Search Results (1,884)

Population aging is an inevitable trend in contemporary society, and the application of technologies such as human–machine interaction, assistive healthcare, and robotics in daily service sectors continues to increase. The lower limb exoskeleton rehabilitation robot has great potential in areas such as enhancing human physical functions, rehabilitation training, and assisting the elderly and disabled. This paper integrates the structural characteristics of the human lower limb, motion mechanics, and gait features to design a biomimetic exoskeleton structure and proposes a human–machine integrated lower limb exoskeleton rehabilitation robot. Human gait data are collected using the Optitrack optical 3D motion capture system. SolidWorks 3D modeling software Version 2021 is used to create a virtual prototype of the exoskeleton, and kinematic analysis is performed using the standard Denavit–Hartenberg (D-H) parameter method. Kinematic simulations are carried out using the Matlab Robotic Toolbox Version R2018a with the derived D-H parameters. A physical prototype was fabricated and tested to verify the validity of the structural design and gait parameters. A controller based on BP fuzzy neural network PID control is designed to ensure the stability of human walking. By comparing two sets of simulation results, it is shown that the BP fuzzy neural network PID control outperforms the other two control methods in terms of overshoot and settling time. The specific conclusions are as follows: after multiple walking gait tests, the robot’s walking process proved to be relatively safe and stable; when using BP fuzzy neural network PID control, there is no significant oscillation, with an overshoot of 5.5% and a settling time of 0.49 s, but the speed was slow, with a walking speed of approximately 0.18 m/s, a stride length of about 32 cm, and a gait cycle duration of approximately 1.8 s. The model proposed in this paper can effectively assist patients in recovering their ability to walk. However, the lower limb exoskeleton rehabilitation robot still faces challenges, such as a slow speed, large size, and heavy weight, which need to be optimized and improved in future research. Full article

Parallel-kinematic machines or parallel robots have only been established in a few applications where their advantage over serial kinematics due to their high payload capacity, stiffness, or dynamics with their limited workspace-to-installation-space ratio pays out. However, some applications still have not yet been sufficiently or satisfactorily automated in which parallel robots could be advantageous. As their performance is much more dependent on their complex dimensioning, an automated design tool—not existing yet—is required to optimize the parameterization of parallel robots for applications. Combined structural and dimensional synthesis considers all principally possible kinematic structures and performs a separate dimensioning for each to obtain the best task-specific structure. However, this makes the method computationally demanding. The proposed computationally efficient approach for dimensional synthesis extends multi-objective particle swarm optimization with hierarchical constraints. A cascaded (bilevel) optimization includes the design optimization of components and the redundancy resolution for tasks with rotational symmetry, like milling. Two case studies for different end-effector degrees of freedom demonstrate the broad applicability of the combined structural and dimensional synthesis for symmetric parallel robots with rigid links and serial-kinematic leg chains. The framework produces many possible task-optimal structures despite numerous constraints and can be applied to other problems as an open-source Matlab toolbox. Full article

Grid cells in the brain’s entorhinal cortex are essential for spatial navigation and have inspired advancements in robotic navigation systems. This paper first provides an overview of recent research on grid cell-based navigation in robotics, focusing on deep learning models and algorithms capable of handling uncertainty and dynamic environments. We then present experimental results where a grid cell network was trained using trajectories from a mobile unmanned ground vehicle (UGV) robot. After training, the network’s units exhibited spatially periodic and hexagonal activation patterns characteristic of biological grid cells, as well as responses resembling border cells and head-direction cells. These findings demonstrate that grid cell networks can effectively learn spatial representations from robot trajectories, providing a foundation for developing advanced navigation algorithms for mobile robots. We conclude by discussing current challenges and future research directions in this field. Full article

Domain generalization (DG) has become a pivotal research area in machine learning, focusing on equipping models with the ability to generalize effectively to unseen test domains that differ from the training distribution. This capability is crucial, as real-world data frequently exhibit domain shifts that violate the assumption of independent and identically distributed (i.i.d.) data, resulting in significant declines in model performance. Among the various strategies to address domain generalization, data augmentation has garnered substantial attention as an effective approach for mitigating domain shifts and improving model robustness. In this survey, we examine the role of data augmentation in domain generalization, offering a comprehensive overview of its methods, applications, and challenges. We present a detailed taxonomy of data augmentation techniques, categorized along three dimensions: scope, nature, and training dependency. Additionally, we provide a comparative analysis of key methods, highlighting their strengths and limitations. Finally, we explore the domain-specific applications of data augmentation and analyze their effectiveness in enhancing generalization across various real-world tasks, including computer vision, NLP, speech, and robotics. We conclude by examining key challenges—such as computational cost and augmentation overfitting—and outline promising research directions, with a focus on advancing cross-modal augmentation techniques and developing standardized evaluation benchmarks. Full article

Breast reconstruction following mastectomy or sectorectomy significantly impacts the quality of life and psychological well-being of breast cancer patients. Since its inception in the 1950s, artificial intelligence (AI) has gradually entered the medical field, promising to transform surgical planning, intraoperative guidance, postoperative care, and medical research. This article examines AI applications in breast reconstruction, supported by recent studies. AI shows promise in enhancing imaging for tumor detection and surgical planning, improving microsurgical precision, predicting complications such as flap failure, and optimizing postoperative monitoring. However, challenges remain, including data quality, safety, algorithm transparency, and clinical integration. Despite these shortcomings, AI has the potential to revolutionize breast reconstruction by improving preoperative planning, surgical precision, operative efficiency, and patient outcomes. This review provides a foundation for further research as AI continues to evolve and clinical trials expand its applications, offering greater benefits to patients and healthcare providers. Full article

Grasping objects, from simple tasks to complex fine motor skills, is a key component of our daily activities. Our approach to facilitate the development of advanced prosthetics, robotic hands and human–machine interaction systems consists of collecting and combining surface electromyography (EMG) signals and contextual data of individuals performing manipulation tasks. In this context, the identification of patterns and prediction of hand grasp types is crucial, with cylindrical grasp being one of the most common and functional. Traditional approaches to grasp prediction often rely on unimodal data sources, limiting their ability to capture the complexity of real-world scenarios. In this work, grasp prediction models that integrate both EMG signals and contextual (task- and product-related) information have been explored to improve the prediction of cylindrical grasps during reaching movements. Three model architectures are presented: an EMG processing model based on convolutions that analyzes forearm surface EMG data, a fully connected model for processing contextual information, and a hybrid architecture combining both inputs resulting in a multimodal model. The results show that context has great predictive power. Variables such as object size and weight (product-related) were found to have a greater impact on model performance than task height (task-related). Combining EMG and product context yielded better results than using each data mode separately, confirming the importance of product context in improving EMG-based models of grasping. Full article

Many tasks in a modern household are performed by machines, e.g., a dishwasher or a vacuum cleaner, and in the near future most household tasks will be performed by smart service robots. This will relieve the residents, who in turn can enjoy their free time. This newly gained free time will turn out to cause the so-called spare time rebound effect due to more resource consumption. We roughly quantify this rebound effect and propose a CO₂-budget model to reduce or even avoid it. In modern industry, automation and AI are taking over work from humans, leading to higher productivity of the company as a whole. This is the main reason for economic growth, which leads to environmental problems due to higher consumption of natural resources. We show that, even though the effects of automation at home and in the industry are different (free time versus higher productivity), in the end they both lead to more resource consumption and environmental pollution. We discuss possible solutions to this problem, such as carbon taxes, emissions trading systems, and a carbon budget. Full article

In recent years, the integration of industrial robotics has emerged as a powerful tool in reshaping industries by enhancing production efficiency, reducing waste generation, and optimizing resource utilization. However, industrial robotics, particularly in manufacturing and production, require significant energy that can potentially impact on environmental quality. Despite the growing adoption of artificial intelligence (AI)-based industrial robotics, there is a paucity of literature on the impact of industrial robotics on the ecological footprint (EF), particularly in the context of advanced economies. In this context, this study aims to investigate the impact of industrial robotics, resource efficiency, energy transition, and geopolitical risk EF in G7 countries from 1993 to 2021. The study employed advanced econometric techniques, including Kernel-based Regularized Least Squares (KRLS) and Artificial Neural Network (ANN) machine learning methods. The results unveiled that industrial robotics significantly curtail environmental degradation by impeding the EF. Resource efficiency and energy transition posed a significant and negative impact on the EF. Geopolitical risks and economic growth exacerbate the EF. Based on the results, the study proposes important policy implications for achieving sustainable development. Full article

In this study, we present a gesture-controlled robotic arm system for small assembly lines. Robotic arms are extensively used in industrial applications; however, they typically require special treatment and qualified personnel to set up and operate them. Towards this end, hand gestures can provide a natural way for human–robot interaction, providing a straightforward means for control without the need for significant training of the operators. Our goal is to develop a safe, low-cost, and user-friendly system for environments that often involve non-repetitive and custom automation processes, such as in small factory setups. Our system estimates the 3D position of the user’s joints in real time with the help of AI and real-world data provided by an RGB-D camera. Then, joint coordinates are translated into the robotic arm’s desired poses in a simulated environment (ROS), thus achieving gesture control. Through the experiments we conducted, we show that the system provides the performance required to control a robotic arm effectively and efficiently. Full article

Industry 5.0 aims to integrate humans and machines to achieve greater productivity, personalization, and sustainable development in the production process. Built on the foundation of Industry 4.0 which emphasizes automation, digitalization, and intelligent production processes, Industry 5.0 highlights the importance of human resources in modern manufacturing. Robotic arms have replaced traditional manpower, particularly in flexible manufacturing systems. However, integrating advanced machinery into workflows has increased competition in terms of securing resources, resulting in frequent deadlocks. Various deadlock prevention policies have been proposed to address this issue. Despite these efforts, resolving system deadlocks while achieving the optimal number of reachable states remains challenging. Based on existing research, we have developed a novel deadlock recovery method applicable to various flexible manufacturing systems. We designed an adaptable system and a controller that can restore the system to its fully operational state. Full article

Soft robots with compliant bodies offer safe human–robot interaction as well as adaptability to unstructured dynamic environments. However, the nonlinear dynamics of a soft robot with infinite motion freedom pose various challenges to operation and control engineering. This research explores the motion of a pneumatic soft robot under diverse loading conditions by conducting finite element analysis (FEA) and using machine learning. The pneumatic soft robot consists of two parallel hyper-elastic tubular chambers that convert pneumatic pressure inputs into soft robot motion to mimic an elephant trunk and its motion. The body of each pneumatic chamber consists of a series of bellows to effectively facilitate the expansion, contraction, and bending of the body. The first chamber spans the entire length of the soft robot’s body, and the second chamber spans half of it. This unique asymmetric design enables the soft robot to bend and curl in various ways. Machine learning is used to establish a forward kinematic relationship between the pressure inputs and the motion responses of the soft robot using data from FEA. Accordingly, this research employs an artificial neural network that is trained on FEA data to estimate the reachable workspace of the soft robot for given pressure inputs. The trained neural network demonstrates promising estimation accuracy with an R-squared value of 0.99 and a root mean square error of 0.783. The workspaces of asymmetric double-chamber and single-chamber soft robots were compared, revealing that the double-chamber robot offers approximately 185 times more reachable workspace than the single-chamber soft robot. Full article

The construction industry has traditionally relied on labor-intensive methods, often resulting in inefficiencies, cost overruns, and extended project timelines. Despite advancements in automation and robotics, the potential of programmable construction to address these challenges remains underexplored, particularly in the context of small to medium-scale projects. This study investigates the impact of programmable construction on time, cost, and sustainability, using a detailed case study of a residential project in Italy. This research adopts a comparative approach, analyzing traditional construction techniques versus automated construction systems. Production rates from previous research and real-world applications are used to develop alternative schedules that reflect the efficiencies of these advanced technologies. The findings demonstrate that programmable construction can reduce project timelines by up to 82.6% and achieve cost savings of approximately 40.6%. Automated systems also offer significant environmental advantages, including a 70.25% reduction in carbon emissions and a 70% decrease in energy consumption in several tasks such as soil treatment. This study suggested that programmable construction sites can significantly shorten project timelines and reduce costs. The precision and speed of AI and robotics minimize reliance on human labor, streamline construction processes, and enhance project performance and work quality by reducing human error while promoting sustainability through reduced resource consumption and lower environmental impact. Full article

Background: Democratized access to safe and effective robotic neurorehabilitation for stroke survivors requires innovative, affordable solutions that can be used not only in clinics but also at home. This requires the high usability of the devices involved to minimize costs associated with support from physical therapists or technicians. Methods: This paper describes the early findings of the NeuroExo brain–machine interface (BMI) with an upper-limb robotic exoskeleton for stroke neurorehabilitation. This early feasibility study consisted of a six-week protocol, with an initial training and BMI calibration phase at the clinic followed by 60 sessions of neuromotor therapy at the homes of the participants. Pre- and post-assessments were used to assess users’ compliance and system performance. Results: Participants achieved a compliance rate between $21\%$ and $100\%$, with an average of $69\%$, while maintaining adequate signal quality and a positive perceived BMI performance during home usage with an average Likert scale score of four out of five. Moreover, adequate signal quality was maintained for four out of five participants throughout the protocol. These findings provide valuable insights into essential components for comprehensive rehabilitation therapy for stroke survivors. Furthermore, linear mixed-effects statistical models showed a significant reduction in trial duration (p-value < 0.02) and concomitant changes in brain patterns (p-value < 0.02). Conclusions: the analysis of these findings suggests that a low-cost, safe, simple-to-use BMI system for at-home stroke rehabilitation is feasible. Full article

The development of neuroprosthetic limbs—robotic devices designed to restore lost limb functions for individuals with limb loss or impairment—has made significant strides over the past decade, reaching the stage of successful human clinical trials. A current research focus involves providing somatosensory feedback to these devices, which was shown to improve device control performance and embodiment. However, widespread commercialization and clinical adoption of somatosensory neuroprosthetic limbs remain limited. Biomimetic neuroprosthetics, which seeks to resemble the natural sensory processing of tactile information and to deliver biologically relevant inputs to the nervous system, offer a promising path forward. This method could bridge the gap between existing neurotechnology and the future realization of bionic limbs that more closely mimic biological limbs. In this review, we examine the recent key clinical trials that incorporated somatosensory feedback on neuroprosthetic limbs through biomimetic neurostimulation for individuals with missing or paralyzed limbs. Furthermore, we highlight the potential impact of cutting-edge advances in tactile sensing, encoding strategies, neuroelectronic interfaces, and innovative surgical techniques to create a clinically viable human–machine interface that facilitates natural tactile perception and advanced, closed-loop neuroprosthetic control to improve the quality of life of people with sensorimotor impairments. Full article