Robot Applications Research Articles

The impact of peer effect of industrial robot application on enterprise carbon emission reduction.

The application of intelligent technology, such as industrial robots, is related to the environmental governance effectiveness of enterprises and the realization of the goal of "carbon peak and carbon neutrality". Due to their similar external environments, driven by economic rationality, peer enterprises will mimic the robotics applications of other enterprises, which in turn will affect the enterprises' carbon emissions. However, little literature has explored the impact of industrial robot application on enterprise carbon emissions from the perspective of peer effect. Based on the data of Shanghai and Shenzhen A-share manufacturing listed enterprises in China from 2011 to 2021, this paper explores the impact of industrial robot application on carbon emission reduction of manufacturing enterprises from the perspective of peer effect. It is found that the industry peer effect and regional peer effect brought by the application of industrial robots are conducive to promoting the carbon emission reduction of enterprises. Among them, the industry peer effect of industrial robot applications promotes carbon emission reduction by enhancing the green innovation ability of enterprises, while the regional peer effect promotes carbon emission reduction by improving the service level of enterprises. It is further found that the degree of industry competition and the level of environmental regulation have inverted U-shaped moderating effects on the industrial robot application industry peer effect, regional peer effect, and enterprises' carbon emission reduction, respectively. The results enrich the research on the impact of industrial robot application on carbon emission reduction of manufacturing enterprises and provide policy implications for improving the environmental performance of enterprises.

Advances in the Application of AI Robots in Critical Care: Scoping Review.

In recent epochs, the field of critical medicine has experienced significant advancements due to the integration of artificial intelligence (AI). Specifically, AI robots have evolved from theoretical concepts to being actively implemented in clinical trials and applications. The intensive care unit (ICU), known for its reliance on a vast amount of medical information, presents a promising avenue for the deployment of robotic AI, anticipated to bring substantial improvements to patient care. This review aims to comprehensively summarize the current state of AI robots in the field of critical care by searching for previous studies, developments, and applications of AI robots related to ICU wards. In addition, it seeks to address the ethical challenges arising from their use, including concerns related to safety, patient privacy, responsibility delineation, and cost-benefit analysis. Following the scoping review framework proposed by Arksey and O'Malley and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we conducted a scoping review to delineate the breadth of research in this field of AI robots in ICU and reported the findings. The literature search was carried out on May 1, 2023, across 3 databases: PubMed, Embase, and the IEEE Xplore Digital Library. Eligible publications were initially screened based on their titles and abstracts. Publications that passed the preliminary screening underwent a comprehensive review. Various research characteristics were extracted, summarized, and analyzed from the final publications. Of the 5908 publications screened, 77 (1.3%) underwent a full review. These studies collectively spanned 21 ICU robotics projects, encompassing their system development and testing, clinical trials, and approval processes. Upon an expert-reviewed classification framework, these were categorized into 5 main types: therapeutic assistance robots, nursing assistance robots, rehabilitation assistance robots, telepresence robots, and logistics and disinfection robots. Most of these are already widely deployed and commercialized in ICUs, although a select few remain under testing. All robotic systems and tools are engineered to deliver more personalized, convenient, and intelligent medical services to patients in the ICU, concurrently aiming to reduce the substantial workload on ICU medical staff and promote therapeutic and care procedures. This review further explored the prevailing challenges, particularly focusing on ethical and safety concerns, proposing viable solutions or methodologies, and illustrating the prospective capabilities and potential of AI-driven robotic technologies in the ICU environment. Ultimately, we foresee a pivotal role for robots in a future scenario of a fully automated continuum from admission to discharge within the ICU. This review highlights the potential of AI robots to transform ICU care by improving patient treatment, support, and rehabilitation processes. However, it also recognizes the ethical complexities and operational challenges that come with their implementation, offering possible solutions for future development and optimization.

Progress of anterior subcutaneous internal fixation in the treatment of anterior pelvic ring injury

Anterior subcutaneous internal fixation (INFIX) is one of the current representatives of minimally invasive fixation of injuries to the anterior pelvic ring. The nail insertion point of this technique is located at the anterior inferior iliac spinous screw, with an angle of 30° outward and 20° backward. Screw in at an angle, and note that the screw head should be above the deep fascia and maintain a safe distance of 20 to 25 mm from the bone surface. Its improved versions include 3 or 4 nails INFIX with added pubic tubercle screws, unilateral INFIX, short-rod INFIX, and double INFIX. These improvements further enhance stability. The lateral femoral cutaneous nerve (LFCN) is relatively easy to be damaged during anatomy, so special attention should be paid during the operation. Biomechanical stability has advantages over external fixation, and its application is flexible. It is not limited to pubic ramus fracture, symphyseal separation, etc. It also plays an important role in combined anterior and posterior ring fixation. It can be combined with posterior sacroiliac screws, iliac lumbar screws, etc. Fixed etc. Good clinical results have also been reported in children, pregnant women, and people with contraindications for subcutaneous connecting rods. In addition, the current application of robots, reduction frames and other technologies has greatly reduced the difficulty of reduction and improved the quality of closed reduction, making it possible to fix complex pelvic fracture. This technique has high reduction quality and is as effective as traditional steel plates. A common complication is LFCN injury. Careful exposure and adjustment of the position and depth of internal fixation during surgery can effectively avoid this complication.

Research on Road Pattern Recognition of a Vision-Guided Robot Based on Improved-YOLOv8

In order to promote the accurate recognition and application of visual navigation robots to the environment, this paper carried out research on the road pattern recognition of a vision-guided robot based on improved YOLOv8 on the basis of road pattern calibration and experimental sampling. First, an experimental system for road image shooting was built independently, and 21 different kinds of road pattern image data were obtained by sampling roads with different weather conditions, road materials, and degrees of damage. Second, the road pattern recognition model based on the classical neural network Resnet 18 was constructed for model training and testing, and the initial recognition of road pattern was realized. Third, the YOLOv8 target detection model was introduced to build the road pattern recognition model based on YOLOv8n, and the model was trained and tested, improving road pattern recognition accuracy and recognition response speed by 3.1% and 200%, respectively. Finally, to further improve the accuracy of road pattern recognition, improvement research was carried out on the YOLOv8n road pattern recognition model based on the C2f-ODConv module, the AWD adaptive weight downsampling module, the EMA attention mechanism, and the collaboration of the three modules. Three network architectures, classical CNN (Resnet 18), YOLOv8n, and improved YOLOv8n, were compared. The results show that four different optimization models can further improve the accuracy of road pattern recognition, among which the accuracy of the improved YOLO v8 road pattern recognition model based on multimodule cooperation is the highest, reaching more than 93%.

Application of new features based on artificial intelligent robot technology in medium-scale urban design pedigree and intelligent management and control

Since the 21st century, China has been vigorously developing urban construction, and now the rise of artificial intelligence (AI) has brought new opportunities for urban design and management. Meso-scale cities are the most developed cities in China and play an important role in economic and social development. The current robot technology is mainly divided into industrial robots and service robots, which can play an important role in the development of cities. This paper aims to apply AI robotics technology to the analysis and intelligent management of mesoscale urban design genealogy. Firstly, the development pedigree of the mesoscale city was analyzed, from which the characteristics of previous designs could be clearly understood. Then, the urban management and control system is intelligently designed from many aspects, the characteristics of the robot are analyzed, and the application of the intelligent robot in urban design is introduced. After that, four developing meso‑scale cities in a province were selected as the evaluation objects, and an Analytic Hierarchy Process (AHP) was proposed to evaluate the application effect of robotics technology in meso‑scale cities. The results showed that the overall score of the robot in urban traffic design was greater than 70 points, and the overall score of urban architectural design was greater than 65 points, which was in the acceptable range. In the application score of urban environment design, the influence was about 70 to 80 points, and the aesthetics was more than 75 points. The cultural aspect of the design impact score was no more than 75 points, while the cultural support aspect score was around 80 points. After the weight calculation, the final overall score was 489 points, and the comprehensive average score was 36 points. The overall composite score and average score were both good. This showed that the application of robot technology to the pedigree analysis and intelligent management and control of mesoscale urban design could achieve good results.

Exploration of the Application of Agricultural Intelligent Robots in the Qinghai-Tibet Plateau

In order to improve agricultural efficiency, reduce labor intensity, and improve the quality and yield of agricultural products on the Tibetan Plateau, this paper takes the application of traditional agricultural robots as an example and proposes the application of agricultural robots to agriculture on the Qinghai-Tibet Plateau based on the special breeding environment of the Qinghai-Tibet Plateau combined with the current situation of traditional agriculture. Through the application practice of robots in traditional agriculture, the possibility of the application of agricultural robots in the Qinghai-Tibet Plateau is pointed out, the working methods and advantages of robots in the Qinghai-Tibet Plateau are expounded, and the application of intelligent agricultural robots in the agriculture of the Qinghai-Tibet Plateau is discussed and analyzed. The results show that with the progress of science and technology, intelligent robots have gradually played an important role in various fields, and intelligent robots have broad application prospects in plateau agriculture. The application of agricultural robots in the Qinghai-Tibet Plateau should be aimed at the Qinghai-Tibet Plateau to make some adaptive research and development, intelligent upgrading and demonstration project promotion in order to promote the Qinghai-Tibet Plateau agricultural automation.

Recent Advances in Robotics and Intelligent Robots Applications

Robotics research has a unique allure for both academia and the industry due to its potential for groundbreaking innovation and real-world applications [...]

Research Status and Development Trend of Multi-arm Collaborative Robots

Abstract: Industrial robots are mainly used in metal forming, automotive, and electrical and electronics industries. After decades of unremitting efforts, industrial robots have achieved great success. However, problems such as low safety, weak environmental adaptability, and insufficient humancomputer interaction ability of industrial robots have greatly limited the application of industrial robots, and multi-arm collaborative robots came into being in this case. Multi-arm collaborative robots offer high flexibility and precision. Over the years, the application and development of multiarm collaborative robots have been paid more and more attention. This paper aims to summarize the patents related to multi-arm cooperative robots, including their principles, classification, latest progress and future development, in order to meet the needs of the real world. According to the structural characteristics of multi-arm collaborative robots and the requirements of multi-arm collaborative robots application field, the most typical mobile collaborative robots and stationary dual-arm collaborative robots in multi-arm collaborative robots are summarized. By investigating various patents of multi-arm collaborative robots the main problems of multi-arm collaborative robots are summarized and analyzed, such as collisions or conflicts between robotic arms, low power-to-weight ratio, loose structures, and heavy weight of the robot body. In addition, the development trend of multi-arm collaborative robots is also discussed. Multi-arm robots have broad prospects and potential in future applications and development. With the advancement of new technologies, multi-arm collaborative robots will become more efficient, intelligent, safe, and flexible. More relevant patents are expected to be invented in the future.

Towards a high-energy efficiency world: Assessing the impact of artificial intelligence on urban energy efficiency

Global warming has increased the frequency of natural disasters, making enhancing energy efficiency an inevitable choice to address the worsening global climate risks. Whether industrial robots, as a crucial component of artificial intelligence, can contribute to improving energy efficiency remains a subject of debate. This study employs the SBM and super-efficiency SBM models to assess the energy efficiency of 262 Chinese cities from 2010 to 2020. We investigate the impact and mechanisms of industrial robots at the city level on energy efficiency. The research findings indicate that the application of industrial robots promotes enhanced energy efficiency, which remains robust after subjecting it to a battery of tests, including SYS-GMM and instrumental variable analyses. However, a significant nonlinear relationship exists between industrial robot utilization and energy efficiency, with diminishing marginal effects. Mechanism analysis reveals that the fundamental mechanisms facilitating this improvement are the digital economy, industrial structural adjustments, and innovations in green technology. Heterogeneity analysis suggests that industrial robots have a more pronounced impact on cities with higher energy efficiency, greater manufacturing levels in resource-dependent areas, and lower population densities. Furthermore, the application of industrial robots can influence the energy efficiency of neighboring regions through spatial spillover effects.

Industrial Robot Digital Twin Application Technology

This paper mainly discusses the core position of the course "Industrial robot Application Technology" in the national double high construction professional group and the innovation of teaching methods. The "task-driven, process" of production-teaching collaboration built through digital twin technology aims to cultivate students' design development. The importance of the course "Industrial Robot Application Technology" is introduced. Teachers' teaching innovation team, national production training base and national science and technology innovation service platform support the digital development of this technology in intelligent manufacturing to create a digital twin teaching platform. In the course material content-based teaching design and the docking of industrial needs, including and industrial robot operation certification. In addition, the action-oriented teaching concept, ideological and political materials are integrated into the teaching design, and the paper emphasizes the teaching material compilation method and three-dimensional teaching material design. At the same time, it also has a potential impact on the expected model innovation of the application and promotion of teaching materials and continuing education.

Improving path planning for mobile robots in complex orchard environments: the continuous bidirectional Quick-RRT* algorithm.

Efficient obstacle-avoidance path planning is critical for orchards with numerous irregular obstacles. This paper presents a continuous bidirectional Quick-RRT* (CBQ-RRT*) algorithm based on the bidirectional RRT (Bi-RRT) and Quick-RRT* algorithms and proposes an expansion cost function that evaluates path smoothness and length to overcome the limitations of the Quick-RRT* algorithm for non-holonomic mobile robot applications. To improve the zigzag between dual trees caused by the dual-tree expansion of the Bi-RRT algorithm, CBQ-RRT* proposes the CreateConnectNode optimization method, which effectively solves the path smoothness problem at the junction of dual trees. Simulations conducted on the ROS platform showed that the CBQ-RRT* outperformed the unidirectional Quick-RRT* in terms of efficiency for various orchard layouts and terrain conditions. Compared to Bi-RRT*, CBQ-RRT* reduced the average path length and maximum heading angle by 8.5% and 21.7%, respectively. In addition, field tests confirmed the superior performance of the CBQ-RRT*, as evidenced by an average maximum path lateral error of 0.334m, a significant improvement over Bi-RRT* and Quick-RRT*. These improvements demonstrate the effectiveness of the CBQ-RRT* in complex orchard environments.

Impact of industrial robot on labour productivity: Empirical study based on industry panel data

This empirical analysis explores the impact of industrial robots on labour productivity using panel data of 17 Chinese industries from 2006 to 2021. The results reveal that the development of industrial robots significantly improves labour productivity; a series of robustness tests validate this outcome. However, the impact of industrial robots on labour productivity varies across industry types. The influence coefficient of the low-density robotics industry is larger than that of the high-density robotics industry. Furthermore, although the scale of industrial robot usage before 2012 was smaller than that after 2012, its effect on labour productivity was more significant. Our findings indicate the possibility of diminishing marginal effect of industrial robots in promoting labour productivity. The mechanism analysis demonstrates that human capital level has a complete intermediating effect between industrial robots and labour productivity. Thus, industrial robot applications can contribute to labour productivity by optimising human capital structure. These findings provide crucial insights for governments and policy makers to improve labour productivity and economic growth.

Multi-scene application of intelligent inspection robot based on computer vision in power plant

As industries develop, the automation and intelligence level of power plants is constantly improving, and the application of patrol robots is also increasingly widespread. This research combines computer vision technology and particle swarm optimization algorithm to build an obstacle recognition model and obstacle avoidance model of an intelligent patrol robot in a power plant respectively. Firstly, the traditional convolutional recurrent neural network is optimized, and the obstacle recognition model of an intelligent patrol robot is built by combining the connection timing classification algorithm. Then, the artificial potential field method optimizes the traditional particle swarm optimization algorithm, and an obstacle avoidance model of an intelligent patrol robot is built. The performance of the two models was tested, and it was found that the highest precision, recall, and F1 values of the identification model were 0.978, 0.974, and 0.975. The highest precision, recall, and F1 values of the obstacle avoidance model were 0.97, 0.96, and 0.96 respectively. The two optimization models designed in this research have better performance. In conclusion, the two models in this study are superior to the traditional methods in recognition effect and obstacle avoidance efficiency, providing an effective technical scheme for intelligent patrol inspection of power plants.

Simplified Strategy for Trajectory Tracking Application of a Passive Suspension Rover-Type Mobile Robot

In the present work, based on an approximate modelling of a rover-type robot and a proportional control law, a simplified trajectory tracking strategy for a passive suspension rover-type mobile robot was developed. This strategy achieves trajectory tracking and the autonomous displacement of a rover, of which its configuration involves complex kinematics and dynamics. All these lineaments reduce the complexity of the analysis, the number of electronic components to implement, the computational requirements and the energy consumption. The robotic system used is based on the Shrimp rover, which is a robot with a passive suspension that is capable of carrying out displacements over rough terrain. The tests were performed using numerical simulations with different desired trajectories, and also using experimental tests using a passive suspension rover-type mobile robot.

Enhancing the Mobility of Small-Scale Robots Via Nonlinear Structural Springs Exhibiting Negative Stiffness

Abstract Compared to traditional robotic systems, small-scale robots, ranging from several millimeters to micrometres in size, are capable of reaching narrower and vulnerable regions with minimal damage. However, conventional small-scale robots’ limited maneuverability and controlability hinder their ability to effectively navigate in the intricate environments, such as the gastrointestinal tract. Self-propelled capsule robots driven by vibrations and impacts emerge as a promising solution, holding the potentials to enhance diagnostic accuracy, enable targeted drug delivery, and alleviate patient discomfort during gastrointestinal endoscopic procedures. This paper builds upon our previous work on self-propelled capsule robots, exploring the potential of nonlinear connecting springs to enhance its propulsion capabilities. Leveraging a mathematical model for self-propelling robots with a von Mises truss spring, which is verified using a finite element model, we investigate the effects of negative stiffness and snap-back within the nonlinear structural spring on the robots’ propelling speed. Our analysis reveals that the negative stiffness of the von Mises truss can significantly reduce the sensitivity of the propelling speed to excitation frequency. As a result, the capsule robot exhibits a remarkably wider operational band where it maintains a high average propelling speed, surpassing its linear counterpart. This work sheds light on the potential for developing customized nonlinear structural systems for diverse scenarios in small-scale robot applications, opening up new possibilities for enhanced functionality and maneuverability in various biomedical applications.

Composite continuum robots: Accurate modeling and model reduction

Models of continuum robots often trade off accuracy and speed. The constant curvature assumption model can perform fast calculations but with poor accuracy. Static models can obtain good calculation accuracy but with slower computation speed. In this paper, a precise and efficient method for establishing a non-constant curvature kinematic model for composite continuum robots is proposed. First, an algorithm based on the Beam Constraint Model is proposed to establish the static model of the continuum robot. The situation in which friction changes gradually in continuum robots is considered in the static model. Then, the Kepler elliptic curve is used to simplify the static model to build a non-constant curvature kinematic model of the continuum robot. Then, complex inverse kinematics solutions are transformed into explicit sets of equations by mathematical transformation. Finally, simulations and experiments are designed to demonstrate the algorithm proposed in this paper. The simulation results show that the proposed algorithm is 1572 times faster than the Levenberg–Marquardt algorithm. The experimental results show that the average error of the static model is 0.065 mm, accounting for 0.13% of its length. The maximum error is 0.309 mm, accounting for 0.618% of its length. The positioning accuracy of the non-constant curvature model proposed in this paper is 2.02 times that of the constant curvature model. The results show that the simplified kinematic model algorithm based on the static model has high accuracy and computational efficiency. This research is crucial for the practical application of continuum robots in engineering.

Electronic skin with shape sensing and Bending-Insensitive pressure sensing for soft robotic grippers object recognition

Soft robotic grippers rely on accurate contact surface information to perform gripping and sorting tasks safely. Difficulty in obtaining accurate contact surface information for recognizing objects with a single type of sensor hinders the advanced capabilities of soft robotic grippers. To obtain contact surface information more accurately for object recognition, in this paper a shape-pressure dual-mode electronic skin for contact surface detection of soft robotic grippers is proposed. It consists of pressure sensing units and curvature sensing units. The pressure sensing unit is insensitive to curvature through an ingenious structural design. It can detect pressure in the range of 0–––7.5 N with a measurement error of 3.92 %. The curvature sensing unit can detect curvature in the range of 0–––0.5 cm−1 with a sensitivity of 63.66 % / cm-1. The experimental results show that the electronic skin can help the soft robotic gripper obtain information on the contact surface. The electronic skin is expected to accelerate the application of soft robots in advanced tasks such as recognition and classification.

A soft robot tactile fingertip for grasping surface posture detection

Abstract The application field of robots is gradually extending from traditional industrial manufacturing to commercial services, medical care and other fields. Stable grasping is a necessary prerequisite for various complex robot application scenarios. The existing research on robot tactile sensor mainly focus on sensing tactile information, such as pressure or sliding. However, grasping surface posture as a tactile information that has a significant impact on the stability of grasping is often ignored. This study proposed a novel soft robot tactile fingertip that can not only detect the grasping force but also simultaneously detect the grasping surface posture. It is capable of recognizing 19 grasping surface postures in real-time using Mamdani fuzzy control method. The soft robot tactile fingertip has a simple structure and easy to fabricate at low cost. The application example of the two-finger mechanical hand using the soft tactile fingertip samples for stable grasping is demonstrated. This study shows that the soft robot tactile fingertip has great application prospects in the filed of robot intelligent grasping.

Deep reinforcement learning and decoupling proportional-integral-derivative control of a humanoid cable-driven hybrid robot

Aiming at the application of robots in service, medical treatment, rehabilitation, and other fields, a humanoid cable-driven hybrid robot by imitating the structure of human arm is designed in this article. The robot is composed of a cable–spool–pulley system, series mechanism, and coaxial spherical parallel mechanism, which can achieve six degrees of freedom movement in space. A challenge with cable driving is that the movement of the rear end joints (such as pitch and roll) can alter the length or tension of the cable driven by the frontend joints, resulting in joint coupling. This interference can lead to a decrease in the motion accuracy of the robotic arm. In addition, it also affects the durability of cables or mechanical components such as bearings and pulleys. Considering the joint motion coupling phenomenon caused by the cable-driven, the decoupling method is proposed, and the kinematic model of the robot is established. To solve the nonlinear coupling characteristics and the uncertainty of the dynamics parameters, a controller is proposed for the humanoid cable-driven hybrid robot, combining proportional-integral-derivative (PID) control based on decoupling method (DC-PID) and the double delay deep deterministic policy gradient (TD3) deep reinforcement learning algorithm. The trajectory tracking of the end-effector position and orientation are controlled by using DC-PID and TD3. And the simulation results show that the proposed control method has good trajectory tracking and convergence performance according to trajectory tracking error and training reward. Finally, the humanoid cable-driven hybrid robot prototype is developed. The experimental results of coupling compensation show that the average maximum error of the joint is reduced by 77.58% after considering the coupling compensation of the joint. The results of the controller validation experiments show that the DC-PID controller reduces the maximum error in each axis by 11.54%, 35.29%, and 40.16%, respectively, compared to the open-loop experiments.

Gas source localization using Dueling Deep Q-Network with an olfactory quadruped robot

Gas source localization is one of the most common applications of a gas-sensitive mobile robot. However, most of the existing work focuses on rule-based algorithms for wheeled robots, which are difficult to apply in complex terrain with obstacles. In this article, we propose an olfactory quadruped robot to perform the gas source localization task using the Dueling Deep Q-Network (Dueling DQN) algorithm. For training, we designed a set of environments and imported gas dispersion data from computational fluid dynamics (CFD) software to construct a simulator. The olfactory quadruped robot was trained in this simulator using the Dueling DQN algorithm to learn how to find the gas source. The trained neural network was then deployed on the olfactory quadruped robot. Our method has been tested in both simulation and real environments. The olfactory quadruped robot can traverse rugged terrain in real experiments and efficiently find gas sources, demonstrating that our method is highly robust and has important practical value.