Robot Applications Research Articles

A contact sensor-free framework for ground reaction force observation in heavy-legged robots considering unknown loads

As a direct response to robot movements and load distributions, the ground reaction force (GRF) is pivotal for heavy-legged robot (HLR) applications. This study presents a technical framework for GRF monitoring in an electric cylinder-driven HLR, eliminating the need for information on body motion, load distributions, and measured servo outputs. Traditional joint space-based dynamics are extended to include servo currents, compensating for the influence of unknown servo outputs. An essential contribution is incorporating the impact of floating bases and unknown loads into a virtual spatial force (VSF) applied to the HLR hip joint. The VSF is obtained through the nonlinear disturbance observer when the HLR is in a stable contact phase. Subsequently, a high-order GRF observer (HOGO), compensated with VSF, enables GRF observations without the pre-acquired body movement and load distribution data. In contrast to conventional GRF observations, the proposed framework could determine virtual payloads acting on the hip joint while ensuring precise GRF monitoring without requiring supplementary sensors. The GRF observations of the proposed framework are experimentally superior to those of the conventional methods with unknown HLR body motion and load information.

A sense-drive-feedback integrated soft tactile actuator for bionic flytrap with high sensitivity, large deformation, and low-power consumption

In recent years, soft actuators have attracted much attention due to their potential application in soft robots, artificial muscles, and human–machine interaction interfaces. However, it is still challenging to achieve intelligent integration of sensing and driving functions. Herein, inspired by natural flytrap, a novel soft intelligent actuator is developed based on sense-drive-feedback integration in response to pressure stimuli. The actuator is fabricated using MXene/CNT paper to construct sensing and driving modules. In terms of actuation, the Joule heating effect and heterogeneous composites endow the device with large deformation under low-power consumption. As for sensing, highly conductive MXene/CNT paper contributes to superior sensitivity over a large linear range. Thereon, a portable soft tactile actuator is constructed to grasp and manipulate objects based on manual operation. Moreover, a soft petal actuator that mimics the feeding action of flytrap was prepared to actively sense and grasp external objects, where its weight and size can also be reflected in real time. Furthermore, integration of the actuators with human hands can enable its real-time control via bending the fingers. Therefore, this study provides a significant paradigm to construct intelligent soft tactile actuators towards various practical applications.

A Review of Orchard Robot Research

In modern agricultural production, orchards, as one of the important agricultural fields, are facing difficulties such as labor shortage, low production efficiency, and rising costs. To cope with these difficulties, orchard robots were introduced as an emerging technology. With the rapid development of robotics, artificial intelligence and machine learning, the application of intelligent robots in the agricultural field is becoming more and more mature. Advances in sensing technology, navigation technology, image recognition and other technologies have provided technical support for the research and application of orchard robots. Through the precise operation of orchard robots, precise management of orchard crops can be realized, including precise fertilization, pest and disease monitoring, directional irrigation, etc., so as to improve the yield and quality of orchards. This increases production efficiency, reduces costs, increases yields, and drives agriculture towards sustainable development. This paper combs the research status of picking robots by combining literature collation and market research, shows the scientific research results of various types of orchard picking robots at home and abroad, makes an in-depth analysis of the existing problems, and discusses the basic system composition of orchard picking robots Existing problems, and propose solutions to the problems found. Orchard picking robot has a huge application space in China, but due to the difficulty of research and development, there are still few economical and practical orchard picking robot products at home and abroad. It is necessary to increase the research and development of intelligent picking robots, integrate multidisciplinary high and new technologies, vigorously carry out innovative research on intelligent picking technology and equipment, improve the precision, portability, and low cost of robots, and promote the early application of orchard picking robots in China.

Calibration Optimization of Kinematics and Dynamics for Delta Robot Driven by Integrated Joints in Machining Task

For the application of Delta robots with a 3-R(RPaR) configuration in machining tasks, this paper constructed a 54-parameter kinematic error model and a simplified dynamic model incorporating an integrated joint’s position error and friction, respectively. Utilizing Singular Value Decomposition (SVD) of the Linear Model Coefficient Matrix (LMCM) and the coefficient chart, a criterion for identifiability of error components is established. For good identification results, the optimal measurement surface with Fourier series form is obtained using a combination of the Hook–Jeeves Direct Search Algorithm (DSA) and Inner Point Method (IPM). The friction coefficients and other dynamic parameters are obtained through fitting the integrated joint torque-angle pairs measured along specific trajectories using nonlinear least squares regression. The validation of the calibration process is conducted through simulations and experiments. The calibration results provide a foundation for the precise control of integrated joints and the high-precision motion of robots.

Physical Robots in Education: A Systematic Review Based on the Technological Pedagogical Content Knowledge Framework

Driven by the wave of artificial intelligence, the educational practice and application of robots have become increasingly common. Despite extensive coverage in the literature on various aspects of educational robots, there are still unexplored avenues, particularly regarding robotic support, robotic personality, and challenges in their applications. This study presented a systematic review of high-quality empirical research on the use of physical robots in educational settings. A total of 92 relevant papers from the Web of Science database were analyzed. Employing the technological pedagogical content knowledge (TPCK) framework, we investigated research questions across seven components, including the learning domain, teaching strategy, robot types, learning results, problems with using robots, robotic support, and robotic personality. The findings revealed that robots are most prevalently employed in language learning applications. When opting for teaching strategies, educators tend to favor those that incorporate physical interaction. Concurrently, humanoid robots emerge as the preferred choice among many. These robots, in human–robot interaction scenarios, often exhibit an agreeable personality. In terms of evaluating learning results, cognitive aspects like thinking, creativity, self-regulation, and inquiry ability are especially emphasized. Such results are frequently influenced by the informational and emotional support provided by robots. Nonetheless, challenges encountered by teachers, learners, and robots in this process are not to be overlooked. The findings of this study contributed to future applications of robotics in education.

A goal-conditioned policy search method with multi-timescale value function tuning

PurposeCurrent reinforcement learning (RL) algorithms are facing issues such as low learning efficiency and poor generalization performance, which significantly limit their practical application in real robots. This paper aims to adopt a hybrid model-based and model-free policy search method with multi-timescale value function tuning, aiming to allow robots to learn complex motion planning skills in multi-goal and multi-constraint environments with a few interactions.Design/methodology/approachA goal-conditioned model-based and model-free search method with multi-timescale value function tuning is proposed in this paper. First, the authors construct a multi-goal, multi-constrained policy optimization approach that fuses model-based policy optimization with goal-conditioned, model-free learning. Soft constraints on states and controls are applied to ensure fast and stable policy iteration. Second, an uncertainty-aware multi-timescale value function learning method is proposed, which constructs a multi-timescale value function network and adaptively chooses the value function planning timescales according to the value prediction uncertainty. It implicitly reduces the value representation complexity and improves the generalization performance of the policy.FindingsThe algorithm enables physical robots to learn generalized skills in real-world environments through a handful of trials. The simulation and experimental results show that the algorithm outperforms other relevant model-based and model-free RL algorithms.Originality/valueThis paper combines goal-conditioned RL and the model predictive path integral method into a unified model-based policy search framework, which improves the learning efficiency and policy optimality of motor skill learning in multi-goal and multi-constrained environments. An uncertainty-aware multi-timescale value function learning and selection method is proposed to overcome long horizon problems, improve optimal policy resolution and therefore enhance the generalization ability of goal-conditioned RL.

Novel View Synthesis with Neural Radiance Fields for Industrial Robot Applications

Abstract. Neural Radiance Fields (NeRFs) have become a rapidly growing research field with the potential to revolutionize typical photogrammetric workflows, such as those used for 3D scene reconstruction. As input, NeRFs require multi-view images with corresponding camera poses as well as the interior orientation. In the typical NeRF workflow, the camera poses and the interior orientation are estimated in advance with Structure from Motion (SfM). But the quality of the resulting novel views, which depends on different parameters such as the number and distribution of available images, the accuracy of the related camera poses and interior orientation, but also the reflection characteristics of the depicted scene, is difficult to predict. In addition, SfM is a time-consuming pre-processing step, and its robustness and quality strongly depend on the image content. Furthermore, the undefined scaling factor of SfM hinders subsequent steps in which metric information is required. In this paper, we evaluate the potential of NeRFs for industrial robot applications. To start with, we propose an alternative to SfM pre-processing: we capture the input images with a calibrated camera that is attached to the end effector of an industrial robot and determine accurate camera poses with metric scale based on the robot kinematics. We then investigate the quality of the novel views by comparing them to ground truth, and by computing an internal quality measure based on ensemble methods. For evaluation purposes, we acquire multiple datasets that pose challenges for reconstruction typical of industrial applications, like reflective objects, poor texture, and fine structures. We show that the robot-based pose determination reaches similar accuracy as SfM in non-demanding cases, while having clear advantages in more challenging scenarios. We also report results of the novel view quality for different NeRF approaches, showing that an additional online pose refinement may be disadvantageous. Finally, we present first results of applying the ensemble method to estimate the quality of the synthetic novel view in the absence of a ground truth.

On-chip integration of vector force sensor based on AlGaInP micro-LEDs with interface light coupling modulated by microstructures

Optical principle-based force sensors offer significant advantages in detection applications for surgical robots and wearable devices. With the rapid development in these fields, there is an increasingly urgent demand for improving sensor performance and reducing sensor size. Here, we present a monolithic micro-vector force sensor by integrating the sapphire based AlGaInP micro light-emitting diodes (micro-LEDs) and a surface micro-structured polydimethylsiloxane (PDMS) elastomer. The sensor primarily operates based on the overlapping between the emission spectra and the detection spectra of the multiple quantum well (MQW) structure in the micro-LEDs and the modulation of the reflectance of the sapphire interface by highly elastic microstructures. This monolithic micro-vector force sensor is characterized by its small footprint with a diameter of only ∼7 mm, and it exhibits good real-time response performance with response sensitivities to normal force and shear force of 0.38 nA/mN (0–2 N) and 1.3 nA/mN (0–200 mN). Furthermore, this monolithic integrated device can be manufactured on a large scale and at low cost using wafer-level microfabrication processes. Additionally, the sensor operates in the biologically safe red light spectrum, thus offering broad prospects for applications in fields such as biomedicine and wearable devices.

Robotics in dental and facial esthetics

Dental and facial esthetics is an essential part of modern stomatology. With the development of science and technology and the advancement of interdisciplinary studies, the research of robot technology in the field of dental and facial esthetics is becoming more extensive, and its clinical applications are gradually increasing. This article provides insights into the research and application of robot technology in dental hard tissue esthetics, periodontal soft tissue esthetics, dentition space esthetics, and maxillofacial esthetics. It also highlights the current limitations and development trends and provides references for future research and application of robots in oral esthetics.

Social entertainment robot based on artificial intelligence algorithm application in interactive mode of music classroom

With the rapid development of artificial intelligence technology, in-depth research on the application of social entertainment robots in the field of education has become increasingly important. Therefore, this study aims to explore how to use social entertainment robots based on artificial intelligence algorithms to improve the interactive mode in music classrooms, in order to enhance learning effectiveness and classroom atmosphere. The study introduced the detection algorithm of social entertainment robots and designed a complete detection process for accurately locating and identifying social entertainment robots in music classrooms. A machine data classification detection algorithm has been proposed to accurately determine the behavior and function of social entertainment robots. The social entertainment robot teaching module guides students to actively participate in music classroom activities through simulated conversations and interactive demonstrations. Interactive tests were conducted in actual music classrooms to evaluate the effectiveness of social entertainment robots in the classroom and student feedback. Through experiments, it has been proven that social entertainment robots based on artificial intelligence algorithms can effectively enhance the interactivity and fun of music classrooms. It not only promotes communication and cooperation among students, but also enhances learning participation.

Biped Robots Control in Gusty Environments with Adaptive Exploration Based DDPG.

As technology rapidly evolves, the application of bipedal robots in various environments has widely expanded. These robots, compared to their wheeled counterparts, exhibit a greater degree of freedom and a higher complexity in control, making the challenge of maintaining balance and stability under changing wind speeds particularly intricate. Overcoming this challenge is critical as it enables bipedal robots to sustain more stable gaits during outdoor tasks, thereby increasing safety and enhancing operational efficiency in outdoor settings. To transcend the constraints of existing methodologies, this research introduces an adaptive bio-inspired exploration framework for bipedal robots facing wind disturbances, which is based on the Deep Deterministic Policy Gradient (DDPG) approach. This framework allows the robots to perceive their bodily states through wind force inputs and adaptively modify their exploration coefficients. Additionally, to address the convergence challenges posed by sparse rewards, this study incorporates Hindsight Experience Replay (HER) and a reward-reshaping strategy to provide safer and more effective training guidance for the agents. Simulation outcomes reveal that robots utilizing this advanced method can more swiftly explore behaviors that contribute to stability in complex conditions, and demonstrate improvements in training speed and walking distance over traditional DDPG algorithms.

Research and Implementation of Pneumatic Amphibious Soft Bionic Robot

To meet the requirements of amphibious exploration, ocean exploration, and military reconnaissance tasks, a pneumatic amphibious soft bionic robot was developed by taking advantage of the structural characteristics, motion forms, and propulsion mechanisms of the sea lion fore-flippers, inchworms, Carangidae tails, and dolphin tails. Using silicone rubber as the main material of the robot, combined with the driving mechanism of the pneumatic soft bionic actuator, and based on the theory of mechanism design, a systematic structural design of the pneumatic amphibious soft bionic robot was carried out from the aspects of flippers, tail, head–neck, and trunk. Then, a numerical simulation algorithm was used to analyze the main executing mechanisms and their coordinated motion performance of the soft bionic robot and to verify the rationality and feasibility of the robot structure design and motion forms. With the use of rapid prototyping technology to complete the construction of the robot prototype body, based on the motion amplitude, frequency, and phase of the bionic prototype, the main execution mechanisms of the robot were controlled through a pneumatic system to carry out experimental testing. The results show that the performance of the robot is consistent with the original design and numerical simulation predictions, and it can achieve certain maneuverability, flexibility, and environmental adaptability. The significance of this work is the development of a pneumatic soft bionic robot suitable for amphibious environments, which provides a new idea for the bionic design and application of pneumatic soft robots.

Robot base position and spacecraft cabin angle optimization via homogeneous stiffness domain index with nonlinear stiffness characteristics

The use of mobile robots for machining large components has received considerable research interest for the application of industrial robots in the machinery manufacturing sector. However, the low structural stiffness of industrial robots can result in poor machining quality under the action of cutting forces. Therefore, this paper proposes a simultaneous optimization method the mobile robot base position and cabin angle using homogeneous stiffness domain (HSD) index for large spacecraft cabins. First, a nonlinear joint stiffness model that considers the gravity compensator mechanism is established to describe the stiffness characteristics of heavy-duty robots more accurately. Subsequently, a HSD index is proposed to evaluate the overall stiffness values and stiffness fluctuation for all robot postures in the machining program. An optimization model is then established based on the HSD under the constraints of machining accessibility, joint angle limitation and singularity. The optimal base position and cabin angle are determined simultaneously using the sparrow search algorithm. Finally, simulation and milling experiments are used to demonstrate that the optimization method proposed in this paper can effectively improve the machining quality.

Development of a Multi-Robot System for Pier Construction

The construction industry is a challenging field for the application of robots. In particular, bridge construction, which involves many tasks at great heights, makes it difficult to implement robots. To construct a bridge, it is necessary to build numerous piers that can support the bridge deck. Pier construction involves a series of tasks including rebar connection, formwork installation, concrete pouring, formwork dismantling, and formwork reinstallation. These activities require working at heights, presenting a significant risk of falls. If bridge construction could be performed remotely using robots instead of relying on human labor, it would greatly contribute to the safety of bridge construction. This paper proposes a multi-robot system capable of remote operation and automation for rebar structure connection, concrete pouring, and concrete vibrating tasks in pier construction. The proposed multi-robot system for pier construction is composed of three robot systems. Each robot system consists of a robot arm mounted on a mobile robot that can move along rails. And to apply the proposed system to a construction site, it is essential to implement a compliance control algorithm that adapts to external forces. In this paper, we propose an admittance control that takes into account the weight of the tool for the compliance control of the proposed robot, which performs tasks by switching between various construction tools of different weights. Furthermore, we propose a synchronization control method for the multi-robot system to connect reinforcing structures. We validated the proposed algorithm through simulation. Furthermore, we developed a prototype of the proposed system to verify the feasibility of the suggested hardware design and control.

What did you hear and what did you see? Understanding the transparency of facial recognition and speech recognition systems during human–robot interaction

As social robots begin to assume various social roles in society, the demand for understanding how social robots work and communicate grows rapidly. While literature on explainable artificial intelligence suggests that transparency about a social robot’s working mechanism can evoke users’ positive attitudes, transparency may also have negative outcomes. This study investigates the paradoxical effects of the transparency of facial recognition technology and speech recognition technology in human–robot interactions. Based on a lab experiment and combined analyses of users’ quantitative and qualitative responses, this study suggests that the transparency of facial recognition technology in human–robot interaction increases users’ social presence, reduces privacy concerns, and enhances users’ acceptance of robots. However, exposure to both facial and speech recognition technologies revives users’ privacy worries. This study further parses users’ open-ended evaluation of the prospective application of social robots’ tracking technologies and discusses the theoretical, practical, and ethical value of the findings.

Reprogrammable, Recyclable Origami Robots Controlled by Magnetic Fields

Origami, the art of paper folding, has emerged as a versatile technique for crafting intricate 3D structures from 2D sheets. Combined with the magnetic actuation, origami paper becomes the building blocks for cost‐effective, wirelessly controllable magnetic robots. Herein, a biodegradable magnetic paper with excellent formability and recyclability is developed, facilitating its convenient utilization and disposal. The programable magnetic paper, fabricated with specific magnetization and crease patterns, enables the transformation of 2D sheets into predetermined 3D structures. Leveraging the lightweight and pliable nature of paper‐based materials, exceptional control of origami robots with fast response is demonstrated, enabling a wide range of locomotion. Furthermore, the paper‐based approach enables the incorporation of electronic functionality into magnetic actuators. By introducing conductive nanoparticles into magnetic paper, an electrically conductive substance is created. Constructing electronic circuits and integrating electronic components onto the paper‐based printed circuit board platform enables the repairing of broken circuits inside complicated equipment and optical sensing of surrounding environments in conjunction with locomotive robots. The origami robots have a huge potential to be facilitated in diverse fields with various functions, demonstrating complex locomotion, and integrating chemical, optical, thermal, and mechanical sensors for monitoring environmental conditions in hard‐to‐reach locations. The array of possibilities holds significant promise for the widespread application of these origami magnetic robots across a diverse spectrum of research fields in soft robotics.

Gamified learning experience based on digital media and the application of virtual teaching robots in dance teaching

The research aims to explore the application of gamified learning experience based on digital media and virtual teaching robots in dance teaching. A virtual teaching robot system is designed, which combines technologies such as 3D motion capture, pose calculation, loss function constraint, and similarity matching. Through methods such as bone orientation, digital media audio-visual synchronization, and analysis of robot motion effects, virtual teaching robots have been able to present dance movements. Integrating virtual teaching robots into the teaching process through gamified learning methods. By introducing elements such as tasks, challenges, and rewards into learning, it is possible to stimulate students’ interest and enthusiasm in learning, and improve learning outcomes. The application of digital media based virtual teaching robots in dance teaching has a good effect. The overall structure of the system is reasonable, and the application of virtual reality technology allows students to learn dance firsthand. Through the analysis of skill teaching effectiveness and comprehensive teaching effectiveness, it was found that students have better participation and learning outcomes when learning dance through virtual teaching robots. The implementation of this system can enhance students’ learning enthusiasm and effectiveness, providing a new way for innovation in dance teaching.

Technology and Care: Starting From Human-Centered Compassion

The rapid development of technology in recent years has not only transformed everyday life but also profoundly impacted the fields of healthcare and caregiving. Approaches to care that are technology-based and human-centered may be used to provide care services that are more efficient, accurate, and personalized and that, ultimately, improve our quality of life and overall health. First, intelligent health monitoring systems can generate customized health management plans based on individual health conditions and needs, allowing chronic diseases to be managed more effectively and helping prevent disease onset. Examples of these systems include smart medication dispensers (Gargioni et al., 2024) and fall prevention exercise apps for older adult patients (Czuber et al., 2024). Also, in clinical settings, especially in the context of monitoring patients in operating rooms and intensive care units, visualization technologies are using psychology and cognitive neuroscience principles to leverage human sensory perception to improve caregiver understanding of information. Patient data are presented using different shapes, colors, and animation frequencies, which are more effectively perceived, integrated, and interpreted than other formats (e.g., numbers). This helps healthcare professionals effectively perceive and identify potential medical issues, enhancing their situational awareness, helping them make better decisions, and improving patient safety (Gasciauskaite et al., 2023). In the field of rehabilitation, Krishnan et al. (2024) developed a wearable rehabilitation device to facilitate gait rehabilitation in post-stroke patients that exhibits better durability, modularity, and usability than previous technologies. In addition, for patients with upper limb impairments who experience varying degrees of ability loss in performing activities of daily living, robotic platforms have been introduced to facilitate intensive and repetitive exercises that strengthen motor skills and neuroplasticity (Bucchieri et al., 2023). In conclusion, human-centered technology care is a field ripe with potential that combines the power of technology with human-centered care to improve support and service capabilities. Articles in this issue explore the application of social robots in nursing practice, the applicability of smart technology in cardiac and elderly care, and the opportunities for next-generation smart care. We look forward to seeing more human-centered technology care solutions in the future that further promote health, well-being, and comprehensive social development.

Entertainment robots based on digital new media application in real-time error correction mode for Chinese English translation

With the assistance of digital new media technology, virtual entertainment robots, as a new learning experience mode, can effectively enhance the interactive process of e-learning learning. This article studies the application of entertainment robots based on digital new media in real-time error correction mode for Chinese English translation. Through experiments, it has been verified that the flexible use of deep learning technology can significantly improve user satisfaction and translation accuracy, and has already improved the level of error correction and positioning. This article first introduces the existing mainstream machine learning models, including supervised neural network models and attention mechanisms. On this basis, the system was optimized to further improve its performance. At the same time, this article proposes new improvement plans to address the shortcomings of current mainstream translation systems. We conducted comparative experiments on the error correction model of the proposed adaptive algorithm for specific error types, and also tested it using real datasets. Research has shown that using adaptive algorithms based on reinforcement deep learning can not only significantly optimize the error correction efficiency of our system, but also flexibly adapt to the needs of various optimization strategies.

New Technology, Warm Care: Social Robots in Nursing Practice and Research

Recent, rapid advancements in technology have enabled the introduction and integration of robots into everyday life. Different from the traditional image of robots as cold and mechanical, social robots are designed to emulate human interaction patterns, improving the user experience and facilitating social interactivity. Thus, social robots represent a promising new care intervention. In this article, after defining social robots and explaining the factors influencing "human-robot interaction", the authors discuss the effectiveness of social robots in the context of providing care to patients with dementia and autism as well as to pediatric patients. Finally, current cases in which PARO, a social robot, has been used in nursing are described, and key challenges and suggestions for future social robot applications are given. Current evidence indicates social robots must be developed and designed to adhere to a people-centered approach to achieve better robot-assisted care outcomes, be better accepted by patients, and better enable patients to open up emotionally and maintain good physical, mental, and social well-being.