A walking robot called human: lessons to be learned from neural control of locomotion

Duysens, Jacques; Crommert, H.W.A.A. van de; Smits-Engelsman, B.C.M.; Helm, F.C.T. van der

doi:10.1016/s0021-9290(01)00187-7

Cited by 33 publications

(31 citation statements)

References 59 publications

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“…The method used in this work for solving the extended problem (11), with fixed penalty parameters c i , d i , and d eq i , is related to the method of moving asymptotes, 'MMA', from [24,25]. But there are three major modifications:…”

Section: Optimization Methods For the Extended Problem With Fixed Penamentioning

confidence: 99%

“…But first some words on the general approach used in this work for solving (9), namely to repeatedly solve the extended problem (11) for increasing values of the penalty parameters. In the first iteration of this 'parametric loop', the components of the vectors c, d, and d eq (i.e.…”

Section: The Extended Problem With Artificial Variablesmentioning

confidence: 99%

“…where k, l, n, g, and c are vectors of Lagrange multipliers for the various constraints in (11). The first four of these multiplier vectors are required to be non-negative.…”

Section: The Line Search Part When Solving the Extended Problemmentioning

confidence: 99%

“…the forces needed or the energy used. The idea 1128 A. ERIKSSON AND K. SVANBERG is that the neural system creates a movement plan according to such criteria [11], with deviations due to internal restrictions or external agents handled through a feedback system [5,12].…”

Section: Introductionmentioning

confidence: 99%

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Optimization in simulations of human movement planning

Eriksson¹,

Svanberg²

2011

Numerical Meth Engineering

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SUMMARYThis paper discusses optimization algorithms in movement simulations for models of humans, humanoid robots or other mechanisms. Targeted movements between two configurations define a dynamically redundant system, for which there is freedom in the choice of control force time variations. A previously developed formulation for the treatment of targeted dynamics for mechanisms was used as a basis. The paper describes the development of an algorithm related to the method of moving asymptotes for the necessary optimization. The algorithm is specifically adapted to problems which are large and non-linear but sparse, and which include very high numbers of design variables as well as constraints. In particular, non-linear equality constraints from dynamic equilibrium equations are important. The optimization algorithm was developed to include these, but also in order to allow successively increasing penalty factors for constraint violations. The resulting setting was shown to be able to handle the systems established, robustly giving convergence to at least a local minimum also for very distant start iterates. The existence of very closely situated local optima, representing very similar movements, was discovered for the problem formulation, calling for an ad hoc method for finding the best of these local optima.

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Section: Optimization Methods For the Extended Problem With Fixed Penamentioning

confidence: 99%

Section: The Extended Problem With Artificial Variablesmentioning

confidence: 99%

“…where k, l, n, g, and c are vectors of Lagrange multipliers for the various constraints in (11). The first four of these multiplier vectors are required to be non-negative.…”

Section: The Line Search Part When Solving the Extended Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimization in simulations of human movement planning

Eriksson¹,

Svanberg²

2011

Numerical Meth Engineering

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“…This is also emphasized by simplified models, in which the kinematics and kinetics are not fully resolved. The redundant actuator system allows very complex movements under neural control, but causes computational difficulties when attempts are made to simulate the behavior [11][12][13][14]. The exact functioning of the control system is not fully known, but a common approach is to assume that the muscular forces are chosen from some pre-defined optimization rule: minimum forces, nominal efforts, activation, energy consumption, or maximum smoothness [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Temporal finite element formulation of optimal control in mechanisms

Eriksson

Nordmark

2010

Computer Methods in Applied Mechanics and Engineering

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Reliable and Precise Gait Modeling for a Quadruped Robot

Düffert

Hoffmann

2006

RoboCup 2005: Robot Soccer World Cup IX

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We present a parametric walk model for a four-legged robot. The walk model is improved using a genetic algorithm, but unlike previous approaches, the fitness is determined in a run that closely resembles the later application. We thus not only achieve high speeds, but also a high degree of flexibility. In addition to the walking model being flexible, we present a means of automatically calibrating the walking engine. This allows for highly precise robot control and greatly improved odometry accuracy. Lastly, we show how the motion model can be extended to integrate specialized motions to further increase locomotion speed without compromising flexibility.

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A walking robot called human: lessons to be learned from neural control of locomotion

Cited by 33 publications

References 59 publications

Optimization in simulations of human movement planning

Optimization in simulations of human movement planning

Temporal finite element formulation of optimal control in mechanisms

Reliable and Precise Gait Modeling for a Quadruped Robot

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