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The purpose of this paper is to detail the design and prototyping of a smart automation solution for de-strapping plastic bonding straps on shipping pallets, which are loaded with multiple containers secured by a top-cover as they move on a conveyor belt.

The adopted design methodology to have the system perform its function entails using the least number of sensors and actuators to arrive at an economic solution from a system design viewpoint. Two prototypes of the robotic structure are designed and built, one in a research laboratory and another in an industrial plant, to perform localized cutting and grabbing of the plastic straps, with the help of a custom-designed passive localizing structure. The proposed structure is engineered to locate the plastic straps using one degree of freedom (DOF) only. An additional strap removal mechanism is designed to collect the straps and prevent them from interfering with the conveyor.

The functionality of the system is validated by performing full-process tests on the developed prototypes in a laboratory setting and under real-life operating conditions at BMW Group facilities. Testing showed that the proposed localization system meets the specified requirements and can be generalized and adapted to other industrial processes with similar requirements.

The proposed automated system for de-strapping pallets can be deployed in assembly or manufacturing facilities that receive parts in standard shipping pallets that are used worldwide.

To the best of the authors’ knowledge, this is the first mechanically smart system that is used for the automated removal of straps from shipping pallets used in assembly facilities. The two main novelties of the proposed design are the robustness of the strap localization without the need for computer vision and a large number of DOF, and the critical placement and choice of the cutting and gripping tools to minimize the number of needed actuators.

This paper aims to evaluate four different simultaneous localization and mapping (SLAM) systems in the context of localization of multi-legged walking robots equipped with compact RGB-D sensors. This paper identifies problems related to in-motion data acquisition in a legged robot and evaluates the particular building blocks and concepts applied in contemporary SLAM systems against these problems. The SLAM systems are evaluated on two independent experimental set-ups, applying a well-established methodology and performance metrics.

Four feature-based SLAM architectures are evaluated with respect to their suitability for localization of multi-legged walking robots. The evaluation methodology is based on the computation of the absolute trajectory error (ATE) and relative pose error (RPE), which are performance metrics well-established in the robotics community. Four sequences of RGB-D frames acquired in two independent experiments using two different six-legged walking robots are used in the evaluation process.

The experiments revealed that the predominant problem characteristics of the legged robots as platforms for SLAM are the abrupt and unpredictable sensor motions, as well as oscillations and vibrations, which corrupt the images captured in-motion. The tested adaptive gait allowed the evaluated SLAM systems to reconstruct proper trajectories. The bundle adjustment-based SLAM systems produced best results, thanks to the use of a map, which enables to establish a large number of constraints for the estimated trajectory.

The evaluation was performed using indoor mockups of terrain. Experiments in more natural and challenging environments are envisioned as part of future research.

The lack of accurate self-localization methods is considered as one of the most important limitations of walking robots. Thus, the evaluation of the state-of-the-art SLAM methods on legged platforms may be useful for all researchers working on walking robots’ autonomy and their use in various applications, such as search, security, agriculture and mining.

The main contribution lies in the integration of the state-of-the-art SLAM methods on walking robots and their thorough experimental evaluation using a well-established methodology. Moreover, a SLAM system designed especially for RGB-D sensors and real-world applications is presented in details.

The purpose of this paper is to provide new practical viewpoints and a framework regarding the management of technology transition from old to new technology through an in‐depth case study of Fanuc.

Due to the exploratory nature of this research and our interest in identifying key factors that affect the progress of technological transition, the authors selected the grounded theory style of data interpretation.

This case study identifies three key factors common to Fanuc's two major technological transitions and provides a new framework for the successful transition.

This paper provides new practical viewpoints regarding the management of technology transition from old to new technology.

This paper aims to illustrate the integration of multiple heterogeneous sensors into a fused filament fabrication (FFF) system and the implementation of multi-sensor data fusion technologies to support the development of a “smart” machine capable of monitoring the manufacturing process and part quality as it is being built.

Starting from off-the-shelf FFF components, the paper discusses the issues related to how the machine architecture and the FFF process itself must be redesigned to accommodate heterogeneous sensors and how data from such sensors can be integrated. The usefulness of the approach is discussed through illustration of detectable, example defects.

Through aggregation of heterogeneous in-process data, a smart FFF system developed upon the architectural choices discussed in this work has the potential to recognise a number of process-related issues leading to defective parts.

Although the implementation is specific to a type of FFF hardware and type of processed material, the conclusions are of general validity for material extrusion processes of polymers.

Effective in-process sensing enables timely detection of process or part quality issues, thus allowing for early process termination or application of corrective actions, leading to significant savings for high value-added parts.

While most current literature on FFF process monitoring has focused on monitoring selected process variables, in this work a wider perspective is gained by aggregation of heterogeneous sensors, with particular focus on achieving co-localisation in space and time of the sensor data acquired within the same fabrication process. This allows for the detection of issues that no sensor alone could reliably detect.

This work analyzes a pharmaceutical supply chain (PSC) in terms of supply chain visibility (SCV). The current good distribution practice (GDP) guideline demands increased visibility from firms. The purpose of this paper is to propose a solution for SCV enhancements based on automatic identification (Auto-ID) technologies.

The authors qualitatively analyze data from ten case studies of actors in a PSC. A review of Auto-ID technologies supports the derivation of solutions to enhance SCV.

This work shows that the functionalities of Auto-ID technologies offered by current practical monitoring solutions and challenges created by the GDP guideline necessitate further SCV enhancements. To enhance SCV, the authors propose three solutions: securPharm with passive radio frequency identification tags, transport containers with sensor nodes, and an SCV dashboard.

This study is limited to a PSC in Germany and is therefore not intended to be exhaustive. Thus, the results serve as a foundation for further analyses.

This study provides an overview of the functionality of Auto-ID technologies. In juxtaposition with the influence of the GDP guideline, the use of our Auto-ID-based solutions can help to enhance SCV.

This work analyzes a PSC in Germany, with consideration given to the influence of current legislation. Based on a multiple-case-study design, the authors derive three Auto-ID-based solutions for enhancing SCV.

This paper gives a review of the finite element techniques (FE) applied in the area of material processing. The latest trends in metal forming, non‐metal forming, powder metallurgy and composite material processing are briefly discussed. The range of applications of finite elements on these subjects is extremely wide and cannot be presented in a single paper; therefore the aim of the paper is to give FE researchers/users only an encyclopaedic view of the different possibilities that exist today in the various fields mentioned above. An appendix included at the end of the paper presents a bibliography on finite element applications in material processing for 1994‐1996, where 1,370 references are listed. This bibliography is an updating of the paper written by Brannberg and Mackerle which has been published in Engineering Computations, Vol. 11 No. 5, 1994, pp. 413‐55.

The aim of this paper is to explore the brain–computer interface (BCI) as a methodology for generating awareness and increasing reliable use cases of the same so that an individual's quality of life can be enhanced via neuroscience and neural networks, and risk evaluation of certain experiments of BCI can be conducted in a proactive manner.

This paper puts forward an efficient approach for an existing BCI device, which can enhance the performance of an electroencephalography (EEG) signal classifier in a composite multiclass problem and investigates the effects of sampling rate on feature extraction and multiple channels on the accuracy of a complex multiclass EEG signal. A one-dimensional convolutional neural network architecture is used to further classify and improve the quality of the EEG signals, and other algorithms are applied to test their variability. The paper further also dwells upon the combination of internet of things multimedia technology to be integrated with a customized design BCI network based on a conventionally used system known as the message query telemetry transport.

At the end of our implementation stage, 98% accuracy was achieved in a binary classification problem of classifying digit and non-digit stimuli, and 36% accuracy was observed in the classification of signals resulting from stimuli of digits 0 to 9.

BCI, also known as the neural-control interface, is a device that helps a user reliably interact with a computer using only his/her brain activity, which is measured usually via EEG. An EEG machine is a quality device used for observing the neural activity and electric signals generated in certain parts of the human brain, which in turn can help us in studying the different core components of the human brain and how it functions to improve the quality of human life in general.

The purpose of this paper is to develop an automated disassembly cell that is flexible and robust to the physical variations of a product. In this way it is capable of dealing with any model of product, regardless of the level of detail in the supplied information.

The concept of cognitive robotics is used to replicate human level expertise in terms of perception and decision making. As a result, difficulties with respect to the uncertainties and variations of the product in the disassembly process are resolved.

Cognitive functions, namely reasoning and execution monitoring, can be used in basic behaviour control to address problems in variations of the disassembly process due to variations in the product's structure particularly across different models of the product.

The paper provides a practical approach to formulating the disassembly domain and behaviour control of the cognitive robotic agent via a high‐level logical programming language that combines domain‐specific heuristic knowledge with search to deal with variations in products and uncertainties that arise during the disassembly process.

Full disassembly automation that is flexible and robust to the uncertainties that may arise potentially replaces human labour in a difficult and hazardous task. Consequently, the disassembly process will be more economically feasible, especially in developed countries.

The paper provides a practical approach to the basic cognitive functions that replicate the human expert's behaviour to the disassembly cell.

The purpose of this paper is to present the methodology that was used to perform system identification of a dynamically unstable tilt-rotor from flight test data. The method incorporated wavelet transform into the maximum likelihood principle formulation, emphasizing both time and frequency responses. Using wavelets allowed to additionally filter noise in the data, and this increased the estimation quality. This approach did not require measurement and process noise modeling in contrast to the Kalman filter usage for parameter estimation.

In the study, lateral-directional stability and control derivatives of an unstable tiltrotor in hover were estimated. This was performed by applying the maximum likelihood output error method. The estimated model response was decomposed using the Mallat pyramid and matched to wavelet coefficients obtained directly from measurements. In addition, a coherence-based weighting function was used to put more emphasis on the most reliable data. For comparison, the same set of data was used to identify a model with the same structure using the maximum likelihood principle with an incorporated Kalman filter.

It was found that maximum likelihood principle and wavelet transform allowed for estimating aerodynamic coefficients of a dynamically unstable aircraft. The estimation was performed with high accuracy.

The designed method can be used for system identification of unstable aircraft and when additional noise is present (e.g. when noise due to turbulence was observable during the flight test or higher noise levels were present in the sensors data).

The paper presents verification of a wavelet-based maximum likelihood principle output error method using flight test data.

The motivation for robotics research in the agricultural field has sparked in consequence of the increasing world population and decreasing agricultural labor availability. This paper aims to analyze the state of the art of pruning and harvesting manipulators used in agriculture.

A research was performed on papers that corresponded to specific keywords. Ten papers were selected based on a set of attributes that made them adequate for review.

The pruning manipulators were used in two different scenarios: grapevines and apple trees. These manipulators showed that a light-controlled environment could reduce visual errors and that prismatic joints on the manipulator are advantageous to obtain a higher reach. The harvesting manipulators were used for three types of fruits: strawberries, tomatoes and apples. These manipulators revealed that different kinematic configurations are required for different kinds of end-effectors, as some of these tools only require movement in the horizontal axis and others are required to reach the target with a broad range of orientations.

This work serves to reduce the gap in the literature regarding agricultural manipulators and will support new developments of novel solutions related to agricultural robotic grasping and manipulation.