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

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.

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A Model of the Human Spine: Forward and Inverse Kinematics

22nd Biennial Mechanisms Conference: Flexible Mechanisms, Dynamics, and Analysis ◽

10.1115/detc1992-0426 ◽

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..

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Velocity and Force Observers for the Control of Robot Manipulators

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4024995 ◽

2013 ◽

Vol 135 (6) ◽

Cited By ~ 1

Author(s):

Marco A. Arteaga–Pérez ◽

Juan C. Rivera–Dueñas ◽

Alejandro Gutiérrez–Giles

Keyword(s):

Dynamic Model ◽

Tracking Control ◽

Inverse Kinematics ◽

Robot Manipulator ◽

Robot Manipulators ◽

Asymptotical Stability ◽

Joint Angles ◽

Force Tracking

In this paper, position/force tracking control for rigid robot manipulators interacting with its environment is considered. It is assumed that only joint angles are available for feedback, so that velocity and force observers are designed. The principle of orthogonalization is employed for this particular purpose and some of its main properties are fully exploited to guarantee local asymptotical stability. Only the force observer requires the dynamic model of the robot manipulator for implementation, and the scheme is developed directly in workspace coordinates, so that no inverse kinematics is required. The proposed approach is tested experimentally and compared with a well–known algorithm.

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Joint angle variations analyses of the two link planar manipulator in welding by using inverse kinematics

Robotica ◽

10.1017/s0263574705002274 ◽

2005 ◽

Vol 24 (3) ◽

pp. 355-363 ◽

Cited By ~ 1

Author(s):

S. Bulut ◽

M. B. Terzioǧlu

Keyword(s):

Angular Velocity ◽

Data Collection ◽

Inverse Kinematics ◽

Joint Angle ◽

Joint Angles ◽

End Effector ◽

Fortran 90

In this paper, the joint angles of a two link planar manipulator are calculated by using inverse kinematics equations together with some geometric equalities. For a given position of the end-effector the joint angle and angular velocity of the links are derived. The analyses contains many equations which have to be solved. However, the solutions are rather cumbersome and complicated, therefore a program is written in Fortran 90 in order to do, the whole calculation and data collection. The results are given at the end of this paper.

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The importance of a consistent workflow to estimate muscle-tendon lengths based on joint angles from the conventional gait model

10.1101/2021.03.10.21253257 ◽

2021 ◽

Author(s):

Hans Kainz ◽

Michael H Schwartz

Keyword(s):

Cerebral Palsy ◽

Inverse Kinematics ◽

Clinical Decision Making ◽

Joint Kinematics ◽

Joint Angles ◽

Modified Model ◽

Model Driven ◽

Tendon Length ◽

Musculoskeletal Models ◽

Children With Cerebral Palsy

AbstractBackgroundMusculoskeletal models enable us to estimate muscle-tendon length, which has been shown to improve clinical decision-making and outcomes in children with cerebral palsy. Most clinical gait analysis services, however, do not include muscle-tendon length estimation in their clinical routine. This is due, in part, to a lack of knowledge and trust in the musculoskeletal models, and to the complexity involved in the workflow to obtain the muscle-tendon length.Research questionCan the joint angles obtained with the conventional gait model (CGM) be used to generate accurate muscle-tendon length estimates?MethodsThree-dimensional motion capture data of 15 children with cerebral palsy and 15 typically developing children were retrospectively analyzed and used to estimate muscle-tendon length with the following four modelling frameworks: (1) 2392-OSM-IK-angles: standard OpenSim workflow including scaling, inverse kinematics and muscle analysis; (2) 2392-OSM-CGM-angle: generic 2392-OpenSim model driven with joint angles from the CGM; (3) modif-OSM-IK-angles: standard OpenSim workflow including inverse kinematics and a modified model with segment coordinate systems and joint degrees-of-freedom similar to the CGM; (4) modif-OSM-CGM-angles: modified model driven with joint angles from the CGM. Joint kinematics and muscle-tendon length were compared between the different modelling frameworks.ResultsLarge differences in hip joint kinematics were observed between the CGM and the 2392-OpenSim model. The modif-OSM showed similar kinematics as the CGM. Muscle-tendon length obtained with modif-OSM-IK-angles and modif-OSM-CGM-angles were similar, whereas large differences in some muscle-tendon length were observed between 2392-OSM-IK-angles and 2392-OSM-CGM-angles.SignificanceThe modif-OSM-CGM-angles framework enabled us to estimate muscle-tendon lengths without the need for scaling a musculoskeletal model and running inverse kinematics. Hence, muscle-tendon length estimates can be obtained simply, without the need for the complexity, knowledge and time required for musculoskeletal modeling and associated software. An instruction showing how the framework can be used in a clinical setting is provided on https://github.com/HansUniVie/MuscleLength.

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Symbolic Computation of Inverse Kinematics for General 6R Manipulators Based on Raghavan and Roth’s Solution

Volume 10: 44th Mechanisms and Robotics Conference (MR) ◽

10.1115/detc2020-22231 ◽

2020 ◽

Author(s):

Keisuke Arikawa

Keyword(s):

Characteristic Polynomial ◽

Symbolic Computation ◽

Inverse Kinematics ◽

Structural Parameters ◽

Efficient Computation ◽

Joint Angles ◽

Arbitrary Geometries ◽

The Matrix ◽

Rational Polynomials ◽

Symbolic Constraints

Abstract We discuss the symbolic computation of inverse kinematics for serial 6R manipulators with arbitrary geometries (general 6R manipulators) based on Raghavan and Roth’s solution. The elements of the matrices required in the solution were symbolically calculated. In the symbolic computation, an algorithm for simplifying polynomials upon considering the symbolic constraints (constraints of the trigonometric functions and those of the rotation matrix), a method for symbolic elimination of the joint variables, and an efficient computation of the rational polynomials are presented. The elements of the matrix whose determinant produces a 16th-order single variable polynomial (characteristic polynomial) were symbolically calculated by using structural parameters (parameters that define the geometry of the manipulator) and hand configuration parameters (parameters that define the hand configuration). The symbolic determinant of the matrix consists of huge number of terms even when each element is replaced by a single symbol. Instead of expressing the coefficients in a characteristic polynomial by structural parameters and hand configuration parameters, we substituted appropriate rational numbers that strictly satisfy the constraints of the symbols for the elements of the matrix and calculated the determinant (numerical error free calculation). By numerically calculating the real roots of the rational characteristic polynomial and the joint angles for each root, we verified the formulation for the symbolic computation.

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Solution for Ill-Posed Inverse Kinematics of Robot Arm by Network Inversion

Journal of Robotics ◽

10.1155/2010/870923 ◽

2010 ◽

Vol 2010 ◽

pp. 1-9 ◽

Cited By ~ 3

Author(s):

Takehiko Ogawa ◽

Hajime Kanada

Keyword(s):

Neural Network ◽

Inverse Problems ◽

Inverse Kinematics ◽

Robot Arm ◽

Joint Angles ◽

End Effector ◽

Inverse Kinematics Problem ◽

Ill Posed ◽

The Given

In the context of controlling a robot arm with multiple joints, the method of estimating the joint angles from the given end-effector coordinates is called inverse kinematics, which is a type of inverse problems. Network inversion has been proposed as a method for solving inverse problems by using a multilayer neural network. In this paper, network inversion is introduced as a method to solve the inverse kinematics problem of a robot arm with multiple joints, where the joint angles are estimated from the given end-effector coordinates. In general, inverse problems are affected by ill-posedness, which implies that the existence, uniqueness, and stability of their solutions are not guaranteed. In this paper, we show the effectiveness of applying network inversion with regularization, by which ill-posedness can be reduced, to the ill-posed inverse kinematics of an actual robot arm with multiple joints.

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Redundancy coordination of multiple robotic devices for welding through genetic algorithm

Robotica ◽

10.1017/s0263574799001976 ◽

2000 ◽

Vol 18 (6) ◽

pp. 669-676 ◽

Cited By ~ 23

Author(s):

L. Wu ◽

K. Cui ◽

S. B. Chen

Keyword(s):

Genetic Algorithm ◽

Inverse Kinematics ◽

Multiple Criteria ◽

Joint Angles ◽

Coordination Problem ◽

Robot System ◽

Singular Configurations ◽

Robotic Devices ◽

Pseudo Inverse ◽

Gravity Direction

In this paper we consider the problem of coordinating multiple motion devices for welding. We focus on the problem of coordinating a three-axis positioning table and a six-axis manipulator. The problem is complex as there are nine axes involved and a number of permutations are possible which achieve the same movements of the weld torch. The system is redundant and the robot has singular configurations. As a result, manual programming of the robot system is rather difficult to complete.Our approach to the coordination problem is based on a subdivision of tasks. The welding table is coordinated to align the weld point surface to be anti-parallel to the gravity direction. The six-axis robot is constrained to move the weld torch along the weld trajectory. Robot coordination is achieved by placing the positioning table in a good maneuverability position, i.e. far from its singular configurations and far from the motion limits of the six-axis arm and the motion limits of the track. While considering multiple criteria, including the welding orientation, a Genetic Algorithm was employed to globally optimize six relevant redundant degrees of the multiple robotic system for welding. The joint angles of the arm were generated by inverse kinematics. Our method of redundancy coordination is superior to pseudo-inverse techniques, for it is more global and accurate.

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Utilization of multilayer perceptron for determining the inverse kinematics of an industrial robotic manipulator

International Journal of Advanced Robotic Systems ◽

10.1177/1729881420925283 ◽

2021 ◽

Vol 18 (4) ◽

pp. 172988142092528

Author(s):

Sandi Baressi Šegota ◽

Nikola Anđelić ◽

Vedran Mrzljak ◽

Ivan Lorencin ◽

Ivan Kuric ◽

...

Keyword(s):

Multilayer Perceptron ◽

Inverse Kinematics ◽

Absolute Error ◽

Robotic Manipulator ◽

Inverse Kinematic ◽

Percentage Error ◽

Joint Angles ◽

Transformation Matrices ◽

Error Metrics

Inverse kinematic equations allow the determination of the joint angles necessary for the robotic manipulator to place a tool into a predefined position. Determining this equation is vital but a complex work. In this article, an artificial neural network, more specifically, a feed-forward type, multilayer perceptron (MLP), is trained, so that it could be used to calculate the inverse kinematics for a robotic manipulator. First, direct kinematics of a robotic manipulator are determined using Denavit–Hartenberg method and a dataset of 15,000 points is generated using the calculated homogenous transformation matrices. Following that, multiple MLPs are trained with 10,240 different hyperparameter combinations to find the best. Each trained MLP is evaluated using the R 2 and mean absolute error metrics and the architectures of the MLPs that achieved the best results are presented. Results show a successful regression for the first five joints (percentage error being less than 0.1%) but a comparatively poor regression for the final joint due to the configuration of the robotic manipulator.

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Quick Motion Path Plan for Joint-Robot to Pick Tomato under Free-Obstacle

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.229-231.2225 ◽

2012 ◽

Vol 229-231 ◽

pp. 2225-2228

Author(s):

Jian Jun Yin ◽

Chun Xie ◽

Chuan Yu Wu ◽

S.Mittal Gauri ◽

Simon X. Yang

Keyword(s):

Real Time ◽

Inverse Kinematics ◽

Energy Optimization ◽

Constant Speed ◽

Motion Path ◽

Robot Arm ◽

Joint Angles ◽

Object Point ◽

Straight Path

To improve the efficiency of picking the fruit, a kind of quick motion path plan method for fruit-picking robot under free-obstacle was proposed in this paper. Firstly, the method obtained a series of points along straight path at equal internal, and the gripper center was designed to pass thorough these points. Inverse kinematics formulas of the robot arm were used to solve joint angles of the robot arm when the gripper center will pass thorough each point. To make the robot arm guide the gripper to reach quickly the object point, the joint angles were optimized to determinate according to the principle of energy optimization. The test of picking tomato showed that the method can both reproduce the motion of the gripper along linear points at constant speed and keep the shortest motion path, which are benefit to grip the fruit steadily. The method has a low amount of calculation, better real-time and may provide a reference for joint robot to pick the fruit quickly.

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