The development of a human-robot interface for industrial collaborative system

Industrial robots have been identified as one of the most effective solutions for optimising output and quality within many industries. However, there are a number of manufacturing applications involving complex tasks and inconstant components which prohibit the use of fully automated solutions in the foreseeable future. A breakthrough in robotic technologies and changes in safety legislations have supported the creation of robots that coexist and assist humans in industrial applications. It has been broadly recognised that human-robot collaborative systems would be a realistic solution as an advanced production system with wide range of applications and high economic impact. This type of system can utilise the best of both worlds, where the robot can perform simple tasks that require high repeatability while the human performs tasks that require judgement and dexterity of the human hands. Robots in such system will operate as “intelligent assistants”. In a collaborative working environment, robot and human share the same working area, and interact with each other. This level of interface will require effective ways of communication and collaboration to avoid unwanted conflicts. This project aims to create a user interface for industrial collaborative robot system through integration of current robotic technologies. The robotic system is designed for seamless collaboration with a human in close proximity. The system is capable to communicate with the human via the exchange of gestures, as well as visual signal which operators can observe and comprehend at a glance. The main objective of this PhD is to develop a Human-Robot Interface (HRI) for communication with an industrial collaborative robot during collaboration in proximity. The system is developed in conjunction with a small scale collaborative robot system which has been integrated using off-the-shelf components. The system should be capable of receiving input from the human user via an intuitive method as well as indicating its status to the user ii effectively. The HRI will be developed using a combination of hardware integrations and software developments. The software and the control framework were developed in a way that is applicable to other industrial robots in the future. The developed gesture command system is demonstrated on a heavy duty industrial robot

The detection of concealed firearm carrying trough CCTV: the role of affect recognition

This research aimed to explore whether the recognition of offenders with a concealed firearm by a human operator might be based on the recognition of affective (negative) state derived from non-verbal behaviour that is accessible from CCTV images. Since a firearm is concealed, it has been assumed that human observers would respond to subtle cues which individuals inherently produce whilst carrying a hidden firearm. These cues are believed to be reflected in the body language of those carrying firearms and might be apprehended by observers at a conscious or subconscious level. Another hypothesis is that the ability to recognize the carrier of concealed firearm in the CCTV footage might be affected by other factors, such as the skills in decoding an affective state of others and the viewpoint of observation of the surveillance targets. In order to give a theoretical and experimental basis for these hypotheses the first objective was to examine the extant literature to determine what is known about recognition of affect from non-verbal cues (e.g. facial expressions and body movement), and how it can be applied to the detection of human mal-intent. A second objective was to explore this subject in relation to the detection of concealed firearm carrying through performing a number of experimental studies. The studies employed experts, i.e. CCTV operators and mainly the lay people as participants. Also, various experimental techniques such as questionnaires and eye-tracking registration were used to investigate the topic. The results show that human observers seem to use visual indicators of affective state of surveillance targets to make a decision whether or not the individuals are carrying a concealed firearm. The most prominent cues were face, and upper body of surveillance targets, gait, posture and arm movements. The test of decoding ability did not show sufficient relationship with the ability to detect a concealed firearm bearer. The performance on the task might be view dependent. Further research into this topic will be needed to generate strategies that would support reliable detection of concealed firearm carrying through employing of related affective behavioural cues

Chapter From the Lab to the Real World: Affect Recognition Using Multiple Cues and Modalities

Interdisciplinary concept of dissipative soliton is unfolded in connection with ultrafast fibre lasers. The different mode-locking techniques as well as experimental realizations of dissipative soliton fibre lasers are surveyed briefly with an emphasis on their energy scalability. Basic topics of the dissipative soliton theory are elucidated in connection with concepts of energy scalability and stability. It is shown that the parametric space of dissipative soliton has reduced dimension and comparatively simple structure that simplifies the analysis and optimization of ultrafast fibre lasers. The main destabilization scenarios are described and the limits of energy scalability are connected with impact of optical turbulence and stimulated Raman scattering. The fast and slow dynamics of vector dissipative solitons are exposed

Towards objective human performance measurement for maritime safety: A new psychophysiological data-driven machine learning method

Human errors significantly contribute to transport accidents. Human performance measurement (HPM) is crucial to ensure human reliability and reduce human errors. However, how to address and reduce the subjective bias introduced by assessors in HPM and seafarer certification remains a key research challenge. This paper aims to develop a new psychophysiological data-driven machine learning method to realize the effective HPM in the maritime sector. It conducts experiments using a functional Near-Infrared Spectroscopy (fNIRS) technology and compares the performance of two groups in a maritime case (i.e. experienced and inexperienced seafarers in terms of different qualifications by certificates), via an Artificial Neural Network (ANN) model. The results have generated insightful implications and new contributions, including (1) the introduction of an objective criterion for assessors to monitor, assess, and support seafarer training and certification for maritime authorities; (2) the quantification of human response under specific missions, which serves as an index for a shipping company to evaluate seafarer reliability; (3) a supportive tool to evaluate human performance in complex emerging systems (e.g. Maritime Autonomous Surface Ship (MASS)) design for ship manufactures and shipbuilders

Three Essays on the Impact of Cuteness on Consumer Behavior

Over the last decade, the world has seen a rise in the popularity of cute stimuli. Adorable baby pictures, fluffy puppy videos, and whimsical emojis seem ever-present in social media news feeds and friends’ posts. In addition, products marketed toward adults that feature cute characters drive sales worth billions of dollars. The growing presence of cute stimuli in our daily lives is accompanied by emerging research on their social and behavioral impacts. Correspondingly, the current dissertation consists of three essays that contribute to the literature on cuteness by empirically testing the impacts of cuteness in the marketplace. To extend the literature on cuteness, the first essay examines the effects of exposure to cute images on risk-seeking behavior, the second essay tests the effect of salespeople’s cute facial features (i.e., babyface) on online consumer engagement, and the third essay proposes a novel construct that induces the perception of cuteness through auditory cuteness cues: cute voice

Building long-term relationships with virtual and robotic characters: the role of remembering

With the recent advances, today people are able to communicate with embodied (virtual/robotic) entities using natural ways of communication. In order to use them in our daily lives, they need to be intelligent enough to make long-term relationships with us and this is highly challenging. Previous work on long-term interaction frequently reported that after the novelty effect disappeared, users' interest into the interaction decreased with time. Our primary goal in this study was to develop a system that can still keep the attention of the users after the first interaction. Incorporating the notion of time, we think that the key to long-term interaction is the recall of past memories during current conversation. For this purpose, we developed a long-term interaction framework with remembering and dialogue planning capability. In order to see the effect of remembering on users, we designed a tutoring application and measured the changes in social presence and task engagement levels according to the existence of memory. Different from previous work, users' interest in our system did not decrease with time with the important contributions of remembering to the engagement level of user

What do Collaborations with the Arts Have to Say About Human-Robot Interaction?

This is a collection of papers presented at the workshop What Do Collaborations with the Arts Have to Say About HRI , held at the 2010 Human-Robot Interaction Conference, in Osaka, Japan

Affective Computing

This book provides an overview of state of the art research in Affective Computing. It presents new ideas, original results and practical experiences in this increasingly important research field. The book consists of 23 chapters categorized into four sections. Since one of the most important means of human communication is facial expression, the first section of this book (Chapters 1 to 7) presents a research on synthesis and recognition of facial expressions. Given that we not only use the face but also body movements to express ourselves, in the second section (Chapters 8 to 11) we present a research on perception and generation of emotional expressions by using full-body motions. The third section of the book (Chapters 12 to 16) presents computational models on emotion, as well as findings from neuroscience research. In the last section of the book (Chapters 17 to 22) we present applications related to affective computing