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This research paper aims to optimize the calibration process for an ABB IRB 120 robot, specifically for robotic orbital milling applications, by introducing and validating the use of the observability index and telescopic ballbar for accuracy enhancement.

The study uses the telescopic ballbar and an observability index for the calibration of an ABB IRB 120 robot, focusing on robotic orbital milling. Comparative simulation analysis selects the O₃ index. Experimental tests, both static and dynamic, evaluate the proposed calibration approach within the robot’s workspace.

The proposed calibration approach significantly reduces circularity errors, particularly in robotic orbital milling, showcasing effectiveness in both static and dynamic modes at various tool center point speeds.

The study focuses on a specific robot model and application (robotic orbital milling), limiting generalizability. Further research could explore diverse robot models and applications.

The findings offer practical benefits by enhancing the accuracy of robotic systems, particularly in precision tasks like orbital milling, providing a valuable calibration method.

While primarily technological, improved robotic precision can have social implications, potentially influencing fields where robotic applications are crucial, such as manufacturing and automation.

This study’s distinctiveness lies in advancing the accuracy and precision of industrial robots during circular motions, specifically tailored for orbital milling applications. The innovative approach synergistically uses the observability index and telescopic ballbar to achieve these objectives.

The purpose of this paper is to achieve optimal climbing control of the gas-insulated switchgear (GIS) robot, as the authors know that the GIS inspection robot is a kind of artificial intelligent mobile equipment which auxiliary or even substitute human labor drive on the inner wall of the gas-insulated metal enclosed switchgear. The GIS equipment fault inspection and maintenance can be realized through the robot manipulator on the mobile platform and the camera carried on the fuselage, and it is a kind of intelligent equipment for operation. To realize the inspection and operation of the GIS equipment pipeline without blind spots, the robot is required to be able to travel on any wall inside the pipeline, especially the top of the pipeline and both right and left sides of the pipeline, which requires the flexible climbing of the GIS inspection robot. The robot device has a certain adsorption function to ensure that the robot is fully attached to the wall surface. At the same time, the robot manipulator can be used for collision-free obstacle avoidance operation planning in the narrow operation space inside the GIS equipment.

The above two technologies are the key that the robot completes the GIS equipment inspections. Based on this, this paper focuses on modeling and analysis of the chassis adsorption characteristics for the GIS inspection robot. At the same time, the Denavit Hartenberg (D-H) coordinate model of the robot arm system has been established, and the kinematics forward and inverse solutions of the robot manipulator system have been derived.

The reachable working space point cloud diagram of the robot manipulator in MATLAB has been obtained based on the kinematics analysis, and the operation trajectory planning of the robot manipulator using the robot toolbox has been obtained. The simulation results show that the robot manipulator system can realize the movement without collision and obstacle avoidance. The space can cover the entire GIS pipeline so as to achieve no blind area operation.

Finally, the GIS inspection robot physical prototype system has been developed through system integration design, and the inspection, maintenance operation experiment has been carried out in the actual GIS equipment. The entire robot system can complete the GIS equipment inspection operation soundly and improve the operation efficiency. The research in this paper has important theoretical significance and practical application value for the optimization design and practical research of the GIS inspection robot system.

Visual feedback control is a promising solution for robots work in unstructured environments, and this is accomplished by estimation of the time derivative relationship between the image features and the robot moving. While some of the drawbacks associated with most visual servoing (VS) approaches include the vision–motor mapping computation and the robots’ dynamic performance, the problem of designing optimal and more effective VS systems still remains challenging. Thus, the purpose of this paper is to propose and evaluate the VS method for robots in an unstructured environment.

This paper presents a new model-free VS control of a robotic manipulator, for which an adaptive estimator aid by network learning is proposed using online estimation of the vision–motor mapping relationship in an environment without the knowledge of statistical noise. Based on the adaptive estimator, a model-free VS schema was constructed by introducing an active disturbance rejection control (ADRC). In our schema, the VS system was designed independently of the robot kinematic model.

The various simulations and experiments were conducted to verify the proposed approach by using an eye-in-hand robot manipulator without calibration and vision depth information, which can improve the autonomous maneuverability of the robot and also allow the robot to adapt its motion according to the image feature changes in real time. In the current method, the image feature trajectory was stable in the camera field range, and the robot’s end motion trajectory did not exhibit shock retreat. The results showed that the steady-state errors of image features was within 19.74 pixels, the robot positioning was stable within 1.53 mm and 0.0373 rad and the convergence rate of the control system was less than 7.21 s in real grasping tasks.

Compared with traditional Kalman filtering for image-based VS and position-based VS methods, this paper adopts the model-free VS method based on the adaptive mapping estimator combination with the ADRC controller, which is effective for improving the dynamic performance of robot systems. The proposed model-free VS schema is suitable for robots’ grasping manipulation in unstructured environments.

The heterogeneous character of Industry 4.0 opens opportunities for studies to understand the difficulties and challenges found in the transformation process of manufacturers. This article aims to present a critical analysis of the modernization process of an Industry 3.0 automated cell into a fully autonomous cell of Industry 4.0. The objective is to elucidate the difficulties found in this transition process and the possible ways to overcome the challenges, focusing on the management perspective.

For this, the needed steps for the technology transition were defined and the main I4.0 enabling technologies were applied, such as the application of machine learning algorithms to control quality parameters in milling.

The main challenges found were related to the obsolescence of the equipment present in the cell, challenges in data integration and communication protocols, in addition to the training of people who work actively in the project team. The difficulties faced were discussed based on similar studies in the literature and possible solutions for each challenge.

This understanding of possible barriers in the modernization process, and the step-by-step defined for this transition, can be important references for professionals working in manufacturing industries and researchers who aim to deepen their studies in this important and disruptive stage of world industrialization.

The use of a force sensor to estimate the external force of manipulator not only needs to deal with the signal noise of the sensor itself but also needs to solve the coupling interference of the sensor itself, especially the axial force. The purpose of this paper is to develop a three-dimensional fiber Bragg grating (FBG) wrist force sensor, which has a simple structure and reduces the coupling influence between several axes.

A particular separation elastic structure with four FBGs is devised for the three-axial force sensor. One FBG is suspended on the profile of central cylinder and the other three FBGs are pasted on the elastic beam surface of the over and under measuring bodies, respectively. Finite element analysis (FEA) simulation has been implemented to the strain distribution characteristics, the output characteristics of each direction and the coupling effects of the structure. Furthermore, theoretical derivation and experimental results are used to compare, which have a good consistency.

The experiment results show that the maximum repeatability error of the sensor is 6.75%, the maximum nonlinear error is 5.36%, the maximum coupling interference is 4.73% and the minimum sensitivity is 1.58 pm/N.

A three-dimensional force sensor based on FBG adopts a particular separation elastic structure. The sensor can reduce the coupling influence between several axes, especially the coupling interference in the z-direction is 0.

The introduction of robots as value-adding “workers” on the shop floor triggers complex changes to manufacturing work. Such changes involve highly entangled relationships between technology, organisation and people. Understanding such entanglements requires a holistic assessment of contemporary robotised manufacturing work, to anticipate the dynamically emerging opportunities and risks of robotised work.

A systematic literature review of 87 papers was conducted to capture relevant themes of change in robotised manufacturing work. The literature was analysed using a thematic analysis approach, with Checkland’s soft systems thinking as an analytical framework.

Based on the literature analysis, the authors present a systemic conceptualisation of robotised manufacturing work. Specifically, the conceptualisation highlights four entangled themes of change: work, organisation of labour, workers’ (experiences) and the firm’s environment. Moreover, the authors discuss the complex patterns of interactions between these objects as relationships that defy straightforward cause–effect models.

The findings draw attention to complex interactions between robotisation and manufacturing work. It can, therefore, inform strategic decisions and support projects for robotisation from a holistic perspective.

The authors present a novel approach to studying and designing robotised manufacturing work as a conceptual system. In particular, the paper shifts the focus towards crucial properties of the system, which are subject to complex changes alongside the introduction of robot technology in manufacturing. Soft systems thinking enables new research avenues to explain complex phenomena at the intersection of robotisation and manufacturing work.

The selection of sensitive temperature measurement points is the premise of thermal error modeling and compensation. However, most of the sensitive temperature measurement point selection methods do not consider the influence of the variability of thermal sensitive points on thermal error modeling and compensation. This paper considers the variability of thermal sensitive points, and aims to propose a sensitive temperature measurement point selection method and thermal error modeling method that can reduce the influence of thermal sensitive point variability.

Taking the truss robot as the experimental object, the finite element method is used to construct the simulation model of the truss robot, and the temperature measurement point layout scheme is designed based on the simulation model to collect the temperature and thermal error data. After the clustering of the temperature measurement point data is completed, the improved attention mechanism is used to extract the temperature data of the key time steps of the temperature measurement points in each category for thermal error modeling.

By comparing with the thermal error modeling method of the conventional fixed sensitive temperature measurement points, it is proved that the method proposed in this paper is more flexible in the processing of sensitive temperature measurement points and more stable in prediction accuracy.

The Grey Attention-Long Short Term Memory (GA-LSTM) thermal error prediction model proposed in this paper can reduce the influence of the variability of thermal sensitive points on the accuracy of thermal error modeling in long-term processing, and improve the accuracy of thermal error prediction model, which has certain application value. It has guiding significance for thermal error compensation prediction.

In complex environments, a spherical robot has great application value. When the pendulum spherical robot is stopped or disturbed, there will be a periodic oscillation. This situation will seriously affect the stability of the spherical robot. Therefore, this paper aims to propose a control method based on backstepping and disturbance observers for oscillation suppression.

This paper analyzes the mechanism of oscillation. The oscillation model of the spherical robot is constructed and the relationship between the oscillation and the internal structure of the sphere is analyzed. Based on the oscillation model, the authors design the oscillation suppression control of the spherical robot using the backstepping method. At the same time, a disturbance observer is added to suppress the disturbance.

It is found that the control system based on backstepping and disturbance observer is simple and efficient for nonlinear models. Compared with the PID controller commonly used in engineering, this control method has a better control effect.

The proposed method can provide a reliable and effective stability scheme for spherical robots. The problem of instability in real motion is solved.

In this paper, the oscillation model of a spherical robot is innovatively constructed. Second, a new backstepping control method combined with a disturbance observer for the spherical robot is proposed to suppress the oscillation.

This study aims to address the issue of random movement and non coordination between docking mechanisms and locking mechanisms, and proposes a comprehensive dynamic docking control architecture that integrates perception, planning, and motion control.

Firstly, the proposed dynamic docking control architecture uses laser sensors and a charge-coupled device camera to perceive the pose of the target. The sensor data are mapped to a high-dimensional potential field space and fused to reduce interference caused by detection noise. Next, a new potential function based on multi-dimensional space is developed for docking path planning, which enables the docking mechanism based on Stewart platform to rapidly converge to the target axis of the locking mechanism, which improves the adaptability and terminal docking accuracy of the docking state. Finally, to achieve precise tracking and flexible docking in the final stage, the system combines a self-impedance controller and an impedance control algorithm based on the planned trajectory.

Extensive simulations and experiments have been conducted to validate the effectiveness of the dynamic docking system and its control architecture. The results indicate that even if the target moves randomly, the system can successfully achieve accurate, stable and flexible dynamic docking.

This research can provide technical guidance and reference for docking task of unmanned vehicles under the ground conditions. It can also provide ideas for space docking missions, such as space simulator docking.

The purpose of this paper is to provide details of biomimetic and neuromorphic sensor research and developments and discuss their applications in robotics.

Following a short introduction, this first provides examples of recent biomimetic gripping and sensing skin research and developments. It then considers neuromorphic vision sensing technology and its potential robotic applications. Finally, brief conclusions are drawn.

Biomimetics aims to exploit mechanisms, structures and signal processing techniques which occur in the natural world. Biomimetic sensors and control techniques can impart robots with a range of enhanced capabilities such as learning, gripping and multidimensional tactile sensing. Neuromorphic vision sensors offer several key operation benefits over conventional frame-based imaging techniques. Robotic applications are still largely at the research stage but uses are anticipated in enhanced safety systems in autonomous vehicles and in robotic gripping.

This illustrates how tactile and imaging sensors based on biological principles can contribute to imparting robots with enhanced capabilities.