Dynamic modeling in the simulation, optimization, and control of bipedal and quadrupedal robots

Hardt, Michael; Stryk, Oskar von

doi:10.1002/zamm.200310068

Cited by 16 publications

(17 citation statements)

References 47 publications

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“…An alternative formulation of the ABA has been derived which results in an O(n) closed-form expression for the inverse mass matrix [RKDJ91] (see also [HvS03] for details). This recursive approach is modular and flexible as it facilitates the exchange of submodels and the reuse of other model parts without having to reformulate the complete model as it is the case, e.g., with the Euler-Lagrange method.…”

Section: Modeling and Simulation Of Locomotion Dynamicsmentioning

confidence: 99%

“…Commonly trajectory tracking control is applied in a hierarchical, decentralized scheme where the setpoints for the joint angles of the humanoid robot are updated in a constant frequency between 1 and 10 ms [HvS03]. The feedback control schemes of the joints, e.g., PD or PID, are usually operated independently of each other.…”

Section: Control and Stability Of Bipedal Gaitsmentioning

confidence: 99%

“…Among the various, recursive, numerical algorithms which satisfy these criteria the ABA [Fea87] has been selected in the alternative formulation of [RKDJ91] (cf. [HvS03]). It can be implemented highly modular as all calculations and parameters can occur as an exchange of information between links.…”

Section: Model-based Trajectory Optimization and Control For Humanoidmentioning

confidence: 99%

“…The question of finding optimal trajectories of the n joint motor torques τ (t), 0 ≤ t ≤ t f , w.r.t. to time, energy and/or stability leads to optimal control problems [HvS03]. A forward dynamics simulation and optimization approach analogous to the biomechanical optimization in Sect.…”

Section: Model-based Trajectory Optimization and Control For Humanoidmentioning

confidence: 99%

“…The approach requires solving the inverse kinematics problem for the dependent states which, in the case of legged systems, are generally the contact leg states. For most leg configurations, this problem is easily solved using knowledge of the relative hip and foot contact locations [HvS03].…”

Section: Model-based Trajectory Optimization and Control For Humanoidmentioning

confidence: 99%

See 4 more Smart Citations

Walking, Running and Kicking of Humanoid Robots and Humans

Stelzer

Stryk

2008

From Nano to Space

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Summary. In this paper key aspects and several methods for modeling, simulation, optimization and control of the locomotion of humanoid robots and humans are discussed. Similarities and differences between walking and running of humanoid robots and humans are outlined. They represent several, different steps towards the ultimate goals of understanding and predicting human motion by validated simulation models and of developing humanoid robots with human like performance in walking and running. Numerical and experimental results are presented for modelbased optimal control as well as for hardware-in-the-loop optimization of humanoid robot walking and for forward dynamics simulation and optimization of a human kicking motion.

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Section: Modeling and Simulation Of Locomotion Dynamicsmentioning

confidence: 99%

Section: Control and Stability Of Bipedal Gaitsmentioning

confidence: 99%

Section: Model-based Trajectory Optimization and Control For Humanoidmentioning

confidence: 99%

Section: Model-based Trajectory Optimization and Control For Humanoidmentioning

confidence: 99%

Section: Model-based Trajectory Optimization and Control For Humanoidmentioning

confidence: 99%

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Walking, Running and Kicking of Humanoid Robots and Humans

Stelzer

Stryk

2008

From Nano to Space

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Concurrent motion planning and reaction load distribution for redundant dynamic systems under external holonomic constraints

Kim

Abdel‐Malek

Xiang

et al. 2011

Numerical Meth Engineering

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SUMMARYDynamics of mechanical systems during motion usually involves reaction forces and moments due to the interaction with external objects or constraints from the environment. The problems of determining the external reaction loads have often been addressed in the literature in terms of forward dynamics solution methods for non-redundant systems. In this article, we propose a numerical formulation of distributing the external reaction loads of redundant systems concurrently with optimal motion planning and simulation. For dynamic equilibrium, the resultant reaction loads that include the effects of inertia, gravity, and general applied loads are distributed to each contact point. Unknown reactions are determined along with the system configuration at each time step using iterative non-linear optimization algorithm. The required actuator torques as well as the motion trajectories are obtained while satisfying the given holonomic constraints. The formulation is applied with two types of discretization methods for the reactions, and several example motions of multi-rigid-body systems, such as a simple 3-degree-of-freedom mechanical manipulator and a highly articulated whole-body human mechanism, are demonstrated. The example results are compared with the cases where the reactions are pre-assigned. The proposed numerical formulation demonstrates the realistic distribution of external reaction loads and the associated goal-oriented motions that are dynamically consistent, and provides alternate schemes for hybrid motion/force control of redundant robots.

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Efficient forward dynamics simulation and optimization of human body dynamics

Stelzer

Stryk

2006

Z Angew Math Mech

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The modeling of the time dependent, dynamic behavior of the human musculoskeletal system results in a large scale mechanical multibody system. This consists of submodels for the skeleton, wobbling masses, muscles and tendons as redundant actuators. Optimization models are required for the simulation of the muscle groups involved in a motion. In contrast to the inverse dynamics simulation the forward dynamics simulation enables to consider very general problem statements in principle. The paper presents a new approach to the forward dynamics simulation and optimization of human body dynamics which overcomes the enormous computational cost of current approaches for solving the resulting optimal control problems. The presented approach is based on a suitable modeling of the dynamics of the musculoskeletal system in combination with a tailored direct collocation method for optimal control. First numerical results for a human kick demonstrate an improvement in computational time of two orders of magnitude when compared to standard methods.

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Dynamic modeling in the simulation, optimization, and control of bipedal and quadrupedal robots

Cited by 16 publications

References 47 publications

Walking, Running and Kicking of Humanoid Robots and Humans

Walking, Running and Kicking of Humanoid Robots and Humans

Concurrent motion planning and reaction load distribution for redundant dynamic systems under external holonomic constraints

Efficient forward dynamics simulation and optimization of human body dynamics

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