In recent years, autonomous control based on contact inspections in unknown environments is a new hot and difficult point in robotics research. This paper presents a new control law for unmanned aerial manipulator (UAM) to perform contact inspection tasks on vertical surfaces. The selected circular image feature decouples the position and attitude of UAM, so an image-based impedance control is proposed to control the position and track the contact force. The developed controller uses geometric methods to control the attitude. In addition, the designed aerial manipulator decouples the roll and pitch of the UAV from the UAV, which improves the system’s stability. Experiments have been carried out to demonstrate the feasibility of this method.

Joint actuators with a compact size and high power density are necessary for robots with rotating joints, making the hydraulic actuator (HyA) an ideal actuator candidate. This paper presents a joint HyA with the following characteristics: compact installation size of 70 mm × 92.5 mm × 145 mm, low weight of 1.93 kg, high output torque of 742.2 Nm/531.09 Nm in two directions under 210 bar, high torque to weight ratio of 265.5 Nm/kg, low internal leakage of about 9 mL/min, zero external leakage, low starting pressure of 0.26 MPa/0.39 MPa in two directions, and a large rotation angle of 135 degrees. Compared with HyAs that have been applied in robot joints, the HyA proposed in this paper can greatly reduce the joint weight, reduce the joint size, and ensure the control performance of the joint movement.

In addition, a dynamic model of the HyA is established. Based on this, some dynamic design suggestions are given. Furthermore, a simple position and torque control algorithm for the HyA is proposed. Finally, some experiments are carried out to verify the performance of the HyA.

Digital identity systems are not devised for their own sake, rather they are developed by institutions as part of their pursuit of specific goals—such as economic, social, and developmental outcomes through enabling individual rights and facilitating access to basic services and entitlements. A growing number of organizations and institutions are advancing specific principles, frameworks, and “imaginaries” of what “good” digital identity looks like—yet it is often not clear how much influence they have or what their underlying worldview is to those designing, developing, and deploying these systems. This paper introduces sociopolitical configurations as a means of studying these underlying worldviews. Sociopolitical configurations combine elements from technological frames, expectations, and imaginations as well as developmental discourses to provide a basis for critically examining three key documents in this space.

Modern approaches for exploration path planning generally do not assume any structural information regarding the operational area. Therefore, they offer good performance when the region of interest is entirely unknown. However, for some applications such as plantation forest surveying, partial information regarding the survey area is known before the exploration process. Because the region of interest consists only of the lower portions of the tree stems themselves, the ground and high-elevation sections of the environment are unimportant and do not need to be observed. Due to these unconventional conditions, existing methods favoring faster survey speeds produce suboptimal surveys as they do not try and ensure even coverage across the entire exploration volume, while methods that favor reconstruction accuracy produce excessively long survey times. This work proposes a structured exploration approach specifically for plantation forests utilizing a lawnmowing pattern to maximize coverage while minimizing re-visited regions, guiding the unmanned aerial vehicle to visit all areas. Experiments are conducted in various environments, with comparisons made to state-of-the-art exploration planners regarding survey time and coverage. Results suggest that the proposed methods produce surveys with significantly more predictable coverage and survey times at the expense of a longer survey.

In the era of rapid product update and intense competition, aesthetic design has been increasingly important in various fields, as aesthetic feelings of customers largely influence their purchase preferences. However, the quantification of aesthetic feeling is still a very subjective process due to vague evaluations. The determination of form parameters according to aesthetics is difficult hitherto. Aesthetic measure recently arises as a prominent tool for this purpose using formulas derived from aesthetic theory. But as revealed by existing studies, it needs to be customized with deterministic and objective methods to be reliable in practice use. To facilitate this application, this paper proposes an evolutionary form design method, integrating aesthetic dimension selection and parameter optimization. After summarizing initial aesthetic dimensions, aesthetic dimension selection based on expert decision-making and particle swarm optimization (PSO) is carried out. With filtered aesthetic dimensions, design parameters are optimized with NSGA-II (non-dominated sorting genetic algorithm). The quality of pareto solutions obtained to be design schemes is assessed by three criteria to conduct sensitivity analysis of cross and mutation probability and population size. Our experiment using bicycle form design shows that the proposed evolutionary form design method can generate numerous and variant aesthetic design schemes rapidly. This is very useful for both product redesign and innovative new product development.

Recently, deep learning methods have achieved considerable performance in gesture recognition using surface electromyography signals. However, improving the recognition accuracy in multi-subject gesture recognition remains a challenging problem. In this study, we aimed to improve recognition performance by adding subject-specific prior knowledge to provide guidance for multi-subject gesture recognition. We proposed a time–frequency feature transform suite (TFFT) that takes the maps generated by continuous wavelet transform (CWT) as input. The TFFT can be connected to a neural network to obtain an end-to-end architecture. Thus, we integrated the suite into traditional neural networks, such as convolutional neural networks and long short-term memory, to adjust the intermediate features. The results of comparative experiments showed that the deep learning models with the TFFT suite based on CWT improved the recognition performance of the original architectures without the TFFT suite in gesture recognition tasks. Our proposed TFFT suite has promising applications in multi-subject gesture recognition and prosthetic control.

Many classic networks grow by hooking small components via vertices. We introduce a class of networks that grows by fusing the edges of a small graph to an edge chosen uniformly at random from the network. For this random edge-hooking network, we study the local degree profile, that is, the evolution of the average degree of a vertex over time. For a special subclass, we further determine the exact distribution and an asymptotic gamma-type distribution. We also study the “core,” which consists of the well-anchored edges that experience fusing. A central limit theorem emerges for the size of the core.

At the end, we look at an alternative model of randomness attained by preferential hooking, favoring edges that experience more fusing. Under preferential hooking, the core still follows a Gaussian law but with different parameters. Throughout, Pólya urns are systematically used as a method of proof.