Table of Contents

Various 3-UPU architectures feature two rigid bodies connected to one another through three kinematic chains (limbs) of universal–prismatic–universal (UPU) type. They were first proposed in the last decade of the 20th century and have animated discussions among researchers for more-or-less two decades. Such discussions brought to light many features of lower-mobility parallel manipulators (PMs) that were unknown until then. The discussions also showed that such architectures may be sized into translational PMs, parallel wrists, or even reconfigurable (metamorphic) PMs. Even though commercial robots with these architectures have not yet been built, the interest in them remains. Consequently, a review of the literature on these architectures, highlighting their contribution to the progress of lower-mobility PM design, is still of interest for the scientific community. This paper aims at presenting a critical review of the results that have been obtained up until now. Full article

The problem of inverse kinematics is essential to consider while dealing with the robot’s mechanical structure in almost all applications. Since the solution of the inverse kinematics problem is very complex, many research efforts have been working towards getting the approximate solution of this problem. However, for some applications, working with the approximate robot’s model is neither sufficient nor efficient. In this paper, an adaptive inverse kinematics methodology is developed to solve the inverse kinematics problem in such a way that compensate for unknown uncertainty in the Jacobian matrix of the serial kinematic chain robot manipulators. The proposed methodology is based on continuous second order sliding mode strategy (CSOSM-AIK). The salient advantage of the CSOSM-AIK approach is that it does not require the availability of the kinematics model or Jacobian matrix of the robot manipulators from joint space variables to Cartesian space variables. The global stability of the closed-loop system with CSOSM-AIK methodology is proven using the Lyapunov theorem. In order to demonstrate the robustness and effectiveness of the proposed methodology, some simulations are conducted. Full article

Wireless sensor networks (WSN) are used by engineers to record the behavior of structures. The sensors provide data to be used by engineers to make informed choices and prioritize decisions concerning maintenance procedures, required repairs, and potential infrastructure replacements. However, reliable data collection in the field remains a challenge. The information obtained by the sensors in the field frequently needs further processing, either at the decision-making headquarters or in the office. Although WSN allows data collection and analysis, there is often a gap between WSN data analysis results and the way decisions are made in industry. The industry depends on inspectors’ decisions, so it is of vital necessity to improve the inspectors’ access in the field to data collected from sensors. This paper presents the results of an experiment that shows the way Augmented Reality (AR) may improve the availability of WSN data to inspectors. AR is a tool which overlays the known attributes of an object with the corresponding position on the headset screen. In this way, it allows the integration of reality with a virtual representation provided by a computer in real time. These additional synthetic overlays supply data that may be unavailable otherwise, but it may also display additional contextual information. The experiment reported in this paper involves the application of a smart Strain Gauge Platform, which automatically measures strain for different applications, using a wireless sensor. In this experiment, an AR headset was used to improve actionable data visualization. The results of the reported experiment indicate that since the AR headset makes it possible to visualize information collected from the sensors in a graphic form in real time, it enables automatic, effective, reliable, and instant communication from a smart low-cost sensor strain gauge to a database. Moreover, it allows inspectors to observe augmented data and compare it across time and space, which then leads to appropriate prioritization of infrastructure management decisions based on accurate observations. Full article

Robot manipulators are often tasked with working in environments with vibrations and are subject to load uncertainty. Providing an accurate tracking control design with implementable torque input for these robots is a complex topic. This paper presents two approaches to solve this problem. The approaches consider joint space tracking control design in the presence of nonlinear uncertain torques caused by external vibration and payload variation. The properties of the uncertain torques are used in both approaches. The first approach is based on the boundedness property, while the second approach considers the differentiability and boundedness together. The controllers derived from each approach differ from the perspectives of accuracy, control effort, and disturbance properties. A Lyapunov-based analysis is utilized to guarantee the stability of the control design in each case. Simulation results validate the approaches and demonstrate the performance of the controllers. The derived controllers show stable results at the cost of the mentioned properties. Full article

Meal assistant robots form a very important part of the assistive robotics sector since self-feeding is a priority activity of daily living (ADL) for people suffering from physical disabilities like tetraplegia. A quick survey of the current trends in this domain reveals that, while tremendous progress has been made in the development of assistive robots for the feeding of solid foods, the task of feeding liquids from a cup remains largely underdeveloped. Therefore, this paper describes an assistive robot that focuses specifically on the feeding of liquids from a cup using tactile feedback through force sensors with direct human–robot interaction (HRI). The main focus of this paper is the application of reinforcement learning (RL) to learn what the best robotic actions are, based on the force applied by the user. A model of the application environment is developed based on the Markov decision process and a software training procedure is designed for quick development and testing. Five of the commonly used RL algorithms are investigated, with the intention of finding the best fit for training, and the system is tested in an experimental study. The preliminary results show a high degree of acceptance by the participants. Feedback from the users indicates that the assistive robot functions intuitively and effectively. Full article