Robot dynamics: equations and algorithms

Featherstone, Roy; Orin, David E.

doi:10.1109/robot.2000.844153

Cited by 205 publications

(149 citation statements)

References 32 publications

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“…The controller implementation sets up the QP problem described in section 4 using the current contact state from the simulation. It uses our C++ implementation of recursive dynamics equations [FO00] to compute various dynamical quantities needed for the optimization such as the inertial matrix of the system and gravitational and centrifugal forces on the system. The QP is solved using SQOPT [GMS97].…”

Section: Methodsmentioning

confidence: 99%

Simulation of Human Motion Data using Short‐Horizon Model‐Predictive Control

Silva

Abe

Popović

2008

Computer Graphics Forum

110

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Section: Methodsmentioning

confidence: 99%

Simulation of Human Motion Data using Short‐Horizon Model‐Predictive Control

Silva

Abe

Popović

2008

Computer Graphics Forum

110

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“…The torques required to move the robot and the human arm could be assessed by passive calibration of a particular movement or dynamic models of the robot [16] and the human arm [17].…”

Section: Patient Contribution Estimationmentioning

confidence: 99%

Estimating the patient�s contribution during robot-assisted therapy

Guidali¹,

Keller²,

Klamroth-Marganska³

et al. 2013

JRRD

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Abstract-Robot-assisted therapy has become increasingly common in neurorehabilitation. Sophisticated controllers have been developed for robots to assist and cooperate with the patient. It is difficult for the patient to judge to what extent the robot contributes to the execution of a movement. Therefore, methods to comprehensively quantify the patient's contribution and provide feedback are of key importance. We developed a method comprehensively to estimate the patient's contribution by combining kinematic measures and the motor assistance applied. Inverse dynamic models of the robot and the passive human arm calculate the required torques to move the robot and the arm and build, together with the recorded motor torque, a metric (in percentage) that represents the patient's contribution to the movement. To evaluate the developed metric, 12 nondisabled subjects and 7 patients with neurological problems simulated instructed movement contributions. The results are compared with a common performance metric. The estimation shows very satisfying results for both groups, even though the arm model used was strongly simplified. Displaying this metric to patients during therapy can potentially motivate them to actively participate in the training.

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“…Real-time simulation for such systems poses a challenge for software developers and for analysts [4,5,6,7]. This paper presents an approach for replacing the linear actuator and rigid link bodies in the multibody system model with passive force and constraint elements whose kinematic and dynamic equations are expressed in terms of the attached body state variables [8,9]. The spatial displacement and velocity of any number of markers fixed in the element are computed, and this information is used to compute interaction between the element and other parts of a model.…”

Section: Introductionmentioning

confidence: 99%

Decoupling Kinematic Loops for Real-Time Multibody Dynamic Simulations

Omar

2016

MATEC Web of Conferences

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Abstract. Earth moving equipment are typically equipped with hydraulic cylinder actuators to perform the designated tasks. Multibody modelling of such systems results in models with kinematic loops that couples the motion variables of the loop bodies. Iterative solutions will be needed to satisfy the loop constraints and the applied constraints, which require evaluation of the constraint Jacobean matrix. The size of the Jacobean matrix and the associated projections depends on the number of motion variables in each kinematic loop. Consequently, the computational cost significantly increases as the number of variables in the kinematic loop increases. Real-time control and hybrid hardware-in-the-loop systems both require efficient and fast computational algorithms. Eliminating the kinematic loops can improve the computational efficiency and effectiveness of the control algorithms. This paper presents an efficient approach to eliminate the coupling due to the cylinder-rod connections and consequently the kinematic loops in the multibody models leading to efficient simulation. The proposed approach calculates the spatial accelerations and inertia forces of the actuator bodies and the interaction forces with other components. The actuator forces are then projected onto the connecting bodies leading to exact dynamics of the system.

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Robot dynamics: equations and algorithms

Cited by 205 publications

References 32 publications

Simulation of Human Motion Data using Short‐Horizon Model‐Predictive Control

Simulation of Human Motion Data using Short‐Horizon Model‐Predictive Control

Estimating the patient�s contribution during robot-assisted therapy

Decoupling Kinematic Loops for Real-Time Multibody Dynamic Simulations

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