This paper deals with the kinematic calibration of a two-axis over-constrained solar tracker. In an over-constrained mechanism, it is difficult to derive the constraint among geometric errors and then the accurate error model can not be achieved. This paper presents a new error modeling method with considering constraints among geometric errors to derive a complete, continuous and minimal model. The constraint among geometric errors are derived from an algebraic approach with strict theoretical proof. Furthermore, an iterative Tikhonov regularization method is presented for parameter identification and the iteration termination condition is related to the measurement noise. The traditional Tikhonov regularization method is a posterior method that depends on the measurement data in calibration, while the proposed regularization method is a prior method with clear physical meaning. The kinematic calibration of the two-axis solar tracker is used to validate the proposed methods. The orientation error of the solar tracker is reduced by more than 95% after kinematic calibration.Note to Practitioners—The geometric error constraints are proven in the article to exist in an over-constrained two-axis solar tracker, which leads to the unsuitability of previous calibration methods. With our proposed method, the accuracy of the solar tracker has been improved by 95% after calibration. In our calibration, the error modeling method can be extended to other over-constrained robots, and the Tikhonov regularization method has good stability and accuracy in error identification. In future research, we will address the problem of deformation and joint clearance in kinematic calibration.

Large-scale pre-training has proven to be an effective method for improving performance across different tasks. Current person search methods use ImageNet pre-trained models for feature extraction, yet it is not an optimal solution due to the gap between the pre-training task and person search task (as a downstream task). Therefore, in this paper, we focus on pre-training for person search, which involves detecting and re-identifying individuals simultaneously. Although labeled data for person search is scarce, datasets for two sub-tasks person detection and re-identification are relatively abundant. To this end, we propose a hybrid pre-training framework specifically designed for person search using sub-task data only. It consists of a hybrid learning paradigm that handles data with different kinds of supervisions, and an intra-task alignment module that alleviates domain discrepancy under limited resources. To the best of our knowledge, this is the first work that investigates how to support full-task pre-training using sub-task data. Extensive experiments demonstrate that our pre-trained model can achieve significant improvements across diverse protocols, such as person search method, fine-tuning data, pre-training data and model backbone. For example, our model improves ResNet50 based NAE by 10.3% relative improvement w.r.t. mAP. Our code and pre-trained models are released for plug-and-play usage to the person search community (https://github.com/personsearch/PretrainPS).

This paper proposes a novel contact force sensing and control method for the inserting operation during precise assembly process, which is based on a micromanipulator integrated with force sensors. At first, theoretical analysis is carried out to calculate the admissible contact force between the gripped holes and the pegs. The contact force thresholds which are smaller than the admissible contact forces are adopted in the control algorithm to avoid the rotating of the gripped holes during assembly process. The force sensors are calibrated using an ATI force sensor and the conversing coefficients are calculated. The admissible contact forces are tested when different contact distance and preload force are adopted. The performance of the proposed contact force sensing and control method is verified by carrying out the task of applying contact force on the surface of the gripped holes with different contacting speeds. The results indicate that the contact force can be adjusted to be smaller than the threshold 1 and the peg-in-hole assembly can be completed successfully.Note to Practitioners—This paper proposes a novel contact force sensing method during the inserting operation. Compared with the traditional contact force sensing method, this paper adopts the force sensor integrated into the micromanipulator instead of commercial force sensor to detect the contact force between two parts. To ensure the assembling precision, the theoretical analysis is conducted to calculated the admissible contact force to avoid the sliding and rotating of the gripped micro part during assembling. This work efficiently simplifies the contact force sensing and control process, where complex calibration process needn’t to be carried out to eliminate the influence of the mass of the micromanipulator on the testing results. In addition, the assembling costs are reduced by replacing commercial force sensors with strain gauges.

Person search is an integrated task of multiple sub-tasks such as foreground/background classification, bounding box regression and person re-identification. Therefore, person search is a typical multi-task learning problem, especially when solved in an end-to-end manner. Recently, some works enhance person search features by exploiting various auxiliary information, e.g. person joint keypoints, body part position, attributes, etc., which brings in more tasks and further complexifies a person search model. The inconsistent convergence rate of each task could potentially harm the model optimization. A straightforward solution is to manually assign different weights to different tasks, compensating for the diverse convergence rates. However, given the special case of person search, i.e. with a large number of tasks, it is impractical to weight the tasks manually. To this end, we propose a Grouped Adaptive Loss Weighting (GALW) method which adjusts the weight of each task automatically and dynamically. Specifically, we group tasks according to their convergence rates. Tasks within the same group share the same learnable weight, which is dynamically assigned by considering the loss uncertainty. Experimental results on two typical benchmarks, CUHK-SYSU and PRW, demonstrate the effectiveness of our method.

Text-to-image synthesis is a challenging task that generates realistic images from a textual description. The description contains limited information compared with the corresponding image and is ambiguous and abstract, which will complicate the generation and lead to low-quality images. To address this problem, we propose a novel generation text-to-image synthesis method, called RiFeGAN2, to enrich the given description. To improve the enrichment quality while accelerating the enrichment process, RiFeGAN2 exploits a domain-specific constrained model to limit the search scope and then uses an attention-based caption matching model to refine the compatible candidate captions based on constrained prior knowledge. To improve the semantic consistency between the given description and the synthesized results, RiFeGAN2 employs improved SAEMs, SAEM2s, to compact better features of the retrieved captions and effectively emphasize the descriptions via incorporating centre-attention layers. Finally, multi-caption attentional GANs are exploited to synthesize images from those features. Experiments performed on widely-used datasets show that the models can generate vivid images from enriched captions and effectually improve the semantic consistency.

Text-to-image synthesis is a challenging task that generates realistic images from a textual sequence, which usually contains limited information compared with the corresponding image and so is ambiguous and abstractive. The limited textual information only describes a scene partly, which will complicate the generation with complementing the other details implicitly and lead to low-quality images. To address this problem, we propose a novel rich feature generating text-to-image synthesis, called RiFeGAN, to enrich the given description. In order to provide additional visual details and avoid conflicting, RiFeGAN exploits an attention-based caption matching model to select and refine the compatible candidate captions from prior knowledge. Given enriched captions, RiFeGAN uses self-attentional embedding mixtures to extract features across them effectually and handle the diverging features further. Then it exploits multi-captions attentional generative adversarial networks to synthesize images from those features. The experiments conducted on widely-used datasets show that the models can generate images from enriched captions effectually and improve the results significantly.

With the rapid rise of computer vision and driverless technology, vehicle model recognition plays a huge role in the common application and industry field. While fine-grained vehicle model recognition is often influenced by multi-level information, such as the image perspective, inter-feature similarity, vehicle details. Furthermore, pivotal regions extraction and fine-grained feature learning have become a vital obstacle to the fine-grained recognition of vehicle models. In this paper, we propose an iterative discrimination CNN (ID-CNN) based on selective multi-convolutional region (SMCR) feature extraction. The SMCR features, which consist of global and local SMCR features, are extracted from the original image with higher activation response value. As for ID-CNN, we use the global and local SMCR features iteratively to localize deep pivotal features and concatenate them together into a fully-connected fusion layer to predict the vehicle categories. We get better results and improve the accuracy to 91.8% on Stanford Cars-196 dataset and to 96.2% on CompCars dataset.

A high machining precision is the most important goal for the fabrication of three dimensional nanometer scale structure. The force-based atomic force microscope (AFM) can reach a very high precision in the structure machining, but the machining depth is difficult to predict for different sample material or machining parameters. The nano machining system in this paper adopts traditional displacement-based machining method, aiming at a few hundred nanometers machining depth. The machining tool (a probe) is kept motionless during the structure machining, so the structure size is determined by a high precise x/y/z three degree of freedoms (DOF) stage. A probe suspension with bridge-type amplification mechanism is designed and optimized, through the theoretical analysis and finite element analysis (FEA), the amplification factor, Z-direction stiffness and first natural frequency of the probe suspension are about 5, 5000N/m, 130HZ, respectively.

This paper presents experimental analysis of laser interferometry-based closed-loop robust motion tracking control for flexure-based four-bar micro/nano manipulator. To enhance the accuracy of micro/nano manipulation, laser interferometry realized robust motion tracking control is established with the experimental facility. This paper contains brief discussions about the error sources associated with the laser interferometry-based sensing and measurement technique, along with detailed error analysis and estimation. Comparative error analysis of capacitive position sensor-based system and laser interferometry-based system is also presented. Robust control demonstrates high precision and accurate motion tracking of the four-bar flexure-based mechanism. The experimental results demonstrate precise motion tracking, where resultant closed-loop position tracking error is of the order of ± 20 nm, and a steady-state error of about ±10 nm. With the experimental study and error analysis, we offer evidence that the laser interferometry-based closed-loop robust motion tracking control can minimize positioning and tracking errors during dynamic motion.

This paper presents the dynamic modelling and performance evaluation methodologies of a flexure-base mechanism for ultra-precision grinding operations. The mechanical design of the mechanism is briefly described. A piezoelectric actuator is used to drive the moving platform. A flexure-based structure is utilized to guide the moving platform and to provide preload for the piezoelectric actuator. By simplifying the Hertzian contact as a linear spring and damping component, a bilinear dynamic model is developed to investigate the dynamic characteristics of the flexure-based mechanism. Based on the established model, the separation phenomenon of the moving platform from the piezoelectric actuator is analyzed. The influence of the control voltage on the maximum overshoot is also investigated. The slope and cycloidal command signals are used to reduce and/or avoid the overshoot of such flexure-based mechanism under step command signal actuation condition. The effects of the rising time of the command signals on the maximum overshoot and the settling time are studied.

This paper presents the forward kinematics and stiffness estimation methodologies of a flexure-based five-bar micro-manipulator. The mechanical design of the micro-manipulator is firstly descried. Base on the configuration of the proposed flexure-based micro-manipulator, the whole system has been divided into a five-bar parallel mechanism and an amplification mechanism. Two mathematical expressions describing the path traced by the tips of two passive links connected to each other are obtained. The Cartesian coordinates of the end-effector attached to one of the passive links is obtained according to the geometric relationship. The amplification factor of the lever mechanism is also derived based on the analytical solution of the four-bar linkage. By simplifying the flexure hinge as an ideal revolution joint with a linear torsional spring, the stiffness of the compliant five-bar mechanism is derived in both actuation and Cartesian spaces. It is noted that the stiffness of the compliant five-bar mechanism is positional dependant, and reaches up to infinite value at the singular configuration.

Fully automated microassembly procedure has become an essential subject of interest among microsystem researchers due to the promising potential of the establishment of fully functional Micro Electro Mechanical System (MEMS) devices and significant cost reduction on large number of consumer based products once the system is realized. In the heart of microassembly, the development of high precision micrograsping technique for dexterous manipulation of minute parts becomes the key factor which determines the success of microassembly process. This paper presents the development of a microgripper for the purpose of manipulating micro sized objects in precision environment. The gripper is realized based on compliant mechanism concept that uses flexure hinge to replace the traditional rigid hinge due to its favorable advantages of eliminating the backlash and coulomb friction problems. Modeling of this gripper was performed utilizing a combination of Pseudo Rigid Body Model (PRBM) and Finite Element Analysis (FEA) which effectively accelerates the prototyping procedure. The gripper was cut out of aluminum plate using electro-discharge machining (EDM) technique and experimental studies were conducted to investigate the performance as well as for model verification. The result between experiment and computational analysis has shown high degree of agreement. High displacement amplification was successfully recorded and maximum stroke of 100 mum can be achieved.