Position Trajectory Research Articles

ANN Enhanced Hybrid Force/Position Controller of Robot Manipulators for Fiber Placement

In practice, most industrial robot manipulators use PID (Proportional + Integral + Derivative) controllers, thanks to their simplicity and adequate performance under certain conditions. Normally, this type of controller has a good performance in tasks where the robot moves freely, performing movements without contact with its environment. However, complications arise in applications such as the AFP (Automated Fiber Placement) process, where a high degree of precision and repeatability is required in the control of parameters such as position and compression force for the production of composite parts. The control of these parameters is a major challenge in terms of quality and productivity of the final product, mainly due to the complex geometry of the part and the type of tooling with which the AFP system is equipped. In the last decades, several control system approaches have been proposed in the literature, such as classical, adaptive or sliding mode control theory based methodologies. Nevertheless, such strategies present difficulties to change their dynamics since their design consider only some set of disturbances. This article presents a novel intelligent type control algorithm based on back-propagation neural networks (BP-NNs) combined with classical PID/PI control schemes for force/position control in manipulator robots. The PID/PI controllers are responsible for the main control action, while the BP-NNs contributes with its ability to estimate and compensate online the dynamic variations of the AFP process. It is proven that the proposed control achieves both, stability in the Lyapunov sense for the desired interaction force between the end-effector and the environment, and position trajectory tracking for the robot tip in Cartesian space. The performance and efficiency of the proposed control is evaluated by numerical simulations in MATLAB-Simulink environment, obtaining as results that the errors for the desired force and the tracking of complex trajectories are reduced to a range below 5% in root mean square error (RMSE).

Particles with constant speed and random velocity in 3+1 space-time: separation of the space variables

We consider a particle in 3+1 space-time dimensions which constantly moves at speed of light c, randomly changing its velocity which can be represented by a point on the surface of a sphere of radius c. The velocity performs an isotropic Wiener process on this surface so that the velocity trajectories are almost everywhere continuous although not differentiable, while the position trajectories are continuous and differentiable. Together with the process that describes the particle in the ‘rest frame’, where the diffusion of velocity on the surface of the sphere is isotropic, the entire family of anisotropic processes which result from Lorentz boosts is also described. The focus of this article is on stochastic evolution in space. We identify a reduced set of variables whose stochastic evolution is autonomous from the remaining variables, but, nevertheless, carry all the relevant information concerning the spatial properties of the process. The associated stochastic equations as well the Forward Kolmogorov equation are considerably simplified compared to those of the complete set of position and velocity variables.

Dynamics modeling and nonlinear attitude controller design for a rocket-type unmanned aerial vehicle

This paper presents an altitude and attitude control system for a newly designed rocket-type unmanned aerial vehicle (UAV) propelled by a gimbal-based coaxial rotor system (GCRS) enabling thrust vector control (TVC). The GCRS is the only means of actuation available to control the UAV’s orientation, and the flight dynamics identify the primary control difficulty as the highly nonlinear and tightly coupled control distribution problem. To address this, the study presents detailed derivations of attitude flight dynamics and a control strategy to track the desired attitude trajectory. First, a Proportional-Integral-Derivative (PID) control algorithm is developed based on the formulation of linear matrix inequality (LMI) to ensure robust stability and performance. Second, an optimization algorithm using the Levenberg–Marquardt (LM) method is introduced to solve the nonlinear inverse mapping problem between the control law and the actual actuator outputs, addressing the nonlinear coupled control input distribution problem of the GCRS. In summary, the main contribution is the proposal of a new TVC UAV system based on GCRS. The PID control algorithm and LM algorithm were designed to solve the distribution problem of the actuation model and confirm altitude and attitude tracking missions. Finally, to validate the flight properties of the rocket-type UAV and the performance of the proposed control algorithm, several numerical simulations were conducted. The results indicate that the tightly coupled control input nonlinear inverse problem was successfully solved, and the proposed control algorithm achieved effective attitude stabilization even in the presence of disturbances.

Event-triggered adaptive deep neural network sliding mode control design for unmanned aerial vehicle systems

This paper proposes an event-triggered adaptive attitude maneuver control strategy based on deep neural network (DNN) for quadcopters to improve the attitude tracking performance and attenuate the effects of nonlinear and uncertainty. First, we establish an affine function model constructed to describe the dynamic model of quadcopters. The DNN trained over a large time scale is used to approximate the nonlinearity and uncertainty. Then, an adaptive sliding mode control (SMC) is combined with the DNN to achieve accurate tracking of attitude trajectory. Furthermore, to reduce unnecessary data transmissions, an event-trigger mechanism is combined with the designed DNN-driven adaptive SMC. The Lyapunov-based stability analysis is used to prove the stability of the closed loop system. The simulation results show the good performance of the proposed control strategy.

The prototype of the front-end electronics for STCF muon detector

The muon detector (MUD), serving as the outermost detector of the high-precision spectrometer in STCF, is used to provide muon identification in the presence of a significant pion background. The accuracy of muon identification relies heavily on excellent momentum resolution, which can be determined by their flight trajectory positions. Physical simulations of the measurement precision for reconstructed muons indicate that a spatial resolution of 2 cm is required. In the barrel MUD, a double-ended readout is required to determine the hit position, with a time resolution requirement of approximately 500 ps. To meet the readout requirements of the MUD, a comprehensive scheme for the front-end readout electronics of the STCF MUD is proposed, and a prototype of MUD readout electronics is developed. To validate the final 8-channel application-specific integrated circuit (ASIC) design, an 8-channel time-to-digital converter (TDC) is implemented using a field programmable gate array (FPGA). The results of the electronics tests demonstrate that the average root mean square (RMS) precision ranges from 14 to 16 ps for each channel within a 1∼20 ns time interval. In the joint test with the detector, the system achieves a single-channel RMS precision of 297 ps, with a detection efficiency exceeding 95.5%. All indicators meet project requirements. This validates that the prototype is capable of preliminary evaluation and parameter optimization of the STCF MUD.

Neural Tracking of Perceived Parent, but Not Peer, Norms Is Associated with Longitudinal Changes in Adolescent Attitudes about Externalizing Behaviors.

Adolescents' perceptions of parent and peer norms about externalizing behaviors influence the extent to which they adopt similar attitudes, yet little is known about how the trajectories of perceived parent and peer norms are related to trajectories of personal attitudes across adolescence. Neural development of midline regions implicated in self-other processing may underlie developmental changes in parent and peer influence. Here, we examined whether neural processing of perceived parent and peer norms in midline regions during self-evaluations would be associated with trajectories of personal attitudes about externalizing behaviors. Trajectories of adolescents' perceived parent and peer norms were examined longitudinally with functional neuroimaging (n = 165; ages 11-16 years across three waves; 86 girls, 79 boys; 29.7% White, 21.8% Black, 35.8% Latinx, 12.7% other/multiracial). Behavioral results showed perceived parent norms were less permissive than adolescents' own attitudes about externalizing behaviors, whereas perceived peer norms were more permissive than adolescents' own attitudes, effects that increased from early to middle adolescence. Although younger adolescents reported less permissive attitudes when they spontaneously tracked perceived parent norms in the ventromedial and medial pFCs during self-evaluations, this effect weakened as they aged. No brain-behavior effects were found when tracking perceived peer norms. These findings elucidate how perceived parent and peer norms change in parallel with personal attitudes about externalizing behaviors from early to middle adolescence and underscore the importance of spontaneous neural tracking of perceived parent norms during self-evaluations for buffering permissive personal attitudes, particularly in early adolescence.

Numerical and experimental study of tip leakage vortex structures and dynamics in a mixed-flow waterjet pump

Abstract Driven by the pressure difference between pressure side and suction side, the tip leakage flow (TFL) and forms the tip leakage vortex (TLV), which interact with main flow and leads to instability of flow as well as cavitation in a mixed-flow waterjet pump. In this work, the characteristics of TLV are investigated by experiment based on high-speed photography (HSP) and the numerical simulation. The TLV trajectories predicted by numerical simulation’s shows a good agreement with the experiment test results. Cavitation of the TLV core due to low pressure forms a TLV core cavitation trajectory line. The start position of TLV trajectories and the relative angle between the TLV trajectory and the blade are significantly affected by the blade tip loading. The unsteady primary tip leakage vortex (PTLV) evolution process can be summarised in three stages of splitting, developing, and collapsing.

Transient instability characteristics of fluid film bearings induced by bubble inclusion

Bubble inclusion in the tribo-pair leads to two-phase fluid lubrication. Upon the initial introduction of air bubbles to the tribo-pair, it can lead to instability in the operational state. A numerical model is formulated by coupling the fluid Reynolds equation, bubble dynamics equation, and rotor dynamics equation. Various parameters, such as hydrodynamic pressure, fluid carrying capacity, rotor trajectory, and equilibrium position, are employed to characterize the impact of operational and fluid interface parameters on the bubble entrainment process. The findings reveal that the hydrodynamic pressure plays a crucial role in establishing the correlation between velocity and fluctuations in kinetic parameters. Surface tension predominantly influences bubbles within the dispersion region, while surface dilatational viscosity affects the entire domain. Lower surface dilatational viscosity or neglecting surface tension can trigger larger fluctuations in the rotor trajectory. Changes in liquid-phase viscosity result in fluctuations in bubble behavior and dynamics parameters, influenced by the equilibrium position and the effect of hydrodynamic pressure. Higher initial gas-phase volume fractions lead to a more pronounced reduction in fluid-carrying capacity and increased system instability.

Scalable-produced micro-elastic triboelectric sensing ground for all-weather large-scale applications

Large-scale smart floor will be one potential form for a human–machine interface, but is also facing many difficulties and challenges in the materials science and information engineering. Here, we present a straightforward method for fabricating large-scale micro-elastic triboelectric nanogenerator (ME-TENG) arrays, which can be assembled as triboelectric sensing ground for real-time interface interaction and all-weather monitoring. ME-TENG unit of 6 by 6 arrays offers a maximum power density of 5.84 mW·m−2 under a load resistance of 70 MΩ. It displays a sensitivity of 132.48 mV/kPa in the pressure range of 0 to 180 kPa. The electrical output signals have remained stable in the durability tests over 10 days with various weather conditions. Furthermore, successfully demonstrated a regional comprehensive security monitoring (RCSM) system for enabling all-weather monitoring of the features, position, and movement trajectories of an object. Even more gratifying is that the ME-TENG unit of 30 cm by 30 cm prepared by a simple and fast “all-in-one” method, takes costs of 14.84 dollars/m2 and the production period of 2 h, compared with the most cost-effective piezoelectric sensors, the cost and duration were reduced by 98.67 % and 42.86 %, respectively. This work will greatly contribute to the development and implementation of large-scale sensing and the multidimensional security system.

Nanoscopic Imaging of Self-Propelled Ultrasmall Catalytic Nanomotors.

Ultrasmall nanomotors (<100 nm) are highly desirable nanomachines for their size-specific advantages over their larger counterparts in applications spanning nanomedicine, directed assembly, active sensing, and environmental remediation. While there are extensive studies on motors larger than 100 nm, the design and understanding of ultrasmall nanomotors have been scant due to the lack of high-resolution imaging of their propelled motions with orientation and shape details resolved. Here, we report the imaging of the propelled motions of catalytically powered ultrasmall nanomotors─hundreds of them─at the nanometer resolution using liquid-phase transmission electron microscopy. These nanomotors are Pt nanoparticles of asymmetric shapes ("tadpoles" and "boomerangs"), which are colloidally synthesized and observed to be fueled by the catalyzed decomposition of NaBH4 in solution. Statistical analysis of the orientation and position trajectories of fueled and unfueled motors, coupled with finite element simulation, reveals that the shape asymmetry alone is sufficient to induce local chemical concentration gradient and self-diffusiophoresis to act against random Brownian motion. Our work elucidates the colloidal design and fundamental forces involved in the motions of ultrasmall nanomotors, which hold promise as active nanomachines to perform tasks in confined environments such as drug delivery and chemical sensing.

Cueing Flight Object Trajectory and Safety Prediction Based on SLAM Technology

With the rapid development of artificial intelligence and robot technology, SLAM technology, as a key component, has been paid more and more attention. SLAM technology enables robots to autonomously navigate, build maps, and achieve accurate positioning in unknown environments, providing strong support for the autonomy and intelligence of robots and unmanned vehicles. In this paper, the position prediction method of flying object based on SLAM technology and the application of EvolveGCN model in behavior prediction are introduced. First, through the fusion of SLAM technology and liDAR data, we can accurately predict the position and movement trajectory of flying objects, thereby improving the safety and efficiency of the system. Secondly, with the EvolveGCN model, we are able to capture dynamic changes in the environment and achieve accurate predictions of the behavior of flying objects. Through experimental verification, the prediction accuracy of our method has been significantly improved in both simulation and real environment, which indicates the feasibility and effectiveness of the method in practical application, and provides an important reference and technical support for the development of autonomous navigation, aerial surveillance and other fields.

Mobilising new frontiers in digital transformation research: A problematization review

AbstractIn this paper, we mobilise new frontiers in digital transformation (DT) research by deconstructing the literature's underlying assumptions and analysing their correspondence with current theory. To do so, we conduct a problematization review across the fields of IS, strategy and entrepreneurship, organisation theory and management studies, to capture the multidimensionality of DT research. Unlike systematic literature reviews commonly found in DT research, a problematization review critically questions how theoretical contributions have been constructed in past research to develop novel theoretical questions. Our findings offer three contributions. First, we uncover five research trajectories, each with its own in‐house assumptions about the nature of digital technologies and how organisations, groups and individuals interact with those technologies and the data they generate. Second, we show how individual studies within the identified research trajectories position themselves against prior research, pointing at six distinct processes of constructing theoretical contributions. Finally, we mobilise new frontiers of research by questioning DT research field assumptions that cut across the five research trajectories. We conclude by discussing the theoretical implications of our problematization review for further DT research.

The configuration and detection characteristics of a 3D holographic LWD instrument antenna system based on multi-component EM induction theory

In order to improve the precision of electromagnetic resistivity logging, while ensuring the reliability of boundary detection and anisotropy identification, this study starts from the theory of multi-component electromagnetic induction. Through deriving the formula of component responses, the analytical expressions of electromagnetic wave fields are obtained for various coil pairs of azimuthal instruments. A 3D holographic LWD instrument antenna configuration is constructed, with co-planar and non-coplanar, tilted and orthogonal coil combinations, all components of the magnetic field tensor Hxx,Hxy,Hxz,Hyx,Hyy,Hyz,Hzx,Hzx,HzzHxx,Hxy,Hxz,Hyx,Hyy,Hyz,Hzx,Hzx,Hzz are successfully resolved. With the combination of some or all components, functional signals are generated for specific detection objectives. Numerical simulation of the response of those signals shows that the distance of boundary detection and anisotropy identification capability are superior to traditional geo-signals. The response relationship shows that as the contrast of the formation resistivity decreases and the well deviation angle increases, the anisotropy identification capability gradually strengthens, and the signal strength and identification capability are better at frequency in the range of 400–600 kHz. The proposed antenna configuration has the potential to significantly enhance formation evaluation and to improve geo-steering capability, contributing technology-wise to the optimal landing trajectory and best wellbore position, resulting in cost saving and maximize oil & gas production.

Imitating via manipulability: Geometry-aware combined DMP with via-point and speed adaptation

Manipulability ellipsoid on the Riemannian manifold provides an effective criterion for guiding the regulation of robot postures in an efficient and natural manner. While many manipulability-based learning by demonstration (LbD) methods achieve favorable performance in human-like manipulation, the adaptation of movement skills encapsulated in Symmetric Positive Definite (SPD) matrices and the implementation of manipulability-relevant skills related to velocity scaling are still largely open. In this paper, we develop a new framework based on Geometry-Aware Combined Dynamic Movement Primitives (GA-CDMP) for learning movement skills from demonstrations. The GA-CDMP model not only adapts learned trajectories to SPD-via-points but also establishes a correlation between SPD-based trajectories and position trajectories, enabling the adaptation of learned SPD-based trajectories across different non-linear motion velocity scales. The experimental evaluations show that our proposed method exhibits remarkable precision in passing through via-points positioned at a distance from the demonstration, while also substantially improving the invariant shape similarity in manipulability by approximately 65% compared to extended Kernelized Movement Primitives (KMPs). Moreover, our velocity adaptive approach achieves impressive success rates of up to 94% and demonstrates a notable reduction in execution time (11.72 ± 0.45 s), representing an almost 10% decrease in comparison to the state-of-the-art method employed in water-carrying tasks.

Convex distributed robust model predictive control for collision and obstacle avoidance

AbstractThe article proposes a convex distributed robust model predictive control algorithm for collision avoidance in multi‐agent systems with additive disturbances, which utilizes the concept of tube model predictive control to address the disturbances. To tackle the coupled collision avoidance constraints, each agent integrates assumed nominal position and input trajectories from its neighbors, rather than relying on actual ones. Compatibility constraints are then designed using the normal vector of the constructed separating hyperplanes for collision avoidance and the residual collision avoidance margin of the optimal solutions at the last time step to restrict deviations between assumed and actual trajectories and ensure consistency among the agents. The nonconvex collision and obstacle avoidance constraints are convexified using the concept of safe sets, transforming them into time‐varying closed polyhedral constraints while accounting for the impact of disturbances. Particularly, the residual collision avoidance margin is incorporated into the construction of safe sets for the satisfaction of coupled collision avoidance constraints. Consequently, the original collision avoidance optimal control problems can be efficiently and simultaneously solved for all agents using standard quadratic programming techniques. Under the design of local terminal constraint sets for each agent, the proposed algorithm ensures a rigorous analysis of robust constraint satisfaction for collision avoidance constraints, as well as an assessment of recursive feasibility and closed‐loop stability. The effectiveness of the algorithm is demonstrated through an illustrative example, and simulation results validate its ability to successfully achieve collision and obstacle avoidance even in the presence of disturbances.

A virtual reference trajectory scheme for tracking control of wheeled mobile robots with slip disturbances

The problem of trajectory tracking control for wheeled mobile robots with slip disturbances is investigated in this paper. A virtual reference trajectory scheme is proposed based on disturbance compensations. Different from the conventional design philosophy of adding disturbance compensations to the controller, the proposed scheme integrates slip disturbance estimates into the reference trajectory. The proposed scheme consists of two parts. First, the slip disturbance estimates and the reference trajectory are fused to generate a virtual reference trajectory, where the slip disturbances are estimated using a disturbance approximation method. Second, a controller is designed to enable the wheeled mobile robot’s odometry position and yaw angle track the virtual reference trajectory, and then the actual position of the wheeled mobile robot track the position of the real reference trajectory. Comparison simulations and experiments are provided to illustrate the validity and advantages of the proposed scheme.

Research on trajectory learning and modification method based on improved dynamic movement primitives

Traditional robot trajectory planning and programming methods often struggle to adapt to changing working requirements, leading to repeated programming in manufacturing processes. To address these challenges, a trajectory learning and modification method based on improved Dynamic Movement Primitives (DMPs), called FDC-DMP, is proposed. The method introduces an improved force-controlled dynamic coupling term (FDCT) that uses virtual force as coupling force. This enhancement enables precise and flexible shape modifications within the target trajectory range. The paper also dissects the core dynamic systems of DMP to achieve the reproduction and generalization of both robot position and pose trajectories. The practical feasibility of the proposed method in manufacturing is demonstrated through two case studies on trajectory planning for bus body polishing.

AI-Driven solid-state device to enable natural control of upper-extremity robotic exoskeletons

The paper introduces the ‘Smart Handle’, a compact, lightweight, fully solid-state control device based on machine learning (ML) models, enabling intuitive manipulation across all three degrees-of-freedom (DOF) of translation and orientation. This handle-type control device utilizes low-cost, easily accessible sensors and materials. It addresses the need for natural control mechanisms in upper-extremity exoskeletons used in rehabilitation and occupational settings, offering position and orientation trajectories crucial for kinematic models. Additionally, individuals with motor difficulties using mobility chairs could benefit from its ability to learn and interpret diverse inputs as control commands. Through empirical study, it was demonstrated that the device adeptly learns and outputs continuous translation and rotation information from training sets focused on single DOF motions. Personalized calibration enables consistent movement classification with over 95% accuracy. Successful proof-of-concept experiments were conducted in exoskeleton and wheeled robot control applications, showcasing the Smart Handle's adaptability and reliability in diverse settings. This research underscores the device's potential across multiple domains, providing a foundation for enhanced intuitive control mechanisms in various technological applications.

The dynamic outcomes of service recovery in tourism services: A latent growth modeling approach

The cross-sectional focus of service recovery literature can lead to a static effect or snapshot view of consumer responses; however, the dynamic outcomes of service recovery should play an important role in driving performance. Using a longitudinal approach, this study focuses on the dynamic changes in customers’ psychological responses during service recovery events. This study provides strong evidence that customer forgiveness and attitudes shift positively when service recovery efforts increase rapidly. We also no difference in the initial value of customer forgiveness, whereas its rate of change rises sharply toward behavioral intentions. Interestingly, the mean of customer forgiveness drops between T1 and T2 and increases sharply again at T3 (constituting a U-shaped curve). In contrast, the mean of customer attitudes steadily increases from T1 to T3. Therefore, we demonstrate that customer forgiveness and attitude trajectories vary, and their impacts can be extended or diluted during service recovery events. Thus, our research highlights that dynamic pathways are essential because they significantly influence behavioral intentions beyond the impact of the static levels, resulting in dynamic pathways playing a fundamental role in driving recovery performance.

Comparing Video Analysis to Computerized Detection of Limb Position for the Diagnosis of Movement Control during Back Squat Exercise with Overload.

Incorrect limb position while lifting heavy weights might compromise athlete success during weightlifting performance, similar to the way that it increases the risk of muscle injuries during resistance exercises, regardless of the individual's level of experience. However, practitioners might not have the necessary background knowledge for self-supervision of limb position and adjustment of the lifting position when improper movement occurs. Therefore, the computerized analysis of movement patterns might assist people in detecting changes in limb position during exercises with different loads or enhance the analysis of an observer with expertise in weightlifting exercises. In this study, hidden Markov models (HMMs) were employed to automate the detection of joint position and barbell trajectory during back squat exercises. Ten volunteers performed three lift movements each with a 0, 50, and 75% load based on body weight. A smartphone was used to record the movements in the sagittal plane, providing information for the analysis of variance and identifying significant position changes by video analysis (p < 0.05). Data from individuals performing the same movements with no added weight load were used to train the HMMs to identify changes in the pattern. A comparison of HMMs and human experts revealed between 40% and 90% agreement, indicating the reliability of HMMs for identifying changes in the control of movements with added weight load. In addition, the results highlighted that HMMs can detect changes imperceptible to the human visual analysis.