Multi-contact Locomotion and Percep- tion on the Humanoid Robot - - PowerPoint PPT Presentation

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Multi-contact Locomotion and Percep- tion on the Humanoid Robot - - PowerPoint PPT Presentation

Mar 15, 2024 392 likes •570 views

Multi-contact Locomotion and Percep- tion on the Humanoid Robot HRP-2 J. Carpentier C. Quang-Pham A. Del Prete M. Kudruss N. Mansard M. Naveau O. Stasse S. Tonneau Gepetto, LAAS-CNRS, Toulouse, France Int. Conf. on Humanoid Robotics,

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Multi-contact Locomotion and Percep- tion on the Humanoid Robot HRP-2

  • Int. Conf. on Humanoid Robotics,

10th Workshop on Humanoid Soccer Robots Seoul, Korea, November 3rd, 2015

  • J. Carpentier
  • C. Quang-Pham
  • A. Del Prete
  • M. Kudruss
  • N. Mansard
  • M. Naveau
  • O. Stasse
  • S. Tonneau

Gepetto, LAAS-CNRS, Toulouse, France

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Motivations Uncertainity, planning and control Conclusions

Presentation overview

1 Motivations

Applications Results

2 Uncertainity, planning and control

Motion generation Planning complex contact sequences Noise in the contact surfaces Noise in the localization Control and underactuation

3 Conclusions

10th WS on Humanoid Soccer Robots – 2/16

  • N. Mansard and O. Stasse
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SLIDE 3

Motivations Uncertainity, planning and control Conclusions

Table of Contents

1 Motivations

Applications Results

2 Uncertainity, planning and control 3 Conclusions

10th WS on Humanoid Soccer Robots – 3/16

  • N. Mansard and O. Stasse
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SLIDE 4

Motivations Uncertainity, planning and control Conclusions Applications

Humanoids in Factory like environment

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  • N. Mansard and O. Stasse
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SLIDE 5

Motivations Uncertainity, planning and control Conclusions Results

Humanoid robot HRP-2 evolving on stairs

[Kudruss, Humanoids 2015] [Carpentier, ICRA 2016 submitted] Previous work [Luo, ICRA 2014] [Vaillant, Humanoids 2014] [Noda, ICRA 2014]

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  • N. Mansard and O. Stasse
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SLIDE 6

Motivations Uncertainity, planning and control Conclusions Results

Humanoid robot HRP-2 stepping down

[Cuong, IEEE Trans. on Mechatronics 2014]

10th WS on Humanoid Soccer Robots – 6/16

  • N. Mansard and O. Stasse
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SLIDE 7

Motivations Uncertainity, planning and control Conclusions

Table of Contents

1 Motivations 2 Uncertainity, planning and control

Motion generation Planning complex contact sequences Noise in the contact surfaces Noise in the localization Control and underactuation

3 Conclusions

10th WS on Humanoid Soccer Robots – 7/16

  • N. Mansard and O. Stasse
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SLIDE 8

Motivations Uncertainity, planning and control Conclusions Motion generation

Motion generation: the general problem

min f (u(t), v(t)) g(u(t), v(t)) < 0 h(u(t), v(t)) = 0

          

with u(t) the control and v(t) the environment model Which v(t) for multi- contact control ?

t CoM ^ q Balance (under-actuated part) GIK A general problem on the time window

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  • N. Mansard and O. Stasse
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SLIDE 9

Motivations Uncertainity, planning and control Conclusions Planning complex contact sequences

Planning complex contact sequences

Fast conctact planner from environment CAD (near real-time) [Tonneau, ISRR2015] Evident need of dense mapping as input Preparing force control using robust balance

[Del Prete, ICRA 2016 Submitted]

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  • N. Mansard and O. Stasse
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Motivations Uncertainity, planning and control Conclusions Noise in the contact surfaces

Problems with the environment model

Noise in the contact surfaces Torque control

Online adaptation to un- known terrain

Solution

10th WS on Humanoid Soccer Robots – 10/16

  • N. Mansard and O. Stasse
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SLIDE 11

Motivations Uncertainity, planning and control Conclusions Noise in the contact surfaces

Problems with the environment model

Noise in the contact surfaces Torque control

Online adaptation to un- known terrain

Solution Torque control for some humanoid robots (HRP-2) is difficult to achieve

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  • N. Mansard and O. Stasse
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Motivations Uncertainity, planning and control Conclusions Noise in the contact surfaces

Torque control

Torque control on a stiff-actuation robot Using end-effector force-torque sensors + IMU + encoders Efficient reconstruction of the motor torques Feedforward on the reconstructed torques (= friction compensation) Feedback on the force sensors (= perfect contact tracking)

[Del Prete, IJHR 2015]

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Motivations Uncertainity, planning and control Conclusions Noise in the localization

Problems with the environment model

Noise in the localization Rigid robot are good to localize locally SLAM in large environment and use for planning is a challenge In general geometric environment are simple for planning Direct use of geometric models is sometimes preferable Noise due to foot landing and robot Replanning and fast control are necessary

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  • N. Mansard and O. Stasse
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Motivations Uncertainity, planning and control Conclusions Control and underactuation

Contact and underactuation

m(¨ c − g) = Nc

i=1 fi

mc×(¨ c − g) = Nc

i=1 pi ×fi

      

t CoM ^ q Balance (under-actuated part) GIK A general problem on the time window

Challenges in Multi-contacts locomotion The general template model includes Quadratic Constraint which can be concave The problem is NP-Hard with c or f as free variables Are the real problems that hard ? Open problem : real-time computation with pi also free variables ?

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  • N. Mansard and O. Stasse
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SLIDE 15

Motivations Uncertainity, planning and control Conclusions Control and underactuation

Model-predictive control for 3D locomotion

Fast optimal control for central-dynamics pattern generation Near real-time ( 80ms per cycle), ready for MPC Optimize the COM trajectory while keeping the angular momentum low On-going connection with the IMU+force sensor Submitted to ICRA 2016

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Motivations Uncertainity, planning and control Conclusions

Table of Contents

1 Motivations 2 Uncertainity, planning and control 3 Conclusions

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SLIDE 17

Motivations Uncertainity, planning and control Conclusions

Conclusions and Perspectives

Conclusions

Human environments are still very challenging due to symmetries, lack of textures, occlusion. Including a-priori knowledge helps. Real-time multi-contact based motion generation is difficult Choosing from scratch new contact might be difficult unless candidates are already known.

Perspectives

Efficient formulation might be found Stochastique approach of control

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  • N. Mansard and O. Stasse