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To the industrial application of intelligent and connected vehicles (ICVs), the robustness and accuracy of environmental perception are critical in challenging conditions. However, the accuracy of perception is closely related to the performance of sensors configured on the vehicle. To enhance sensors’ performance further to improve the accuracy of environmental perception, this paper aims to introduce an obstacle detection method based on the depth fusion of lidar and radar in challenging conditions, which could reduce the false rate resulting from sensors’ misdetection.

Firstly, a multi-layer self-calibration method is proposed based on the spatial and temporal relationships. Next, a depth fusion model is proposed to improve the performance of obstacle detection in challenging conditions. Finally, the study tests are carried out in challenging conditions, including straight unstructured road, unstructured road with rough surface and unstructured road with heavy dust or mist.

The experimental tests in challenging conditions demonstrate that the depth fusion model, comparing with the use of a single sensor, can filter out the false alarm of radar and point clouds of dust or mist received by lidar. So, the accuracy of objects detection is also improved under challenging conditions.

A multi-layer self-calibration method is conducive to improve the accuracy of the calibration and reduce the workload of manual calibration. Next, a depth fusion model based on lidar and radar can effectively get high precision by way of filtering out the false alarm of radar and point clouds of dust or mist received by lidar, which could improve ICVs’ performance in challenging conditions.

For some special manipulators such as the ones work at the space station, nuclear or some other unmanned environments, the overload, collision, vibration, temperature change or release of the internal stress would affect the structural parameters. And thus the operation precision might constantly decrease in long-term use. In these unmanned environments, the unattended manipulators should calibrate itself when they execute high precision operations or proceed self-maintenances. The purpose of this paper is to propose an automatic visual assistant on-line calibration (AVOC) method based on multi-markers.

A camera fixed on the end of the manipulator is used to measure one to three identification points, which forms an unstable multi-sensor eye-in-hand system. A Gaussian motion method which combines the linear quadratic regulator control and extended Kalman filter together is proposed to make the manipulator track the planned trajectories when its inaccurate structural parameters form uncertain motion errors. And a Monte-Carlo method is proposed to form a high precision and stable signal acquisition when the visual system has measurement errors and intermittent signal feedback. An automatic sampling process is adopted to select the optimal measurement points basing on their variances.

Data analysis and experiment results prove the efficiency and feasibility of the method proposed in this paper. With this method, the positioning accuracy is largely promoted from about 2 mm to 0.04–0.05 mm.

Experiments were carried out successfully on a manipulator in a life sciences glove box that will work at the Chinese space station. It is a low cost and efficient manipulator calibration method. The whole autonomic calibration process takes less than 10 min and requires no human intervention. In addition, this method not only can be used in the calibration of other unmanned articulated manipulator that works in deep ocean, nuclear industry or space but also be useful for the maintenance work in modern factories owing a lot of industrial robots.

The purpose of this paper is to present a novel concentration estimation model for improving the accuracy and robustness of low-cost electronic noses (e-noses) with metal oxide semiconductor sensors in indoor air contaminant monitoring and overcome the potential sensor drift.

In the quantification model, a piecewise linearly weighted artificial neural network ensemble model (PLWE-ANN) with an embedded self-calibration module based on a threshold network is studied.

The nonlinear estimation problem of sensor array-based e-noses can be effectively transformed into a piecewise linear estimation through linear weighted neural networks ensemble activated by a threshold network.

In this paper, a number of experimental results have been presented, and it also demonstrates that the proposed model has very good accuracy and robustness in real-time indoor monitoring of formaldehyde.

This paper aims to present a highly accessible and affordable tracking model for earthmoving operations in an attempt to overcome some of the limitations of current tracking models.

The proposed methodology involves four main processes: acquiring onsite terrestrial images, processing the images into 3D scaled cloud data, extracting volumetric measurements and crew productivity estimations from multiple point clouds using Delaunay triangulation and conducting earned value/schedule analysis and forecasting the remaining scope of work based on the estimated performance. For validation, the tracking model was compared with an observation-based tracking approach for a backfilling site. It was also used for tracking a coarse base aggregate inventory for a road construction project.

The presented model has proved to be a practical and accurate tracking approach that algorithmically estimates and forecasts all performance parameters from the captured data.

The proposed model is unique in extracting accurate volumetric measurements directly from multiple point clouds in a developed code using Delaunay triangulation instead of extracting them from textured models in modelling software which is neither automated nor time-effective. Furthermore, the presented model uses a self-calibration approach aiming to eliminate the pre-calibration procedure required before image capturing for each camera intended to be used. Thus, any worker onsite can directly capture the required images with an easily accessible camera (e.g. handheld camera or a smartphone) and can be sent to any processing device via e-mail, cloud-based storage or any communication application (e.g. WhatsApp).

Large eddy simulation (LES) is widely used in prediction of turbulent flow. The purpose of this paper is to propose a new dynamic mixed nonlinear subgrid‐scale (SGS) model (DMNM), in order to improve LES precision of complex turbulent flow, such as flow including separation or rotation.

The SGS stress in DMNM consists of scale‐similarity part and eddy‐viscosity part. The scale‐similarity part is used to describe the energy transfer of scales that are close to the cut‐off explicitly. The eddy‐viscosity part represents energy transfer of the other scales between smaller than grid‐filter size and larger than grid‐filter size. The model is demonstrated through two examples; one is channel flow and another is surface‐mounted cube flow. The computed results are compared with prior experimental data, and the behavior of DMNM is analyzed.

The proposed model has the following characteristics. First, DMNM exhibits significant flexibility in self‐calibration of the model coefficients. Second, it does not require alignment of the principal axes of the SGS stress tensor and the resolved strain rate tensor. Third, since both the rotating part and scale‐similarity part are considered in the new model, flow with rotation and separation is easily simulated. Compared with the prior experimental data, DMNM gives more accurate results in both examples.

The SGS model DMNM proposed in the paper could capture the detail vortex characteristics more accurately. It has the advantage in simulation of complex flow, including more separations.

Semiconductor manufacturing industry requires highly accurate robot operation with short install/setup downtime.

We develop a fast, low cost and easy‐to‐operate calibration system for wafer‐handling robots. The system is defined by a fixture and a simple compensation algorithm. Given robot repeatability, end effector uncertainties, and the tolerance requirements of wafer placement points, we derive fixture design and placement specifications based on a statistical tolerance model.

By employing the fixture‐based calibration, we successfully relax the tolerance requirement of the end effector by 20 times.

Semiconductor manufacturing requires fast and easy‐to‐operate calibration systems for wafer‐handling robots. In this paper, we describe a new methodology to solve this problem using fixtures. We develop fixture design criteria and a simple compensate algorithm to satisfy calibration requirements. We also verify our approach by a physical example.

This paper aims to define imminent and future key aspects in innovative production machines and systems but more specifically to focus on the automation and control aspects.

The foresight analysis is based on the state‐of‐the‐art of current manufacturing technologies with a setup of key enabling features and a roadmap research.

The paper finds that more integration of current and future technology development is required to build a strong platform for various applications featured with interoperability, trust, security and protection. Autonomy and close collaboration aspects in machines remain as crucial targets for the near future. An immediate action is required on smart strategies for the design patterns and agents to enable intuitive components for high quality dynamic user interfaces. This will allow rapid configuration and adaptation to new manufacturing tasks with highly improved machine learning.

The paper describes the future of key aspects required to move the production, automation and control systems forward.

This paper seeks to establish parallel robots with strong performance characteristics in handling and assembly processes.

The presented work introduces concepts and solutions related to the improvement of parallel kinematic mechanisms. Structural design topics and modeling approaches are as well considered as control schemes and new machine components particularly designed for high‐dynamic parallel robots. The results have been achieved by a unique interdisciplinary research group linking knowledge from mechanical engineering, electrical engineering and computer science.

The paper found numerous individually applicable methods leading to an improved efficiency of parallel robots. Several of the developments have been already implemented and validated by various self‐built machine prototypes and a new control system.

Owing to higher stiffness, accuracy and improved dynamic behavior parallel robots proved to be an efficient and suitable supplement to serial robots. By means of the various developments contributed in this paper, the promising potential of this class of robots is once more emphasized and further strengthened.

How to model blind image deblurring that arises when a camera undergoes ego-motion while observing a static and close scene. In particular, this paper aims to detail how the blurry image can be restored under a sequence of the linear model of the point spread function (PSF) that are derived from the 6-degree of freedom (DOF) camera’s accurate path during the long exposure time.

There are two existing techniques, namely, an estimation of the PSF and a blind image deconvolution. Based on online and short-period inertial measurement unit (IMU) self-calibration, this motion path has discretized a sequence of the uniform speed of 3-DOF rectilinear motion, which unites with a 3-DOF rotational motion to form a discrete 6-DOF camera’s path. These PSFs are evaluated through the discrete path, then combine with a blurry image to restoration through deconvolution.

This paper describes to build a hardware attachment, which is composed of a consumer camera, an inexpensive IMU and a 3-DOF motion mechanism to the best of the knowledge, together with experimental results demonstrating its overall effectiveness.

First, the paper proposes that a high-precision 6-DOF motion platform periodically adjusts the speed of a three-axis rotational motion and a three-axis rectilinear motion in a short time to compensate the bias of the gyroscope and the accelerometer. Second, this paper establishes a model of 6-DOF motion and emphasizes on rotational motion, translational motion and scene depth motion. Third, this paper addresses a novel model of the discrete path that the motion during long exposure time is discretized at a uniform speed, then to estimate a sequence of PSFs.

The purpose of this paper is to provide a comprehensive review of a non-contact full-field optical measurement technique known as digital image correlation (DIC).

The approach of this review paper is to introduce the research pertaining to DIC. It comprehensively covers crucial facets including its principles, historical development, core challenges, current research status and practical applications. Additionally, it delves into unresolved issues and outlines future research objectives.

The findings of this review encompass essential aspects of DIC, including core issues like the subpixel registration algorithm, camera calibration, measurement of surface deformation in 3D complex structures and applications in ultra-high-temperature settings. Additionally, the review presents the prevailing strategies for addressing these challenges, the most recent advancements in DIC applications across quasi-static, dynamic, ultra-high-temperature, large-scale and micro-scale engineering domains, along with key directions for future research endeavors.

This review holds a substantial value as it furnishes a comprehensive and in-depth introduction to DIC, while also spotlighting its prospective applications.