The ongoing modernization of today's workplaces within the framework of industry 4.0 is leading to radical new developments and forcing fundamental changes to the way companies and factories are established. The purpose of this paper is to develop and improve a concept that organizes the workplace on a basic level in order to meet the upcoming challenge of accounting for robots as key players in it. There are several organizational structures that include the traditional hierarchical structures as well as the modern agile management models that are rising in the world of industry 4.0. Within it, the usage of robots is increasing due to the vast and multiple functions each robot is able to perform. This implies automation, but it also implies that different forms of collaboration with humans are becoming necessary as factories are tending to produce customized products. Individualized products require different methods and different skills which span different sets of abilities of humans and robots. How does this increase of robots' usage affect the applied organization structures and flows of the decision-making processes? What are necessary changes that need to be considered to maximize the efficiency and effectiveness of a workplace with robots that already existing structures do not compensate for? This paper highlights the existing gap and addresses the required changes in the organization models. Following that is an evaluation and outlook on that validates the legitimacy of the approach.

Hybrid teams of industry 4.0: A work place considering robots as key players

We present a new approach for the design of cobotic systems. It is based on several steps with increasing complexity: Activity analysis, basic design, detailed design and realization. A particular attention is paid to human factors and human systems interactions. Different simulation levels are required to provide flexibility and adaptability.

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Human Factors Issues for the Design of a Cobotic System

For some years now, the use of social, anthropomorphic robots in various situations has been on the rise. These are robots developed to interact with humans and are equipped with corresponding extremities. They already support human users in various industries, such as retail, gastronomy, hotels, education and healthcare. During such Human-Robot Interaction (HRI) scenarios, physical touch plays a central role in the various applications of social robots as interactive non-verbal behaviour is a key factor in making the interaction more natural. Shaking hands is a simple, natural interaction used commonly in many social contexts and is seen as a symbol of greeting, farewell and congratulations. In this paper, we take a look at the existing state of Human-Robot Handshaking research, categorise the works based on their focus areas, draw out the major findings of these areas while analysing their pitfalls. We mainly see that some form of synchronisation exists during the different phases of the interaction. In addition to this, we also find that additional factors like gaze, voice facial expressions etc. can affect the perception of a robotic handshake and that internal factors like personality and mood can affect the way in which handshaking behaviours are executed by humans. Based on the findings and insights, we finally discuss possible ways forward for research on such physically interactive behaviours.

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Human-Robot Handshaking: A Review

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The use of social, anthropomorphic robots to support humans in various industries has been on the rise. During Human-Robot Interaction (HRI), physically interactive non-verbal behaviour is key for more natural interactions. Handshaking is one such natural interaction used commonly in many social contexts. It is one of the first non-verbal interactions which takes place and should, therefore, be part of the repertoire of a social robot. In this paper, we explore the existing state of Human-Robot Handshaking and discuss possible ways forward for such physically interactive behaviours.

Advances in Human-Robot Handshaking

A social robot has to ascertain the personality of the human interacting with it through verbal or non-verbal communication. While existing literature focuses on facial feature recognition for non-verbal communication, we propose using handshake patterns to classify the personality of the concerned human. Due to the largely random nature of the gestures in handshakes, general pattern recognition methods cannot be used to classify them effectively. This paper proposes to classify a handshake into two labels: dominant and submissive, by using the K-Nearest Neighbor algorithm on apriori ranked statistical markers extracted from the training data. For the purpose of data acquisition, a wearable device attached to the wrist of the robot was designed. The wearable was fitted with a 9-axis accelerometer and a flex sensor. The training data consists of accelerometric data from handshakes with 12 different individuals, with each individual exhibiting both a dominant and submissive personality trait. Mutual Information and Statistical Dependence are used to rank statistical markers extracted from the training data. The best ranked features were used to classify the handshakes using K-Nearest Neighbor algorithm and classify the handshake into the two class labels. The proposed methodology classifies data with an accuracy of 75.0% when tested on 8 different handshakes.

Classifying human-robot interaction using handshake data

Due to the recent developments in the domain of wireless network, the demand of multi frequency antenna has increased significantly. Wi-Fi has become a very popular choice available to the end users, providing them broadband connectivity and an alternative to cable and digital subscriber line, while X band finds extensive use in RADAR and satellite communication systems, and the proposed design can be put to use where there is a need for integration of both technologies. In this study, the authors have developed a novel U-shaped slot antenna for Wi-Fi and X band applications which gives a significantly higher gain and better performance than the existing antennas aimed for the same application to validate the model the structure was simulated in HFSS and operating frequencies for the proposed antenna were in compliance with the IEEE 802.11 WLAN protocol and the X band range. The structure has a fairly simple design and is having a fairly compact dimension of 40 mm × 40 mm × 1.6 mm. Moreover, the obtained radiation pattern demonstrates that proposed antenna has a significantly unidirectional pattern in both E-plane and H-plane. In addition to the unidirectional radiation pattern, a significant reduction in conduction loss was obtained.

Multiband High-Gain Antenna with CPW Feed for Wi-Fi, WI-MAX and X Band Application

Development of automatic framework for efficient characterization of brain lesions is a significant research concern due to the complex properties exhibited by the brain tissues. This study focuses on observing the properties of such composite structures in order to identify optimal features for characterizing the properties of normal and abnormal brain tissues. This work initially applies Fuzzy C Mean algorithm to identify the region of interest. After segmentation, four different types of features are extracted from the region of interest. These features include first-order parameters, Gray-level Co-occurrence Matrix (GLCM) parameters, Laws texture features, and Gray-Level Run-Length Matrix (GLRLM) parameters. These identification features were ranked in order of pertinence with the help of Mutual Information and Statistical Dependence-based feature ranking algorithms. Based on the inference obtained from the Mutual Information and Statistical Dependence-based feature ranking algorithms, twelve best features are selected for characterizing the properties of the normal and abnormal brain tissues.

Anish Mukherjee

Papers

Classifying human-robot interaction using handshake data

Multiband High-Gain Antenna with CPW Feed for Wi-Fi, WI-MAX and X Band Application

Feature Ranking of Spatial Domain Features for Efficient Characterization of Stroke Lesions