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Materials made from active, living, or robotic components can display emergent properties arising from local sensing and computation. Here, we realize a freestanding active metabeam with piezoelectric elements and electronic feed-forward control that gives rise to an odd micropolar elasticity absent in energy-conserving media. The non-reciprocal odd modulus enables bending and shearing cycles that convert electrical energy into mechanical work, and vice versa. The sign of this elastic modulus is linked to a non-Hermitian topological index that determines the localization of vibrational modes to sample boundaries. At finite frequency, we can also tune the phase angle of the active modulus to produce a direction-dependent bending modulus and control non-Hermitian vibrational properties. Our continuum approach, built on symmetries and conservation laws, could be exploited to design others systems such as synthetic biofilaments and membranes with feed-forward control loops. Mechanical metamaterials can be engineered with properties not possible in ordinary materials. Here the authors demonstrate and study an active metamaterial with self-sensing characteristics that enables odd elastic properties not observed in passive media.

The maintenance of high-voltage transmission lines mainly relies on manual work. When workers climb power towers, tower climbing robots are needed to provide safety ropes. In this paper, a power tower climbing robot with a new structure is designed, and its three-dimensional model is created and simulated. Finally, combined with the motor selection in each robot component, the tower climbing robot prototype was made and tested. The designed climbing robot can complete the locking and crawl on the power tower.

In multi-robot collaboration, individual failures can propagate to other robots due to the topological coupling between them. Existing fault diagnosis models are designed for single robots and fail to meet the practical requirements of multi-robot scenarios. To address this, this study develops a federated learning-based fault self-diagnosis model for individual robots and a multi-robot mutual diagnosis model that accounts for group behavior consistency. This approach effectively isolates faulty robots in multi-robot systems. Initially, each robot’s local data is encoded using the Gramian Angular Field (GAF) to generate two-dimensional time-frequency plots, creating local fault datasets. Next, a federated learning framework is established, where fault models for different robots are pre-trained using the local fault datasets. The local model parameters from multiple robots are then aggregated for shared learning, mitigating the potential knowledge shift during individual robot training. Finally, a multi-robot mutual diagnosis model is developed, incorporating group speed and direction consistency to ensure fault diagnosis based on behavioral coherence. Experimental results demonstrate that the proposed self-diagnosis model accurately identifies faults in individual robot components, while the mutual diagnosis model effectively recognizes system-wide faults.

The growing market for lightweight robots inspires new use-cases, such as collaborative manipulators for human-centered automation. However, widespread adoption faces obstacles due to high R&D costs and longer design cycles, although rapid advances in mechatronic engineering have effectively narrowed the design space to affordable robot components, turning the development of lightweight robots into a component selection and integration challenge. Recognizing this transformation, we demonstrate a practical framework for designing lightweight industrial manipulators using a case-study of indigenously developed 5 Degrees-of-Freedom (DOF) cobot prototype. Our framework incorporates off-the-shelf sensors, actuators, gears, and links for Design for Manufacturing and Assembly (DFMA), along with complete virtual prototyping. The design cycle time is reduced by approximately 40% at the cost of cobot real-time performance deviating within 2.5% of the target metric. Our physical prototype, having repeatability of 0.05mm calculated as per the procedure defined in ISO 9283:1998, validates the cost-effective nature of the framework for creating lightweight manipulators, benefiting robotic startups, R&D organizations, and educational institutes without access to expensive in-house fabrication setups.

Force sensors on climbing robots give important information to the robot control system, however, off-the-shelf sensors can be both heavy and bulky. We investigate the optimisation of a lightweight integrated force sensor made of piezoelectric material for the multi-limbed climbing robot MAGNETO. We focus on three design objectives for this piezoelectric component. The first is to develop a lightweight component with minimal compliance that can be embedded in the foot of the climbing robot. The second objective is to ensure that the component has sensing capability to replace the off-the-shelf force sensor. Finally, the component should be robust for a range of climbing configurations. To this end, we focus on a compliance minimisation problem with constrained voltage and volume fraction. We present structurally optimised designs that satisfy the three main design criteria and improve upon baseline results from a reference component. Our computational study demonstrates that the optimisation of embedded robotic components with piezoelectric sensing is worthy of future investigation.

The past decade of soft robotics has delivered impactful and promising contributions to society and has seen exponentially increasing interest from scientists and engineers. This interest has resulted in growth of the number of researchers participating in the field and the quantity of their resulting contributions, stressing the community's ability to comprehend and build upon the literature. In this work, a data‐driven review is presented that addresses the recent surge of research by providing a quantitative snapshot of the field. Relevant data are catalogued with three levels of analysis. First, publication‐level analysis explores high‐level trends in the field and bibliometric relationships across the more detailed analyses. Second, device‐level analysis examines the tethering of robots and the incorporation of component types (actuators, sensors, controllers, power sources) into each robot. Finally, component‐level analysis investigates the compliances, material compositions, and “function media” (energetic methods by which components operate) of each soft robotic component in the analyzed literature. The reported data indicate a significant reliance on elastomeric materials, electrical and fluidic media, and physical tethering; meanwhile, controllers and power sources remain underdeveloped relative to actuators and sensors. These gaps in the surveyed literature are elaborated upon, and promising future directions for the field of soft robotics are identified. The field of soft robotics has become challenging to digest due to diverse contributions from a growing spectrum of researchers. This data‐driven review provides a holistic, quantitative snapshot of the field by synthesizing content from hundreds of publications. Highlights include historical trends, rising hot topics, and identification of areas that will further advance the field.

Edible robots and robotic food — edible systems that perceive, process and act upon stimulation — could open a new range of opportunities in health care, environmental management and the promotion of healthier eating habits. For example, they could enable precise drug delivery and in vivo health monitoring, deliver autonomously targeted nutrition in emergency situations, reduce waste in farming, facilitate wild animal vaccination and produce novel gastronomical experiences. Here, we take a robot designer perspective to identify edible materials that could serve as functional components of edible robots and robotic food, such as bodies, actuators, sensors, and computational components and energy sources, describe recent examples of integration, and discuss the open challenges in the field. Edible robots and robotic food that perceive, process and react to stimuli offer opportunities to develop new medical applications, emergency food-delivery systems, waste-reduction strategies in farming and novel gastronomic experiences. This Perspective surveys edible materials that can be used to manufacture robotic components and discusses examples of edible robots and robotic food.

This study presents a comprehensive approach for optimizing the acquisition, utilization, and maintenance of ABLVR vascular robots in healthcare settings. Medical robotics, particularly in vascular treatments, necessitates precise resource allocation and optimization due to the complex nature of robot and operator maintenance. Traditional heuristic methods, though intuitive, often fail to achieve global optimization. To address these challenges, this research introduces a novel strategy, combining mathematical modeling, a hybrid genetic algorithm, and ARIMA time series forecasting. Considering the dynamic healthcare environment, our approach includes a robust resource allocation model for robotic vessels and operators. We incorporate the unique requirements of the adaptive learning process for operators and the maintenance needs of robotic components. The hybrid genetic algorithm, integrating simulated annealing and greedy approaches, efficiently solves the optimization problem. Additionally, ARIMA time series forecasting predicts the demand for vascular robots, further enhancing the adaptability of our strategy. Experimental results demonstrate the superiority of our approach in terms of optimization, transparency, and convergence speed from other state-of-the-art methods. The source code is available at https://github.com/ybfo/improved-GA .

With increasing customer demand, industry 4.0 gained a lot of interest, which is based on smart factories. In smart factories, robotic components are vulnerable to failure due to various industrial operations such as assembly, manufacturing, and product handling. Timely fault detection and diagnosis (FDD) is important to keep the industrial operation smooth. Previously, only the unloaded-based FDD algorithms were considered for the industrial robotic system. In the industrial environment, the robot is working under various working conditions such as speeds, loads, and motions. Hence, to reduce the domain discrepancy between the lab scale and the real working environment, we conducted experimentations under various working conditions. For that purpose, an extensive experimental setup is prepared to perform a series of various experiments mimicking the real environmental condition. In addition, in previous research work, various machine learning (ML) and deep learning (DL) approaches were proposed for robotic arm component fault detection. However, various issues are related to the DL and ML approaches. The ML models are problem-specific, and complex in computations. The DL model needs a huge amount of data. The DL model is composed of various layers that have not been thoroughly explored; as a result, the fault detection model lacks a comprehensive explanation. To overcome these issues, the transfer learning (TL) model is considered with the diverse experimental scenarios. The main contribution is to increase the generalization capabilities of the robotic PHM in the context of previously available research work. For that purpose, the VGG16 model is used because of its autonomous feature extractions for fault classification. The data are collected under a variety of different operating conditions such as loadings, speeds, and motion patterns. The 1D signal is converted to a 2D signal (scalogram) to perform the TL model. The proposed approach shows effective fault detection performance and has the capabilities of generalization under variable working conditions.

During robot operation, robot components like links and joints may experience collisions or excess loads that can lead to structural damages or cracks. A crack in a structural component can degrade the overall performance of the structure. This study examines the influence of cracks on the vibration characteristics of a baseline robot link. The approach uses the finite element method to simulate the dynamics of planar robot link models with and without artificial cracks with different sizes, locations, and orientations in the ABAQUS software. The robot link models include one intact model and five defective models with cracks. A rectangular crack with a fixed length of 1 mm and a varying width from 0.001 to 0.1 mm is applied to a specific location along the robot link. Finite element analysis and machine learning are used to simulate and characterize the vibration of each robot link with one fixed end and one free end. The vibration responses are measured at the free end. The measured vibration data are then transformed into two-dimensional (2D) image data using the Gramian Angular Summation Field method. A convolutional neural network is then trained with the image data for crack detection and analysis. The results indicate that the proposed method demonstrates 98.25% accuracy on the data generated by the simulation experiments.