may 14, 2014 on the dynamics of human locomotion and co-design of lower limb assistive devices 1 on...

1May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices

On the dynamics of human locomotion and co-design of lower

limb assistive devices

Jesse van den Kieboom

Biorobotics Laboratory, EPFL, Lausanne

PhD oral exam, May 14, 2014

Supervisor: prof. Auke Jan Ijspeert


INtroduction

QuickTime™ and a decompressor

are needed to see this picture.

Source: youtube.com


introduction



Source: youtube.com


Introduction

• Body versus mind, embodied intelligence

• Morphology determines how interaction takes place

• Strong notion in natural system

• Adaptation of morphology is a product of natural evolution

Motivation


Introduction

• EVRYON: evolving morphologies for human-robot symbiotic interaction (7th framework programme ICT-2007.8.5 Embodied intelligence)

• Incorporate morphological design prominently into the design phase

• Not always obvious, since performance is a combination of morphology and control

• Take inspiration from natural processes to co-design morphology and control using dynamical simulations

Motivation


Introduction

• Development of a methodology for the co-design of bipedal machines, with a case study in wearable lower limb devices

• Study of principles of human gait optimization and control

• Rigorous modeling of coupled dynamical systems and rigid body dynamics suitable for locomotion and co-design

Topics


Overview

1. Introduction

2. Dynamical systems

3. Human gait optimization

4. Co-design methodology for wearable devices

5. Conclusion


Dynamical systems


Dynamical systems

•Generally: a system which changes over time

•Fundamentally multi-domain

•Specifically interested in

•Control dynamics

•Rigid body dynamics

•Neuro-mechanical dynamics

10

May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices

Dynamical systems

• A framework for modeling and integrating multi-domain, coupled dynamical systems

• Motivations

• Free/open

• Expressive/modeling

• Performance

• Educational

codyn - coupled dynamical systems

11


Dynamical systems

Modeled after IJspeert et al. (2007)

12


Dynamical systems

• Articulated rigid body dynamics essential tool in robotics research (modeling, simulation, control)

• Hard problem, a variety of simulators existing today

• Accuracy vs. performance vs. modeling effort

codyn - Rigid Body Dynamics

13


Dynamical systems

Free/open

Accurate

FastMulti-

domainHard

contacts

Multi-body

contacts

Modeling

Embedded

Bullet/ode

+ - ++ - - ++ - -

OpenSim

+ + - +/- - + +/- -

Robotran

- + - + - - +/- -

Codyn + + ++ + + - ++ +

14


Dynamical systems

• Multi-domain: rigid body dynamics formulation indifferent from other dynamics, well suited for neuro-mechanical/musculo-skeletal modeling (Taga, 1995; Geyer, 2010)

• Equations of motion derived from (Featherstone, 2008), transformed into coupled dynamics formulation of codyn

• General purpose, 3D articulated rigid body dynamics

• Entirely user extensible, customized joint models, contact models

• State of the art: Availability of inverse, forward, closed chain, hard contacts, Jacobians, etc.

codyn - Rigid Body Dynamics

15


Dynamical systems



16


Dynamical systems



17


Dynamical systems



18


Dynamical systems





19


dynamical systems

• Generalized coordinates for RBD provides accuracy

• When implemented naively, has very poor performance

• Automatically translate models to efficient representation

• Fast, optimized code

• Suitable for Real Time systems

• Suitable for low-resource, embedded systems (for example micro-controllers (Knüsel, 2013))

codyn - Performance

20


dynamical systems

codyn - Performance

21


dynamical systemshttp://www.codyn.net/

22


Human gait optimization

23


human gait optimization

1. What is the minimal, sufficient model for human gait optimization?

2. What are the objectives leading to stable, human gait?

3. What is the role of impedance modulation for human gait?

4. How prominent is the morphology for optimization of human gait?

24



• Extensively studied

• Neuro muscolu-skeletal control using oscillators and entrainment (Taga, 1995)

• Musculo-skeletal reflex models (Geyer, 2010)

• Optimal control approaches (Mombaur, 2010)

• Passive dynamic walkers (McGeer, 1990; Wisse 2005;

Kuo 2007)

25



• Motivation

• To obtain a minimal, sufficient optimization model to explain human locomotion

• Using simple, local control only, few parameters

• Main inspiration: passive dynamic walkers

Collins (2001)

26



1. Human like gaits occur implicitly using impedance control by optimizing only for mechanical energy expenditure

2. Gait quality increases with increasing modulation of impedance

3. Energy expenditure decreases with increasing modulation of impedance

Hypotheses

27



Level of abstraction

• 2D, planar walking

• Segmented legs (upper leg, lower leg, foot)

• Perfect torque actuators at the joints

28



Control• Joint level variable impedance control

• Optimize reference trajectory, variable stiffness and variable damping

• Various levels of variable impedance: const, step, ppoly

29


Objective Until1 time max time

2 speed match 95%

3 std speed 0.1

4 assist time 0

5non contact

time0

6 torque -


Optimization

• Particle Swarm Optimization (Kennedy, 1995)

• Codyn for modeling of control and rigid body dynamics

30





31



Single step (stance to stance)

32




Hip, knee, ankle

33



Human like gaits occur implicitly using impedance control by optimizing only for mechanical energy expenditure

Gait quality increases with increasing modulation of impedance

Energy expenditure decreases with increasing modulation of impedance

Hypotheses

✓

✓

✓

34



Transfer of methodology to a human-like platform

1. Effect of morphological differences to bipedal gait obtained from optimization

2. Role of variable impedance under perturbation

35



Source: IIT website


36




• Improved objectives due to hard-contact modeling in codyn (remove std speed and non-

contact-time)

• Optimize for (mechanical) cost of transport instead of desired speed

• Adaptation of foot length to size of the robot

• Limit maximum torques to platform limits (30N)

Same optimization methodology except:

37



38



• Foot length important for obtaining robust gaits

• Mass distribution influences stability, making it harder to optimize

• Resulting gait slow, maximum torque limits

Observations

39




40




• Apply force perturbations during window in swing phase

• N(80, 5) Newton

• N(150, 50) Milliseconds

• Same optimization methodology

Role of variable impedance under perturbation

41






42


human gait optimizationHip, knee, ankle

43




• Successfully optimized human-like gait to robot morphology

• Foot length is very important for obtaining gait

• Mass distribution influences stability

• Variable impedance modulation significant under perturbation

44


Morphology/control co-design

45


co-design

• Many successful (some commercial) wearable lower limb devices

• Impressive results: allows for paraplegics to walk again

• Design is always anthropomorphic

• Potential for exploitation of morphology

Motivation

Ekso ReWalk Rex

46


co-design

• Development of a co-design methodology, using evolutionary inspired processes

• EVRYON: use case for a non-anthropomorphic, lower limb wearable device for locomotion

• A framework for the iterative co-design of wearable devices, focussing on open-ended exploration of solutions

Problem domain

47


co-design



48


co-design



49


co-design

• Same human model as in previous studies (2D, planar)

• Looking at mechanical dynamics only

• Augmentation of the hip and knee only (2 dof parallel structures)

• Parallel structures involving 3 human body attachment joints, 3 free wearable robot joints and 4 wearable robot segments

Level of abstraction

50


co-design

51


co-design

• Wearable robot attachment placement

• Active joint placement

• Wearable robot segment lengths

• Wearable robot initial joint angles

Morphological space

52


co-design













53


co-design

• Solutions obtained were never self-stable, torso was constraint to be upright (mass-distribution)

• Ground contact modeling problematic, unstable

• Almost all solutions made use of structural singularities

• Main conclusion: promising, however available tools are insufficient

54


co-design

• Optimization no longer only considering continuous parameters only

• Particle Swarm Optimization in itself not suitable

• Consider Genetic Algorithms (Goldberg, 1987) or Genetic Programming (Koza, 1992)

• Many parameters which significantly alter performance, PSO found to perform better for locomotion (Bourquin, 2004)

• A novel PSO for co-design

55


co-design

• Meta morphic particle swarm optimization

• Extension of PSO principles to simultaneous exploration/optimization of solution structures and its parameters

• Suitable for problems where solution structures can be enumerated

56


co-design• Structural parameters (MMPSO)

• Topology

• Actuator placement

• Morphological parameters

• Attachment location

• Wearable robot joint location

• Control parameters

• Joint angle pattern, stiffness pattern, damping pattern

57


co-design







58


co-design

Topo Mass CoM (up)Segment

sizePower

283.3

(+13.3)1.07 (-0.03) 0.3 - 0.46 462

583.9

(+13.9)1.02 (-0.08) 0.2 - 0.52 748

383.5

(+13.5)1.03 (-0.07) 0.1 - 0.64 1018

59


co-design

• Development of a complete framework for co-design (dynamics/control, optimization, simulation)

• Successfully optimize human like gaits with parallel structures

• Automatic and simultaneous exploration of solution structures and their parameters

• Mass distribution particularly important

• Actuator placement favors actuator on the torso

60


Conclusion

61


conclusion

• Open and freely available, framework for modeling of multi-domain coupled dynamics systems

• State of the art, competitive rigid body dynamics simulator

• Novel particle swarm optimization based algorithm for co-optimization of solution structures and their parameters

• Robust optimization of human gait from first principles using simple, local impedance control

• Front to end framework for the co-design of morphology and control of robotic structures

Main contributions

62


conclusion

• Human locomotion

• Extension of methodology to 3D

• Transfer of control to robotic platform

• Feedback based variable impedance control

Discussion

63


conclusion

• Co-design

• Methodology shown to be feasible

• Should provide input in a larger, iterative design process

• Open-ended search does not provide complete solutions

Discussion

64


conclusion

• Co-design

• Methodology shown to be feasible

• Should provide input in a larger, iterative design process

• Open-ended search does not provide complete solutions

• Only looked at mechanics so far, no human-in-the-loop

• Integration of neuro/musculo skeletal models, studying human adaptation

Discussion

65


conclusion

• Within constraints of EVRYON, obtained wearable robot solutions did not provide the aimed for dynamic symbiosis purely using evolutionary algorithms

• Applying the methodology to smaller subsystems may be more promising

• Use an iterative design approach (engineer-in-the-loop), rather than a front-to-end automatic approach

• Importance of human-in-the-loop cannot be ignored, but tools need to be suitable for such simulations

Lessons learned

66


Questions

67


Co-design

68


References1. Ijspeert, A. & Crespi, A. & Ryczko, D. & Cabelguen, J.-M. (2007). From swimming to walking with

a salamander robot driven by a spinal cord model. Science,315, 1416—1420.

2. Taga, G. (1995). A model of the neuro-musculo-skeletal system for human locomotion. I. Emergence of basic gait.Biological Cybernetics,73, 97—111.

3. Geyer, H. & Herr, H. (2010). A Muscle-Reflex Model That Encodes Principles of Legged Mechanics Produces Human Walking Dynamics and Muscle Activities. Neural Systems and Rehabilitation Engineering, IEEE Transactions on,18, 263—273.

4. Mcgeer, T. (1990). Passive dynamic walking. the international journal of robotics research,9, 62—82.

5. Wisse, M. (2005). Three additions to passive dynamic walking: actuation, an upper body, and 3D stability.International Journal of Humanoid Robotics,2, 459—478.

6. Kuo, A. D. (2007). The six determinants of gait and the inverted pendulum analogy: A dynamic walking perspective.Human movement science,26, 617—656.

7. Mombaur, K. & Truong, A. & Laumond, J. (2010). From human to humanoid locomotion—an inverse optimal control approach. Autonomous robots,28, 369—383.

8. Knüsel, J. (2013). Modeling a diversity of salamander motor behaviors with coupled abstract oscillators and a robot. EPFL.

9. Goldberg, D. E. & others , . (1989). Genetic algorithms in search, optimization, and machine learning. Addison-wesley Reading Menlo Park,412.

10.Koza, J. R. (1992). Genetic programming: on the programming of computers by means of natural selection. MIT press,1.

11.Bourquin, Y. & Ijspeert, A. J. & Harvey, I. (2004). Self-organization of locomotion in modular robots. Unpublished Diploma Thesis, http://birg. epfl. ch/page53073. html.

12.Kennedy, J. & Eberhart, R. (1995). Particle swarm optimization. Proceedings of IEEE international conference on neural networks,4, 1942—1948 vol.4.

13.Featherstone, R. (2008). Rigid body dynamics algorithms. Springer New York,49.

69




Step controller kinematics


70


HUMAN GAIT OPTIMIZATION

may 14, 2014 on the dynamics of human locomotion and co-design of lower limb assistive devices 1 on...

Documents