Showing papers on "Kinematics published in 2018"

Gaia Data Release 2: Mapping the Milky Way disc kinematics

Abstract: To illustrate the potential of GDR2, we provide a first look at the kinematics of the Milky Way disc, within a radius of several kiloparsecs around the Sun. We benefit for the first time from a sample of 6.4 million F-G-K stars with full 6D phase-space coordinates, precise parallaxes, and precise Galactic cylindrical velocities . From this sample, we extracted a sub-sample of 3.2 million giant stars to map the velocity field of the Galactic disc from $\sim$5~kpc to $\sim$13~kpc from the Galactic centre and up to 2~kpc above and below the plane. We also study the distribution of 0.3 million solar neighbourhood stars ($r < 200$~pc), with median velocity uncertainties of 0.4~km/s, in velocity space and use the full sample to examine how the over-densities evolve in more distant regions. GDR2 allows us to draw 3D maps of the Galactocentric median velocities and velocity dispersions with unprecedented accuracy, precision, and spatial resolution. The maps show the complexity and richness of the velocity field of the galactic disc. We observe streaming motions in all the components of the velocities as well as patterns in the velocity dispersions. For example, we confirm the previously reported negative and positive galactocentric radial velocity gradients in the inner and outer disc, respectively. Here, we see them as part of a non-axisymmetric kinematic oscillation, and we map its azimuthal and vertical behaviour. We also witness a new global arrangement of stars in the velocity plane of the solar neighbourhood and in distant regions in which stars are organised in thin substructures with the shape of circular arches that are oriented approximately along the horizontal direction in the $U-V$ plane. Moreover, in distant regions, we see variations in the velocity substructures more clearly than ever before, in particular, variations in the velocity of the Hercules stream. (abridged)

Control of human gait stability through foot placement

Abstract: During human walking, the centre of mass (CoM) is outside the base of support for most of the time, which poses a challenge to stabilizing the gait pattern. Nevertheless, most of us are able to walk without substantial problems. In this review, we aim to provide an integrative overview of how humans cope with an underactuated gait pattern. A central idea that emerges from the literature is that foot placement is crucial in maintaining a stable gait pattern. In this review, we explore this idea; we first describe mechanical models and concepts that have been used to predict how foot placement can be used to control gait stability. These concepts, such as for instance the extrapolated CoM concept, the foot placement estimator concept and the capture point concept, provide explicit predictions on where to place the foot relative to the body at each step, such that gait is stabilized. Next, we describe empirical findings on foot placement during human gait in unperturbed and perturbed conditions. We conclude that humans show behaviour that is largely in accordance with the aforementioned concepts, with foot placement being actively coordinated to body CoM kinematics during the preceding step. In this section, we also address the requirements for such control in terms of the sensory information and the motor strategies that can implement such control, as well as the parts of the central nervous system that may be involved. We show that visual, vestibular and proprioceptive information contribute to estimation of the state of the CoM. Foot placement is adjusted to variations in CoM state mainly by modulation of hip abductor muscle activity during the swing phase of gait, and this process appears to be under spinal and supraspinal, including cortical, control. We conclude with a description of how control of foot placement can be impaired in humans, using ageing as a primary example and with some reference to pathology, and we address alternative strategies available to stabilize gait, which include modulation of ankle moments in the stance leg and changes in body angular momentum, such as rapid trunk tilts. Finally, for future research, we believe that especially the integration of consideration of environmental constraints on foot placement with balance control deserves attention.

Neural Kinematic Networks for Unsupervised Motion Retargetting

Abstract: We propose a recurrent neural network architecture with a Forward Kinematics layer and cycle consistency based adversarial training objective for unsupervised motion retargetting. Our network captures the high-level properties of an input motion by the forward kinematics layer, and adapts them to a target character with different skeleton bone lengths (e.g., shorter, longer arms etc.). Collecting paired motion training sequences from different characters is expensive. Instead, our network utilizes cycle consistency to learn to solve the Inverse Kinematics problem in an unsupervised manner. Our method works online, i.e., it adapts the motion sequence on-the-fly as new frames are received. In our experiments, we use the Mixamo animation data1 to test our method for a variety of motions and characters and achieve state-of-the-art results. We also demonstrate motion retargetting from monocular human videos to 3D characters using an off-the-shelf 3D pose estimator.

A public dataset of overground and treadmill walking kinematics and kinetics in healthy individuals

Abstract: In a typical clinical gait analysis, the gait patterns of pathological individuals are commonly compared with the typically faster, comfortable pace of healthy subjects. However, due to potential bias related to gait speed, this comparison may not be valid. Publicly available gait datasets have failed to address this issue. Therefore, the goal of this study was to present a publicly available dataset of 42 healthy volunteers (24 young adults and 18 older adults) who walked both overground and on a treadmill at a range of gait speeds. Their lower-extremity and pelvis kinematics were measured using a three-dimensional (3D) motion-capture system. The external forces during both overground and treadmill walking were collected using force plates and an instrumented treadmill, respectively. The results include both raw and processed kinematic and kinetic data in different file formats: c3d and ASCII files. In addition, a metadata file is provided that contain demographic and anthropometric data and data related to each file in the dataset. All data are available at Figshare (DOI: 10.6084/m9.figshare.5722711). We foresee several applications of this public dataset, including to examine the influences of speed, age, and environment (overground vs. treadmill) on gait biomechanics, to meet educational needs, and, with the inclusion of additional participants, to use as a normative dataset.

Estimation of Vertical Ground Reaction Forces and Sagittal Knee Kinematics During Running Using Three Inertial Sensors.

Abstract: Analysis of running mechanics has traditionally been limited to a gait laboratory using either force plates or an instrumented treadmill in combination with a full-body optical motion capture system. With the introduction of inertial motion capture systems, it becomes possible to measure kinematics in any environment. However, kinetic information could not be provided with such technology. Furthermore, numerous body-worn sensors are required for a full-body motion analysis. The aim of this study is to examine the validity of a method to estimate sagittal knee joint angles and vertical ground reaction forces during running using an ambulatory minimal body-worn sensor setup. Two concatenated artificial neural networks were trained (using data from eight healthy subjects) to estimate the kinematics and kinetics of the runners. The first artificial neural network maps the information (orientation and acceleration) of three inertial sensors (placed at the lower legs and pelvis) to lower-body joint angles. The estimated joint angles in combination with measured vertical accelerations are input to a second artificial neural network that estimates vertical ground reaction forces. To validate our approach, estimated joint angles were compared to both inertial and optical references, while kinetic output was compared to measured vertical ground reaction forces from an instrumented treadmill. Performance was evaluated using two scenarios: training and evaluating on a single subject and training on multiple subjects and evaluating on a different subject. The estimated kinematics and kinetics of most subjects show excellent agreement (ρ>0.99) with the reference, for single subject training. Knee flexion/extension angles are estimated with a mean RMSE 0.9) is still achieved for seven of the eight different evaluated subjects. The performance of multiple subject learning depends on the diversity in the training dataset, as differences in accuracy were found for the different evaluated subjects.

Kinematics, Dynamics, and Control of a Cable-Driven Hyper-Redundant Manipulator

Abstract: A cable-driven hyper-redundant manipulator has superior dexterity for confined space applications. However, the modeling and control considering the cables are very complex. In this paper, we established the kinematics and dynamics models and proposed a dynamics control strategy. The multilevel mapping between the motors, cables, joints, and end-effector was first analyzed. The corresponding kinematics equations were derived and solved by combining analytical and numerical methods. Especially, the cable coupling relationship was established and a decoupling method was addressed to compensate the coupled motion between cables. Furthermore, we derived the dynamics equations including the cable forces and the joint variables. Considering practical control requirements, the cables’ forces were distributed by simplifying the dynamics equations and obtaining the minimal solutions. Then, we presented a dynamics control strategy, which uses the forward and inverse kinematics of multilevel mapping for motion resolution and compensation, and computes the feedforward torques for the motors using recursive dynamics and “cable force–motor torque” relationship. Finally, a prototype and a truss inspection experiment system were developed to verify the corresponding models and methods. Experiment results show that the derived kinematic and the dynamic equations, and the proposed dynamic control strategy are effective.

A New Kind of Accurate Calibration Method for Robotic Kinematic Parameters Based on the Extended Kalman and Particle Filter Algorithm

Abstract: Precise positioning of a robot plays an very important role in advanced industrial applications, and this paper presents a new kinematic calibration method based on the extended Kalman filter (EKF) and particle filter (PF) algorithm that can significantly improves the positioning accuracy of the robot. Kinematic and its error models of a robot are established, and its kinematic parameters are identified by using the EKF algorithm first. But the EKF algorithm has a kind of linear truncation error and it is useful for the Gauss noise system in general, so its identified accuracy will be affected for the highly nonlinear robot kinematic system with a non-Gauss noise system. The PF algorithm can solve this with non-Gauss noise and a high nonlinear problem well, but its calibration accuracy and efficiency are affected by the prior distribution of the initial values. Therefore, this paper proposes to use the calibration value of the EKF algorithm as the prior value of the PF algorithm, and then, the PF algorithm is used further to calibrate the kinematic parameters of the robot. Enough experiments have been carried out, and the experimental results validated the viability of the proposed method with the robot positioning accuracy improved significantly.

An analysis of the shear-transfer actions in reinforced concrete members without transverse reinforcement based on refined experimental measurements

Abstract: A traditional difficulty in the understanding of the role of the various shear transfer actions in members without transverse reinforcement has been a lack of detailed measurements on the development of shear cracking and their associated kinematics during the process of failure. In this paper, this issue is addressed on the basis of an experimental program on 20 beams investigated by means of digital image correlation. The measurements are shown to allow a clear understanding of the mechanisms leading to shear failure and their evolution (transfer of forces between the various potential shear-carrying actions) during the loading process. The amount of shear carried by the various potential shear-transfer actions is estimated for varying levels of load accounting for the cracking pattern and actual kinematics on the basis of fundamental constitutive laws for concrete and steel. The results are shown to be consistent and provide a rational basis for the understanding of the phenomenon of shear transfer in reinforced concrete members without transverse reinforcement.

Formation Control of Nonholonomic Mobile Robots Without Position and Velocity Measurements

Abstract: Most existing formation control approaches are based on the assumption that the global/relative position and/or velocity measurements of mobile robots are directly available. To extend the application domain and to improve the formation control performance, it is extremely necessary to avoid the use of position and velocity measurements in the design of formation controllers. In this paper, we propose new leader-following formation tracking control schemes for nonholonomic mobile robots with onboard perspective cameras, without using both position and velocity measurements. To address the unavailability issue of position measurements, the leader–follower kinematics model in the image space is developed, which can facilitate the complete elimination of measurement/estimation of the position information. Furthermore, feedback information from the perspective camera of the follower robot is used to design adaptive observers to estimate the leader linear velocity for feedforward compensation, which can handle the absence of velocity measurements such that the proposed schemes can be applied to control formations of mobile robots without mutual communication abilities. By using the Lyapunov stability theory, a rigorous stability analysis based on the nonlinear formation dynamics is provided to show that the global stability of the combined observer–controller closed-loop system can be guaranteed. Both simulation and experimental results are also given to demonstrate the performance of the proposed formation tracking control schemes.

On Higher Order Inverse Kinematics Methods in Time-Optimal Trajectory Planning for Kinematically Redundant Manipulators

Abstract: Time-optimal motion control will only find industrial applications if the optimal motions can actually be performed by standard industrial robots. This is not ensured by any optimal motion planning scheme proposed up to now. The limiting aspect rendering all these schemes impractical is the insufficient continuity of the motion trajectories. In this paper, a time-optimal path following along a predefined end-effector path is addressed for kinematically redundant robots, where nonredundant robots are included as special cases. As prerequisite explicit expressions for the higher order inverse kinematics are presented. Kinematic redundancy is resolved and exploited within the trajectory planning using the joint space decomposition and a novel pseudoinverse-based solution of the higher order inverse kinematics. The approaches are demonstrated for two examples of kinematically redundant manipulators performing time-optimal motions along prescribed end-effector paths in compliance with technological constraints. The optimization results are experimentally validated.

Finite Element Method-Based Kinematics and Closed-Loop Control of Soft, Continuum Manipulators.

Abstract: This article presents a modeling methodology and experimental validation for soft manipulators to obtain forward kinematic model (FKM) and inverse kinematic model (IKM) under quasi-static conditions (in the literature, these manipulators are usually classified as continuum robots. However, their main characteristic of interest in this article is that they create motion by deformation, as opposed to the classical use of articulations). It offers a way to obtain the kinematic characteristics of this type of soft robots that is suitable for offline path planning and position control. The modeling methodology presented relies on continuum mechanics, which does not provide analytic solutions in the general case. Our approach proposes a real-time numerical integration strategy based on finite element method with a numerical optimization based on Lagrange multipliers to obtain FKM and IKM. To reduce the dimension of the problem, at each step, a projection of the model to the constraint space (gathering actuators, sensors, and end-effector) is performed to obtain the smallest number possible of mathematical equations to be solved. This methodology is applied to obtain the kinematics of two different manipulators with complex structural geometry. An experimental comparison is also performed in one of the robots, between two other geometric approaches and the approach that is showcased in this article. A closed-loop controller based on a state estimator is proposed. The controller is experimentally validated and its robustness is evaluated using Lypunov stability method.

Modeling and calibration of high-order joint-dependent kinematic errors for industrial robots

Abstract: Robot positioning accuracy is critically important in many manufacturing applications. While geometric errors such as imprecise link length and assembly misalignment dominate positioning errors in industrial robots, significant errors also arise from non-uniformities in bearing systems and strain wave gearings. These errors are characteristically more complicated than the fixed geometric errors in link lengths and assembly. Typical robot calibration methods only consider constant kinematic errors, thus, neglecting complex kinematic errors and limiting the accuracy to which robots can be calibrated. In contrast to typical calibration methods, this paper considers models containing both constant and joint-dependent kinematic errors. Constituent robot kinematic error sources are identified and kinematic error models are classified for each error source. The constituent models are generalized into a single robot kinematic error model with both constant and high-order joint-dependent error terms. Maximum likelihood estimation is utilized to identify error model parameters using measurements obtained over the measurable joint space by a laser tracker. Experiments comparing the proposed and traditional calibration methods implemented on a FANUC LR Mate 200i robot are presented and analyzed. While the traditional constant kinematic error model describes 79.4% of the measured error, the proposed modeling framework, constructed from measurements of 250 poses, describes 97.0% of the measured error. The results demonstrate that nearly 20% of the kinematic error in this study can be attributed to complex, joint-dependent error sources.

Misalignment Compensation for Full Human-Exoskeleton Kinematic Compatibility: State of the Art and Evaluation

Abstract: The use of exoskeletons by the elderly, disabled people, heavy labor workers, and soldiers can have great social and economic benefit. However, limitations in usability are impeding the widespread adoption of exoskeletal devices. Kinematic compatibility, comfort, volume, mass, simplicity, expandability, and the ability to transmit forces, relative angles between the exoskeleton and the human, and the donning and doffing procedure need to be considered. Over the last decades, a large number of exoskeletons have been developed, to assert kinematic compatibility and compensate for misalignment. To such a degree, that it has become difficult to keep an overview of the different strategies. Therefore, this review article presents an extensive overview of different misalignment compensation strategies existing in the literature. Further, these strategies are organized in nine categories, evaluated and discussed around the exoskeleton's application domain and its specific requirements and needs.

Prediction of Cutting Forces in Five-Axis Milling Using Feed Drive Current Measurements

Abstract: This paper presents a model to identify cutting forces from the feed drive current measurements in five-axis milling processes. The friction, equivalent inertia, and the frequency response function of the structural disturbance of three translational ( X – Y – Z ) and two rotational ( A – C ) drives are identified. The disturbances caused by the structural modes are compensated through disturbance Kalman Filters for each drive. The kinematics of the five-axis machine tool is modeled using the Denavit-Hartenberg method and compensated motor current on the drives are transformed to the tool coordinate frame to obtain cutting forces. The application of the proposed method is demonstrated experimentally by machining a five-axis part on a Quaser UX600 machining center. LSV-2 communication protocol of the Heidenhain computer numerical control (CNC) is used to collect commanded, noise-free digital motor currents, drive speeds, tool center point position, tool orientation, spindle speed, and tangential velocity from the CNC via Ethernet connection.

Modeling the Kinematics of Human Locomotion Over Continuously Varying Speeds and Inclines

Abstract: Powered knee and ankle prostheses can perform a limited number of discrete ambulation tasks. This is largely due to their control architecture, which uses a finite-state machine to select among a set of task-specific controllers. A non-switching controller that supports a continuum of tasks is expected to better facilitate normative biomechanics. This paper introduces a predictive model that represents gait kinematics as a continuous function of gait cycle percentage, speed, and incline. The basis model consists of two parts: basis functions that produce kinematic trajectories over the gait cycle and task functions that smoothly alter the weight of basis functions in response to task. Kinematic data from 10 able-bodied subjects walking at 27 combinations of speed and incline generate training and validation data for this data-driven model. Convex optimization accurately fits the model to experimental data. Automated model order reduction improves predictive abilities by capturing only the most important kinematic changes due to walking tasks. Constraints on a range of motion and jerk ensure the safety and comfort of the user. This model produces a smooth continuum of trajectories over task, an impossibility for finite-state control algorithms. Random sub-sampling validation indicates that basis modeling predicts untrained kinematics more accurately than linear interpolation.

Hand-Object Contact Force Estimation from Markerless Visual Tracking

Abstract: We consider the problem of estimating realistic contact forces during manipulation, backed with ground-truth measurements, using vision alone. Interaction forces are usually measured by mounting force transducers onto the manipulated objects or the hands. Those are costly, cumbersome, and alter the objects’ physical properties and their perception by the human sense of touch. Our work establishes that interaction forces can be estimated in a cost-effective, reliable, non-intrusive way using vision. This is a complex and challenging problem. Indeed, in multi-contact, a given motion can generally be caused by an infinity of possible force distributions. To alleviate the limitations of traditional models based on inverse optimization, we collect and release the first large-scale dataset on manipulation kinodynamics as 3.2 hours of synchronized force and motion measurements under 193 object-grasp configurations. We learn a mapping between high-level kinematic features based on the equations of motion and the underlying manipulation forces using recurrent neural networks (RNN). The RNN predictions are consistently refined using physics-based optimization through second-order cone programming (SOCP). We show that our method can successfully capture interaction forces compatible with both the observations and the way humans intuitively manipulate objects, using a single RGB-D camera.

A unified formulation for mechanical joints with and without clearances/bushings and/or stops in the framework of multibody systems

Abstract: Virtually all machines and mechanisms use mechanical joints that are not perfect from the kinematic point of view and for which tolerances, in the fitting of their components, are specified. Together with such controlled clearances, mechanical joints may require the use of bushing elements, such as those used in vehicle suspensions. Furthermore, in many situations the joints exhibit limits (stops) in their translational or rotational motion that have to be taken into account when modeling them. The dynamic response of the mechanical systems that use such realistic mechanical joints is largely dependent on their characteristic dimensions and material properties of the compliant elements, implying that correct models of these systems must include realistic models of the bushing/clearance joints and of the joint stops. Several works addressed the modeling of imperfect joints to account for the existence of clearances and bushings, generally independently of the formulation of the perfect kinematic joints. This work proposes a formulation in which both perfect and clearance/bushing joints share the same kinematic information making their modeling data similar and enabling their easy permutation in the context of multibody systems modeling. The proposed methodology is suitable for the most common mechanical joints and easily extended to many other joint types benefiting the exploration of a wide number of modeling applications, including the representation of cut-joints required for some formulations in multibody dynamics. The formulation presented in this work is applied to several demonstrative examples of spatial mechanisms to show the need to consider the type of imperfect joints and/or joints with stops modeling in practical applications.

Kinematic Distances: A Monte Carlo Method

Abstract: Distances to high mass star forming regions (HMSFRs) in the Milky Way are a crucial constraint on the structure of the Galaxy. Only kinematic distances are available for a majority of the HMSFRs in the Milky Way. Here we compare the kinematic and parallax distances of 75 Galactic HMSFRs to assess the accuracy of kinematic distances. We derive the kinematic distances using three different methods: the traditional method using the Brand & Blitz (1993) rotation curve (Method A), the traditional method using the Reid et al. (2014) rotation curve and updated Solar motion parameters (Method B), and a Monte Carlo technique (Method C). Methods B and C produce kinematic distances closest to the parallax distances, with median differences of 13% (0.43 kpc) and 17% (0.42 kpc), respectively. Except in the vicinity of the tangent point, the kinematic distance uncertainties derived by Method C are smaller than those of Methods A and B. In a large region of the Galaxy, the Method C kinematic distances constrain both the distances and the Galactocentric positions of HMSFRs more accurately than parallax distances. Beyond the tangent point along longitude=30 degrees, for example, the Method C kinematic distance uncertainties reach a minimum of 10% of the parallax distance uncertainty at a distance of 14 kpc. We develop a prescription for deriving and applying the Method C kinematic distances and distance uncertainties. The code to generate the Method C kinematic distances is publicly available and may be utilized through an on-line tool.

The Feasibility and Benefits of In Situ Exploration of `Oumuamua-like objects

Abstract: A rapid accumulation of observations and interpretation have followed in the wake of 1I `Oumuamua's passage through the inner Solar System. We briefly outline the consequences that this first detection of an interstellar asteroid implies for the planet-forming process, and we assess the near-term prospects for detecting and observing (both remotely and in situ) future Solar System visitors of this type. Drawing on detailed heat-transfer calculations that take both `Oumuamua's unusual shape and its chaotic tumbling into account, we affirm that the lack of a detectable coma in deep images of the object very likely arises from the presence of a radiation-modified coating of high molecular weight material (rather than a refractory bulk composition). Assuming that `Oumuamua is a typical representative of a larger population with a kinematic distribution similar to Population I stars in the local galactic neighborhood, we calculate expected arrival rates, impact parameters and velocities of similar objects and assess their prospects for detection using operational and forthcoming facilities. Using `Oumuamua as a proof-of-concept, we assess the prospects for missions that intercept interstellar objects (ISOs) using conventional chemical propulsion. Using a "launch on detection" paradigm, we estimate wait times of order 10 years between favorable mission opportunities with the detection capabilities of the Large-Scale Synoptic Survey Telescope (LSST), a figure that will be refined as the population of interstellar asteroids becomes observationally better constrained.