Kinematic Framework Research Articles

PB-Trajectron: Physics bounded neural network for generalized trajectory prediction

Vehicle trajectory prediction is a critical technology in autonomous driving systems, as the quality of prediction directly affects downstream path planning and vehicle control. Although recent studies have shown that models combining kinematic rules with data-driven methods exhibit better interpretability in trajectory prediction, these models often adopt fixed constraint strengths, limiting their generalization ability in diverse traffic scenarios. This fixed constraint strength design restricts the model’s adaptability to different traffic environments.To address this issue, we propose PB-Trajectron, a dynamic integration mechanism that enhances the model’s prediction performance and generalization capability. PB-Trajectron is a dynamic integration mechanism that combines vehicle kinematic rules with deep learning prediction models for generalized vehicle trajectory prediction. The model integrates physics-based motion models and Deep Neural Networks (DNNs) to provide reasonable physical explanations for predicting vehicle trajectories at different speeds. First, we establish two vehicle kinematic constraints applicable to different prediction scenarios, enabling the agent to switch between these constraints based on the causal relationships between vehicle motion states to avoid generating non-physical trajectory results. Second, we propose a kinematic constraint decision framework based on velocity thresholds, which demonstrates adaptive adjustment, allowing the agent to switch tasks according to real-time state conditions and adaptively adjust the contribution of different physical constraints based on actual vehicle speeds. Finally, we investigate the advantages of PB-Trajectron in Out-of-Domain(OOD) generalization.Cross-scenario experiments on the nuScenes dataset show that, compared to previous methods, PB-Trajectron reduces the final displacement error by 7.69% when the prediction step length is 4. Furthermore, out-of-domain generalization tests on the INTERACTION dataset demonstrate that PB-Trajectron achieves a 12.23% reduction in average prediction error on datasets from different countries and scenarios compared to previous methods. The proposed mechanism can better adapt to complex and diverse traffic scenarios, laying the foundation for explainable and robust autonomous driving systems.

A four-node inverse curved shell element coupling MITC method for deformation reconstruction of plate and shell structures

In the field of structural deformation monitoring, the inverse finite element method (iFEM) has significant engineering value as a structural health monitoring technique that provides timely and reliable warnings for shell structures. However, existing inverse finite elements are mainly based on first-order shear deformation theory and kirchhoff-love theory, which are not suitable for deformation reconstruction in plate and shell structures of arbitrary thickness. This study integrates iFEM with the Mixed Interpolation of Tensorial Components (MITC) method to develop a novel four-node quadrilateral inverse curved shell element, named iMICS(inverse Mixed Interpolation Curved Shell)4, aimed at enhancing the accuracy and efficiency of deformation reconstruction in complex plate and shell structures. The method uses the MITC4 shell element as the kinematic framework and applies the least squares variational principle to achieve deformation reconstruction, effectively alleviating shear and membrane locking issues across structures of varying thickness. Numerical examples validate the superior performance of the iMICS4 element, demonstrating its promising application prospects.

A feedforward kinematic error controller with an angular positioning deviations model for backlash compensation of industrial robots

The accuracy of industrial robots, typically on the order of several millimeters, has inhibited their adoption in advanced aerospace manufacturing applications, such as robotic machining. For this reason, extensive investigations have been conducted to develop solutions to improve their accuracy. These solutions can be categorized into offline and online compensation strategies, both of which have advantages and limitations. Offline compensation has been shown to improve the accuracy of industrial robots by two to three orders of magnitude; however, the sensitivity of these solutions to environmental changes and external disturbances can degrade their performance. In contrast, online compensation, which utilizes real-time measurement and compensation, is robust to these changes; however, the performance of these solutions is limited by the causality of time, preventing them from compensating fast changing errors such as backlash. In this work, a novel kinematic modeling strategy, which models the robot’s angular positioning deviations to predict when backlash will occur, is integrated into a novel online kinematic error compensation framework to leverage the advantages of both solutions. By integrating the proposed kinematic model into the online compensation framework, backlash errors can be predicted and preemptively compensated to improve performance. The performance of the combined system was evaluated using ball bar measurements, where it was shown to improve the circularity of the ball bar motion by 25 %.

A Study of Drilling Parameter Optimization of Functionally Graded Material Steel–Aluminum Alloy Using 3D Finite Element Analysis

Composite materials, such as aluminum alloy FGMs, provide advantageous weight reduction properties compared to homogenous pure structures while still preserving sufficient stiffness for diverse applications. Despite various research on drilling simulation concepts and ideas for these materials, there still needs to be an agreement on the process modeling. Researchers have looked into a lot of different numerical methods, including Lagrangian, Eulerian, arbitrary Lagrangian–Eulerian (ALE), and coupled Eulerian–Lagrangian (CEL), to find solutions to problems like divergence issues and too much mesh distribution, which become more of a problem at higher speeds. This research provides a global analysis of bottom-up meshing for eleven 1 mm layers using ABAQUS® software. It combines the internal surface contact approach with the Lagrangian domain’s kinematic framework. The model uses the Johnson–Cook constitutive equation to precisely predict cutting forces, stress, and strain distributions, optimizing cutting parameters to improve drilling performance. According to Taguchi analysis, the most favorable parameters for reducing cutting force and improving performance are a rotational speed of 700 rpm, a feed rate of 1 mm/s, and a depth of cut of 3 mm. The findings suggest that increasing the feed rate and depth of cut substantially affects the cutting force, while the rotational speed has a comparatively little effect. These ideal settings serve as a foundation for improving FGM drilling efficiency.

A finite deformation formulation for amorphous glassy polymers under moderate and impact strain rates: Application to adhesive films

This work describes a novel finite strain hypo-elastic formulation for amorphous thermoset polymers working in the glassy regime. The constitutive formulation is able to describe time and pressure-dependent behaviour under loading rates ranging from quasi-static to impact-type loading conditions and to account for thermo-mechanical coupling effects. A non-linear pressure dependent potential function is introduced to capture the non-associative visco-plastic potential flow for volumetric plastic strain control. The formulation also incorporates an internal state or deformation resistance variable to enable the description of the polymer hardening — softening behaviour, as well as a novel relation for the dissipative fraction of the plastic work in terms of the equivalent accumulated plastic strain. The constitutive model is formulated within a finite strain kinematics framework, and full details of its numerical implementation into the finite element method using a fully implicit Euler Backward algorithm are given. Model calibration is carried out for three different thermosetting resins (PR520 and RTM6 Epoxies, and MMA adhesives).To illustrate the model capabilities, two case studies are investigated: (i) the de-formation behaviour of epoxy under moderate and impact-type loading conditions under isothermal and fully adiabatic conditions, and (ii) the fracture behaviour of adhesive layers used to bond stiff metallic substrates. Results on RTM6 epoxy show that at, e.g., 60% true strain, 48.5% of the rate of plastic work is dissipative, and that the corresponding predicted increase in temperature due to the locally dissipated heat is consistent with published calorimetry data on a similar thermoset polymer. It was also found that, by coupling the proposed constitutive model with a cohesive zone model, it was possible to predict accurately the effect of an MMA adhesive layer thickness and pressure-dependency on the growth of an interfacial crack between the MMA adhesive and the metallic substrates. The proposed model should constitute a generic phenomenological formulation for the mechanical behaviour prediction of a wide range of thermoset polymers under confined and quasi-adiabatic conditions such as in composites and adhesive joints.

Kinematic Analysis and Application to Control Logic Development for RHex Robot Locomotion.

This study explores the kinematic model of the popular RHex hexapod robots which have garnered considerable interest for their locomotion capabilities. We study the influence of tripod trajectory parameters on the RHex robot's movement, aiming to craft a precise kinematic model that enhances walking mechanisms. This model serves as a cornerstone for refining robot control strategies, enabling tailored performance enhancements or specific motion patterns. Validation conducted on a bespoke test bed confirms the model's efficacy in predicting spatial movements, albeit with minor deviations due to motor load variations and control system dynamics. In particular, the derived kinematic framework offers valuable insights for advancing control logic, particularly navigating in flat terrains, thereby broadening the RHex robot's application spectrum.

Dynamic assessment of spine movement patterns using an RGB-D camera and deep learning

In clinical practice, functional limitations in patients with low back pain are subjectively assessed, potentially leading to misdiagnosis and prolonged pain. This paper proposes an objective deep learning (DL) markerless motion capture system that uses a red–green–blue-depth (RGB-D) camera to measure the kinematics of the spine during flexion–extension (FE) through: 1) the development and validation of a DL semantic segmentation algorithm that segments the back into four anatomical classes and 2) the development and validation of a framework that uses these segmentations to measure spine kinematics during FE. Twenty participants performed ten cycles of FE with drawn-on point markers while being recorded with an RGB-D camera. Five of these participants also performed an additional trial where they were recorded with an optical motion capture (OPT) system. The DL algorithm was trained to segment the back and pelvis into four anatomical classes: upper back, lower back, spine, and pelvis. A kinematic framework was then developed to refine these segmentations into upper spine, lower spine, and pelvis masks, which were used to measure spine kinematics after obtaining 3D global coordinates of the mask corners. The segmentation algorithm achieved high accuracy, and the root mean square error (RMSE) between ground truth and predicted lumbar kinematics was < 4°. When comparing markerless and OPT kinematics, RMSE values were < 6°. This work demonstrates the feasibility of using markerless motion capture to assess FE spine movement in clinical settings. Future work will expand the studied movement directions and test on different demographics.

From a distance: Shuttleworth revisited.

The Shuttleworth equation: a linear stress-strain relation ubiquitously used in modeling the behavior of soft surfaces. Its validity in the realm of materials subject to large deformation is a topic of current debate. Here, we allow for large deformation by deriving the constitutive behavior of the surface from the general framework of finite kinematics. We distinguish cases of finite and infinitesimal surface relaxation preceding an infinitesimal applied deformation. The Shuttleworth equation identifies as the Cauchy stress measure in the fully linearized setting. We show that both in finite and linearized cases, measured elastic constants depend on the utilized stress measure. In addition, we discuss the physical implications of our results and analyze the impact of surface relaxation on the estimation of surface elastic moduli in the light of two different test cases.

Kinematics and dynamics of non-developable origami

Non-developable origami is a unique type of origami structures that cannot be unfolded into a flat sheet without stretching. In this work, we study the kinematics and dynamics of such origami, theoretically, numerically and experimentally, considering rigid panels and stretchable creases. Unlike developable origami, we find that non-developable origami exhibits distinct folding angle relationships, leading to several separate branches in its kinematic configuration space with snap-through and locking behaviours. By modelling the creases as stretchable torsional springs, we derive a dynamic model to analyse the deployment of non-developable origami structures, from single-vertex origami to origami chains. Our dynamic model unveils the snap-through behaviour between the two kinematics branches of the single-vertex non-developable origami structure, which is further validated by experiments with excellent agreement. We believe that our kinematic and dynamic framework of non-developable origami will greatly expand the current design space of origami structures and guide the design of novel origami actuators.

Design, Analysis, and Kinematic Framework of a Long-Range Magnetic Anchored and Guided Endoscope

Design, Analysis, and Kinematic Framework of a Long-Range Magnetic Anchored and Guided Endoscope

Accelerating galaxy dynamical modeling using a neural network for joint lensing and kinematic analyses

Strong gravitational lensing is a powerful tool to provide constraints on galaxy mass distributions and cosmological parameters, such as the Hubble constant, H0. Nevertheless, inference of such parameters from images of lensing systems is not trivial as parameter degeneracies can limit the precision in the measured lens mass and cosmological results. External information on the mass of the lens, in the form of kinematic measurements, is needed to ensure a precise and unbiased inference. Traditionally, such kinematic information has been included in the inference after the image modeling, using spherical Jeans approximations to match the measured velocity dispersion integrated within an aperture. However, as spatially resolved kinematic measurements become available via IFU data, more sophisticated dynamical modeling is necessary. Such kinematic modeling is expensive, and constitutes a computational bottleneck that we aim to overcome with our Stellar Kinematics Neural Network (SKiNN). SKiNN emulates axisymmetric modeling using a neural network, quickly synthesizing from a given mass model a kinematic map that can be compared to the observations to evaluate a likelihood. With a joint lensing plus kinematic framework, this likelihood constrains the mass model at the same time as the imaging data. We show that SKiNN’s emulation of a kinematic map is accurate to a considerably better precision than can be measured (better than 1% in almost all cases). Using SKiNN speeds up the likelihood evaluation by a factor of ~200. This speedup makes dynamical modeling economical, and enables lens modelers to make effective use of modern data quality in the JWST era.

Ratcheting–Fatigue Damage Assessment of Additively Manufactured SS304L and AlSi10Mg Samples under Asymmetric Stress Cycles

The present study aims to investigate the interaction of ratcheting and fatigue phenomena for additively manufactured (AM) samples of SS304L and AlSi10Mg undergoing uniaxial asymmetric stress cycles. Overall damage was accumulated through fatigue and ratcheting on AM samples prepared from three-dimensional-printed plates along vertical and horizontal directions. Fatigue damage was evaluated based on the strain energy density fatigue approach and ratcheting damage was calculated through use of an isotropic–kinematic hardening framework. The isotropic description through the Lee–Zavrel (L–Z) model formed the initial and concentric expansion of yield surfaces while the Ahmadzadeh–Varvani (A–V) kinematic hardening rule translated yield surfaces into the deviatoric stress space. Ratcheting of AM samples was simulated using finite element analysis through use of triangular and quadrilateral elements. Ratcheting values of the AM samples were simulated on the basis of Chaboche’s materials model. The predicted and simulated ratcheting damage curves placed above the experimental fatigue–ratcheting experimental data while predicted fatigue damage curves collapsed below the measured values. The overall damage was formulated to partition damage weights due to fatigue and ratcheting phenomena.

Mechanical origin of shape memory performance for crosslinked epoxy networks

The mechanical principle of microstructural change governing the shape formation and restoration process of shape memory epoxy (SME) was analyzed on a subcontinuum scale. A series of processes to program and operate the microstructure of highly crosslinked networks across the phase transition temperature range was implemented in molecular dynamics simulations. This elucidated the mechanisms by which the chemical composition of resins and crosslinkers, the degree of orientation of each chain, and the topology of the entire network characterize the shape memory behavior of the system. The strategy for analyzing the mechanical behavior of molecules by classifying them into translation, rotation, and deformation based on the classical kinematic framework was particularly effective in clarifying the structure–property relationship. The results showed that, during the shape programming process, each molecular component of the SME was rearranged to different levels depending on its stiffness, forming local residual stresses. The principle leading to shape recovery as the subsequent thermal load removes residual stress and the resulting change in the mechanical anisotropy of the entire system were also successfully comprehended.

Kinetostatic Optimization for Kinematic Redundancy Planning of Nimbl’Bot Robot

AbstractIn manufacturing industry, computer numerical control (CNC) machines are often preferred over industrial serial robots (ISR) for machining tasks. Indeed, CNC machines offer high positioning accuracy, which leads to slight dimensional deviation on the final product. However, these machines have a restricted workspace generating limitations in the machining work. Conversely, ISR are typically characterized by a larger workspace. ISR have already shown satisfactory performance in tasks like polishing, grinding, and deburring. This paper proposes a kinematic redundant robot composed of a novel two degrees-of-freedom mechanism with a closed kinematic chain. After describing a task-priority inverse kinematic control framework used for joint trajectory planning exploiting the robot kinematic redundancy, the paper analyses the kinetostatic performance of this robot depending on the considered control tasks. Moreover, two kinetostatic tasks are introduced and employed to improve the robot performance. Simulation results show how the robot better performs when the optimization tasks are active.

Using satellite observations of ocean variables to improve estimates of water mass (trans)formation

For the first time, an accurate and complete picture of Mixed Layer (ML) water mass dynamics can be inferred at high spatio-temporal resolution via the material derivative derived from Sea Surface Salinity/Temperature (SSS/T) and Currents (SSC). The product between this satellite derived material derivative and in-situ derived Mixed Layer Depth (MLD) provides a satellite based kinematic approach to the water mass (trans)formation framework (WMT/F) above ML. We compare this approach to the standard thermodynamic approach based on air-sea fluxes provided by satellites, an ocean state estimate and in-situ observations. Southern Hemisphere surface density flux and water mass (trans)formation framework (WMT/F) were analysed in geographic and potential density space for the year 2014. Surface density flux differences between the satellite derived thermodynamic and kinematic approaches and ECCO (an ocean state estimate) underline: 1) air-sea heat fluxes dominate variability in the thermodynamic approach; and 2) fine scale structures from the satellite derived kinematic approach are most likely geophysical and not artefacts from noise in SSS/T or SSC—as suggested by a series of smoothing experiments. Additionally, ECCO revealed surface density flux integrated over ML are positively biased as compared to similar estimates assuming that surface conditions are homogeneous over ML—in part owing to the e-folding nature of shortwave solar radiation. Major differences between the satellite derived kinematic and thermodynamic approaches are associated to: 1) lateral mixing and mesoscale dynamics in the kinematic framework; 2) vertical excursions of, and vertical velocities through the ML base; and 3) interactions between ML horizontal velocities and ML base spatial gradients.

An inverse kinematics framework of mobile manipulator based on unique domain constraint

This paper presents the collision-free solution to the inverse kinematics of a mobile manipulator. The main contribution is to propose a novel inverse kinematics solution framework combining unique domains. The inverse kinematics problem is formulated as an optimization problem in this work. Firstly, the objective function is simplified by the kinematics decoupling method based on improved disconnection and re-connection. Then, the joint variables are decoupled and constrained, and an approach to initialize the variables is proposed. An inverse kinematic solution framework for the manipulator body to avoid obstacles is further presented, and CMA-ES is applied to the framework. Finally, the simulation results demonstrate the effectiveness of the proposed method.

Global Navigation Satellite System Real-Time Kinematic Positioning Framework for Precise Operation of a Swarm of Moving Vehicles.

The global navigation satellite system (GNSS) real-time kinematic (RTK) technique is used to achieve relative positioning centimeter levels among multiple agents on the move. A typical GNSS RTK estimates the relative positions of multiple rover receivers with respect to a single-base receiver. In a fleet of rover GNSS receivers, this approach is inefficient because each rover receiver only uses GNSS measurements of its own and those sent from a single-base receiver. In this study, we propose a novel GNSS RTK framework that facilitates the precise positioning of a swarm of moving vehicles through the GNSS measurements of multiple receivers and broadcasts fixed-integer ambiguities of GNSS carrier phases. The proposed framework not only provides efficient RTK positioning but also reliable performance with a limited number of GNSS satellites in view. Our experimental flight tests with six GNSS receivers showed that the systematic procedure of the proposed framework could maintain lower than 6 cm of 3D RMS positioning errors, whereas the conventional RTK failed to resolve the correct integer ambiguities of double difference carrier phase measurements more than 13% in five out of nine total baselines.

Ratcheting evaluation of additively manufactured 4043 aluminum samples through a combined isotropic–kinematic hardening framework

Ratcheting evaluation of additively manufactured 4043 aluminum samples through a combined isotropic–kinematic hardening framework

An ISB-consistent upper limb model based on the Denavit-Hartenberg convention for joint angle estimation during prolonged rehabilitation exercises

An accurate and robust joint kinematics description is a crucial prerequisite for the development of tele- rehabilitation applications. Miniaturized wearable sensors (IMUs), along with the use of efficient sensor fusion algorithms, represent a suitable solution. However, orientation drift can affect real-time joint kinematics over prolonged acquisition. In this work, we proposed an optimization kinematics framework which exploits a Denavit-Hartenberg (DH) model of the upper limbs, compliant with the guidelines of the International Society of Biomechanics. The accuracy of the estimated 3D shoulder and elbow kinematics was evaluated using data recorded by two IMUs attached to a robotic arm during a 20- minute planar exercise and compared with the errors obtained using a model-free approach. The upper limb was modelled as a three-segment chain including trunk, upper arm (UA), and the forearm (FA) (Fig. 1). Shoulder and elbow joints were modelled with three (φ1, φ2, φ3) and two degrees of freedom (φ4, φ6), respectively. The carrying angle (φ5) was introduced as a fixed subject-specific parameter to describe the physiological abduction of the FA with respect to the UA. The joint angles were computed in an optimization process which minimizes at each time step the difference between the orientation predicted using the DH model and the corresponding orientation estimated by a sensor fusion algorithm without the magnetometer [1]. In addition, joint angle constraints were set based on the maximum angular velocity, the physiological angular limits, and the specific range of movement when known a-priori . To validate the methods, two IMUs (Xsens – MTw) were attached to the UA and FA of a robotic arm (Kinova – Jaco2) programmed to mimic a shoulder flexion-extension (φ1, φ2, φ3), an elbow flexion-extension (φ4), and a forearm prono-supination (φ6), simultaneously, for 20 minutes (~150 cycles) [3]. The φ5 was equal to zero for the robot. IMU and reference data were collected at 100 Hz. The gyroscope offset was computed during a preliminary acquisition and then subtracted from the measurements. The accuracy was evaluated in terms of root mean square difference (RMSD) between the model-based optimized and robot reference joint angles. Furthermore, the RMSD corresponding to the Euler inversion of the FA-to-UA relative orientation (model-free) was also computed. The RMSD (deg) for the joint angles obtained with the optimization (model-free) process amounted to 1 (10.2), 0.4 (0.2), 0.9 (1.4), 3.0 (3.1), 0 (8.2), and 1 (6) for φ1, φ2, φ3, φ4, φ5, φ6, respectively. The proposed methods allowed to reduce joint angles errors of almost a factor 10 over long period compared to model-free approach (1.1 vs 8.9 deg on average). These results if further confirmed on human experiments can contribute to the design of tele-rehabilitation applications relying on the accurate upper limb joint kinematics estimates. Funded by Sardegna Ricerche (POR FESR 2014/2020).

Surface pattern formation induced by oscillatory loading of frontally polymerized gels

Frontal polymerization (FP) is a rapid, energy-efficient technique for the manufacturing of polymeric materials and composites. It has also emerged as a way to rapidly alter the shape of partially cured polymeric materials through mechanical deformation. The first objective of this paper is to introduce a coupled thermo-chemo-mechanical theory capable of describing within the framework of nonlinear kinematics the evolution of deformation and temperature fields during frontal polymerization of gels. At the heart of the proposed theory is the introduction of eigenstrains corresponding to the phase transformation prompted by the frontal curing process. Another objective is to introduce a novel bio-inspired oscillatory loading-induced patterning technique in which we form thermoset polymeric materials with periodic surface topography patterns by applying oscillatory uniaxial loads to partially cured gels during FP. After a detailed presentation of the experimental methodology, the theoretical predictions are compared with experimental results.