Grating displacement sensing is regarded as one of the key technologies for achieving cross-scale nanopositioning. This paper proposes a real-time grating sensing correction and compensation technology to enhance the performance of the embedded grating displacement sensor in Cross-scale piezoelectric actuators (CSPAs), thereby enabling nano-scale motion positioning of CSPAs. Firstly, based on the principle of diffracted image reflection, a miniaturized grating sensing unit that can be monolithically integrated with the CSPA structure is designed. Secondly, an online self-correction algorithm based on amplitude iteration is proposed to dynamically eliminate DC offset and amplitude imbalance errors in the signals. Furthermore, a real-time error compensation strategy is constructed to compensate for inherent periodic errors and measurement lag errors of the system induced by stick-slip effects. Experimental results demonstrate that, with the proposed technology, the embedded grating displacement sensor can achieve a detection resolution of 0.9 nm within the full stroke. The CSPA integrated with this sensor achieved a positioning accuracy within ±1.3 nm over its scanning range, and a full-stroke bidirectional positioning consistency of 3.093 ± 1.358 nm.

This paper reports an asymmetric Bouc-Wen (ABW) hysteresis model and a hybrid control algorithm based on multi-modal Bayesian gradient optimization (MBGO) for trajectory tracking in the micro-positioning phase of a piezoelectric positioning platform. First, a system-level dynamic model capable of expressing hysteresis nonlinearity is established based on the asymmetric Bouc-Wen model. Second, an MBGO parameter identification algorithm based on Particle Swarm Optimization (PSO) is proposed to improve the characterization capability of the hysteresis model. Subsequently, a feedforward adaptive fuzzy PID (FF-AFPID) composite controller is designed by compensating the hysteresis nonlinearity through the ABW inverse model while dynamically adjusting PID parameters with adaptive fuzzy rules. Through triangular and sinusoidal trajectory tracking experiments, the root mean square errors were reduced to 0.112 nm and 0.103 nm by the FF-AFPID, with an improvement of 74.944%, 57.088% (triangular) and 77.511%, 57.083% (sinusoidal) over the FF-PID and FF-FPID algorithms, respectively. The results demonstrate that the trajectory tracking of performance the positioning platform in micro-positioning phase was significantly enhanced by FF-AFPID, with the maximum error being suppressed to sub-nanometer levels.

The fabrication of Nanoimprint Lithography (NIL) masters serves as the initial process in the manufacturing of devices such as silicon photonic chips using NIL. Repairing defects and modifying structures on an NIL master accurately is critical for NIL manufacturing. This study proposes an innovative scanning electron microscope (SEM)-based in-situ nanomanipulation technique to address the macro-micro-nano cross-scale nanopositioning issues necessary for repair functions. A macro/micro closed-loop control system was designed, which includes a frequency/voltage (f/u) proportional controller, a real-time direct inverse hysteresis compensation feedforward controller, a grating displacement sensor, and a cross-scale nanopositioning platform. The performance testing of the nano-manipulation approach resulted in a repair range of 22.18×20.92×10.06 mm3, meeting the repair requirements for most silicon photo chip NIL masters in terms of size. The system achieved a repair accuracy at 4.946 nm (X-axis), 4.663 nm (Y-axis), and 4.679 nm (Z-axis). As a demonstrate, the repair functionality testing confirmed the ability of the system to remove target structures such as cantilever beams and detach adhered particles from a master surface.

Contouring control of 3D trajectory is critical in multi-axial machine tool, scanning stage and other precision automation systems. Currently, most contouring controllers are based on Frenet frames, thus their limited applicability to nonzero-curvature 3D trajectories rather than zero-curvature ones which exist ubiquitously in multi-axial motion systems. This paper proposes an optimal vector-referenced moving frame based contouring controller (OVRMFCC) suitable for contouring control of 3D zero-curvature trajectories. Firstly, the optimal vector-referenced moving frame (OVRMF) capable of framing arbitrary finite-length smooth 3D trajectory regardless of its curvature was proposed. Then, a contouring controller (OVRMFCC) based on OVRMF was designed, followed by derivation of its analytical form and proof of its convergence. Finally, this controller was deployed to an FPGA-based controller target with comprehensive comparison experiments on a triaxial system. Experimental results indicates that OVRMFCC reduces at least 46.6% maximum contour error, and 25.0% root-mean-square contour error compared to cross-coupled controller. Besides, OVRMFCC achieves almost the same precision on trajectories with nonzero curvature or curvature singularities compared to TCF. It still maintains high-precision contour tracking on trajectories with continuous zero-curvature segments or planned discrete trajectories with sharp curvature changes, while TCF crashes or leads to several times larger contour error. Note to Practitioners—This work is motivated by the increasing need of contouring control of 3D trajectory in precision automation systems. The mainstream 3D contouring controllers, like task coordinated frame method and model predicted contouring controller, are invalid for zero-curvature 3D trajectory which ubiquitously exists in motion system since they are based on Frenet frame which fails to be defined where curvature is zero. Although cross-coupled controller can tackle zero-curvature 3D trajectory, it proves inefficient in reducing contour error as it is commonly model-free and not specifically designed. To solve this problem, we proposed an optimal vector referenced moving frame (OVRMF) for arbitrary infinite-length smooth 3D trajectory framing and proves its existence strictly. The main advantage of OVRMF lies in its independence of curvature, thus its existence everywhere as long as the curve is$C^{1}$continuous. And based on OVRMF a contouring controller (OVRMFCC) is designed which combines the benefit of both TCF and OVRMF. With this, OVRMFCC can track any infinite-length$C^{3}$-continuous 3D trajectory, which fill the gap of traditional 3D contouring controller. Experimental results reveal that OVRMFCC maintains high precision regardless of trajectory curvature, which outperforms both cross-coupled controller and TCF.

Multi-agent trajectory prediction plays an increasingly critical role in intelligent transportation systems. Despite significant progress in this field, several key challenges remain unresolved. Future trajectories of agents are jointly influenced by individual behavior patterns and the surrounding environment, while most existing methods extract temporal features at a single time scale, limiting their capacity to capture complex temporal dependencies within trajectory sequences. Moreover, many current approaches employ numerically precise formulations for interaction modeling, which are misaligned with the imprecise nature of real-world social behavior. To address these limitations, we propose a multi-agent trajectory prediction model that combines multi-scale temporal encoding and egocentric scan-based spatial representation. Temporally, we leverage a sliding-window-based multi-scale temporal encoder to capture trajectory features across diverse time scales. Spatially, we partition the surrounding environment into multiple egocentric bins to represent social zones, thereby simulating real-world interaction patterns. Experimental results on public benchmark datasets ETH/UCY and SDD demonstrate that our model outperforms existing approaches.

Neural architecture search (NAS) has shown great promise in automatically designing lightweight models. However, conventional approaches are insufficient in training the supernet and pay little attention to actual robot hardware resources. To meet such challenges, we propose RAM-NAS, a resource-aware multi-objective NAS method that focuses on improving the supernet pretrain and resource-awareness on robot hardware devices. We introduce the concept of subnets mutual distillation, which refers to mutually distilling all subnets sampled by the sandwich rule. Additionally, we utilize the Decoupled Knowledge Distillation (DKD) loss to enhance logits distillation performance. To expedite the search process with consideration for hardware resources, we used data from three types of robotic edge hardware to train Latency Surrogate predictors. These predictors facilitated the estimation of hardware inference latency during the search phase, enabling a unified multi-objective evolutionary search to balance model accuracy and latency trade-offs. Our discovered model family, RAM-NAS models, can achieve top-1 accuracy ranging from 76.7% to 81.4% on ImageNet. In addition, the resource-aware multi-objective NAS we employ significantly reduces the model’s inference latency on edge hardware for robots. We conducted experiments on downstream tasks to verify the scalability of our methods. The inference time for detection and segmentation is reduced on all three hardware types compared to MobileNetv3-based methods. Our work fills the gap in NAS for robot hardware resource-aware.

This paper presents a precision centimeter-range positioner based on a Lorentz force actuator using flexure guides. An additional digital-to-analog converter and an operational amplifier (op amp) circuit together with a suitable controller are used to enhance the positioning accuracy to the nanometer level. First, a suitable coil is designed for the actuator based on the stiffness of the flexure guide model. The flexure mechanism and actuator performance are then verified with finite element analysis. Based on these, a means to enhance the positioning performance electronically is presented together with the control scheme. Finally, a prototype is fabricated, and the performance is evaluated. This positioner features a range of 10 mm with a resolution of 10 nm. The proposed scheme can be extended to other systems.

As a wearable robot, an exoskeleton provides a direct transfer of mechanical power to assist or augment the wearer’s movement with an anthropomorphic configuration. When an exoskeleton is used to facilitate the wearer’s movement, a motion generation process often plays an important role in high-level control. One of the main challenges in this area is to generate in real time a reference trajectory that is parallel with human intention and can adapt to different situations. In this paper, we first describe a novel motion modeling method based on probabilistic movement primitive (ProMP) for a lower limb exoskeleton, which is a new and powerful representative tool for generating motion trajectories. To adapt the trajectory to different situations when the exoskeleton is used by different wearers, we propose a novel motion learning scheme based on black-box optimization (BBO) PI BB combined with ProMP. The motion model is first learned by ProMP offline, which can generate reference trajectories for use by exoskeleton controllers online. PI BB is adopted to learn and update the model for online trajectory generation, which provides the capability of adaptation of the system and eliminates the effects of uncertainties. Simulations and experiments involving six subjects using the lower limb exoskeleton HEXO demonstrate the effectiveness of the proposed methods.

We proposed a lower extremity exoskeleton for power amplification that perceives intended human motion via humanexoskeleton interaction signals measured by biomedical or mechanical sensors, and estimates human gait trajectories to implement corresponding actions quickly and accurately. In this study, torque sensors mounted on the exoskeleton links are proposed for obtaining physical human-robot interaction (pHRI) torque information directly. A Kalman smoother is adopted for eliminating noise and smoothing the signal data. Simultaneously, the mapping from the pHRI torque to the human gait trajectory is defined. The mapping is derived from the real-time state of the robotic exoskeleton during movement. The walking phase is identified by the threshold approach using ground reaction force. Based on phase identification, the human gait can be estimated by applying the proposed algorithm, and then the gait is regarded as the reference input for the controller. A proportional-integral-derivative control strategy is constructed to drive the robotic exoskeleton to follow the human gait trajectory. Experiments were performed on a human subject who walked on the floor at a natural speed wearing the robotic exoskeleton. Experimental results show the effectiveness of the proposed strategy.

In robot-assisted upper limb rehabilitation, detecting the intentions of hemiplegic patients is essential towards assisting the patients to actively exercise instead of driving passive motions. Many interactive channels, such as voice, EMG and EEG, have been studied to estimate the motion intentions. However, limitations of these techniques, such as high complexity, have constrained their applications in practice. In this paper, we integrate a virtual environment and a low-cost motion sensor into a novel control strategy to detect motion intentions for a rehabilitation robot. Several bimanual motion sequences are intuitively programmed by a professional therapist for subjects to repeat. The strategy uses the unaffected arm and the programmed motion sequence to estimate the motion intentions of the affected arm. We adopt this strategy in Mirror Therapy, a widely-practised therapeutic intervention method. Experiments have been conducted to validate the control strategy.

The kinematic modelling has been applied to many controllers of under-actuated manipulators. Most of these studies assume that the control process is conducted within the workspace. However, as such a kinematic model cannot describe the situations when the stable grasping is violated in the real environment, these controllers may fail unexpectedly. In this paper, we propose a combination of kinematics based Workspace Analysis (WA) and Gaussian Process Classification (GPC) to model the success rates of control actions in the theoretical workspace. We also use the Gaussian Process Regression (GPR) to model the residual between the prediction of the WA and the ground truth data. We then apply this integrated model, Gaussian Processes enhanced Workspace Analysis (GP-WA), into an optimal controller. The optimal controller is implemented on a planar under-actuated gripper with two three-phalanx fingers. Two sets of simulation experiments are carried out to validate our method. The results demonstrate that the optimal manipulation controller based on GP-WA achieves high control accuracy for manipulating a wide range of objects.

Many recent studies describe micromanipulation systems by using complex Analytic Forward Models (AFM), but such models are difficult to build and incapable of describing unmodelable factors, such as manufacturing defects. In this work, we propose the Enhanced Analytic Forward Model (EAFM), an integrated model of the AFM and the Heteroscedastic Gaussian Processes (HGP). The EAFM can compensate the shortfalls of the AFM by training the HGP on the residual of the AFM. This also allows the HGP to learn the repeatability of the micromanipulation system. Based on the EAFM, we further contribute an optimal position controller for improving the accuracy and the repeatability. This optimal EAFM controller is implemented and tested on a three degree-of-freedom micromanipulator based micromanipulation system. Two sets of real-world experiments are carried out to verify our method. The results demonstrate that the controller using EAFM can statistically achieve higher accuracy and repeatability than solely using the AFM.

A high-precision pointing mechanism for intersatellite optical communication is presented in this paper, which employs flexure hinges as the passive joints and orients its moving platform by means of six piezoelectric actuators. The proposed mechanism features submicroradian resolution and microradian repeatability within a submilliradian pointing range. A corner-filleted flexure hinge considering manufacturing tolerance with high motion accuracy and large displacement is designed based on performance analysis, and the theoretical analysis is also validated by FEA simulation and experimental testing within a 5% error margin. The parameters of the parallel mechanism are synthesized by multiobjective optimization on the basis of inverse kinematics model. Moreover, the workspace determination and the FEA analysis of the optimized mechanism are also performed. Finally, a high-precision pointing prototype is fabricated and the performance testing has been implemented, which validate the effectiveness of the proposed system.

A continuum manipulator with triangular notches is proposed for the potential medical applications, which is driven in the plane by wires embedded in bilateral symmetry channels. The focus of this present research is a mechanics-based kinematic model of the proposed continuum manipulator using the Timoshenko beam theory to map the driven load to the manipulator shape. In the proposed model, the continuum manipulator is divided into several V-shape units, each of which consists of two 2-node Timoshenko beam elements. Compared with previous approaches, our proposed model discards the constant curvature approximation, in which the distributed force caused by the interface contact between the wire and the V-shape unit is also considered simultaneously. The proposed mechanics model is validated experimentally on a segment of Nitinol flexible manipulator, which illustrates the effectiveness of our model to describe the continuum manipulator shaping.

Full parallel manipulator systems with flexure hinges are widely applied in many fields demanding high precision, whose dynamic performances influence the system positioning accuracy, so it is very necessary to analyze the structural dynamics in detail. The dynamic analyses of this kind of systems are relatively complex for they belong to spatial multi-loops mechanisms. In this paper, a simplified modeling is proposed which can estimates the natural characters. This method can provide an effective generalized model to estimate lower natural frequencies of the parallel manipulators with flexure hinges needless to deduce the complicated dynamics model. Firstly, the system kinematics model is established, based on which the flexibility matrix is deduced. And then the dynamics equation can be formulated, via which the lower natural frequencies can be calculated. It is validated that the method proposes an effective and concise estimating model for generalized parallel manipulator systems' dynamics characters

Although micro-motion parallel manipulators have been used widely, their special architectures make it impossible to be applied in the condition demanding relative large workspace. In this paper, a novel large workspace flexure hinge-based parallel manipulator is presented, which can attain the sub-micron scale accuracy over the cubic centimeter workspace. This manipulator system is a 6-PSS parallel mechanism, in which piezoceramic motors actuate the prismatic joints, precision linear encoders detect the actuation displacements, and as the key technology, a kind of novel wide-range flexure hinges are utilized as passive spherical joints. Because of the adoption of wide-range flexure hinges, the whole system features large workspace differing from the conventional micro-motion flexure hinge-based parallel manipulators. The kinematics analysis of the whole mechanism is performed based on the stiffness model of the flexure hinges via FEM theory. The wide-range flexure hinges move over large space during the course of self-deformation, so the kinematics model is a typical geometrical nonlinear problem. In this paper, a Newton-Raphson increment iterative scheme for the kinematics model solution procedure of the whole mechanism is presented based on Updated Lagrange Formulation. And finally, the numerical calculation results about the theoretical model are given.

Parallel-structure flexure mechanisms are increasingly designed due to their superior characteristics. This paper explores a novel six degree-of-freedom large workspace flexure parallel mechanism based on the concept of wide-range flexure hinge, which can attain sub-micron scale accuracy over cubic centimeter motion range. The geometric dimensions of the flexure hinges utilized in this mechanism as passive joints will influence the system stiffness directly and other properties indirectly such as the workspace, load-carrying capacity, and driving-load capacity etc. In this paper, the stiffness model of individual flexure hinge is established firstly, and then the stiffness of the whole flexure mechanism is modeled via assembling stiffness matrices and formulating constraint equations. Based on the system stiffness model of the whole mechanism, the stiffness atlases' analysis is presented which provides theoretical principles for designing and developing this kind of flexure parallel mechanism in further. Finally, a 6-PSS large workspace flexure parallel mechanism prototype is proposed according to the analysis results, which will be utilized in the precision positioning.