Industrial Robot Research Articles

Design of a force-controlled end-effector based on the pneumatic artificial muscle for robotic polishing

Purpose Polishing is a crucial process in mechanical manufacturing. The use of industrial robots to automate polishing is an inevitable trend in future developments. However, current robotic polishing tools are too large to reach inside deep holes or grooves in workpieces. This study aims to use a pneumatic artificial muscle (PAM) as the actuator and designs a force-controlled end-effector to reach inside the deep and narrow areas in the workpiece. Design/methodology/approach This approach first addresses the challenge of converting the tensile force generated by the PAM into pushing force through mechanism design. In addition, a dynamics model of the end-effector was established based on the three-element model of the PAM. A combined control strategy was proposed to enhance force control accuracy and adaptability during the polishing process. Findings Experiments were conducted on a robotic platform equipped with the proposed end-effector. The experimental results demonstrate that the end-effector can polish the inner end face of holes or grooves with diameters as small as 80 mm and depths reaching 200 mm. By implementing the combined control strategies, the target force tracking error was reduced by 48.66% compared to the use of the PID controller alone. Originality/value A new force-controlled end-effector based on the PAM is designed for robotic polishing. According to the experimental result, this end-effector can polish not only the outer surfaces of the workpiece but also the internal surfaces of workpieces with deep holes or grooves specifically. By using the combined control strategy proposed in this paper, the end-effector significantly improves force control precision and polishing quality.

Role of AI, Automation & Robotics in Pharmaceutical Industry

The pharmaceutical industry is undergoing a massive transformation driven by technological advancements and leapfrogging further due to the integration of Artificial intelligence (AI), automation, and robotics. These technologies are being deployed to cover various aspects, including drug discovery, manufacturing, supply chain, and patient care. AI's ability to process and analyze massive data sets allows researchers to identify new drug candidates faster or improve on current ones through various strategies. Automation transforms repetitive tasks and increases accuracy, but most importantly, it frees people up from work that they need not do and concentrates on the jobs that still require human involvement. Conversely, when integrated with AI, robots enhance the production process by enabling speedy, accurate, and scalable manufacturing. Robotics are now being used in pharmacies for medication dispensing and are very efficient. Furthermore, all these innovations driven by AI, Automation, and robotics also contribute to personalized medicine by extending tailored treatments based on individual patient data. This paper explores the role of AI, Automation, and Robotics in the Pharmaceutical industry and its benefits. The ongoing evolution of such technologies holds immense potential to address the growing needs and handle industry challenges such as increasing demand, regulatory compliance, and global health needs to ultimately lead the pharmaceutical industry towards a more efficient, adaptive, and patient-centered approach.

Function-Oriented Applicability Evaluation of Technical Folding Based on Expert Knowledge

Technical folding systems are rigid, three-dimensional surface structures that can be folded at their joints. Due to their foldable structure, technical folding systems can produce lightweight structures that have a high load-bearing capacity and can also flexibly change their shape. In order to develop products with technical folding in a function-oriented product development process, a folding principle must be defined that realises the individual functions. Since there is hardly any expert knowledge on technical folding, in general, and on the use of the technical folding principle in particular, technical folding is not very widespread despite its promising properties. Therefore, there is a need for a software-supported evaluation of whether functions can be realised by the principle of technical folding. The software-supported applicability evaluation (EvalTech) developed here is based on case-based reasoning methods, as these can artificially simulate evaluations based on expert knowledge. The expert knowledge used for the evaluation includes functions, function structures and properties of known technical folding systems, which are compared with functions and function structures of the development task. EvalTech was successfully used in the development and realisation of a large-scale foldable cover for industrial robots.

A Biomimetic Pose Estimation and Target Perception Strategy for Transmission Line Maintenance UAVs

High-voltage overhead power lines serve as the carrier of power transmission and are crucial to the stable operation of the power system. Therefore, it is particularly important to detect and remove foreign objects attached to transmission lines, as soon as possible. In this context, the widespread promotion and application of smart robots in the power industry can help address the increasingly complex challenges faced by the industry and ensure the efficient, economical, and safe operation of the power grid system. This article proposes a bionic-based UAV pose estimation and target perception strategy, which aims to address the lack of pattern recognition and automatic tracking capabilities of traditional power line inspection UAVs, as well as the poor robustness of visual odometry. Compared with the existing UAV environmental perception solutions, the bionic target perception algorithm proposed in this article can efficiently extract point and line features from infrared images and realize the target detection and automatic tracking function of small multi-rotor drones in the power line scenario, with low power consumption.

Energy consumption analysis for a 6-axis industrial robot in the packing process

Energy consumption analysis for a 6-axis industrial robot in the packing process

Investigating the effect of customer-robot interaction experience on customer engagement behavior and co-creation value: a mixed methods study

ABSTRACT This study investigated the effect of robots on customer engagement behavior, employing a mixed methods approach. The qualitative study involved in-depth interviews with a total of 25 customers to explore the effects and constructed a theoretical model. A quantitative investigation then evaluated the proposed model based on a sample of 558 valid respondents. The findings demonstrated that customer interaction with service robots affected customer engagement behavior, through customer perceived hedonic, functional, and symbolic benefits. These effects influenced customer happiness and organizational transaction value, facilitating value co-creation between robots, customers, and firms. The impact of robots on customer engagement behavior was contingent upon travel companions and purpose, but not employee support. This study contributes to the literature by enhancing an understanding of service robot performance and customer engagement behavior, offering practical insights for the effective management of robots in the hotel industry.

A pan-European survey of robotic training for gastrointestinal surgery: European Robotic Surgery Consensus (ERSC) initiative.

There has been a recent rapid growth in the adoption of robotic systems across Europe. This study aimed to capture the current state of robotic training in gastrointestinal (GI) surgery and to identify potential challenges and barriers to training within Europe. A pan-European survey was designed to account for the opinion of the following GI surgery groups: (i) experts/independent practitioners; (ii) trainees with robotic access; (iii) trainees without robotic access; (iv) robotic industry representatives. The survey explored various aspects, including stakeholder opinions on bedside assisting, console operations, challenges faced and performance assessment. It was distributed through multiple European surgical societies and industry, in addition to social media and snowball sampling, between December 2023 and March 2024. A total of 1360 participants responded, with valid/complete responses from 1045 participants across 38 European countries. Six hundred and ninety-five (68.0%) experts and trainees were not aware of a dedicated robotic training curriculum for trainees, with 13/23 (56.5%) industry representatives not incorporating training for trainees in their programme. Among trainees with access to robotic systems, 94/195 (48.2%) had not performed any robotic cases, citing challenges including a lack of certified robotic trainers and training lists. Both experts and trainees agreed that trainees should start bedside assisting and operating on the console earlier than they currently do. Assessment tools of trainee performance were not being used by 139/479 (29.0%) participants. This pan-European survey highlights the need for a standardised robotic curriculum to address the gap in visceral training, assessment and certification. A greater emphasis may be required on implementing robotic training earlier through simulation training, dual console learning, bedside assisting, key clinical performance indicators, and assessment tools. The findings will guide the development of a pan-European consensus on the essential components of a comprehensive training programme for GI robotic surgery.

Influence of financial inclusion and industrial robots on the efficiency of China's OFDI: A heterogeneous spatial stochastic frontier analysis

Enhancing the efficacy of China's outward foreign direct investment (COFDI) constitutes a significant avenue for realizing the sustainable development of China's economy. This study analyzes panel data from 63 countries from 2010 to 2021 to create a heterogeneous spatial stochastic frontier model to examine how financial inclusion and industrial robotics in host nations affect COFDI efficiency. The findings are as follows. To begin with, COFDI manifests positive spatial spillover effects across countries. Moreover, from a spatial perspective, financial inclusion diminishes COFDI efficiency, whereas the interaction between financial inclusion and industrial robots increases its efficiency. Industrial robots, thus, counteract the diminishing impact of financial inclusion on COFDI efficiency. Additionally, concerning the heterogeneity of location choice and investment motivation in COFDI, the interaction between financial inclusion and industrial robots in developed economies augments COFDI efficiency as opposed to emerging market economies. Conversely, technology-seeking investment mitigates the adverse effects of financial inclusion on the efficacy of COFDI when juxtaposed with resource-seeking investment. This study provides theoretical insights and practical recommendations for improving the efficiency and guiding the destination selection of COFDI.

Gender earnings gap in Chinese firms: Can it be narrowed by industrial robots?

Gender earnings gap in Chinese firms: Can it be narrowed by industrial robots?

A digital twin framework for anomaly detection in industrial robot system based on multiple physics-informed hybrid convolutional autoencoder

The robot number in industry is growing up rapidly. Building anomaly detection system for them can improve the security of these expensive devices. The article implements an anomaly detection framework based on digital twin, which are built by a hybrid convolutional autoencoder. The framework shares those neural network weight files as digital assets, users can use them to estimate the possible output from real input. It approximates the dynamic relationship between motion, current, temperature and vibration with hybrid convolution. Considering the limited generalization performance of direct data-driven methods in practical physical systems, this article introduces physical information methods to improve the constraint function of neural network. The influence of multiple physical fields on current is established by a unified neural network. Terminals detect anomaly with KL divergence between really current and estimated current. The article collects operational data from real robots and verifies it, and the experiment shows that the RMSE for current estimation is below 1.5 %, the F1-score in anomaly detection is over 98.23 %, false positive is below 1 %, false negative is below 1.7 %. The relevant technologies are gradually being promoted and applied in enterprises.

Predefined time convergence guaranteed performance control for uncertain systems based on reinforcement learning

The prescribed performance control method (PPCM) is commonly employed to ensure the guaranteed performance control of non-linear systems. However, traditional approaches suffer from certain drawbacks, such as the dependence of parameter settings for the performance constraint function on the initial tracking error value and the inability to specify the convergence time of tracking error according to engineering requirements. This paper focuses on designing a fault tolerant control strategy with prescribed convergence time and prescribed transient performance for uncertain systems, considering parameter perturbance, actuator faults, and unknown initial states. Firstly, we introduce an error conversion function that transforms the tracking error with any initial value into a new error variable starting from zero. This resolves the issue of depending on the initial value of tracking error in setting parameters for the performance constraint function in prescribed performance control methods. Subsequently, we derive a novel Lyapunov stability criterion for predefined time (PDT) convergence and design a fault-tolerant control strategy using backstepping control method while ensuring prescribed convergence time and prescribed performance. In this approach, we propose a new online reinforcement learning intelligent algorithm to estimate compound interference caused by actuator faults, control saturation constraint increment, system parameter perturbation, and external interference. The theoretical proof establishes predefined time convergence of the closed-loop system. Finally, numerical simulations are conducted on industrial robots with actuator faults to validate the effectiveness of our designed control strategy.

Digital twin-driven virtual commissioning for robotic machining enhanced by machine learning

Robotic machining has been increasingly applied in intelligent manufacturing production lines. Compared with the traditional machine tools, commissioning for robotic machining system (RMS) is particularly important due to the low accuracy of industrial robots (IRs). Traditional site commissioning has large workload and is difficult to handle the multi-source errors. Since digital twin (DT) provides strategies for staying synchronized with the physical entities in whole lifecycle, a DT-driven virtual commissioning (VC) system for RMS is developed in this study to improve machining accuracy and reduce the difficulty of commissioning. Firstly, the framework of DT-driven VC system is designed including several function modules such as interaction, data pre-processing, DT model of RMS (RMSDT), and optimization service. Since RMSDT is the kernel of precise VC, a machine learning-enhanced RMSDT oriented to actual machining path prediction is then constructed based on a proposed joint error equivalent strategy, which can fully consider the coupled multi-source errors of machining robot. After that, a practical consistency retention method for RMSDT is proposed based on a stepwise updating strategy, where the model performance can be maintained with low updating costs. Finally, a visual VC system is developed for the experimental 6-degree of freedom robotic milling platform to verify the feasibility and effectiveness of the VC framework. Multiple experiments are also performed to test the performance of RMSDT and contour error compensation. This study has useful reference for the enterprises engaged in RMS and has positive significance for promoting the robotic machining.

Is “Intelligence” More Capable of Promoting the Development of the Tertiary Industry? — Analysis of the Impact of Artificial Intelligence on Different Categories of Industries

In the new scientific and technological revolution round, artificial intelligence (AI) technology has become a key leading force for industrial change. Research shows that AI not only promoted technical transformation and industry upgrades but also played a significant role in the rapid development of emerging industries. Based on the installed number of industrial robots and the industrial data by the National Bureau of Statistics, this study establishes a theoretical framework with the econometric model and compares the impact of AI on different categories of industries through empirical analysis. Our results show that AI not only promotes economic growth but also plays a key role in promoting the tertiary industry. Hence, optimization of industrial structure and economic upgrade can be induced.

Adaptive wireless-powered network based on CNN near-field positioning by a dual-band metasurface

With the improvement of industry, the connectivity of electronic devices gradually shift from wired to wireless. As a solution for power delivery, the non-contact power transfer holds promising ways to charge for moving terminals, enabling battery-free sensing, processing, and communication. Based on a dual-band metasurface, this study proposes an adaptive wireless-powered network (AWPN) to realize the simultaneous wireless localization and non-contact power supply. It first achieves localization with 3 cm resolution on a single-input single-output (SISO) system, by combining space-time-coding (STC) and convolutional neural network (CNN). With precise position information, AWPN real-time aligns power beams to the terminals for stable energy transmission. Then, battery-free terminals enable to perceive the environmental data and uploads the results. From the measurement results, AWPN gets more than 98% CNN classification accuracy and can tolerate certain environmental changes. Thus, being adaptive and contactless, our study will propel the advancement in Internet of Things (IoT), intelligent metasurface, and the robot industry.

Does industrial robot adoption inhibit environmental pollution in China? An empirical study on energy consumption and green technology innovation

In this research, we develop a theoretical model to examine the relationship between the adoption of industrial robots and environmental pollution. Additionally, we explore the mechanisms through which the implementation of industrial robots influences pollutant emissions. Our findings indicate that the adoption of industrial robots has a negative effect on environmental pollution. This conclusion is drawn from a panel data analysis of 283 cities in China from 2006 to 2020, utilizing a double fixed effect model. Further mechanism tests reveal that the use of industrial robots contributes to a reduction in pollution emissions by optimizing the energy consumption structure and enhancing energy efficiency. Meanwhile, industrial robot usage decreases pollution emissions by enhancing pollution treatment capabilities and boosting green technological innovation. Furthermore, regions characterized by heightened environmental awareness demonstrate a more significant improvement in environmental pollution levels when industrial robots are applied to their production process. This study ultimately demonstrates that integrating industrial robots can lead to a reduction in pollutant emissions, thereby yielding sustainable benefits for the environment.

Error compensation for industrial robots combining kinematics characterized by conformal geometric algebra and fused observability-index-based measurement strategy

Abstract The geometric errors of industrial robots are key factors affecting positioning accuracy. A new compensation method for industrial robots is proposed based on the kinematics characterizing with Conformal Geometric Algebra (CGA) and measurement strategy with Double Ball Bar (DBB) path point optimization. Firstly, a kinematic error model for the industrial robot is established using CGA, the starting point of the CGA model is modified to simplify the modeling process and reduce computational complexity. Secondly, fused observability index is proposed and the relationship between the number of sampling points on the DBB path and the effectiveness of error parameter is obtained. Thirdly, the adaptive golden spiral optimization algorithm for error parameter identification is proposed, achieving efficient and stable identification of error parameters. Finally, a case study is carried out on a six-degree-of-freedom industrial robot. The validity of measurement strategy and error parameter identification algorithm are confirmed by comparing the residuals and uncertainties of predicted point positions in space with different methods. The spatial compensation results show that, after compensation, the average error and root mean square error of measurement paths are reduced by 36.76% and 33.96%, respectively.

An evolutionary deep learning approach using flexible variable-length dynamic stochastic search for anomaly detection of robot joints

Anomaly detection is crucial for condition monitoring of robot joints. An increasing number of anomaly detection methods based on deep learning have been investigated. However, since the deep learning architectures for anomaly detection are manually designed by trial and error, the design process is time-consuming, and the designed deep learning architectures may not be optimal for specific anomaly detection tasks. In this paper, a Flexible Variable-Length Dynamic Stochastic Search (FVLDSS) is proposed by designing and embedding the encoding and alignment strategies into the original Dynamic Stochastic Search (DSS). Subsequently, an Evolutionary Deep Learning Approach Using FVLDSS (EDLDSS) is proposed to automatically search for an optimal deep learning architecture for anomaly detection. In EDLDSS, Convolutional Neural Network (CNN) is used as the feature extractor, k-nearest neighbors trained only with normal samples is used as the anomaly detector, and FVLDSS is used to simultaneously optimize the network structure and hyperparameters of CNN. Furthermore, an anomaly detection method based on EDLDSS is developed to detect the anomalies in robot joints, in which S-transform spectrograms of vibration signals normalized by Z-score normalization are used as the input of CNN. To validate the performance of EDLDSS, a condition monitoring system using fiber Bragg grating sensors is first established for the industrial robot to acquire vibration signals from robot joints and conduct three experiments. The statistical results demonstrate the feasibility and satisfactory performance of EDLDSS in handling the anomaly detection problem of robot joints.

Labor-Replacing Automation and Finance

We examine the effects of adoption of labor-replacing automation technology on corporate financing. Empirically, using Chinese firm-level panel data on deployment of industrial robots as an example of such automation, we find that robot adoption increases leverage and reduces cost of debt. We hypothesize that the underlying reason is that being a substitute for labor, automation provides a hedge against fluctuations in labor costs. A model based on this hedging argument delivers additional testable predictions concerning determinants of the relation between automation and corporate financing. These relations are borne out in the data, providing support for the proposed mechanism. Our evidence is inconsistent with alternative channels behind the observed relations. This paper was accepted by Victoria Ivashina, finance. Funding: X. Cheng acknowledges support from the Humanities and Social Science Foundation of the Ministry of Education in China [Grant 23YJC840004] and the Fundamental Research Funds for the Central Universities of China [Grant 2722023BQ052]. E. Lyandres acknowledges support from the Coller Foundation and the Henry Crown Institute for Business Research in Israel. K. Zhou acknowledges support from the Major Project of the National Social Science Foundation of China [Grant 20&ZD103], the Key Project of the National Natural Science Foundation of China [Grant 72432005], and the Excellent Young Team Project of the Natural Science Foundation of Guangdong Province of China [Grant 2023B1515040001]. T. Zhou acknowledges support from the National Natural Science Foundation of China [Grant 72003206]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02658 .

Analysis of Current Bipedal Robots by Comparative Method

Concerning the rapid development in the bipedal robot industry, its hard for researchers to figure out their current merits and demerits, making it more difficult to make progress, especially for those who newly step into the robot industry. This paper aims at comparing the most established and creative breakthroughs in bipedal robots to help researchers get a general view of it, simultaneously proposing some thoughts on bipedal robots about how the future utilization of bipedal robots can be.

Surgical robots: history, applications and future prospects

PurposeThe purpose of this study is to provide details of the development and applications of surgical robots.Design/methodology/approachFollowing a short introduction, this first provides a historical background to surgical robot developments. The following sections discuss applications, specialised systems and their uses, remote telesurgery and a short consideration of future prospects. Finally, brief conclusions are drawn.FindingsCatalysed by the needs of NASA and the US Department of Defence, robotic surgery was first conducted in the 1980s with modified industrial robots and the first dedicated surgical robots emerged commercially in the 1990s. These allow surgeons to conduct minimally invasive procedures which lead to significant patient benefits. Advances in communications have enabled long-range telesurgery based on commercial networks. In 2024, a telesurgical technique was conducted on simulated tissue on the International Space Station. Advances in artificial intelligence (AI), augmented reality and virtual reality will enhance the capabilities of future systems as well as contributing to improved surgeon training. In the longer term, AI-assisted surgery may be conducted with limited human intervention and full automation of certain procedures may ultimately arise.Originality/valueThis provides a detailed account of surgical robot developments and applications from their inception in the 1980s to the present day.