Robot Applications Research Articles

Sharp Curve Trajectory Tracking of a Universal Omni-Wheeled Mobile Robot Using a Sliding Mode Controller

Abstract In our previous works, Amarasiri et al. (2022, “Robust Dynamic Modeling and Trajectory Tracking Controller of a Universal Omni-Wheeled Mobile Robot,” ASME Lett. Dyn. Sys. Control 2(4), p. 040902) and Amarasiri et al. (2024, “Investigating Suitable Combinations of Dynamic Models and Control Techniques for Offline Reinforcement Learning Based Navigation: Application of Universal Omni-Wheeled Robots,” ASME Lett. Dyn. Sys. Control 4(2), p. 021007), two dynamic models of a universal omni-wheeled mobile robot (UOWMR), and trajectory-tracking controllers (three linear and one nonlinear) were developed and presented. The purpose of that work was to identify suitable combinations of dynamic models and trajectory-tracking controllers, choosing the combination with the highest tracking accuracy and the lowest solver execution time. The ultimate purpose is to utilize these physics-based tools in a reinforcement learning (RL) agent developed for path planning and navigation in an unstructured environment. Three trajectories were investigated including a smooth path, a sharp curved path, and a smooth path with disturbance forces. The sliding mode controller (SMC) following a trajectory with sharp (right-angled) curves was not investigated in that work. The sharp corners caused indeterminacy in the path derivatives required for the SMC algorithm. In this short article, we complete our studies of the SMC on sharp corner paths utilizing slight trajectory corner smoothing.

Automation in Agriculture: A Robotic Approach to Weed Control for Greenhouse Cucumber Cultivation

The increasing global population has heightened the demand for efficient food production, leading to the adoption of compact cultivation methods such as greenhouses. However, weed growth within these controlled environments significantly challenges crop productivity. The application of robots can be a useful and economic choice. This study presents an innovative, purely mechanical system for weed control in greenhouses, specifically designed to operate autonomously on a monorail. The machine stops in the distance between the two main plant rows (cucumber) and its arm goes into the gap to deploy the weeds by rotating its blades. This is continued repeatedly. The cutting is by the rotational speed of the blade. Variable-speed motions of the blade were at 3500, 2500 and 1500 rpm for 3 types of moulinex, triangular, and circular blades. The movement speed of the arm was 10 and 30 rpm and the forward movement speed of the machine was 30, 40, 50, 60, 80 and 120 rpm. The results showed that different blades, blade speeds and engine speed affect significantly (p <0.05) the percentage of weeds being cut. Although the interactions of these factors have no significant effect on percentage; the average percentages by the blades have significant differences. Although the interactions between these factors were not statistically significant, the comparison of means revealed that at lower blade speeds, the blade type had a pronounced effect on weed-cutting efficiency. As blade speed increased, differences in blade performance diminished. The most effective combination was achieved using Moulinex blades at 3500 rpm with a 10-rpm arm speed, resulting in the highest percentage of weeds cut. These findings suggest that optimizing blade type and speed can significantly enhance the mechanical weed control efficiency in greenhouse environments.

Industrial Robots, Factor Market Distortion, and Productivity

ABSTRACTWe find that robot adoption significantly increases firm‐level TFP, but factor market distortion hinders the promotion effect of robot adoption on firm‐level TFP. Mechanism analysis reveals that the cost‐saving and labor productivity improvements brought about by industrial robot application stimulate market entry by new firms and elevate the exit risk for incumbent enterprises. This dynamic intensifies competition, resulting in an enhanced market environment that contributes to firm‐level TFP. However, factor market distortions impede the survival of the fittest among firms and stifle the competitive effects of market entry and exit. This ultimately leads to sluggish growth in firm‐level TFP.

Technological Applications of Social Robots to Create Healthy and Comfortable Smart Home Environment

Technological Applications of Social Robots to Create Healthy and Comfortable Smart Home Environment

Effect of Upper Robot-Assisted Training on Upper Limb Motor, Daily Life Activities, and Muscular Tone in Patients With Stroke: A Systematic Review and Meta-Analysis.

Upper limb rehabilitation robot is a relatively new technology, but its effectiveness remains debatable due to the inconsistent results of clinical trials. This article intends to assess how upper limb rehabilitation robots help the functional recovery of stroke patients. PubMed, Embase, Cochrane Library, and Web of Science databases were searched for eligible studies to explore the effect of upper limb rehabilitation robots on upper limb motor function, muscle tone, and daily living activities. Eighteen trials with 573 stroke patients met the inclusion criteria. The results showed that compared to conventional rehabilitation training, patients who received upper limb robotic therapy (RT) had significantly improved Fugl-Meyer Upper Extremity Motor Assessment (FMA-UE) scores (weighted mean differences [WMD]: 5.27, 95% confidence intervals [CI]: 3.36, 7.17), Action Research Arm Test (ARAT) scores (WMD: 4.07, 95% CI: -4.14, 12.28), Modified Barthel Index (MBI) scores (WMD: 9.55, 95% CI: 6.37, 12.73), and modified Ashworth Scale (MAS) scores (WMD: -0.28, 95% CI: -0.50, 0.06), with no significant heterogeneity. Upper limb robot-assisted training is superior to conventional training in terms of improving upper limb motor impairment, ability to perform daily living activities, and muscle tone recovery, which supports the application of robots in clinical practice.

Exploring the Implementation Methods of AI-Driven Robots in Media Interaction Experience Design

The purpose of this study is to deeply explore the innovative implementation methods of artificial intelligence (AI)-driven robots in media interactive experience design. Firstly, this paper expounds how AI-driven robot technology can provide users with a more natural and intelligent interactive experience through natural language processing (NLP), speech recognition, computer vision and other technologies, thus enhancing users' sense of participation and immersion. In order to realize the efficient application of AI-driven robot in media interaction, this study proposes a series of implementation methods, including improving the accuracy and personalization of robot interaction by using NLP and deep learning algorithm; Identify user expressions and gestures through computer vision technology to enhance the naturalness of interaction; Multi-modal interactive technology is used to integrate multi-sensory channel information to provide comprehensive interactive experience; Use deep learning technology to train the robot's ability to perceive and understand users' needs, and introduce emotion analysis technology to realize more humanized service; By constructing user portraits, we design personalized interaction schemes that adapt to different scenarios and users. In the experimental part, three different scenes, library, shopping mall and museum, are selected to verify the effectiveness of the design scheme. The results show that the AI-driven robot significantly improves the user's satisfaction and emotional experience under the personalized interaction scheme.

A multimodal educational robots driven via dynamic attention

IntroductionWith the development of artificial intelligence and robotics technology, the application of educational robots in teaching is becoming increasingly popular. However, effectively evaluating and optimizing multimodal educational robots remains a challenge.MethodsThis study introduces Res-ALBEF, a multimodal educational robot framework driven by dynamic attention. Res-ALBEF enhances the ALBEF (Align Before Fuse) method by incorporating residual connections to align visual and textual data more effectively before fusion. In addition, the model integrates a VGG19-based convolutional network for image feature extraction and utilizes a dynamic attention mechanism to dynamically focus on relevant parts of multimodal inputs. Our model was trained using a diverse dataset consisting of 50,000 multimodal educational instances, covering a variety of subjects and instructional content.Results and discussionThe evaluation on an independent validation set of 10,000 samples demonstrated significant performance improvements: the model achieved an overall accuracy of 97.38% in educational content recognition. These results highlight the model's ability to improve alignment and fusion of multimodal information, making it a robust solution for multimodal educational robots.

Ro2En: Robust Neural Environment Encoder for Domain Generalization of Fast Motion Planning

This paper discusses a new issue named domain generalization of fast motion planning in 3D environments, which benefits agility-required robot applications such as autonomous driving and uncrewed aerial vehicle obstacle avoidance flight. The existing work shows that conventional spatial search-based planning algorithms cannot meet the real-time requirement due to high time costs. The end-to-end neural network-based methods achieve an excellent balance between performance and planning speed in the seen environments, but are hard to transfer to new scenarios. To overcome this limitation, we propose a novel Robust Environment Encoder (Ro2En) approach to domain generalization of fast motion planning. Specifically, by demonstrating the reconstructed environment, we find that the previous environment encoder cannot encode the volume information properly, i.e., a volume collapse ensues, which leads to noisy environment modeling. Inspired by this observation, a dual-task auto-encoder is developed. It can not only reconstruct the point cloud of the obstacles, but also align their geometric centers. Experiment results showed that in the new scenarios, Ro2En outperformed previous state-of-the-art conventional and neural alternatives with a much smaller performance variation.

Real-time crop row detection using computer vision- application in agricultural robots

The goal of achieving autonomous navigation for agricultural robots poses significant challenges, mostly arising from the substantial natural variations in crop row images as a result of weather conditions and the growth stages of crops. The processing of the detection algorithm also must be significantly low for real-time applications. In order to address the aforementioned requirements, we propose a crop row detection algorithm that has the following features: Firstly, a projective transformation is applied to transform the camera view and a color-based segmentation is employed to distinguish crop and weed from the background. Secondly, a clustering algorithm is used to differentiate between the crop and weed pixels. Lastly, a robust line-fitting approach is implemented to detect crop rows. The proposed algorithm is evaluated throughout a diverse range of scenarios, and its efficacy is assessed in comparison to four distinct existing solutions. The algorithm achieves an overall intersection over union (IOU) of 0.73 and exhibits robustness in challenging scenarios with high weed growth. The experiments conducted on real-time video featuring challenging scenarios show that our proposed algorithm exhibits a detection accuracy of over 90% and is a viable option for real-time implementation. With the high accuracy and low inference time, the proposed methodology offers a viable solution for autonomous navigation of agricultural robots in a crop field without damaging the crop and thus can serve as a foundation for future research.

Ethical Reflections on the Application of Medical Robots in Healthcare

With the development and utilization of medical robots, more and more members of the public have begun to pay attention to the risks of practicing medical robots and have expressed concerns about their application. In order to deeply study the ethical risks of medical robots applied in health care, this study chooses the literature research method to reflect on the moral and ethical issues involved in medical robots. The study found that current medical robots have many application scenarios, such as: surgery, rehabilitation care, assisted work and telemedicine. These application scenarios of medical robots may bring ethical risks such as attribution of medical responsibility, doctor-patient relationship, and data algorithms. Intensively, the study points out that inadequate laws, immature values, and technological deficiencies are the most significant factors leading to the aforementioned risks. Thus, adequate attention should be paid to the medical career, the intelligent technology of robots should be improved, and the relevant laws and regulations should be actively improved. This study realistically reflects the current situation and risks of medical robot application, and through in-depth reflection, provides inspiration for robots to better serve the medical cause.

Application of Path Planning and Tracking Control Technology in Mower Robots

Path planning and tracking is the most basic technology for mowing robots, among which the performance of algorithms has a great impact on their intelligence and efficiency. Based on the research of relevant references on mower robots, it mainly focuses on complete coverage path planning, path tracking control, and obstacle avoidance path planning. In complete coverage path planning, three methods were introduced, including simple complete coverage planning, optimal complete coverage planning, and hybrid complete coverage planning. In the path tracking control section, the control methods are divided into three types based on whether the control method depends on the robot model and the type of model, namely model free control method, kinematic model-based control method, and dynamic model-based control method. In obstacle avoidance path planning, we introduce the environment detection device and obstacle avoidance planning algorithm. Then the relevant research papers are analyzed in classification, comparing the research and validation methods adopted by the researchers in the form of charts. Finally, we pointed out the limitations of path planning technology in the application of mower robots. Meanwhile, future development trends are predicted.

Robots in Public Libraries: An Empirical Analysis of Current Applications and Future Opportunities

ABSTRACT The paper investigated the current application of robots in public libraries with specific consideration of its future opportunities. Three (3) objectives guided the conduct of the study. The study employed a qualitative research approach. The population for the study comprised librarians who have been certified by the Librarian Registration Council of Nigeria (LRCN) and worked at public library boards. Thematic analysis and content analysis techniques were adopted for this study. The study found that robots bring various advantages, including increased operational efficiency and streamlined access to information, as well as interactive educational opportunities and heightened user involvement. The findings indicate that embracing robotic technology enables public libraries to establish more accessible, dynamic, and user-focused environments, fostering an atmosphere of innovation and technological progress within the realm of information services. It could be concluded that the integration of robots in public libraries plays a vital role in modernizing and improving library services to meet the evolving demands of library users and communities. However, library stakeholders must address potential obstacles, including financial limitations, technological preparedness, and user acceptance. Prioritizing the training of staff, fostering community involvement, and establishing clear policy frameworks enables public libraries to effectively navigate the challenges related to incorporating robotics, ensuring a smooth transition toward a technologically advanced and user-focused library environment.

Engineering the Future: Robotics in the Automotive Industry Explained

The study provides an overview of the potential applications of industrial robots in the automobile sector, which is now the market's most significant client. An overview of the use of robots in global industry is provided in the first section of the article, which also discusses the state of industrial robot utilization. The most popular uses of robots, particularly in the automotive sector, are covered later, along with developments and outlooks for the field's future. The essay concluded with a specific project overview regarding robotized screw tightening on the car seat production line, which served as a model task from actual industrial practice.The many uses of robots in the automobile industry are examined in this review, including material handling, welding, painting, assembly line automation, and quality control. It emphasizes how collaborative robots (cobots) enhance human-robot interaction and how advances in machine learning and artificial intelligence maximize robotic performance. Keywords: Industrial robot; robotics in automotive; production of passenger cars; transport and production; automated assembly line

A novel mobile robot with origami wheels designed for navigating sandy terrains

Abstract Sand robots play a vital role as mobile tools for human exploration of desert regions, facilitating resource transportation and exploration. However, desert areas primarily consist of beaches or dunes, resulting in a highly diverse and complex terrain environment that demands enhanced adaptability from sandy mobile robots. Traditional wheeled robots currently face challenges such as skidding, limited climbing ability, and inadequate obstacle avoidance capabilities in sandy environments. To address these issues and enable effective adaptation to the intricate sand environment, we propose a novel sandy mobile robot equipped with Kresling origami wheels. The origami wheel can dynamically adjust its width and morphology through Kresling origami folding. Experimental tests were conducted to illustrate the impact of width variation on the robot’s mobility velocity, propulsive force, climbing performance, and carrying capacity. The self-folding malleability of the origami wheel empowers the robot to efficiently accomplish diverse tasks, including swift movement on flat sand surfaces, seamless crossing of narrow channels, and intelligent obstacle avoidance. By successfully completing these multimodal tasks while adapting to varying requirements, our robot demonstrates promising prospects for practical applications of mobile robots equipped with origami wheels – paving the way for wider adaptation and utilization of sand mobile robots.

Retraction notice: Application and use of telepresence robots in libraries and information center services: prospect and challenges

Retraction notice: Application and use of telepresence robots in libraries and information center services: prospect and challenges

6-DOFs Robot Placement Based on the Multi-Criteria Procedure for Industrial Applications

Robot acceptance is rapidly increasing in many different industrial applications. The advancement of production systems and machines requires addressing the productivity complexity and flexibility of current manufacturing processes in quasi-real time. Nowadays, robot placement is still achieved via industrial practices based on the expertise of the workers and technicians, with the adoption of offline expensive software that demands time-consuming simulations, detailed time-and-motion mapping activities, and high competencies. Current challenges have been addressed mainly via path planning or robot-to-workpiece location optimization. Numerous solutions, from analytical to physical-based and data-driven formulation, have been discussed in the literature to solve these challenges. In this context, the machine learning approach has proven its superior performance. Nevertheless, the industrial environment is complex to model, generating extra training effort and making the learning procedure, in some cases, inefficient. The industrial problems concern workstation productivity; path-constrained minimal-time motions, considering the actuator’s torque limits; followed by robot vibration and the reduction in its accuracy and lifetime. This paper presents a procedure to find the robot base location for a prescribed task within the robot’s workspace, complying with multiple criteria. The proposed hybrid procedure includes analytical, physical-based, and data-driven modeling to solve the optimization problem. The contribution of the algorithm, for a given user-defined task, is the search for the best robot base location that enables the target points, maximizing the manipulability, avoiding singularities, and minimizing energy consumption. Firstly, the established method was verified using an anthropomorphic robot that considers different levels of a priori kinematics and system dynamics knowledge. The feasibility of the proposed method was evaluated through various simulations for small- and medium-sized robots. Then, a commercial offline program was compared, considering three scenarios and fourteen robots demonstrating an energy reduction in the 7.6–13.2% range. Moreover, the unknown joint dependency in real robot applications was investigated. From 11 robot positions for each active joint, a direct kinematic was appraised with an automatic DH scheme that generates the 3D workspace with an RMSE lower than 65.0 µm. Then, the inverse kinematic was computed using an ANN technique tuned with a genetic algorithm showing an RMSE in an S-shape task close to 702.0 µm. Finally, three experimental campaigns were performed with a set of tasks, repetitions, end-effector velocity, and payloads. The energy consumption reduction was observed in the 12.7–22.9% range. Consequently, the proposed procedure supports the reduction in workstation setup time and energy saving during industrial operations.

The Impact of Industrial Robot Applications on Global Value Chains Division Position

This paper surveys the GVC division position across forty-five countries and fourteen manufacturing sub-sectors during the period from 1999 to 2018 to illustrate the impact of applications of industrial robots on the GVC position. The study proves that an increasing level of utilization of industrial robots significantly improves the GVC division position in the manufacturing sector. By automating repetitive and precision tasks, robots enhance efficiency and productivity, therefore allowing deeper specialization in specific stages of the manufacturing process. This technological progress allows for a more connected global production network and hence better integration into GVC. The study also evaluates the spillover effects across value chains and the moderating impact of industrial agglomeration on smart manufacturing.The empirical results show that the application of industrial robots enhances productivity and encourages innovation in related sectors, thereby driving downstream industry growth and promoting industry specialization.Despite the improvement in GVC division positions, robots also contribute to making global value chains more diversified and resilient. Flexible and adaptive manufacturing systems can adapt to disruptions, such as pandemics and geopolitical tensions, ensuring continuity in production and increasing stability and competitiveness in smart manufacturing systems.

Industry spillover effects of robot applications on labor productivity: Evidence from China

This paper provides new insights into the industry spillover on labor productivity by discussing the spillover effects of robot application among industries in three spillover directions: horizontal, forward, and backward spillover. In addition, this paper measures the three spillovers with the weight matrices from the year 2012 input-output table by the Chinese National Bureau of Statistics. Using a spatial econometric model and 2000–2010 data on China's secondary industry, this paper tests three hypotheses relating to the direct effect and the spillover effects of robot applications. The main results show that the robot application of one industry can improve labor productivity significantly. Moreover, all three kinds of spillover have significant positive effects on the labor productivity of a focal industry. Among them, the forward spillover exerts the greatest impact on labor productivity. The heterogeneity effects exist due to the industry's factor intensity and technological structure etc. The findings of this study could provide several important implications for the development of a given industry and the industry chain of a country as well.

A Novel Multiple Shape Memory Effect in PVDF‐Based Ferroelectric Copolymers and Terpolymers

AbstractShape memory polymers (SMPs) have been extensively investigated because of their wide range of biomedical and robot applications. In most of SMPs, only one temporary shape can be formed and recovered through the mechanism of melting or glass transition. Herein, a multiple shape memory effect (mSME), i.e., formation of at least two temporary shapes, can be realized in polyvinylidene fluoride (PVDF)‐based ferroelectric polymers by exploiting their expanded ferroelectric–paraelectric (F‐P) phase transition temperature range. Although P(VDF‐TrFE) (TrFE: trifluoroethylene) (55/45) copolymer is thought to be a normal ferroelectric, its ferroelectric phase transforms into a paraelectric phase through an intermediate relaxor ferroelectric‐like state and mSME is observed in this extended phase transition temperature range. By incorporating CTFE (chlorotrifluoroethylene) into P(VDF‐TrFE), P(VDF‐TrFE‐CTFE) becomes a relaxor ferroelectric with a further extended phase transition temperature range. The terpolymer exhibits improved mSME and at least three temporary shapes can be formed and recovered. A comparison of SME and structures of several PVDF‐based copolymer and terpolymers suggests that the amount of polar phase is a critical factor affecting the SME. This study not only demonstrates mSME in ferroelectric polymers, which expands their application potential, but also provides an in‐depth understanding of the shape memory mechanism of the polymers.

ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ В ОБРАЗОВАНИИ

The article is devoted to the problem of using robots in the educational process in the training of specialists. The article describes training robots based on the Robot Operating System, i.e. based on a set of software libraries and tools that help create applications for robots. Robot Integration with ROS (Robot Operating System) is the process of combining the hardware and software components of a robot with the ROS infrastructure. This allows the robot to interact with other devices and systems, as well as use a variety of ready-made tools and libraries available within the ecosystem. Robot Integration with ROS (Robot Operating System) is the process of combining the hardware and software components of a robot with the ROS infrastructure. This allows the robot to interact with other devices and systems, as well as use a variety of ready-made tools and libraries available within the ecosystem. Project Development: Using ROS, students can develop their own robotics projects, ranging from simple mobile robots for indoor navigation to complex manipulators for performing tasks in industry. Project Development: Using ROS, students can develop their own robotics projects, ranging from simple mobile robots for indoor navigation to complex manipulators for performing tasks in industry.