pointdynamics ». In : IEEE/RSJ International Conference on Intelligent Robots andSystems. 2011.
[Gri+17] Robert J Griffin, Georg Wiedebach, Sylvain Bertrand, Alexander Leonessa etJerry Pratt. « Walking Stabilization Using Step Timing and Location Adjustment onthe Humanoid Robot, Atlas ». In : IEEE/RSJ International Conference on IntelligentRobots and Systems. 2017.
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references ii.
[Kaj+01a] Shuuji Kajita, Fumio Kanehiro, Kenji Kaneko, Kazuhito Yokoi et Hirohisa Hirukawa.« The 3D Linear Inverted Pendulum Mode : A simple modeling for a biped walkingpattern generation ». In : IEEE/RSJ International Conference on Intelligent Robotsand Systems. 2001.
[Kaj+01b] Shuuji Kajita, Kazuhito Yokoi, Muneharu Saigo et Kazuo Tanie. « Balancing aHumanoid Robot Using Backdrive Concerned Torque Control and Direct AngularMomentum Feedback ». In : IEEE International Conference on Robotics andAutomation. 2001.
[Kaj+03] Shuuji Kajita, Fumio Kanehiro, Kenji Kaneko, Kiyoshi Fujiwara, Kensuke Harada,Kazuhito Yokoi et Hirohisa Hirukawa. « Biped walking pattern generation by usingpreview control of zero-moment point ». In : IEEE International Conference onRobotics and Automation. 2003.
[Kaj+10] Shuuji Kajita, Mitsuharu Morisawa, Kanako Miura, Shin'ichiro Nakaoka,Kensuke Harada, Kenji Kaneko, Fumio Kanehiro et Kazuhito Yokoi. « Biped walkingstabilization based on linear inverted pendulum tracking ». In : IEEE/RSJInternational Conference on Intelligent Robots and Systems. 2010.
[Kan+11] Kenji Kaneko, Fumio Kanehiro, Mitsuharu Morisawa, Kazuhiko Akachi, Gou Miyamori,Atsushi Hayashi et Noriyuki Kanehira. « Humanoid robot HRP-4 - HumanoidRobotics Platform with Lightweight and Slim Body ». In : IEEE/RSJ International Conf.on Intelligent Robots and Systems. 2011.
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references iii.
[Mor+12] Mitsuharu Morisawa, Shuuji Kajita, Fumio Kanehiro, Kenji Kaneko, Kanako Miura etKazuhiro Yokoi. « Balance control based on capture point error compensation forbiped walking on uneven terrain ». In : IEEE-RAS International Conference onHumanoid Robots. 2012.
[Nag99] Ken'ichiro Nagasaka. « Whole-body Motion Generation for a Humanoid Robot byDynamics Filters ». In : PhD thesis (1999). The University of Tokyo, in Japanese.
[Sug09] Tomomichi Sugihara. « Standing stabilizability and stepping maneuver in planarbipedalism based on the best COM-ZMP regulator ». In : IEEE InternationalConference on Robotics and Automation. 2009.
[SY17] Tomomichi Sugihara et Takanobu Yamamoto. « Foot-guided Agile Control of a BipedRobot through ZMP Manipulation ». In : IEEE/RSJ International Conference onIntelligent Robots and Systems. 2017.
[Tak+09] Toru Takenaka, Takashi Matsumoto, Takahide Yoshiike, Tadaaki Hasegawa,Shinya Shirokura, Hiroyuki Kaneko et Atsuo Orita. « Real time motion generationand control for biped robot-4th report : Integrated balance control ». In : IEEE/RSJInternational Conference on Intelligent Robots and Systems. 2009.
[Wie06] Pierre-Brice Wieber. « Trajectory free linear model predictive control for stablewalking in the presence of strong perturbations ». In : IEEE-RAS InternationalConference on Humanoid Robots. 2006.
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