scholarly journals A marker registration method to improve joint angles computed by constrained inverse kinematics

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0252425
Author(s):  
James J. Dunne ◽  
Thomas K. Uchida ◽  
Thor F. Besier ◽  
Scott L. Delp ◽  
Ajay Seth
Keyword(s):  
Inverse Kinematics ◽  
Humanoid Robot ◽  
Reference Frames ◽  
Joint Angles ◽  
Registration Method ◽  
User Input ◽  
Marker Data ◽  
Model Match ◽  
Mass Properties ◽  
Optical Markers

Accurate computation of joint angles from optical marker data using inverse kinematics methods requires that the locations of markers on a model match the locations of experimental markers on participants. Marker registration is the process of positioning the model markers so that they match the locations of the experimental markers. Markers are typically registered using a graphical user interface (GUI), but this method is subjective and may introduce errors and uncertainty to the calculated joint angles and moments. In this investigation, we use OpenSim to isolate and quantify marker registration–based error from other sources of error by analyzing the gait of a bipedal humanoid robot for which segment geometry, mass properties, and joint angles are known. We then propose a marker registration method that is informed by the orientation of anatomical reference frames derived from surface-mounted optical markers as an alternative to user registration using a GUI. The proposed orientation registration method reduced the average root-mean-square error in both joint angles and joint moments by 67% compared to the user registration method, and eliminated variability among users. Our results show that a systematic method for marker registration that reduces subjective user input can make marker registration more accurate and repeatable.

scholarly journals Correction: A marker registration method to improve joint angles computed by constrained inverse kinematics

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254509
Author(s):  
James J. Dunne ◽  
Thomas K. Uchida ◽  
Thor F. Besier ◽  
Scott L. Delp ◽  
Ajay Seth
Keyword(s):  
Inverse Kinematics ◽  
Joint Angles ◽  
Registration Method

A Power Series Based Inverse-Kinematics Solution of Humanoid Robot Finger with Coupled Joints

2018 ◽  
Vol 15 (04) ◽  
pp. 1850016
Author(s):  
Li Jiang ◽  
Bingqian Sun ◽  
Haiwei Gu
Keyword(s):  
Power Series ◽  
Inverse Kinematics ◽  
Humanoid Robot ◽  
Linear Interpolation ◽  
Transcendental Equation ◽  
Small Error ◽  
Robot Hand ◽  
Final Solution ◽  
Joint Angles ◽  
Power Series Expansions

Inverse-kinematics is an emphasis and difficulty in the design and application of the humanoid robot hand with coupled joints because of nonlinearity induced by trigonometric transcendental function. In this paper, a power series based inverse-kinematics algorithm is presented, by which the transcendental equation including trigonometric function can be converted into an algebraic equation. An approximate solution is derived first by means of power series expansions; with 1D linear interpolation for errors compensating, the final solution with small error can then be achieved. For robot with linearly coupled joints, the algorithms based on power series expanded to quadratic and quartic terms are used to calculate the accurate joint angles. For robot with nonlinearly coupled joints, the specific procedures are proposed to select appropriate transmission ratio. Simulation and experimental results demonstrate effectiveness of the proposed inverse kinematics method.

scholarly journals Predicting tibiotalar and subtalar joint angles from skin-marker data with dual-fluoroscopy as a reference standard

2016 ◽  
Vol 49 ◽  
pp. 136-143 ◽  
Author(s):  
Jennifer A. Nichols ◽  
Koren E. Roach ◽  
Niccolo M. Fiorentino ◽  
Andrew E. Anderson
Keyword(s):  
Subtalar Joint ◽  
Reference Standard ◽  
Joint Angles ◽  
Marker Data ◽  
Skin Marker

A Model of the Human Spine: Forward and Inverse Kinematics

1992 ◽  
Author(s):  
Sunil Kumar Agrawal ◽  
Siyan Li ◽  
Glen Desmier
Keyword(s):  
Range Of Motion ◽  
Inverse Kinematics ◽  
Degrees Of Freedom ◽  
Kinematic Model ◽  
Joint Angles ◽  
Human Spine ◽  
Forward And Inverse Kinematics ◽  
Position And Orientation ◽  
Three Degrees Of Freedom ◽  
Adjacent Vertebra

Abstract The human spine is a sophisticated mechanism consisting of 24 vertebrae which are arranged in a series-chain between the pelvis and the skull. By careful articulation of these vertebrae, a human being achieves fine motion of the skull. The spine can be modeled as a series-chain with 24 rigid links, the vertebrae, where each vertebra has three degrees-of-freedom relative to an adjacent vertebra. From the studies in the literature, the vertebral geometry and the range of motion between adjacent vertebrae are well-known. The objectives of this paper are to present a kinematic model of the spine using the available data in the literature and an algorithm to compute the inter vertebral joint angles given the position and orientation of the skull. This algorithm is based on the observation that the backbone can be described analytically by a space curve which is used to find the joint solutions..

scholarly journals Brain-like functor control machine for general humanoid biodynamics

2005 ◽  
Vol 2005 (11) ◽  
pp. 1759-1779 ◽  
Author(s):  
Vladimir Ivancevic ◽  
Nicholas Beagley
Keyword(s):  
Feedback Control ◽  
Control Systems ◽  
Inverse Kinematics ◽  
Humanoid Robot ◽  
Granule Cells ◽  
Nonlinear Feedback ◽  
Spinal Level ◽  
Cortical Level ◽  
Self Organized ◽  
Neuro Fuzzy

A novel, brain-like, hierarchical (affine-neuro-fuzzy-topological) control for biomechanically realistic humanoid-robot biodynamics (HB), formulated previously in [15, 16], is proposed in the form of a tensor-invariant, “meta-cybernetic” functor machine. It represents a physiologically inspired, three-level, nonlinear feedback controller of muscular-like joint actuators. On the spinal level, nominal joint-trajectory tracking is formulated as an affine Hamiltonian control system, resembling the spinal (autogenetic-reflex) “motor servo.” On the cerebellar level, a feedback-control map is proposed in the form of self-organized, oscillatory, neurodynamical system, resembling the associative interaction of excitatory granule cells and inhibitory Purkinje cells. On the cortical level, a topological “hyper-joystick” command space is formulated with a fuzzy-logic feedback-control map defined on it, resembling the regulation of locomotor conditioned reflexes. Finally, both the cerebellar and the cortical control systems are extended to provide translational force control for moving6-degree-of-freedom chains of inverse kinematics.

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