Showing papers on "Kinematics published in 2016"
11 Jul 2016
TL;DR: A framework to synthesize character movements based on high level parameters, such that the produced movements respect the manifold of human motion, trained on a large motion capture dataset, can produce smooth, high quality motion sequences without any manual pre-processing of the training data.
Abstract: We present a framework to synthesize character movements based on high level parameters, such that the produced movements respect the manifold of human motion, trained on a large motion capture dataset. The learned motion manifold, which is represented by the hidden units of a convolutional autoencoder, represents motion data in sparse components which can be combined to produce a wide range of complex movements. To map from high level parameters to the motion manifold, we stack a deep feedforward neural network on top of the trained autoencoder. This network is trained to produce realistic motion sequences from parameters such as a curve over the terrain that the character should follow, or a target location for punching and kicking. The feedforward control network and the motion manifold are trained independently, allowing the user to easily switch between feedforward networks according to the desired interface, without re-training the motion manifold. Once motion is generated it can be edited by performing optimization in the space of the motion manifold. This allows for imposing kinematic constraints, or transforming the style of the motion, while ensuring the edited motion remains natural. As a result, the system can produce smooth, high quality motion sequences without any manual pre-processing of the training data.
542 citations
08 Oct 2016
TL;DR: This work proposes to directly embed a kinematic object model into the deep neutral network learning for general articulated object pose estimation and achieves state-of-the-art result on Human3.6M dataset.
Abstract: Learning articulated object pose is inherently difficult because the pose is high dimensional but has many structural constraints. Most existing work do not model such constraints and does not guarantee the geometric validity of their pose estimation, therefore requiring a post-processing to recover the correct geometry if desired, which is cumbersome and sub-optimal. In this work, we propose to directly embed a kinematic object model into the deep neutral network learning for general articulated object pose estimation. The kinematic function is defined on the appropriately parameterized object motion variables. It is differentiable and can be used in the gradient descent based optimization in network training. The prior knowledge on the object geometric model is fully exploited and the structure is guaranteed to be valid. We show convincing experiment results on a toy example and the 3D human pose estimation problem. For the latter we achieve state-of-the-art result on Human3.6M dataset.
277 citations
TL;DR: An extremely soft robotic manipulator morphology that is composed entirely from low durometer elastomer, powered by pressurized air, and designed to be both modular and durable is presented.
Abstract: This paper presents a robotic manipulation system capable of autonomously positioning a multi-segment soft fluidic elastomer robot in three dimensions. Specifically, we present an extremely soft robotic manipulator morphology that is composed entirely from low durometer elastomer, powered by pressurized air, and designed to be both modular and durable. To understand the deformation of a single arm segment, we develop and experimentally validate a static deformation model. Then, to kinematically model the multi-segment manipulator, we use a piece-wise constant curvature assumption consistent with more traditional continuum manipulators. In addition, we define a complete fabrication process for this new manipulator and use this process to make multiple functional prototypes. In order to power the robot's spatial actuation, a high capacity fluidic drive cylinder array is implemented, providing continuously variable, closed-circuit gas delivery. Next, using real-time data from a vision system, we develop a processing and control algorithm that generates realizable kinematic curvature trajectories and controls the manipulator's configuration along these trajectories. Lastly, we experimentally demonstrate new capabilities offered by this soft fluidic elastomer manipulation system such as entering and advancing through confined three-dimensional environments as well as conforming to goal shape-configurations within a sagittal plane under closed-loop control.
277 citations
TL;DR: In this article, the wavelet-based image segmentation and evaluation (WISE) method was applied to 11 images obtained from multi-epoch Very Long Baseline Array (VLBA) observations made in January-August 2007 at 43 GHz (λ = 7 mm).
Abstract: Context. Very long baseline interferometry (VLBI) imaging of radio emission from extragalactic jets provides a unique probe of physical mechanisms governing the launching, acceleration, and collimation of relativistic outflows.Aims. VLBI imaging of the jet in the nearby active galaxy M 87 enables morphological and kinematic studies to be done on linear scales down to ~100 Schwarzschild radii (R s ).Methods. The two-dimensional structure and kinematics of the jet in M 87 (NGC 4486) have been studied by applying the wavelet-based image segmentation and evaluation (WISE) method to 11 images obtained from multi-epoch Very Long Baseline Array (VLBA) observations made in January-August 2007 at 43 GHz (λ = 7 mm).Results. The WISE analysis recovers a detailed two-dimensional velocity field in the jet in M 87 at sub-parsec scales. The observed evolution of the flow velocity with distance from the jet base can be explained in the framework of MHD jet acceleration and Poynting flux conversion. A linear acceleration regime is observed up to z obs ~ 2 mas. The acceleration is reduced at larger scales, which is consistent with saturation of Poynting flux conversion. Stacked cross correlation analysis of the images reveals a pronounced stratification of the flow. The flow consists of a slow, mildly relativistic layer (moving at β ~ 0.5c ), associated either with instability pattern speed or an outer wind, and a fast, accelerating stream line (with β ~ 0.92, corresponding to a bulk Lorentz factor γ ~ 2.5). A systematic difference of the apparent speeds in the northern and southern limbs of the jet is detected, providing evidence for jet rotation. The angular velocity of the magnetic field line associated with this rotation suggests that the jet in M 87 is launched in the inner part of the disk, at a distance r 0 ~ 5R s from the central engine.Conclusions. The combined results of the analysis imply that MHD acceleration and conversion of Poynting flux to kinetic energy play the dominant roles in collimation and acceleration of the flow in M 87.
210 citations
TL;DR: Two sliding-mode observers are proposed to handle uncertain kinematics and to estimate unknown torques, respectively and a control law is synthesized to guarantee that the desired trajectory can be followed after finite-time with zero tracking error.
Abstract: This paper investigates a difficult problem of tracking control for robotic manipulations with guaranteed high accuracy. Uncertain kinematics, unknown torques including unknown gravitational torque, unknown friction torque, and uncertain dynamics induced by uncertain moment of inertia and disturbance, are addressed. The approach is developed in the framework of observer-based control design. Two sliding-mode observers are proposed to handle uncertain kinematics and to estimate unknown torques, respectively. Using the estimated information, a control law is then synthesized to guarantee that the desired trajectory can be followed after finite-time with zero tracking error. Experimental results are presented to show the performance of the proposed control approach.
198 citations
01 Jan 2016
TL;DR: A hybrid task-space trajectory and force tracking based on fuzzy system and adaptive mechanism that is used to compensate for the external perturbation, kinematics, and dynamics uncertainties is proposed.
Abstract: This paper studies the optimal distribution of feet forces and control of multilegged robots with uncertainties in both kinematics and dynamics. First, a constrained dynamics for multilegged robots and the constrained environment model are established by considering both kinematic and dynamic uncertainties. Under an external wrench for multilegged robots, the foot forces and moments of the supporting legs can be formulated as quadratic programming problems subject to linear and nonlinear constraints. The neurodynamics of recurrent neural network is developed for foot force optimization. For the obtained optimized tip-point force and the motion of legs, we propose a hybrid task-space trajectory and force tracking based on fuzzy system and adaptive mechanism that are used to compensate for the external perturbation, kinematics, and dynamics uncertainties. The tracking of task-space trajectory and constraint force is achieved under unknown dynamical parameters, constraints, and disturbances. Extensive simulations have been provided to verify the effectiveness of the proposed scheme.
179 citations
TL;DR: This study was the first to use only inertial motion capture to estimate 3D GRF&M during gait, providing comparable accuracy with optical motion capture prediction and enables applications that require estimation of the kinetics during walking outside the gait laboratory.
Abstract: Ground reaction forces and moments (GRF&M) are important measures used as input in biomechanical analysis to estimate joint kinetics, which often are used to infer information for many musculoskeletal diseases. Their assessment is conventionally achieved using laboratory-based equipment that cannot be applied in daily life monitoring. In this study, we propose a method to predict GRF&M during walking, using exclusively kinematic information from fully-ambulatory inertial motion capture (IMC). From the equations of motion, we derive the total external forces and moments. Then, we solve the indeterminacy problem during double stance using a distribution algorithm based on a smooth transition assumption. The agreement between the IMC-predicted and reference GRF&M was categorized over normal walking speed as excellent for the vertical (ρ = 0.992, rRMSE = 5.3%), anterior (ρ = 0.965, rRMSE = 9.4%) and sagittal (ρ = 0.933, rRMSE = 12.4%) GRF&M components and as strong for the lateral (ρ = 0.862, rRMSE = 13.1%), frontal (ρ = 0.710, rRMSE = 29.6%), and transverse GRF&M (ρ = 0.826, rRMSE = 18.2%). Sensitivity analysis was performed on the effect of the cut-off frequency used in the filtering of the input kinematics, as well as the threshold velocities for the gait event detection algorithm. This study was the first to use only inertial motion capture to estimate 3D GRF&M during gait, providing comparable accuracy with optical motion capture prediction. This approach enables applications that require estimation of the kinetics during walking outside the gait laboratory.
167 citations
TL;DR: The compensation strategy has been demonstrated on a five-axis machine tool controlled by an industrial CNC with a limited freedom, as well as by a Virtual CNC which allows the incorporation of compensating all 41 errors.
Abstract: This article proposes a method to measure, model and compensate both geometrically dependent and independent volumetric errors of five-axis, serial CNC machine tools. The forward and inverse kinematics of the machine tool are modeled using the screw theory, and the 41 errors of all 5 axes are represented by error motion twists. The component errors of translational drives have been measured with a laser interferometer, and the errors of two rotary drives have been identified with ballbar measurements. The complete volumetric error model of a five-axis machine has been modeled in the machine's coordinate system and proven experimentally. The volumetric errors are mapped to the part coordinates along the tool path, and compensated using the kinematic model of the machine. The compensation strategy has been demonstrated on a five-axis machine tool controlled by an industrial CNC with a limited freedom, as well as by a Virtual CNC which allows the incorporation of compensating all 41 errors.
155 citations
TL;DR: A novel visual servo-based model predictive control method to steer a wheeled mobile robot (WMR) moving in a polar coordinate toward a desired target and its advantage over the conventional methods is illustrated.
Abstract: In this paper, we have developed a novel visual servo-based model predictive control method to steer a wheeled mobile robot (WMR) moving in a polar coordinate toward a desired target. The proposed control scheme has been realized at both kinematics and dynamics levels. The kinematics predictive steering controller generates command of desired velocities that are achieved by employing a low-level motion controller, while the dynamics predictive controller directly generates torques used to steer the WMR to the target. In the presence of both kinematics and dynamics constraints, the control design is carried out using quadratic programming (QP) for optimal performance. The neurodynamic optimization technique, particularly the primal-dual neural network, is employed to solve the QP problems. Theoretical analysis has been first performed to show that the desired velocities can be achieved with the guaranteed stability, as well as with the global convergence to the optimal solutions of formulated convex programming problems. Experiments have then been carried out to validate the effectiveness of the proposed control scheme and illustrate its advantage over the conventional methods.
136 citations
TL;DR: In this paper, the authors show that the MERA network for the ground state of a 1+1-dimensional conformal field theory has the same structural features as kinematic space.
Abstract: We point out that the MERA network for the ground state of a 1+1-dimensional conformal field theory has the same structural features as kinematic space — the geometry of CFT intervals. In holographic theories kinematic space becomes identified with the space of bulk geodesics studied in integral geometry. We argue that in these settings MERA is best viewed as a discretization of the space of bulk geodesics rather than of the bulk geometry itself. As a test of this kinematic proposal, we compare the MERA representation of the thermofield-double state with the space of geodesics in the two-sided BTZ geometry, obtaining a detailed agreement which includes the entwinement sector. We discuss how the kinematic proposal can be extended to excited states by generalizing MERA to a broader class of compression networks.
134 citations
TL;DR: The experimental results demonstrated that the extensively existed sensor misplacement and sensor drift problems can be well solved and the proposed self-contained and environment-independent system is capable of providing consistent tracking of human lower limbs without significant drift.
Abstract: This paper presents a wearable sensor approach to motion measurements of human lower limbs, in which subjects perform specified walking trials at self-administered speeds so that their level walking and stair ascent capacity can be effectively evaluated. After an initial sensor alignment with the reduced error, quaternion is used to represent 3-D orientation and an optimized gradient descent algorithm is deployed to calculate the quaternion derivative. Sensors on the shank offer additional information to accurately determine the instances of both swing and stance phases. The Denavit-Hartenberg convention is used to set up the kinematic chains when the foot stays stationary on the ground, producing state constraints to minimize the estimation error of knee position. The reliability of this system, from the measurement point of view, has been validated by means of the results obtained from a commercial motion tracking system, namely, Vicon, on healthy subjects. The step size error and the position estimation accuracy change are studied. The experimental results demonstrated that the extensively existed sensor misplacement and sensor drift problems can be well solved. The proposed self-contained and environment-independent system is capable of providing consistent tracking of human lower limbs without significant drift.
TL;DR: A methodology is proposed which describes the relationship between lateral and longitudinal accelerations and speeds, and represents a tool to classify car drivers' behaviour as safe or unsafe, and an app for smartphone allows the geo-referenced kinematic parameters of the vehicle to be detected.
Abstract: Speed and acceleration describe the motion of a vehicle. Therefore, these parameters are fundamental to define the behaviour of a driver. To this aim, it is useful to analyse instantaneous and geo-referenced kinematic parameters of the vehicle recorded by real tests on the road. Among all the available methods in the scientific literature, a way for characterizing driver behaviour is the g-g diagram, that shows the longitudinal and lateral accelerations on the y and x-axes, normalized with respect to gravity, recorded on a vehicle during a real test on the road. However, we retain that also speed has to be considered for characterizing drivers' behaviour, being acceleration and speed strictly interrelated. Starting from the g-g diagram, we propose a methodology which describes the relationship between lateral and longitudinal accelerations and speeds, and represents a tool to classify car drivers' behaviour as safe or unsafe. An app for smartphone allows the geo-referenced kinematic parameters of the vehicle to be detected. The experimental survey supporting the methodology was carried out on a rural two-lane road in Southern Italy. Language: en
TL;DR: This paper proposes a hierarchical image-based visual servoing (IBVS) strategy for dynamic positioning of a fully actuated underwater vehicle and demonstrates the influences of the prediction horizon, cost function, closed-loop vehicle dynamics, and predictive velocity reference model on the IBVS system performance.
Abstract: This paper proposes a hierarchical image-based visual servoing (IBVS) strategy for dynamic positioning of a fully actuated underwater vehicle. In the kinematic loop, the desired velocity is generated by a nonlinear model predictive controller, which optimizes a cost function of the predicted image trajectories under the constraints of visibility and velocity. A velocity reference model, representing the desired closed-loop vehicle dynamics, is integrated with an IBVS kinematic model to predict the future trajectories. In the dynamic velocity tracking loop, a neural-network-based model reference adaptive controller is designed to ensure the convergence of the velocity tracking error in the presence of uncertainties associated with vehicle dynamic parameters, water velocity, and thrust forces. Comparative simulations with different control and system configurations are performed to verify the effectiveness of the proposed scheme and to illustrate the influences of the prediction horizon, cost function, closed-loop vehicle dynamics, and predictive velocity reference model on the IBVS system performance.
TL;DR: These findings demonstrate a definable and measurable relationship between the specific features of observed movements and the ability to discriminate intention, providing quantitative evidence of the significance of movement kinematics for anticipating others’ intentional actions.
Abstract: How do we understand the intentions of other people? There has been a longstanding controversy over whether it is possible to understand others’ intentions by simply observing their movements. Here, we show that indeed movement kinematics can form the basis for intention detection. By combining kinematics and psychophysical methods with classification and regression tree (CART) modeling, we found that observers utilized a subset of discriminant kinematic features over the total kinematic pattern in order to detect intention from observation of simple motor acts. Intention discriminability covaried with movement kinematics on a trial-by-trial basis, and was directly related to the expression of discriminative features in the observed movements. These findings demonstrate a definable and measurable relationship between the specific features of observed movements and the ability to discriminate intention, providing quantitative evidence of the significance of movement kinematics for anticipating others’ intentional actions.
TL;DR: A class of absolutely continuous Jacobian transpose robust controllers are derived, which seem to be effective in counteracting uncertain dynamics, unbounded disturbances and (possible) kinematic and/or algorithmic singularities met on the end-effector trajectory.
TL;DR: A rigid-body model of a scapulothoracic joint to describe the kinematics of the scapula relative to the thorax is developed and can be used for inverse and forward dynamics analyses and to compute joint reaction loads.
Abstract: The complexity of shoulder mechanics combined with the movement of skin relative to the scapula makes it difficult to measure shoulder kinematics with sufficient accuracy to distinguish between symptomatic and asymptomatic individuals. Multibody skeletal models can improve motion capture accuracy by reducing the space of possible joint movements, and models are used widely to improve measurement of lower limb kinematics. In this study, we developed a rigid-body model of a scapulothoracic joint to describe the kinematics of the scapula relative to the thorax. This model describes scapular kinematics with four degrees of freedom: 1) elevation and 2) abduction of the scapula on an ellipsoidal thoracic surface, 3) upward rotation of the scapula normal to the thoracic surface, and 4) internal rotation of the scapula to lift the medial border of the scapula off the surface of the thorax. The surface dimensions and joint axes can be customized to match an individual's anthropometry. We compared the model to "gold standard" bone-pin kinematics collected during three shoulder tasks and found modeled scapular kinematics to be accurate to within 2 mm root-mean-squared error for individual bone-pin markers across all markers and movement tasks. As an additional test, we added random and systematic noise to the bone-pin marker data and found that the model reduced kinematic variability due to noise by 65% compared to Euler angles computed without the model. Our scapulothoracic joint model can be used for inverse and forward dynamics analyses and to compute joint reaction loads. The computational performance of the scapulothoracic joint model is well suited for real-time applications; it is freely available for use with OpenSim 3.2, and is customizable and usable with other OpenSim models.
TL;DR: In this article, a 3-degree-of-freedom (3DOF) lower limb rehabilitation robot (LLRR) was developed for the motion recovery in stroke patients, which involves hip, knee and ankle joints and can also be adjusted to fit for the different heights of patients.
01 Oct 2016
TL;DR: The Simmechanics model is developed based on these models to provide high quality visualisation of this robot for simulation of it in Matlab environment and to demonstrate the accuracy of the developed mathematical models.
Abstract: UR robotic arms are from a series of lightweight, fast, easy to program, flexible, and safe robotic arms with 6 degrees of freedom. The fairly open control structure and low level programming access with high control bandwidth have made them of interest for many researchers. This paper presents a complete set of mathematical kinematic and dynamic, Matlab, and Simmechanics models for the UR5 robot. The accuracy of the developed mathematical models are demonstrated through kinematic and dynamic analysis. The Simmechanics model is developed based on these models to provide high quality visualisation of this robot for simulation of it in Matlab environment. The models are developed for public access and readily usable in Matlab environment. A position control system has been developed to demonstrate the use of the models and for cross validation purpose.
TL;DR: Differences in joint kinematics up to 13° were found between the Plug-in-Gait and the gait 2392 OpenSim model, and the majority of these differences were attributed to differences in the anatomical models, which included different anatomical segment frames and joint constraints.
TL;DR: A model that approximates the continuous shape of a continuum manipulator by a serial chain of rigid links, connected by flexible rotational joints is presented, which permits a description of manipulator shape under different loading conditions.
Abstract: Accurate closed-loop control of continuum manipulators requires integration of both models that describe their motion and methods to evaluate manipulator shape. This work presents a model that approximates the continuous shape of a continuum manipulator by a serial chain of rigid links, connected by flexible rotational joints. This rigid-link model permits a description of manipulator shape under different loading conditions. A kinematic controller, based on the manipulator Jacobian of the proposed rigid-link model, is implemented and realizes trajectory tracking, while using the kinematic redundancy of the manipulator to perform a secondary task of avoiding obstacles. The controller is evaluated on an experimental testbed, consisting of a planar tendon-driven continuum manipulator with two bending segments. Fiber Bragg grating (FBG) sensors are used to reconstruct 3-D manipulator shape, and is used as feedback for closed-loop control of the manipulator. Manipulator steering is evaluated for two cases: the first case involving steering around a static obstacle and the second case involving steering along a straight path while avoiding a moving obstacle. Mean trajectory tracking errors are 0.24 and 0.09 mm with maximum errors of 1.37 and 0.52 mm for the first and second cases, respectively. Finally, we demonstrate the possibility of FBG sensors to measure interaction forces, while simultaneously using them for shape sensing.
TL;DR: An origami parallel module that generates two rotations and one translation is integrated with a twisting module and a compliant gripper to form a novel four-degree-of-freedom grasper, leading to the design of two sets of on-board actuation systems.
Abstract: Minimally invasive surgery (MIS) is one of the most challenging techniques for robot designers due to the limited size of access points, the high miniaturization level, and the dexterity needed for performing surgical tasks. Conversely, only a few microfabrication technologies are currently available for developing such small-sized systems, which allow safe operations in human bodies. In order to match these challenges in MIS, both design and integration of actuation systems should proceed in parallel with an identification of most effective transmission mechanisms and kinematics. In this paper, an origami parallel module that generates two rotations and one translation is integrated with a twisting module and a compliant gripper to form a novel four-degree-of-freedom grasper. The rotational motion leads to the pitch and yaw motion of the gripper, while the translational motion is converted to a roll motion of the gripper via the twisting module that is stacked on top of the parallel module. In light of plane-symmetric properties of the origami structure in the parallel module, both inverse and forward kinematics are resolved with a geometric approach, revealing a unique joint space and a kinematic mapping of the parallel module, leading to the design of two sets of on-board actuation systems. During the analysis, bending motion of a central spring and static properties of the compliant gripper are modeled using finite-element methods. The structure of the twisting module for motion transmission of the grasper is designed and fabricated using origami folding techniques. Gripping forces of the compliant gripper are evaluated in experimental tests. Further analyses of the system performance are addressed in accordance with the scaling ratio of miniaturization and the scalability of the system is demonstrated by a millimeter-sized origami parallel module produced by the smart composite microstructure fabrication process.
TL;DR: The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control, and the inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output.
Abstract: This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot’s joint angles.
TL;DR: In this article, a robust iterative learning controller is designed for trajectory tracking on the basis of the linearization of the dynamics and trajectory tracking control of cooperative multiple mobile cranes.
Abstract: This paper addresses the dynamics and trajectory tracking control of cooperative multiple mobile cranes. Compared with a single mobile crane, cooperative cable parallel manipulators for multiple mobile cranes (CPMMC) are more complex in configuration, which have the characters of both series and parallel manipulators. Therefore, for the CPMMC, the forward as well as the inverse kinematics and dynamics include the difficulties of both series and parallel manipulators. However, the closed kinematic chain brings about potential benefits, including sufficient accuracy, higher cost performance, better lifting capacity and security. Firstly, the forward and inverse kinematics of the CPMMC with point mass are derived with elimination method, and the complete dynamic model of the CPMMC is established based on Lagrange equation and the complete kinematics. Secondly, considering the repetitive tasks and high security and precision requirement, a robust iterative learning controller is designed for trajectory tracking on the basis of the linearization of the dynamics. Thirdly, taking the engineering practice into consideration, two case studies are simulated with the same expected trajectory but with different weights of the loads. Finally, the designed controller is compared with traditional PD control algorithm via numerical simulation. The results demonstrate the feasibility and superiority of the CPMMC and designed controller, and provide a theoretical basis for the cooperation of multiple mobile cranes.
TL;DR: This paper introduces a novel architecture of kinematically redundant parallel mechanisms similar to the well-known Gough-Stewart platform, and it is shown that the singularities of this type of mechanism are governed by the orientation of passive links connecting the redundant legs to the platform.
Abstract: This paper introduces a novel architecture of kinematically redundant parallel mechanisms. This family of mechanisms is similar to the well-known Gough–Stewart platform, and it retains its advantages, i.e., the members connecting the base to the moving platform are only subjected to tensile/compressive loads. The proposed architecture exploits kinematic redundancy to avoid singularities and extend the rotational workspace. The novel kinematic architecture is described, and the associated kinematic relationships are developed. Based on the derivation of the Jacobian matrices, it is shown that the singularities of this type of mechanism are governed by the orientation of passive links connecting the redundant legs to the platform. Grassmann geometry is then used to demonstrate that, given some simple geometric assumptions on the architecture, all singularities can be avoided by exploiting the kinematic redundancy. The orientational workspace is then discussed, and a graphical representation is provided for an example architecture comprising nine actuators, whose orientational workspace is shown to be very large. The translational workspace is also studied. Example trajectories are given in order to illustrate the capabilities of the mechanism to produce very large rotation angles without encountering singularities. Computer animations of the trajectories are provided in a multimedia extension of the paper.
TL;DR: The results of this study suggest that rotational head kinematics are the most important parameters for brain injury criteria.
Abstract: Numerous injury criteria have been developed to predict brain injury using the kinematic response of the head during impact. Each criterion utilizes a metric that is some mathematical combination of the velocity and/or acceleration components of translational and/or rotational head motion. Early metrics were based on linear acceleration of the head, but recent injury criteria have shifted towards rotational-based metrics. Currently, there is no universally accepted metric that is suitable for a diverse range of head impacts. In this study, we assessed the capability of fifteen existing kinematic-based metrics for predicting strain-based brain response using four different automotive impact conditions. Tissue-level strains were obtained through finite element model simulation of 660 head impacts including occupant and pedestrian crash tests, and pendulum head impacts. Correlations between head kinematic metrics and predicted brain strain-based metrics were evaluated. Correlations between brain strain and metrics based on angular velocity were highest among those evaluated, while metrics based on linear acceleration were least correlative. BrIC and RVCI were the kinematic metrics with the highest overall correlation; however, each metric had limitations in certain impact conditions. The results of this study suggest that rotational head kinematics are the most important parameters for brain injury criteria.
TL;DR: In this article, a reconfigurable revolute (rR) joint was proposed for a metamorphic parallel mechanism based on three rRPS (r-R joint-prismatic joint-spherical joint) limbs.
TL;DR: Trilateral teleoperation systems with dual-master-single-slave framework are investigated, where a single robotic manipulator constrained by an unknown geometrical environment is controlled by dual masters.
Abstract: Most studies on bilateral teleoperation assume known system kinematics and only consider dynamical uncertainties. However, many practical applications involve tasks with both kinematics and dynamics uncertainties. In this paper, trilateral teleoperation systems with dual-master–single-slave framework are investigated, where a single robotic manipulator constrained by an unknown geometrical environment is controlled by dual masters. The network delay in the teleoperation system is modeled as Markov chain-based stochastic delay, then asymmetric stochastic time-varying delays, kinematics and dynamics uncertainties are all considered in the force–motion control design. First, a unified dynamical model is introduced by incorporating unknown environmental constraints. Then, by exact identification of constraint Jacobian matrix, adaptive neural network approximation method is employed, and the motion/force synchronization with time delays are achieved without persistency of excitation condition. The neural networks and parameter adaptive mechanism are combined to deal with the system uncertainties and unknown kinematics. It is shown that the system is stable with the strict linear matrix inequality-based controllers. Finally, the extensive simulation experiment studies are provided to demonstrate the performance of the proposed approach.
TL;DR: In this article, a full adaptive control strategy for the space debris removal via a tethered space robot with unknown kinematics, dynamics and part of the states is proposed for the target retrieval/de-orbiting.
TL;DR: This paper exploits screw theory expressed via unit dual quaternion representation and its algebra to formulate both the forward (position+velocity) kinematics and pose control of an n -dof robot arm in an efficient way.
TL;DR: In this article, a continuous feed motion is generated by directly planning jerk limited velocity transitions for the drives in the vicinity of sharp corners of the toolpath, which completely eliminates the need for geometry-based path smoothing and feed planning.
Abstract: This paper presents a novel kinematic corner smoothing technique for high-speed CNC machine tools. Typically, reference tool-paths compromised of short G01 moves are geometrically smoothed by means of arcs and splines within the NC system. In this study, a continuous feed motion is generated by directly planning jerk limited velocity transitions for the drives in the vicinity of sharp corners of the tool-path. This approach completely eliminates the need for geometry based path smoothing and feed planning. Contouring errors at sharp corners are controlled analytically by accurately calculating cornering speed and duration. Since proposed approach bases on kinematically planning axis motion profiles, it exploits acceleration and jerk limits of the drives and delivers a near-time optimal motion. Experimental validation and comparisons are presented to show significant improvement in the cycle time and accuracy of contouring Cartesian tool-paths.