The rise of digital technology has led to fundamental changes in how individual and collective perspectives on the past are transmitted and engaged. An immediate implication of these changes relates to the shift away from human communication as a single form of communication about memory towards multiple models which involve non-human (or robotic) agents. These non-human agents are primarily constituted by artificial intelligence (AI)-driven systems, such as search engines and conversational agents, which retrieve information about the past for human users and are increasingly used to generate memory-related content. To account for the growing complexity of memory-related digital communication, the article introduces three agency-based models of such communication: (1) human-to-human; (2) human-to-robot; and (3) robot-to-robot. It discusses examples of communication practices enabled by these models and scrutinises their implications for individual and collective memory transmission. The article concludes by outlining several directions for memory communication research increasingly shaped by non-human agents.

This cross-sectional study investigated staff’s attitudes towards the use of mobile telepresence robots in long-term care (LTC) homes in western Canada. We drew on a Health Technology Assessment Core Model 3.0 to design a survey examining attitudes towards nine domains of mobile telepresence robots. Staff, including nurses, care staff, and managers, from two LTC homes were invited to participate. Statistical analysis of survey data from 181 participants revealed that overall, participants showed positive attitudes towards features and characteristics, self-efficacy on technology use, organizational aspects, clinical effectiveness, and residents and social aspects; neutral attitudes towards residents’ ability to use technology, and costs; and negative attitudes towards safety and privacy. Participants who disclosed their demographic backgrounds tended to exhibit more positive attitudes than participants who did not. Content analysis of textual data identified specific concerns and benefits of using the robots. We discuss options for implementing mobile telepresence robots in LTC.

Aiming at the problem of low accuracy of robot joint fault diagnosis, a fault diagnosis method of robot joint based on BP neural network is designed. In this paper, the UR10 robot is taken as the research object, and the end pose data of the robot are collected in real time. By injecting different joint errors and changing the sampling frequency, the joint fault database is collected and established, and the BP neural network is used for training to obtain the robot neural network fault diagnosis model. The fault diagnosis model can output the joint fault of the input end pose data. And we analyzed the influence of different joint angle errors and different training sets on the accuracy of joint fault diagnosis of the robot. The results show that when the sampling frequency is 250 Hz, the simulation result of joint fault diagnosis accuracy with the fault degree of 0.5° is 99.17%, and the experimental result is 97.87%. Compared with traditional data-driven methods, it has higher accuracy and diagnostic efficiency, and compared with existing machine learning methods, it also achieves a high accuracy while reducing the network complexity. The effectiveness of the BP neural network robot joint fault diagnosis method is verified by experiments.

In this paper, a novel statistical application of large deviation principle (LDP) to the robot trajectory tracking problem is presented. The exit probability of the trajectory from stability zone is evaluated, in the presence of small-amplitude Gaussian and Poisson noise. Afterward, the limit of the partition function for the average tracking error energy is derived by solving a fourth-order system of Euler–Lagrange equations. Stability and computational complexity of the proposed approach is investigated to show the superiority over the Lyapunov method. Finally, the proposed algorithm is validated by Monte Carlo simulations and on the commercially available Omni bundleTM robot.

Industry 4.0 integrated with robotic and digital fabrication technologies have attracted the attention of manufacturing researchers. Autonomous assembly with supervisory control and data acquisition (SCADA) systems holds the promise of greater scalability, adaptability, and potentially evolved design possibilities helping to maintain efficiency, process data for smarter decisions, and communicate system issues to help mitigate downtime. This paper concerns with developing an intelligent control system based on SCADA in the Internet of Things (IoT) platform to process configuration and reconfiguration of an autonomous assembly system. The implementation study certifies the effectiveness of the proposed IoT-based SCADA control system in autonomous assembly.

This paper presents a novel method for modeling a 3-degree of freedom open kinematic chain using quaternions algebra and neural network to solve the inverse kinematic problem. The structure of the network was composed of 3 hidden layers with 25 neurons per layer and 1 output layer. The network was trained using the Bayesian regularization backpropagation. The inverse kinematic problem was modeled as a system of six nonlinear equations and six unknowns. Finally, both models were tested using a straight path to compare the results between the Newton–Raphson method and the network training.

Fossil fuel sources are well suited to fulfill the energy needs of human beings. Unfortunately, there are some limitations and disadvantages pertaining to fossil fuels, some of which are drastic. The main issues are: firstly, there is a finite supply of these fuels, eventually this supply will be exhausted; secondly, burning fossil fuels contributes to global warming, leading to disastrous consequences for the environment and the health of humans. Switching to renewable energy sources is the viable solution to the aforementioned issues. Robots bring numerous benefits in a wide variety of applications. Introducing robots to production environments and other applications results in a remarkable improvement in terms of productivity and efficiency. In this paper, the integration between robots and renewable energy sources is discussed. In other words, two main points are investigated: (1) how can renewable energy be a viable source of energy for robots and (2) how can the renewable energy industry benefit from utilizing robots in the execution of renewable energy-related tasks. Some of the recent developments concerning the integration between robots and renewable energy are reviewed. In addition, more opportunities and expected advancements are also discussed.

This paper concerns with comparing simulation studies for a newly developed concept of turning point to be used in multiple robot path planning. Different critical factors and design parameters are collected and statistical analyses are performed. After configuring different simulation scenarios, the efficient one is evaluated using a robust data envelopment analysis (RDEA). Due to uncertain aspects of various simulations scenarios, robust version of data envelopment analysis is proposed. Here, major criteria in robot path planning are deadlock and conflict avoidance, throughput, mean flow time, and effective total distance travelled. To determine the effective experiment for the proposed simulation model, RDEA is used. A comparative study with respect to different experiments having various simulation setting is developed. The results for a real robotic manufacturing cell system show effectiveness of the proposed process. Also, the efficient simulation software is determined by multiaspect analysis.

This paper investigates a battery snap-in operation performed by an industrial robot. The snap-fit phenomenon is experienced during the insertion of batteries into the battery holder. In order to understand the nature of the snap-fit phenomenon, the large displacement but small deformation theory is used to model the insertion operation. The stability of equilibrium points is analysed in order to determine the so-called trip point. The purpose of the investigation is to augment the displacement control of the robot with force feedback. A microcontroller-based measurement system is implemented to provide the force feedback. The proposed method is valid for a class of snap-fit problems not only for the investigated specific one.

The development of artificial intelligence has created new opportunities and challenges in industries. The competition between robots and humans has elicited extensive attention among legal researchers. In this exploratory study, we addressed issues regarding the introduction of robots to the practice of legal service through a semistructured interviews with lawyers, judges, artificial intelligence experts, and potential clients. An extended robot lawyer technology acceptance model with five facets and 11 elements is proposed in this study. This model highlights two dimensions: ‘legal use’ and ‘perception of trust.’ In summary, this study provides new specific implications and exhibits three characteristics, namely, derivative, macroscopic, and instructive, in the legal services with artificial intelligence. In addition, artificial intelligence robot lawyers are being developed with some of the abilities necessary to substitute for human beings. Nevertheless, working with human lawyers is imperative to produce benefits from this type of reciprocity.

Variable stiffness can improve the capability of human–robot interacting. Based on the mechanism of a flexible rack and gear, a rotational joint actuator named vsaFGR is proposed to regulate the joint stiffness. The flexible gear rack can be regarded as a combination of a non-linear elastic element and a linear adjusting mechanism, providing benefits of compactness. The joint stiffness is in the range of 217–3527 N.m/rad, and it is inversely proportional to the 4th-order of the gear displacement, and nearly independent from the joint angular deflection, providing benefits of quick stiffness regulation in a short displacement of 20 mm. The gear displacement with respect to the flexible gear rack is perpendicular to the joint loading force, so the power required for stiffness regulating is as low as 14.4 W, providing benefits of energy saving. The working principles of vsaFGR are elaborated, followed by presentation on the mechanics model and the prototype. The high compactness, great stiffness range and low power cost of vsaFGR are proved by simulations and experiments.

The Great War ushered in a new era of long-distance combat. For the first time, weapons with a very long range were massively deployed, in previously unheard-of places: under the sea and in the air. Stealth fighting also included espionage and propaganda, now orchestrated on a global scale. In reaction to the carnage in the trenches, a degree of moral rehabilitation came to be conferred on the weapons initially associated with a “cowards’ war”. This in turn encouraged experimentation with the new, unmanned technology that would lead to the first prototypes of guided munitions and drones.

Current robotic systems have achieved great sophistication in kinematic motion, control, and neural processing. One of the most challenging limitations imposed on modern robotics is the portable power source needed to sustain tether-free operation. Energy storage devices such as batteries and combustion engines may be heavy, require a great deal of space, and invariably have a finite energy capacity. Methods to control such devices may also impose limitations as most robotic systems rely on either tethered or radiative communication. The unavoidable repercussion of these limitations is the ultimate reduction of mobility and operation time to achieve specific tasks. To address these challenges, we apply our quasi-wireless powering methodology to show the operation of two robotic devices over a 1×1 m2 surface. Both power and control signals are transmitted simultaneously, producing seamless storage-free functionality over the entire area with a communication technique that is not line-of-sight or radiation dependent. We demonstrate an average power transfer efficiency of 93% using commercially available toy robots and discuss parameters relating to the power and communication performance.

This paper studies the effect of joint flexibility on the dynamic performance of a serial spatial robot arm of rigid links. Three models are developed in this paper. The first and the third models are developed using the multibody dynamics approach, while the second using the classical robotics approach. A numerical algorithm and an experimental test-rig are developed to test the final model. The links' inertial parameters are estimated numerically. Empirical formulae with assumption models are used to estimate the flexibility coefficients. The simulation results show that the joint damping is a major source of inaccuracies, causing trajectory error without a proper feedback controller.

Similar to the traditional adaptive strategies for robot manipulators, the regressor-free adaptive controller design also requires applying Slotine and Li's modification to avoid the feedback of joint accelerations. In this paper, a simple method is proposed to construct a regressor-free adaptive controller for robot manipulators without Slotine and Li's modification. In the new design, the joint acceleration vector is lumped into an unknown time-varying function and the function approximation technique is utilized to cover its effect; therefore, its implementation is free from joint acceleration feedback. The closed-loop stability and boundedness of internal signals are justified by the Lyapunov-like technique. Both simulation and experimental results for a two-link robot are presented to show the effectiveness of the proposed design.

The motivation for this work is the state of modern structural mechanisms that are characterized by growing complexity and ever-increasing demands for rapid and accurate motion. These contradictory requirements are often achieved according to easier and easier structures characterized by flexibility segments. In most of cases, the elasticity of structures appears as an obstacle for a precise and rapid control of motion. The aim of this paper is to explore ways of implementation of structural properties of elasticity with the application of high fidelity models during synthesis and analysis of complex mechanisms. Precisely, the aim is to explore the possibility of using Euler–Bernoulli equation, if not in its original form, then to the same extent with the use of modern knowledge in robotics (based on the knowledge of classical mechanics), and to examine the affordability and confirmation of the method through simulation results for a typical robotic configuration.