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This paper aims to introduce a new acoustic positioning method to solve the problem of space positioning for online inspection robots within the storage tank.

The proposed positioning system comprises two acoustic signal emitters and two receivers. Emitters are brought by the robot into the storage tank. Receivers are mounted on the external edge of the storage tank floor. The spatial coordinate values and motion directions of the robot in the storage tank are calculated by using the proposed acoustic positioning algorithm.

The experiment results and positioning error analysis indicate that the method can obtain the data of robotic space coordinates and motion orientation, while the positioning error of the method can be less than 20 cm. The accuracy reaches the positioning technology level of other tank online inspection robots.

This method not only expands the positioning of the inspection robots from 2D plane to 3D space but also significantly reduces the number of positioning sensors carried by a robot and improves the safety of a robot in the tank.

Automatic robots can improve the efficiency of liquefied petroleum gas (LPG) tank inspection and maintenance, but it is difficult to achieve high-precision spatial positioning and navigation on tank surfaces. The purpose of this paper is to develop a spatial positioning robotic system for tank inspection. The robot can accurately identify and track weld paths. The positioning system can complete robot’s spatial positioning on tank surfaces.

A tank inspection robot with curvature-adaptive transmission mechanisms is designed in this study. A weld path recognition method based on deep learning is proposed to accurately identify and extract weld paths. Integrated multiple sensors, the positioning system is developed to improve the robot’s spatial positioning accuracy. Experiments are conducted on a cylindrical tank to test weld seam tracking accuracy and spatial positioning performance of the robotic system. The practicality of the robotic system is then verified in field tests.

The robot can accurately identify and track weld seams with a maximum drift angle of 4° and a maximum offset distance of ±30 mm. The positioning system has excellent positioning accuracy and stability. The maximum angle and height errors are 3° and 0.08 m, respectively.

The positioning system can improve the autonomous performance of inspection robots and solve the problems of weld path recognition and spatial positioning. Application of the robotic system can promote the automatic inspection and maintenance of LPG tanks.

The purpose of this paper is to improve the positioning accuracy of 6-Dof serial robot by the way of error compensation and sensitivity analysis.

In this paper, the Denavit–Hartenberg matrix is used to construct the kinematics models of the robot; the effects from individual joint and several joints on the end effector are estimated by simulation. Then, an error model based on joint clearance is proposed so that the positioning accuracy at any position of joints can be predicted for compensation. Through the simulation of the curve path, the validity of the error compensation model is verified. Finally, the experimental results show that the error compensation method can improve the positioning accuracy of a two joint exoskeleton robot by nearly 76.46%.

Through the analysis of joint error sensitivity, it is found that the first three joints, especially joint 2, contribute a lot to the positioning accuracy of the robot, which provides guidance for the accuracy allocation of the robot. In addition, this paper creatively puts forward the error model based on joint clearance, and the error compensation method which decouples the positioning accuracy into joint errors.

It provides a new idea for error modeling and error compensation of 6-Dof serial robot. Combining sensitivity analysis results with error compensation can effectively improve the positioning accuracy of the robot, and provide convenience for welding robot and other robots that need high positioning accuracy.

The purpose of the following work was to work out the dependency to allow for the determination of the repeatability positioning error value of the robot at any given point in its workspace, without the necessity of conducting time-consuming measurements while routing a precise surface of repeatability positioning.

The presented dependency permits for the possibility to determine, even at the planning phase, the optimal connection point in the workspace, ensuring the best parameters for the process of machine assembly, without needless overestimation of precision of the utilized equipment. To solve the task the sequential quadratic programming (SQP) method implemented in the MATLAB(R) environment was used. To verify the hypothesis of the compatibility of the empirical distribution with the hypothetical distribution of the robot’s positioning error, the Kolmogorov test was used.

In this paper, it has been demonstrated theoretically and experimentally that the industrial robot accuracy can vary over a very wide range in the workspace. This provides an additional opportunity to increase reliability of the assembly process through the appropriate choice of the point of parts joining. The methodology presented here allows the designer of assembly workstations to rapidly estimate the repeatability of robot positioning and to allocate at the design stage of assembly process the optimal position in the robot workspace to ensure the required precision, without unnecessarily high accuracy of equipment used and, therefore, without inflated costs.

An alternative solution to the stated problem can be the proposed method for determining the robot’s positioning errors, requiring a much smaller amount of measurements to be taken that would be necessary to determine the parameters of the random variable errors of the joint coordinates of the robot and for their verification by the repeatability of positioning in randomly selected points in the workspace. Additionally discussed in the study, the methodology of identifying connection place was designed for typical combinations of machine parts, most frequently encountered in assembly process and was taken into account, typical limitations occurring in actual manufacturing conditions.

The coupling impact of hybrid uncertain errors on the machine precision is complex, as a result of which the designing method with multiple independent error sources under uncertainties remains a challenge. For the purpose of precision improvement, this paper focuses on the robot design and aims to present an assembly precision design method based on uncertain hybrid tolerance allocation (UHTA), to improve the positioning precision of the mechanized robot, as well as realize high precision positioning within the workspace.

The fundamentals of the parallel mechanism are introduced first to implement concept design of a 3-R(4S) &3-SS parallel robot. The kinematic modeling of the robot is carried out, and the performance indexes of the robot are calculated via Jacobian matrix, on the basis of which, the 3D spatial overall workspace can be quantified and visualized, under the constraints of limited rod, to avoid the singular position. The error of the robot is described, and a probabilistic error model is hereby developed to classify the hybrid error sensitivity of each independent uncertain error source by Monte Carlo stochastic method. Most innovatively, a methodology called UHTA is proposed to optimize the robot precision, and the tolerance allocation approach is conducted to reduce the overall error amplitude and improve the robotized positioning precision, on the premise of not increasing assembly cost.

The proposed approach is validated by digital simulation of medical puncture robot. The experiment highlights the mathematical findings that the horizontal plane positioning error of the parallel robotic mechanism can be effectively reduced after using UHTA, and the average precision can be improved by up to 39.54%.

The originality lies in UHTA-based precision design method for parallel robots. The proposed method has widely expanding application scenarios in industrial robots, biomedical robots and other assembly automation fields.

The positioning and grasping of large-size objects have always had problems of low positioning accuracy, slow grasping speed and high application cost compared with ordinary small parts tasks. This paper aims to propose and implement a binocular vision-guided grasping system for large-size object with industrial robot.

To guide the industrial robot to grasp the object with high position and pose accuracy, this study measures the pose of the object by extracting and reconstructing three non-collinear feature points on it. To improve the precision and the robustness of the pose measuring, a coarse-to-fine positioning strategy is proposed. First, a coarse but stable feature is chosen to locate the object in the image and provide initial regions for the fine features. Second, three circular holes are chosen to be the fine features whose centers are extracted with a robust ellipse fitting strategy and thus determine the precise pose and position of the object.

Experimental results show that the proposed system has achieved high robustness and high positioning accuracy of −1 mm and pose accuracy of −0.5 degree.

It is a high accuracy method that can be used for industrial robot vision-guided and grasp location.

This paper aims to present a prototype of medical transportation robot whose positioning accuracy can reach millimeter-level in terms of patient transportation. By using this kind of mobile robot, a fully automatic image diagnosis process among independent CT/PET devices and the image fusion can be achieved.

Following a short introduction, a large-load 4WD-4WS (four-wheel driving and four-wheel steering) mobile robot for carrying patient among multiple medical imaging equipments is developed. At the same time, a specially designed bedplate with self-locking function is also introduced. For further improving the positioning accuracy, the authors proposed a calibration method based on Gaussian process regression (GPR) to process the measuring data of the sensors. The performance of this robot is verified by the calibration experiment and Image fusion experiment. Finally, concluding comments are drawn.

By calibrating the robot’s positioning system through the proposed GPR method, one can obtain the accuracy of the robot’s offset distance and deflection angle, which are 0.50 mm and +0.21°, respectively. Independent repeated trials were then set up to verify this result. Subsequent phantom experiment shows the accuracy of image fusion can be accurate within 0.57 mm in the front-rear direction and 0.83 in the left-right direction, respectively, while the clinical experiment shows that the proposed robot can practically realize the transportation of patient and image fusion between multiple imaging diagnosis devices.

The proposed robot offers an economical image fusion solution for medical institutions whose imaging diagnosis system basically comprises independent MRI, CT and PET devices. Also, a fully automatic diagnosis process can be achieved so that the patient’s suffering of getting in and out of the bed and the doctor’s radiation dose can be obviated.

The general bedplate presented in Section 2 that can be mounted on the CT and PET devices and the self-locking mechanism has realized the catching and releasing motion of the patient on different medical devices. They also provide a detailed method regarding patient handling and orientation maintenance, which was hardly mentioned in previous research. By establishing the positioning system between the robot and different medical equipment, a fully automatic diagnosis process can be achieved so that the patient’s suffering of getting in and out of the bed and the doctor’s radiation dose can be obviated.

The GPR-based method proposed in this paper offers a novel method for enhancing the positioning accuracy of the industrial AGV while the transportation robot proposed in this paper also offers a solution for modern imaging fusion diagnosis, which are basically predicated on the conjoint analysis between different kinds of medical devices.

The present designs of industrial robots or mechanical handling units generally fall into two categories, the simple pick‐and‐place units with two fixed positions per axis, or the more sophisticated type such as Unimate with a very large number of positions per axis and a large memory. Whilst the latter devices are essential for complex operations such as spot welding, paint spraying or palletising there are many applications where only a small number of positions per axis are required, e.g. press loading, conveyor transferring, assembly operations. This paper describes a positioning system that falls between the above two general categories in that it allows a number of positions on each axis to be selected. A detailed description is given of the positioning system which basically consists of a number of mechanical stops attached to indexable bars such that there are a minimum number of 6 positions per axis. These stops are positioned as required and include a fine positioning adjustment. It is found that this system gives a positioning accuracy far greater than those commonly used with robots. The design of the hydraulic system and the control system for the fast to slow traverse are given together with test results obtained from a prototype system. The method of programming and the advantages and disadvantages are specified in a final discussion. In particular how the system can be used in fairly complex operations such as palletising is discussed.

Recently, service robots have been widely used in various fields. The purpose of this paper is to design a restaurant service robot which could be applicable to provide basic service, such as ordering, fetching and sending food, settlement and so on, for the customers in the robot restaurant.

Based on characteristics of wheeled mobile robots, the service robot with two manipulators is designed. Constrained by the DOF, the final positioning accuracy within ±3 cm must be guaranteed to successfully grasp the plate. Segmented positioning method is applied considering the positioning costs and accuracy requirement in the different stages, and the shape‐based matching tracking method is adopted to navigate the robot to the object.

Experiments indicate that the service robot could successfully grasp the plate, from wherever is its initial position; and the proposed algorithms could estimate the robot pose well and accurately evaluate the localization performance.

At present, the service robot could only work in an indoor environment where there is steady illumination.

The service robot is applicable to provide basic service for the customers in the robot restaurant.

The paper gives us a concept of a restaurant service robot and its localization and navigation algorithms. The service robot could provide its real‐time coordinates and arrive at the object with ±2 cm positioning precision, from wherever is its initial position.

The purpose of this paper is to propose a new method based on three-dimensional (3D) vision technologies and human skill integrated deep learning to solve assembly positioning task such as peg-in-hole.

Hybrid camera configuration was used to provide the global and local views. Eye-in-hand mode guided the peg to be in contact with the hole plate using 3D vision in global view. When the peg was in contact with the workpiece surface, eye-to-hand mode provided the local view to accomplish peg-hole positioning based on trained CNN.

The results of assembly positioning experiments proved that the proposed method successfully distinguished the target hole from the other same size holes according to the CNN. The robot planned the motion according to the depth images and human skill guide line. The final positioning precision was good enough for the robot to carry out force controlled assembly.

The developed framework can have an important impact on robotic assembly positioning process, which combine with the existing force-guidance assembly technology as to build a whole set of autonomous assembly technology.

This paper proposed a new approach to the robotic assembly positioning based on 3D visual technologies and human skill integrated deep learning. Dual cameras swapping mode was used to provide visual feedback for the entire assembly motion planning process. The proposed workpiece positioning method provided an effective disturbance rejection, autonomous motion planning and increased overall performance with depth images feedback. The proposed peg-hole positioning method with human skill integrated provided the capability of target perceptual aliasing avoiding and successive motion decision for the robotic assembly manipulation.