Capabilities Of Robots Research Articles

Cross-National Comparison of Technology Innovation Capabilities in Automation and Robotics

In today’s dynamic and global economic environment, technological innovations are a key determinant of international competitiveness. In the field of automation and robotics, the development of modern technologies is of particular significance, influencing not only industrial progress but also social aspects. Automation and robotics play a crucial role in the concept of modern industry, contributing to the creation of more sustainable, flexible, and human-centric production systems. The article aims to conduct an in-depth analysis of the patent activity in automation and robotics in Europe. Patent documentation serves as a source of knowledge about the directions of research, inventive activities, and consequently, the innovative and competitive potential of the economy. The number of patents over time reflects the dynamics of a country’s technological development. The article discusses the use of patent databases to evaluate a country’s innovation in automation and robotics. The conducted analysis of data on European countries enabled the identification of trends based on the International Patent Classification and specialisation from a geographical perspective using revealed comparative technological advantage. The European countries were classified using cluster analysis, demonstrating the diversity, and identifying leaders in each group.

Synthesizing the temporal self: robotic models of episodic and autobiographical memory.

Episodic memories are experienced as belonging to a self that persists in time. We review evidence concerning the nature of human episodic memory and of the sense of self and how these emerge during development, proposing that the younger child experiences a persistent self that supports a subjective experience of remembering. We then explore recent research in cognitive architectures for robotics that has investigated the possibility of forms of synthetic episodic and autobiographical memory. We show that recent advances in generative modeling can support an understanding of the emergence of self and of episodic memory, and that cognitive architectures which include a language capacity are showing progress towards the construction of a narrative self with autobiographical memory capabilities for robots. We conclude by considering the prospects for a more complete model of mental time travel in robotics and the implications of this modeling work for understanding human episodic memory and the self in time. This article is part of the theme issue 'Elements of episodic memory: lessons from 40 years of research'.

A Multifunctional Tactile Sensory System for Robotic Intelligent Identification and Manipulation Perception.

Humans recognize and manipulate objects relying on the multidimensional force features captured by the tactile sense of skin during the manipulation. Since the current sensors integrated in robots cannot support the robots to sense the multiple interaction states between manipulator and objects, achieving human-like perception and analytical capabilities remains a major challenge for service robots. Prompted by the tactile perception involved in robots performing complex tasks, a multimodal tactile sensory system is presented to provide in situ simultaneous sensing for robots when approaching, touching, and manipulating objects. The system comprises a capacitive sensor owning the high sensitivity of 1.11E-2pFmm-1, a triboelectricity nanogenerator with the fast response speed of 30ms, and a pressure sensor array capable of 3D force detection. By Combining transfer learning models, which fuses multimodal tactile information to achieve high-precision (up to 95%) recognition of the multi-featured targets such as random hardness and texture information under random sampling conditions, including random grasp force and velocity. This sensory system is expected to enhance the intelligent recognition and behavior-planning capabilities of autonomous robots when performing complex tasks in undefined surrounding environments.

Analysis and Simulation of Permanent Magnet Adsorption Performance of Wall-Climbing Robot

In response to problems such as insufficient adhesion, difficulty in adjustment, and weak obstacle-crossing capabilities in traditional robots, an innovative design has been developed for a five-wheeled climbing robot equipped with a pendulum-style magnetic control adsorption module. This design effectively reduces the weight of the robot, and sensors on the magnetic adsorption module enable real-time monitoring of magnetic force. Intelligent control adjusts the pendulum angle to modify the magnetic force according to different wall conditions. The magnetic adsorption module, using a Halbach array, enhances the concentration effect of the magnetic field, ensuring excellent performance in high-load tasks such as building maintenance, bridge inspection, and ship cleaning. The five-wheel structural design enhances the stability and obstacle-crossing capability, making it suitable for all-terrain environments. Simulation experiments using Maxwell analyzed the effects of the magnetic gap and the angle between the adsorption module and the wall, and mechanical performance analysis confirmed the robot’s ability to adhere safely and operate stably.

AI-Powered Innovations in Contemporary Manufacturing Procedures: An Extensive Analysis

The industrial sector is undergoing a transformation thanks to artificial intelligence (AI), which is bringing revolutionary changes to a number of areas like robots and automation, supply chain efficiency, predictive maintenance, and quality control and assurance. This thorough analysis investigates AI's significant influence on contemporary manufacturing procedures. Artificial Intelligence (AI) improves machine capabilities in robotics and automation, creating more intelligent and flexible systems. Robots can now complete complicated tasks with more flexibility and precision thanks to AI-driven developments, which boosts manufacturing efficiency and human-robot cooperation. Another crucial area where AI has a big impact is predictive maintenance. With the use of machine learning algorithms and real-time data analysis, artificial intelligence (AI) helps manufacturers anticipate equipment faults before they happen. By taking a proactive stance, unplanned downtime is decreased, resource usage is optimized, and machinery longevity is increased. AI has a significant positive impact on quality assurance and control because to cutting-edge technologies like data analytics and computer vision. Artificial intelligence (AI) solutions facilitate predictive quality management, improve fault identification, and offer real-time monitoring. Higher quality standards, less waste, and more customer happiness are the outcomes of this. Artificial Intelligence (AI) tackles issues related to supplier performance, accurate forecasting, and inventory management in supply chain optimization. Automation and analytics powered by AI simplify supply chain processes, increase transparency, and facilitate improved decision-making, which lowers costs and increases flexibility. All things considered, integrating AI into manufacturing processes offers a strategic advantage by promoting increased accuracy, flexibility, and efficiency. The continued developments in AI technology have the potential to significantly influence how manufacturing develops in the future by creating new avenues for creativity and excellence in the sector.

Optimization of Machine Learning-Based Automatic Target Detection and Locking System on Robots

Background: In recent years, the world of robotics has made significant progress in improving the operational capabilities of robots through target detection and locking systems. These systems play a crucial role in improving the efficiency and effectiveness of critical applications such as defense, security, and industrial automation. However, the main challenge faced is the limitations of the existing system in adapting to unstable environmental conditions and dynamic changes in targets. Objective: This research aims to overcome these challenges by developing a more adaptive and responsive target detection and locking system by integrating two leading machine learning technologies: Convolutional Neural Networks (CNN) for target detection and Long Short-Term Memory (LSTM) for target tracking. Methods: This study uses a quantitative approach to evaluate the effectiveness of the integration of CNNs and LSTMs in target detection and locking systems. Results: The results of the study showed a detection accuracy rate of 95% and a locking accuracy of 90%. The system is proven to be able to adapt to changing operational conditions in real-time and provide consistent performance in a variety of complex and dynamic scenarios. Conclusion: The conclusion of this study is that the integration of CNN and LSTM technologies in target detection and locking systems in robots significantly improves the performance and efficiency of the system, enabling a wider and more complex application.

Recognition of Chinese Electronic Medical Records for Rehabilitation Robots: Information Fusion Classification Strategy.

Named entity recognition is a critical task in the electronic medical record management system for rehabilitation robots. Handwritten documents often contain spelling errors and illegible handwriting, and healthcare professionals frequently use different terminologies. These issues adversely affect the robot's judgment and precise operations. Additionally, the same entity can have different meanings in various contexts, leading to category inconsistencies, which further increase the system's complexity. To address these challenges, a novel medical entity recognition algorithm for Chinese electronic medical records is developed to enhance the processing and understanding capabilities of rehabilitation robots for patient data. This algorithm is based on a fusion classification strategy. Specifically, a preprocessing strategy is proposed according to clinical medical knowledge, which includes redefining entities, removing outliers, and eliminating invalid characters. Subsequently, a medical entity recognition model is developed to identify Chinese electronic medical records, thereby enhancing the data analysis capabilities of rehabilitation robots. To extract semantic information, the ALBERT network is utilized, and BILSTM and MHA networks are combined to capture the dependency relationships between words, overcoming the problem of different meanings for the same entity in different contexts. The CRF network is employed to determine the boundaries of different entities. The research results indicate that the proposed model significantly enhances the recognition accuracy of electronic medical texts by rehabilitation robots, particularly in accurately identifying entities and handling terminology diversity and contextual differences. This model effectively addresses the key challenges faced by rehabilitation robots in processing Chinese electronic medical texts, and holds important theoretical and practical value.

Recent advances on application of continuum robots in minimally invasive surgery

Continuum robots have emerged as pivotal tools in advancing minimally invasive surgery (MIS), offering enhanced flexibility and precision compared to traditional rigid instruments. This review categorizes continuum robots based on structural designs and actuation strategies, highlighting their ability to navigate complex surgical environments with minimal invasiveness. The integration of advanced sensing technologies for shape and environmental perception is examined, underscoring their role in improving surgical accuracy and patient safety. Specific applications in bronchoscopy, gastrointestinal endoscopy, and orthopedic procedures demonstrate the diverse capabilities of continuum robots in clinical settings. Despite challenges in material stiffness and sensor miniaturization, continuum robots show promising potential to reshape the landscape of MIS through continued innovation and research advancements.

Sustainable Impact of Stance Attribution Design Cues for Robots on Human–Robot Relationships—Evidence from the ERSP

With the development of large language model technologies, the capability of social robots to interact emotionally with users has been steadily increasing. However, the existing research insufficiently examines the influence of robot stance attribution design cues on the construction of users’ mental models and their effects on human–robot interaction (HRI). This study innovatively combines mental models with the associative–propositional evaluation (APE) model, unveiling the impact of the stance attribution explanations of this design cue on the construction of user mental models and the interaction between the two types of mental models through EEG experiments and survey investigations. The results found that under the influence of intentional stance explanations (compared to design stance explanations), participants displayed higher error rates, higher θ- and β-band Event-Related Spectral Perturbations (ERSPs), and phase-locking value (PLV). Intentional stance explanations trigger a primarily associatively based mental model of users towards robots, which conflicts with the propositionally based mental models of individuals. Users might adjust or “correct” their immediate reactions caused by stance attribution explanations after logical analysis. This study reveals that stance attribution interpretation can significantly affect users’ mental model construction of robots, which provides a new theoretical framework for exploring human interaction with non-human agents and provides theoretical support for the sustainable development of human–robot relations. It also provides new ideas for designing robots that are more humane and can better interact with human users.

Fast fixed-time extended state observer based command filtering backstepping control of free-flying flexible-joint space robots

System parameter perturbations, external disturbances, and input saturation issues seriously affect the on-orbit operation precision and rapid response capabilities of the free-flying flexible-joint space robots (FFSR). To this end, a fixed-time extended state observer (ESO) based non-singular command filtering backstepping controller is proposed. The fast fixed-time ESO is designed to estimate the parameter perturbations and external disturbances. To avoid the “computational explosion” caused by multiple derivatives of the virtual control law in backstepping control, a second-order command filter is employed to obtain the derivative of virtual control laws. Meanwhile, to reduce the filtering errors induced by the command filter, a non-singular fixed-time filtering error compensation algorithm is developed. Furthermore, considering the constraints of control moment gyroscope (CMG) and joint motors, an anti-saturation compensation algorithm is introduced to drive the system out of the saturated region rapidly, thus reducing actuator saturation effects on the control system performance. Theoretical analysis shows the proposed controller can ensure the system tracking error converges to any arbitrarily small neighborhood of the origin within fixed time. Simulation results demonstrate that the designed non-singular fixed-time command filtering backstepping controller exhibits fast convergence speed and high tracking accuracy.

An experimental study on attitude control of a tailless hummingbird-mimetic flapping-wing robot with defective wings

Hummingbirds demonstrate remarkable robustness to wing defects by maintaining stable hovering during the molting process. However, mimicking these capabilities in flapping-wing robots presents significant challenges. To investigate the effectiveness of various controllers in maintaining the attitude of a hummingbird-mimetic flapping-wing robot against external disturbances under different wing defects, this study implemented a PD controller, a PID controller, and a three-loop feedback controller with a disturbance observer (3L-DOB controller). The flapping-wing robot features a pair of wings and controls its body’s yaw, pitch, and roll rotations by modulating the tension of wing membranes and the neutral positions of wing torsion. The performance of these controllers was evaluated through semi-tethered experiments on a gimbal, employing three Wing Sets: intact wings, one wing was defective, and both wings were defective. The defective wing emulated hummingbird wing during molting, in which the wing area was cropped by 14.1%. As a result, the 3L-DOB controller showed the best performance in terms of responsiveness and accuracy across all Wing Sets, while the PID controller also achieved comparable performance.

Optimization of the mobile robot's path searching based on the A-star algorithm

Notable has been the rapid development of artificial intelligence in recent years, with mobile robots being widely applied in various fields. Greatly facilitating people's daily lives, research on robots has received widespread attention. The obstacle avoidance capability of mobile robots is an important indicator of their intelligence. This article utilizes the A-star algorithm. It optimizes the robot's movement path. The A-star algorithm is characterized by balancing the actual cost from the starting point to the current node with the estimated cost from the endpoint to the current node. This algorithm incorporates elements of both Dijkstra's algorithm and greedy search. By being guided by a heuristic function, A-star efficiently approaches the target point, ensuring optimal path planning. This paper focuses on optimizing the A-star algorithm, enhancing both node expansion during path exploration and search time for paths as the robot navigates through a maze.

Arduino-Controlled Multi-Function Robot with Bluetooth and nRF24L01+ Communication

This paper outlines the design and development of an advanced robotic system that integrates hardware implementation with theoretical simulation to address the need for versatile and user-friendly robotic solutions in various environments. Addressing the issue of limited adaptability in existing robotic systems, we propose a wireless, voice and gesture-controlled robot car with an integrated robotic arm capable of performing complex tasks such as line following, obstacle avoidance, object manipulation, and autonomous navigation over one-kilometer range. To improve operational efficiency and user involvement, this paper designs a multifunctional robotic platform that integrates user-friendly control interfaces with inexpensive, state-of-the-art sensor technologies. To achieve this, we integrate a variety of sensors, including ultrasonic sensors for precise distance measurement, infrared sensors for object detection and line following, an L298 motor driver for controlling geared motors, servo motors for controlling robotic arms, a flex sensor for claw control, and an mpu6050 accelerometer for gesture recognition. The system also uses a custom-made Bluetooth app for remote control, nRF24L01+ for long-range wireless control, and Arduino Mega and Nano for processing and control functions. The results demonstrate the robot functions well in dynamic conditions, and it can be used in hospitals to assist healthcare professionals, in restaurants for food delivery, and in industrial settings for object manipulation. The system’s design proves robust in real-world scenarios, offering significant improvements in accessibility and operational efficiency. This study aligns with Sustainable Development Goals (SDGs) 3 (Good Health and Well-being), 9 (Industry, Innovation, and Infrastructure), and 17 (Partnerships for the Goals). The robotic arm's potential application in healthcare settings advances SDG 3, its contribution to industrial productivity advances SDG 9, and collaborations with tech companies to expand and improve the robot's capabilities promote SDG 17.

A study on design and moving behavior of meal partner robots that can perform eating behavior expression

Dining with people improves our lives and brings a lot of benefits. Nonetheless, many people are compelled to eat by themselves due to the current social situation. We believe that robots can be good meal partners. In this paper, we describe the developed meal partner robots called Mamoru'21 and Mamoru'22. The design of these robots is inherited from a robot for watching over elderly people. Based on previous research that robot's eating behavior expression can improve the mealtime experience, they can perform motions that mimic eating and present food images by monitors. In the first experiment, we confirmed the developed robot could improve the co-eating experience compared to a conventional small humanoid robot, and investigated the keywords for preferable hardware design, such as ‘smaller than a child.’ In the second experiment, we studied the effects of the robot's moving around during mealtime. As a result, no statistically significant negative effects of robot movement during meals were observed in the developed robot's capability. Though this study has some limitations such as shorter experimentation time compared to regular meals, it contributes some insights and knowledge into the behavior and design of meal partner robots.

Design, motions, capabilities, and applications of quadruped robots: a comprehensive review

Robots are becoming integral to society and industries due to their enormous advantages. Among the various categories of mobile robots, including wheeled robot, tracked robot, and legged robots, the latter stands out as a better choice for most field applications due to their adaptability across various terrains. The purpose of this review is to study the locomotion capabilities of quadruped robots and judge their suitability for climbing applications as most unexplored applications of automation and robotics are required to climb. This review explores the locomotion capabilities of quadruped robots. It covers different aspects of quadruped robots like types of legs, leg design, gait patterns, and their mathematical formulations, and types of motions like omnidirectional motion and body sway motion. It also emphasizes its fault-tolerant gait, adaptability, and reliability. The paper also focuses on slope and stair climbing, outlining design requirements and applications. The study includes an examination of the applicability of various gaits under different conditions and the methods for increasing stability without compromising speed. Overall, the review serves as a valuable resource for future research in this field.

Improving Robotic Milling Performance through Active Damping of Low-Frequency Structural Modes

Industrial robots are increasingly prevalent due to their large workspace and cost-effectiveness. However, their limited static and dynamic stiffness can lead to issues like mode coupling chatter and regenerative chatter in robotic milling processes, even at shallow cutting depths. These problems significantly impact performance, product quality, tool longevity, and can damage robot components. An active inertial actuator was deployed at the milling spindle to enhance dynamic stiffness and suppress low-frequency vibrations effectively. It was identified that the characteristics of the actuator change with its mounting orientation, a common scenario in robotic machining processes. This variation has not been reported in the literature. Our study includes the identification of model parameters for the actuator in both horizontal and vertical mountings. Additionally, the novelty of the present work lies in the specific design and implementation of compensation filters tailored for the active inertial actuator in both horizontal and vertical configurations. These filters address the unique challenges posed by low-frequency vibrations in robotic milling, offering significant improvements in dynamic stiffness and vibration suppression. Traditional model-based compensators were effective for vertical mounting, while pole-zero placement techniques with minimum phase systems were optimal for horizontal mounting. These compensators significantly enhanced dynamic stiffness, reducing maximum low-frequency robot structural modes by approximately 100% in horizontal mounting and approximately 214% in the vertical configuration of the actuator. This advancement promises to enhance industrial robot capabilities across diverse machining applications.

A tactile sensing system capable of recognizing objects based on bioinspired self-sensing soft pneumatic actuator

Tactile sensors play an important role when robots perform contact tasks, such as physical information collection, force or displacement control to avoid collision. For these manipulations, excessive contact may cause damage while poor contact cause information loss between the robotic end-effector and the objects. Inspired by skin structure and signal transmission method, this paper proposes a tactile sensing system based on the self-sensing soft pneumatic actuator (S-SPA) capable of providing tactile sensing capability for robots. Based on the adjustable height and compliance characteristics of the S-SPA, the contact process is safe and more tactile information can be collected. And to demonstrate the feasibility and advantage of this system, a robotic hand with S-SPAs could recognize different textures and stiffness of the objects by touching and pinching behaviours to collect physical information of the various objects under the positive work states of the S-SPA. The result shows the recognition accuracy of the fifteen texture plates reaches 99.4%, and the recognition accuracy of the four stiffness cuboids reaches 100%by training a KNN model. This safe and simple tactile sensing system with high recognition accuracies based on S-SPA shows great potential in robotic manipulations and is beneficial to applications in domestic and industrial fields.

Individual and collective manipulation of multifunctional bimodal droplets in three dimensions.

Spatiotemporally controllable droplet manipulation is vital across numerous applications, particularly in miniature droplet robots known for their exceptional deformability. Despite notable advancements, current droplet control methods are predominantly limited to two-dimensional (2D) deformation and motion of an individual droplet, with minimal exploration of 3D manipulation and collective droplet behaviors. Here, we introduce a bimodal actuation strategy, merging magnetic and optical fields, for remote and programmable 3D guidance of individual ferrofluidic droplets and droplet collectives. The magnetic field induces a magnetic dipole force, prompting the formation of droplet collectives. Simultaneously, the optical field triggers isothermal changes in interfacial tension through Marangoni flows, enhancing buoyancy and facilitating 3D movements of individual and collective droplets. Moreover, these droplets can function autonomously as soft robots, capable of transporting objects. Alternatively, when combined with a hydrogel shell, they assemble into jellyfish-like robots, driven by sunlight. These findings present an efficient strategy for droplet manipulation, broadening the capabilities of droplet-based robotics.

Visual route following for tiny autonomous robots.

Navigation is an essential capability for autonomous robots. In particular, visual navigation has been a major research topic in robotics because cameras are lightweight, power-efficient sensors that provide rich information on the environment. However, the main challenge of visual navigation is that it requires substantial computational power and memory for visual processing and storage of the results. As of yet, this has precluded its use on small, extremely resource-constrained robots such as lightweight drones. Inspired by the parsimony of natural intelligence, we propose an insect-inspired approach toward visual navigation that is specifically aimed at extremely resource-restricted robots. It is a route-following approach in which a robot's outbound trajectory is stored as a collection of highly compressed panoramic images together with their spatial relationships as measured with odometry. During the inbound journey, the robot uses a combination of odometry and visual homing to return to the stored locations, with visual homing preventing the buildup of odometric drift. A main advancement of the proposed strategy is that the number of stored compressed images is minimized by spacing them apart as far as the accuracy of odometry allows. To demonstrate the suitability for small systems, we implemented the strategy on a tiny 56-gram drone. The drone could successfully follow routes up to 100 meters with a trajectory representation that consumed less than 20 bytes per meter. The presented method forms a substantial step toward the autonomous visual navigation of tiny robots, facilitating their more widespread application.

Effect of human emotional responses on human-robot team pty in construction

Human-robot collaboration (HRC) is expected to improve construction productivity by integrating robotic capabilities with human expertise. However, the effects of HRC on human emotions are unclear despite their critical impacts on human performance during HRC. This paper examined the inter-relationship between robots' parameters, like movement speed, and emotional responses, during HRC for a construction task. Eighteen participants were observed as they experience various conditions of robots' parameters during a bricklaying task with an arm-type collaborative robot in a laboratory setting. Their brainwaves (i.e., electroencephalogram (EEG) signals) were monitored using an EEG headset to identify emotional responses. Results showed significant emotional variations due to robots' parameters, which could affect the estimated productivity of human-robot teams. This paper demonstrated the emotional changes caused by the robots' parameters during HRC for construction tasks, highlighting the importance of considering emotions as a way to balance the emotional well-being of people and the high productivity of human-robot teams.