Robot Applications Research Articles

The nexus between industrial robot and employment in China: The effects of technology substitution and technology creation

The widespread application of industrial robots has raised our concerns about employment. Based on the data of Chinese micro-enterprises from 2000 to 2019, we use staggered and continuous DID method to measure the impact of industrial robots on the employment in the enterprise level in China. The result of the analysis shows that, under the combined effects of technology substitution and creation, the use of industrial robots has a significant positive impact on the employment of Chinese enterprises. The substitution effect is mainly in medium skilled labor, while the creation effect happens in high skilled and machine auxiliary positions. The analysis of heterogeneity of industry penetration rate of robot shows that, the creation effect in high skill positions and the substitution effect in low and medium skill positions mostly happen in high penetration rate industries, while the substitution effect in high skill positions and the creation effect in low and medium skill positions mostly happen in low and medium penetration rate industries. This result reveals the internal mechanism of the adjustment of employment structure caused by new technology. Based on this, the relevant suggestions are put forward from unemployment security system, corporate social responsibility, training of technical personnel, etc.

Synthetic control with target sticking for on-orbit capturing using a dual-arm space robot

On-orbit capturing is a crucial step in the application of dual-arm space robots, aiming to accurately grasp a moving target and avoid damage to the arms terminal parts (i.e. the end effectors) due to contact force saturation. This paper presents a synthetic control method with target sticking for on-orbit capturing using a dual-arm space robot system from the approaching phase to pre-grasping phase. In the approaching phase, an online trajectory planning method based on visual servoing is proposed to obtain the desired trajectories for end effectors in work space. Then the joint torques are designed by the inverse kinematics and inverse dynamics to track the desired trajectories. Different from most of the current research, a new sticking phase is proposed after the approaching phase and before the pre-grasping phase. In the sticking phase, a force and motion control method is designed to maintain a close relative distance between end effectors and the target, which can avoid saturation of contact forces and avoid damage to the end effectors. In what follows, an impedance control method is proposed in the pre-grasping phase to absorb the impact energy and grasp the target accurately. The synthetic control strategy integrates the controllers of all the phases mentioned above. Automatic switching among the phases is realized by using logic judgment according to the system real states, which is more consistent with the real capturing environment. Finally, numerical simulation results demonstrate the effectiveness of the proposed synthetic control scheme. The results can be applied to the trajectory planning and control of on-orbit capturing, avoiding contact force saturation and ensuring the safety of the system.

Research on Key Technologies of Artificial Intelligence for Intelligent Manufacturing

Intelligent manufacturing, as the main form of future manufacturing, is the highland of a new round of competition in the global manufacturing industry, which will fundamentally change the production and manufacturing methods and technological and economic paradigms formed by human beings since the industrial revolution. At present, the intelligent manufacturing industry is still in the early stage of development, which is characterized by three typical characteristics: continuous growth of market size, strong promotion by national governments and diversification of competition subjects. At present, China's manufacturing industry is facing transformation, and the manufacturing industry is bound to need to transform to intelligent manufacturing. Industrial robots are the main content of current intelligent manufacturing, how to fully apply artificial intelligence technology to the manufacturing of industrial robots, so that its manufacturing efficiency, manufacturing accuracy and other product attributes are improved, to maximize its economic benefits, has a particularly important application value. The application of intelligent manufacturing and robots can not only promote the faster development of manufacturing, but also reconstruct new forms of manufacturing. Based on this, the paper first summarizes the application technology of intelligent manufacturing, and then analyzes the key technology of robot application for reference to relevant workers.

Using Control Barrier Functions to Incorporate Observability: Application to Range-Based Target Tracking

Abstract In many nonlinear systems, the observability of the system is dependent on its state and control input. Thus, incorporating observability into a control scheme can enhance an observer's ability to recover accurate estimates of unmeasured states, minimize estimation error, and ultimately, allow the original control objective to be achieved. The accommodation of observability, however, may conflict with the original control goal at times. In this paper, we propose the use of control barrier functions (CBFs) to enforce observability and thereby facilitate the convergence of the state estimate to the true state while accommodating the original control objectives. Motivated by practical applications for autonomous robots operating in global positioning system-denied environments, we focus on the problem of target tracking for a unicycle model when only the distance to the target is measured. The proposed approach is compared in simulation with a model predictive control (MPC) approach that treats an observability-related metric as part of the cost function, where several different options for the observability metric are explored. It is found that the CBF-based approach achieves control and estimation performance that is comparable to that of the MPC approach, but with significantly less computational complexity. These findings are further experimentally verified in range-based target tracking with a swimming robotic fish.

Reinforcement Learning-Based Simulation of Seal Engraving Robot in the Context of Artificial Intelligence

The rapid development of robotics technology has made people's lives and work more convenient and efficient. The research and simulation of robots combined with reinforcement learning intelligent algorithms have become a hotspot in various fields of robot applications. In view of this, this study is based on deep reinforcement learning convolutional neural networks, combined with point cloud models, proximal strategy optimization algorithms, and flexible action evaluation algorithms. A seal cutting robot based on deep reinforcement learning has been proposed. The final results show that the descent speed of the seal cutting robot with the root mean square difference as the performance standard is about 1% faster than the flexible action evaluation algorithm. About 2% faster than the proximal strategy optimization algorithm. It is about 4% faster than the deep deterministic strategy gradient algorithm. This indicates that the research model has certain advantages in terms of actual accuracy after cutting. The fluctuation of this model is about 10% smaller than the evaluation of flexible actions and about 60% smaller than the gradient of deep deterministic strategies. Therefore, the research model has the highest overall stability without falling into local optima. In addition, compared to the near end strategy optimization algorithm, it falls into local optima, resulting in a low coincidence degree of about 17%. The deep deterministic strategy gradient algorithm has a large fluctuation amplitude during the seal cutting process, and the overall curve is relatively slow, with a final overlap of about 70%. The overlap degree of flexible action evaluation is slightly higher by about 83%. The maximum stability of the model's overlap is best around 90%. Through experiments, it can be found that the seal cutting robot proposed in the study based on deep reinforcement learning maintains certain advantages in performance indicators in various types of tests.

Bioinspired Flexible Capacitive Sensor for Robot Positioning in Unstructured Environments.

The evolution of bionic machines into intelligent robots to adapt to real scenarios is inseparable from positioning sensors. However, traditional positioning methods such as camera arrays, ultrasound, or GPS are limited in narrow concealed spaces, harsh temperatures, or dynamic light fields, which hinder the practical application of special robots. Here, we report a flexible sensor inspired by Gnathonemus petersii that enables robots to achieve contactless and high-precision spatial localization independent of the unstructured features of the environment. Sensors are obtained from low-cost materials (carbon nanotubes and polyimides) and simple structures (fibers) and preparation processes (spin-coating). Experiments and simulations confirmed the high resolution (<1 mm) of the sensor over a large distance detection range (>150 mm) and high bandwidth (0-520 MPa) of contact force. Moreover, the sensing capability is still feasible when the sensor is bent to various curvatures and not affected under harsh conditions such as ultralow temperatures (below -78 °C), ultrahigh temperatures (over 250 °C), darkness, or brightness. We demonstrate the practical potential of the proposed sensors for a biomimetic hyper-redundant continuum robot to locate and avoid collisions in unstructured environments.

Trajectory Control of Designed Experimental Mobile Robot

Due to advancing technology; nowadays mobile robot applications in hospitals have been increased. For that reason, it is very important and necessary to analyze the trajectory of such helping robotic system. However; there are many types of mobile robots have been utilized in hospital applications such as helping nurses. In this simulation study; a designed and controlled mobile robot was controlled by using standard feedback controllers. On the other hand, the robot was also tested with disturbances of ground surface roughness. The simulation results were improved that standard PID controller has superior performance to overcome surface roughness of the robot trajectory.

Insect-Scale Biped Robots Based on Asymmetrical Friction Effect Induced by Magnetic Torque.

Multimodal and controllable locomotion in complex terrain is of great importance for practical applications of insect-scale robots. Robust locomotion plays a particularly critical role. In this study, a locomotion mechanism for magnetic robots based on asymmetrical friction effect induced by magnetic torque is revealed and defined. The defined mechanism overcomes the design constraints imposed by both robot and substrate structures, enabling the realization of multimodal locomotion on complex terrains. Drawing inspiration from human walking and running locomotion, a biped robot based on the mechanism is proposed, which not only exhibits rapid locomotion across substrates with varying friction coefficients but also achieves precise locomotion along patterned trajectories through programmed controlling. Furthermore, apart from its exceptional locomotive capabilities, the biped robot demonstrates remarkable robustness in terms of load-carrying and weight-bearing performance. The presented locomotion and mechanism herein introduce a novel concept for designing magnetic robots while offering extensive possibilities for practical applications in insect-scale robotics.

Calibration strategies for enhancing accuracy in serial industrial robots for orbital milling applications

PurposeThis 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.Design/methodology/approachThe 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.FindingsThe 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.Research limitations/implicationsThe study focuses on a specific robot model and application (robotic orbital milling), limiting generalizability. Further research could explore diverse robot models and applications.Practical implicationsThe findings offer practical benefits by enhancing the accuracy of robotic systems, particularly in precision tasks like orbital milling, providing a valuable calibration method.Social implicationsWhile primarily technological, improved robotic precision can have social implications, potentially influencing fields where robotic applications are crucial, such as manufacturing and automation.Originality/valueThis 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.

Robotics, environmental regulation, and agricultural carbon emissions: an examination of the environmental Kuznets curve theory and moderating effects

IntroductionReducing carbon emissions from agriculture is essential to ensuring food security and human prosperity. As a country with approximately 20% of the global population, China has begun actively practicing the low-carbon agricultural development conception. Against the backdrop of disruptive technologies that continue to be integrated into various industries, the massive application of agricultural robots has opened the way to intelligent agriculture. This paper tries to answer whether there is some non-linear nexus between the application of agricultural robots and agricultural carbon emissions in China. As an essential tool for carbon emission reduction in China, does environmental regulation moderate the nexus between agricultural robot applications and agricultural carbon emissions? If so, how does this effect manifest itself?MethodsThis work takes China as an example by collecting macro-regional panel data from 30 provinces from 2006 to 2019. The environmental Kuznets curve theory is extended to agricultural carbon emissions, and we carried out empirical tests utilizing the panel fixed effects model and the moderating effects model.ResultsThis study verifies the inverted U-shaped nexus between agricultural robotics applications and agricultural carbon emissions in Chinese provinces, i.e., the agricultural carbon emissions (ACE)-Kuznets curve holds. The higher the level of formal environmental regulation, the larger the peak of the ACE-Kuznets curve and the more the inflection point is pushed back. The higher the level of informal environmental regulation, the lower the peak of the ACE-Kuznets curve and the later the inflection point.DiscussionThe findings in this paper represent the first exploration of the environmental Kuznets curve in agricultural carbon emissions. It is noteworthy that the moderating effect of formal environmental regulation does not lower the peak of the curve as we expect. This appearance is attributed to the reality that China is still in a phase of rising agricultural carbon emissions, which is exacerbated by the overlapping positive effects of agricultural robotics applications and formal environmental regulations. Informal environmental regulation is more effective than formal environmental regulation in reducing agricultural carbon emissions at this stage.

Task-Oriented Self-Imitation Learning for Robotic Autonomous Skill Acquisition

The inferior sample efficiency of reinforcement learning (RL) and the requirement for high-quality demonstrations in imitation learning (IL) will hinder their application in real-world robots. To address this challenge, a novel self-evolution framework, named task-oriented self-imitation learning (TOSIL), is proposed. To circumvent external demonstrations, the top-K self-generated trajectories are chosen as expert data from both per-episode exploration and long-term return perspectives. Each transition is assigned a guide reward, which is formulated by these trajectories. The guide rewards update as the agent evolves, encouraging good exploration behaviors. This methodology guarantees that the agent explores in the direction relevant to the task, improving sample efficiency and asymptotic performance. The experimental results on locomotion and manipulation tasks indicate that the proposed framework outperforms other state-of-the-art RL methods. Furthermore, the integration of suboptimal trajectories has the potential to improve the sample efficiency while maintaining performance. This is a significant advancement in autonomous skill acquisition for robots.

The Application of Industrial Robots in Automated Production Lines

Elaborate on the definition, structure, and characteristics of industrial robots, their application in automated production lines, and provide suggestions for balancing and optimizing industrial robots in automated production lines.

Influencing Factors as well as Implementation Path of Synergistic Development of Digitalization and Greening in Manufacturing Industry: Analysis from a Configuration Perspective

&lt;p class="MsoNormal" style="text-align: justify;"&gt;&lt;span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;"&gt;The manufacturing sector must incorporate digitalization and greening as crucial strategies to attain high level of development in the context of the digital economy, given its significant role in quality of environment. Most of existing studies explore net effect of a single or several factors on the digitalization or greening in the manufacturing industry. Limited investigation has been done to integrate various factors impacting the connection and synchronized advancement of digitalization and greening within the manufacturing. Factors affecting the synergy of digitalization and green change within manufacturing are recognized in this article, and a theoretical framework is established to analyze. FsQCA tool is also put into use to survey configurational cases, which contains synergy degree of green digitalization from 30 provinces&amp;rsquo; manufacturing in China. Results show that: (1) Between 2007 and 2020, the development level of digital-green integration in the manufacturing industry shows an upward trend. There are three types of collaborative modes, namely, "industrial robot driven" "market development driven" and "market development driven - industrial robot driven", with each mode consisting of at least one to four equivalent pathways. (2) The marketization level and application of industrial robots are the important external and internal pulling forces that drive the synergy degree of digitalization and green change within manufacturing industry, respectively.(3) The influencing factors of the collaborative mode choice of digital-green development within manufacturing industry are complementary, among which, the degree of marketization is similar to the role of industrial robots in the configuration, which are capable to weaken the negative impact from insufficient government R&amp;amp;D investment support under specific conditions; (4) The non-high collaborative state of digital green in manufacturing industry is mainly due to the low level of marketization, openness, and economic development, as well as the low application rate of industrial robots and software products. This study enriches the theoretical understanding on digitalization and green change of manufacturing industry. Meanwhile, it also provides insights to advance the integrated and synergistic advancement of digital green transformation in manufacturing practice.&lt;/span&gt;&lt;/p&gt;

Semantic segmentation of thermal defects in belt conveyor idlers using thermal image augmentation and U-Net-based convolutional neural networks

The belt conveyor (BC) is the main means of horizontal transportation of bulk materials at mining sites. The sudden fault in BC modules may cause unexpected stops in production lines. With the increasing number of applications of inspection mobile robots in condition monitoring (CM) of industrial infrastructure in hazardous environments, in this article we introduce an image processing pipeline for automatic segmentation of thermal defects in thermal images captured from BC idlers using a mobile robot. This study follows the fact that CM of idler temperature is an important task for preventing sudden breakdowns in BC system networks. We compared the performance of three different types of U-Net-based convolutional neural network architectures for the identification of thermal anomalies using a small number of hand-labeled thermal images. Experiments on the test data set showed that the attention residual U-Net with binary cross entropy as the loss function handled the semantic segmentation problem better than our previous research and other studied U-Net variations.

Investigating Suitable Combinations of Dynamic Models and Control Techniques for Offline Reinforcement Learning Based Navigation: Application of Universal Omni-Wheeled Robots

Abstract Omnidirectional locomotion provides wheeled mobile robots (WMR) with better maneuverability and flexibility, which enhances their energy efficiency and dexterity. Universal omni-wheels are one of the best categories of wheels that can be used to develop a WMR (Amarasiri et al., 2022, “Robust Dynamic Modeling and Trajectory Tracking Controller of a Universal Omni-Wheeled Mobile Robot,” ASME Letters Dyn. Sys. Control., 2(4), p. 040902. 10.1115/1.4055690). We study dynamic modeling and controllers for mobile robots to train in a reinforcement learning (RL)-based navigation algorithm. RL tasks require copious amounts of learning iteration episodes, which makes training very time consuming. The choice of dynamic model and controller has a significant impact on training time. In this paper, we compare a traditional Kane’s equations model to a non-holonomic canonical momenta model (Barhorst, 2019, “Generalized Momenta in Constrained Non-Holonomic Systems—Another Perspective on the Canonical Equations of Motion,” Int. J. Non-Linear Mech., 113, pp. 128–145.). We implemented four controllers: proportional integral derivative, linear quadratic regulator with integral action, pole placement, and a full nonlinear sliding mode controller. We summarize the pros and cons of each of the modeling techniques, and control laws implemented. The outcomes of our analysis will improve RL training time for path generation in unstructured environments.

Current study on PID control method with optimized BP neural network based on particle swarm optimization

At present, robots show great application value in various industries, and the progress of the industry constantly puts forward higher requirements for the performance of robots, and robot control is an important part of robot application. It is difficult for the traditional PID controller to implement online tuning when it is faced with actual objects. Consequently, the neural network algorithm has been included into robot control methods in recent years; the PID controller based on BP neural network is the subject of this research. This study presents the current state of research, the basic concept behind BP neural networks, how they are used in controller design, and how to optimize the PID parameters of BP neural networks using a particle swarm optimization method. To increase system stability, the neural network is coupled with a PID controller. The adaptive learning capability of the neural network is utilized to modify PID control parameters online in real time. Targeting the trouble that it slips into a local minimum easily during the BP-PID self-learning process and the refined PSO algorithm is used to improve it. It makes sure that the BP-PID system converges to the global optimal solution. The proposed method can effectively improve the system control accuracy and control stability.

Kinematic Analysis and Application to Control Logic Development for RHex Robot Locomotion.

This study explores the kinematic model of the popular RHex hexapod robots which have garnered considerable interest for their locomotion capabilities. We study the influence of tripod trajectory parameters on the RHex robot's movement, aiming to craft a precise kinematic model that enhances walking mechanisms. This model serves as a cornerstone for refining robot control strategies, enabling tailored performance enhancements or specific motion patterns. Validation conducted on a bespoke test bed confirms the model's efficacy in predicting spatial movements, albeit with minor deviations due to motor load variations and control system dynamics. In particular, the derived kinematic framework offers valuable insights for advancing control logic, particularly navigating in flat terrains, thereby broadening the RHex robot's application spectrum.

A novel methodology for vision-based path planning and obstacle avoidance in mobile robot applications

ABSTRACT The availability of overhead views of the environments around mobile robots always present useful information provided that these views are correctly perceived. In this paper, a novel approach for vision-based path planning and obstacle avoidance for mobile robots is presented. The approach uses visual perception integrated with a proposed field-based path-planning algorithm to overcome some common path-planning issues such as local minima, problematic destinations, and following long paths around obstacles. An exponential angle field is generated around each obstacle that deviates from the robot’s orientation as it directs towards the destination. Obstacle field is activated or deactivated based on a proposed collision prediction algorithm. To satisfy robot convergence to the goal point, the obstacle field parameters are determined using the Lyapunov stability criterion. The method also proposes a search algorithm for proper exit in the case of robot and/or goal point trapping. Both simulation and experiments are used in validating the algorithm. The results show the effectiveness of the proposed algorithm in converging towards target points, avoiding collision with stationary obstacles and overcoming famous path-planning problems.

Research on maintenance-free design and low friction characteristics of industrial robot sliding components

With the continuous progress of industrial automation, the application of industrial robots in various fields has become increasingly common, and ensuring their efficient and stable operation and reducing maintenance costs is crucial. This study used powder metallurgy technology, combined with multiple sintering-rolling processes and oil-immersed vacuum assistance, to successfully prepare Fe-Cu-Ni-Sn-graphite oil-immersed self-lubricating composite materials to meet this demand. In-depth microstructure and wear surface studies revealed that this porous oil-immersed self-lubricating composite material exhibits long-term low friction and high wear resistance and reduces the friction coefficient by 40% compared to untreated samples after a specific composite process. In addition, the material exhibits excellent friction performance in the high-temperature pin-on-disc friction and wear test machine. Even after continuous sliding for 24 hours, its friction coefficient remains low and stable. The study also found that its lubrication mechanism may be attributed to solid-liquid synergistic lubrication, thanks to the appearance of oil-graphite mixtures around the wear track. This design ensures high rigidity and reduced frictional loss, providing a strong reference for the design and optimization of sliding components of industrial robots, and is highly suitable for the widespread application of industrial robots.

Autonomous Navigation System for a Differential Drive Mobile Robot

Abstract Mobile robotics is a rapidly expanding field of research because of its large number of applications. Autonomous mobile robots (AMRs) are used in multiple areas, such as industrial automation, logistics and warehouse management, museums, hospitals and restaurant assistance, space and ocean exploration, and many others. In this work, a navigation system for a low-cost mobile robot is proposed. It relies on state-of-the-art software suited for designing automated systems, robot applications, and computer vision algorithms. The robot is driven by two wheels powered by Beckhoff motors, controlled using Beckhoff’s TwinCAT 3 automation software in an industrial computer. A separate small-sized Raspberry Pi computer handles the environment perception by processing the information acquired by a Lidar module and a webcam and the localization and planning through the Robot Operating System (ROS). Communication between the two computers yields a complete robot navigation system. This system is tested and subsequently tuned to achieve good performance. Experimental tests show that the navigation system is globally effective, and some limitations related to the robot’s design and its navigation subsystem are discussed. The approach proposed in this work can be extended, adjusted, and used in other types of mobile robots.