Adaptive Coupled Elastic Actuator Developed for Physical Human–Robot Interaction

Considering the interaction between people and machines under safety constraints without utilizing difficult and complex control strategies, this paper proposes a new actuator design—adaptive coupled elastic actuator (ACEA) with adjustable characteristics adaptive to the applied output force and input force. This would provide oncoming robotic systems with an intrinsic compromise between performance and safety in unstructured environments (i.e., exhibiting desired intrinsic lower and higher output impedance depending on different operation situations). Having introduced its concept and design, this paper also presents modeling, control and analysis for the ACEA system, not only to provide useful information to investigate the performance and basic characteristics of the designed system, but also to benefit the design of a more advanced controller in the near future. Finally, experimental results are presented to show the desired properties of the proposed ACEA system.     

Review of Semantic-Free Utterances in Social Human–Robot Interaction

As a young and emerging field in social human–robot interaction (HRI), semantic-free utterances (SFUs) research has been receiving attention over the last decade. SFUs are an auditory interaction means for machines that allow emotion and intent expression, which are composed of vocalizations and sounds without semantic content or language dependence. Currently, SFUs are most commonly utilized in animation movies (e.g., R2-D2, WALL-E, Despicable Me), cartoons (e.g., “Teletubbies,” “Morph,” “La Linea”), and computer games (e.g., The Sims) and hold significant potential for applications in HRI. SFUs are categorized under four general types: Gibberish Speech (GS), Non-Linguistic Utterances (NLUs), Musical Utterances (MU), and Paralinguistic Utterances (PU). By introducing the concept of SFUs and bringing multiple sets of studies in social HRI that have never been analyzed jointly before, this article addresses the need for a comprehensive study of the existing literature for SFUs. It outlines the current grand challenges, open questions, and provides guidelines for future researchers considering to utilize SFU in social HRI.     

A Safe-Control Paradigm for Human–Robot Interaction

This paper introduces a new approach to control a robot manipulator in a way that is safe for humans in the robot's workspace. Conceptually the robot is viewed as a tool with limited autonomy. The limited perception capabilities of automatic systems prohibits the construction of failsafe robots of the capability of people Instead, the goal of our control paradigm is to make the interaction with a robot manipulator safe by making the robot's actions predictable and understandable to the human operator. At the same time the forces the robot applies with any part of its body to its environment have to be controllable and limited. Experimental results are presented of a human-friendly robot controller that is under development for a Barrett Whole Arm Manipulator robot.     

Emotional Feature Extraction Method Based on the Concentration of Phoneme Influence for Human–Robot Interaction

Depending on the emotion of speech, the meaning of the speech or the intention of the speaker differs. Therefore, speech emotion recognition, as well as automatic speech recognition is necessary to communicate precisely between humans and robots for human–robot interaction. In this paper, a novel feature extraction method is proposed for speech emotion recognition using separation of phoneme class. In feature extraction, the signal variation caused by different sentences usually overrides the emotion variation and it lowers the performance of emotion recognition. However, as the proposed method extracts features from speech in parts that correspond to limited ranges of the center of gravity of the spectrum (CoG) and formant frequencies, the effects of phoneme variation on features are reduced. Corresponding to the range of CoG, the obstruent sounds are discriminated from sonorant sounds. Moreover, the sonorant sounds are categorized into four classes by the resonance characteristics revealed by formant frequency. The result shows that the proposed method using 30 different speakers' corpora improves emotion recognition accuracy compared with other methods by 99% significance level. Furthermore, the proposed method was applied to extract several features including prosodic and phonetic features, and was implemented on 'Mung' robots as an emotion recognizer of users.     

Experimental Reaction–Diffusion Chemical Processors for Robot Path Planning

In this paper we discuss the experimental implementation of a chemical reaction–diffusion processor for robot motion planning in terms of finding the shortest collision-free path for a robot moving in an arena with obstacles. These reaction–diffusion chemical processors for robot navigation are not designed to compete with existing silicon-based controllers. These controllers are intended for the incorporation into future generations of soft-bodied robots built of electro- and chemo-active polymers. In this paper we consider the notion of processing as being implicit in the physical medium constituting the body of a soft robot. This work therefore represents some early steps in the employment of excitable media controllers. An image of the arena in which the robot is to navigate is mapped onto a thin-layer chemical medium using a method that allows obstacles to be represented as local changes in the reactant concentrations. Disturbances created by the objects generate diffusive and phase wave fronts. The spreading waves approximate to a repulsive field generated by the obstacles. This repulsive field is then inputted into a discrete model of an excitable reaction–diffusion medium, which computes a tree of shortest paths leading to a selected destination point. Two types of chemical processors are discussed: a disposable palladium processor, which executes arena mapping from a configuration of obstacles, given before an experiment and, a reusable Belousov–Zhabotinsky processor which allows for online path planning and adaptation for dynamically changing configurations of obstacles.     

Adaptive rollover prevention controller for driver–vehicle systems

In the paper, we propose an adaptive rollover prevention controller for heavy vehicles. At first, a design method for an ideal vehicle model is proposed. The designed ideal vehicle model has the property that good rollover prevention performance can be assured even if the driver steering characteristics vary. If the behavior of the actual heavy vehicle tracks that of the designed ideal vehicle model, rollover prevention can be achieved. Therefore, next, to realize good rollover prevention, we propose an adaptive steering controller. In the heavy vehicle system using the controller, the actual heavy vehicle can track the ideal vehicle model. Then, rollover prevention can be achieved. Finally, to demonstrate the usefulness of the proposed controller, numerical simulations are carried out.     

Bias–Variance Analysis for Controlling Adaptive Surface Meshes

This paper presents a statistical methodology for exerting control over adaptive surface meshes. The goal is to develop an adaptive mesh which uses split and merge operations to control the distribution of planar or quadric surface patches. The novelty of the work reported in this paper is to focus on the variance–bias tradeoff that exists between the size of the fitted patches and their associated parameter variances. In particular, we provide an analysis which shows that there is an optimal patch area. This area offers the best compromise between the noise-variance, which decreases with increasing area, and the model-bias, which increases in a polynomial manner with area. The computed optimal areas of the local surface patches are used to exert control over the facets of the adaptive mesh. We use a series of split and merge operations to distribute the faces of the mesh so that each resembles as closely as possible its optimal area. In this way the mesh automatically selects its own density by adjusting the number of control-points or nodes. We provide experiments on both real and synthetic data. This experimentation demonstrates that our mesh is capable of efficiently representing high curvature surface detail.     

In Search of a Narrative for Human–Robot Relationships

Abstract

Fictional robots in literature and film have shaped our cultural image of robots. This paper studies what these images can contribute to our relationship with the real robots that are now entering our everyday lives. A review of science fiction literature and films comes to the conclusion that the predominant theme of the fictional robots – mostly androids and replicants – has been human identity, not robotics itself. Non-humanoid “just-robots” are presented as unproblematic sidekicks. It is argued that this focus has as consequence a tendency toward humanizing even non-humanoid robots in their presentation to the public. This tendency leads to a) breakdowns where technology contradicts the humanoid narrative, and b) a lack of productive narratives about emerging, more complex human-robot relationships.     

Adaptive neuro-fuzzy inference system–based model for elevation–surface area–storage interrelationships

In the developing of an optimal operation schedule for dams and reservoirs, reservoir simulation is one of the critical steps that must be taken into consideration. For reservoirs to have more reliable and flexible optimization models, their simulations must be very accurate. However, a major problem with this simulation is the phenomenon of nonlinearity relationships that exist between some parameters of the reservoir. Some of the conventional methods use a linear approach in solving such problems thereby obtaining not very accurate simulation most especially at extreme values, and this greatly influences the efficiency of the model. One method that has been identified as a possible replacement for ANN and other common regression models currently in use for most analysis involving nonlinear cases in hydrology and water resources–related problems is the adaptive neuro-fuzzy inference system (ANFIS). The use of this method and two other different approaches of the ANN method, namely feedforward back-propagation neural network and radial basis function neural network, were adopted in the current study for the simulation of the relationships that exist between elevation, surface area and storage capacity at Langat reservoir system, Malaysia. Also, another model, auto regression (AR), was developed to compare the analysis of the proposed ANFIS and ANN models. The major revelation from this study is that the use of the proposed ANFIS model would ensure a more accurate simulation than the ANN and the classical AR models. The results obtained showed that the simulations obtained through ANFIS were actually more accurate than those of ANN and AR; it is thus concluded that the use of ANFIS method for simulation of reservoir behavior will give better predictions than the use of any new or existing regression models.     

Contextualization of Geospatial Database Semantics for Human–GIS Interaction

Human interactions with geographical information are contextualized by problem-solving activities which endow meaning to geospatial data and processing. However, existing spatial data models have not taken this aspect of semantics into account. This paper extends spatial data semantics to include not only the contents and schemas, but also the contexts of their use. We specify such a semantic model in terms of three related components: activity-centric context representation, contextualized ontology space, and context mediated semantic exchange. Contextualization of spatial data semantics allows the same underlying data to take multiple semantic forms, and disambiguate spatial concepts based on localized contexts. We demonstrate how such a semantic model supports contextualized interpretation of vague spatial concepts during human–GIS interactions. We employ conversational dialogue as the mechanism to perform collaborative diagnosis of context and to coordinate sharing of meaning across agents and data sources.

Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface

A common assumption in traditional supervised learning is the similar probability distribution of data between the training phase and the testing/operating phase. When transitioning from the training to testing phase, a shift in the probability distribution of input data is known as a covariate shift. Covariate shifts commonly arise in a wide range of real-world systems such as electroencephalogram-based brain–computer interfaces (BCIs). In such systems, there is a necessity for continuous monitoring of the process behavior, and tracking the state of the covariate shifts to decide about initiating adaptation in a timely manner. This paper presents a covariate shift-detection and -adaptation methodology, and its application to motor imagery-based BCIs. A covariate shift-detection test based on an exponential weighted moving average model is used to detect the covariate shift in the features extracted from motor imagery-based brain responses. Following the covariate shift-detection test, the methodology initiates an adaptation by updating the classifier during the testing/operating phase. The usefulness of the proposed method is evaluated using real-world BCI datasets (i.e. BCI competition IV dataset 2A and 2B). The results show a statistically significant improvement in the classification accuracy of the BCI system over traditional learning and semi-supervised learning methods.     

Citizen Science: New Research Challenges for Human–Computer Interaction

Citizen science broadly describes citizen involvement in science. Citizen science has gained significant momentum in recent years, brought about by widespread availability of smartphones and other Internet and communications technologies (ICT) used for collecting and sharing data. Not only are more projects being launched and more members of the public participating, but more human–computer interaction (HCI) researchers are focusing on the design, development, and use of these tools. Together, citizen science and HCI researchers can leverage each other’s skills to speed up science, accelerate learning, and amplify society’s well-being globally as well as locally. The focus of this article is on HCI and biodiversity citizen science as seen primarily through the lens of research in the author’s laboratory. The article is framed around five topics: community, data, technology, design, and a call to save all species, including ourselves. The article ends with a research agenda that focuses on these areas and identifies productive ways for HCI specialists, science researchers, and citizens to collaborate. In a nutshell, while species are disappearing at an alarming rate, citizen scientists who document species’ distributions help to support conservation and educate the public. HCI researchers can empower citizen scientists to dramatically increase what they do and how they do it.     

Space–Time and ALE-VMS Techniques for Patient-Specific Cardiovascular Fluid–Structure Interaction Modeling

This is an extensive overview of the core and special space?Ctime and Arbitrary Lagrangian?CEulerian (ALE) techniques developed by the authors?? research teams for patient-specific cardiovascular fluid?Cstructure interaction (FSI) modeling. The core techniques are the ALE-based variational multiscale (ALE-VMS) method, the Deforming-Spatial-Domain/Stabilized Space?CTime formulation, and the stabilized space?Ctime FSI technique. The special techniques include methods for calculating an estimated zero-pressure arterial geometry, prestressing of the blood vessel wall, a special mapping technique for specifying the velocity profile at an inflow boundary with non-circular shape, techniques for using variable arterial wall thickness, mesh generation techniques for building layers of refined fluid mechanics mesh near the arterial walls, a recipe for pre-FSI computations that improve the convergence of the FSI computations, the Sequentially-Coupled Arterial FSI technique and its multiscale versions, techniques for the projection of fluid?Cstructure interface stresses, calculation of the wall shear stress and oscillatory shear index, arterial-surface extraction and boundary condition techniques, and a scaling technique for specifying a more realistic volumetric flow rate. With results from earlier computations, we show how these core and special FSI techniques work in patient-specific cardiovascular simulations.     

Adaptive output-feedback stabilisation for hybrid PDE–ODE systems with uncertain input disturbances

This paper is concerned with the adaptive output-feedback stabilisation for a class of hybrid partial differential equation (PDE)–ordinary differential equation (ODE) systems with uncertain input disturbances. Remarkably, in this paper, only two boundary measurements of the considered system are available for feedback. Moreover, the unknown parameters involved in the system are allowed to belong to an unknown interval. These two aspects make the considered system essentially different from those in the closely related literature. Inspired by the existing results, an observer is first introduced to estimate the unmeasured states of the original system. Then, by adaptive technique and backstepping method, an adaptive output-feedback controller is successfully constructed, which guarantees that the entire closed-loop system is asymptotically stable, and moreover, the parameter estimates converge to their own real values ultimately. Besides, by the semigroup approach, the well-posedness of the entire closed-loop system is achieved. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method.     

Brain–Computer Interfaces for Multimodal Interaction: A Survey and Principles

For decades, brain–computer interfaces (BCIs) have been used for restoring the communication and mobility of disabled people through applications such as spellers, web browsers, and wheelchair controls. In parallel to advances in computational intelligence and the production of consumer BCI products, BCIs have recently started to be considered as alternative modalities in human–computer interaction (HCI). One of the popular topics in HCI is multimodal interaction (MMI), which deals with combining multiple modalities in order to provide powerful, flexible, adaptable, and natural interfaces. This article discusses the situation of BCI as a modality within MMI research. State-of-the-art, real-time multimodal BCI applications are surveyed in order to demonstrate how BCI can be helpful as a modality in MMI. It is shown that multimodal use of BCIs can improve error handling, task performance, and user experience and that they can broaden the user spectrum. The techniques for employing BCI in MMI are described, and the experimental and technical challenges with some guidelines to overcome these are shown. Issues in input fusion, output fission, integration architectures, and data collection are covered.     

A Stable and Convergent Hodge Decomposition Method for Fluid–Solid Interaction

Fluid–solid interaction has been a challenging subject due to their strong nonlinearity and multidisciplinary nature. Many of the numerical methods for solving FSI problems have struggled with non-convergence and numerical instability. In spite of comprehensive studies, it has still been a challenge to develop a method that guarantees both convergence and stability. Our discussion in this work is restricted to the interaction of viscous incompressible fluid flow and a rigid body. We take the monolithic approach by Gibou and Min (J Comput Phys 231:3245–3263, 2012) that results in an augmented Hodge projection. The projection updates not only the fluid vector field but also the solid velocities. We derive the equivalence between the augmented Hodge projection and the Poisson equation with non-local Robin boundary condition. We prove the existence, uniqueness, and regularity for the weak solution of the Poisson equation, through which the Hodge projection is shown to be unique and orthogonal. We also show the stability of the projection in the sense that the projection does not increase the total kinetic energy of the fluid or the solid. Finally, we discuss a numerical method as a discrete analogue to the Hodge projection, then we show that the unique decomposition and orthogonality also hold in the discrete setting. As one of our main results, we prove that the numerical solution is convergent with at least first-order accuracy. We carry out numerical experiments in two and three dimensions, which validate our analysis and arguments.     

An Adaptive Staggered Discontinuous Galerkin Method for the Steady State Convection–Diffusion Equation

Staggered grid techniques have been applied successfully to many problems. A distinctive advantage is that physical laws arising from the corresponding partial differential equations are automatically preserved. Recently, a staggered discontinuous Galerkin (SDG) method was developed for the convection–diffusion equation. In this paper, we are interested in solving the steady state convection–diffusion equation with a small diffusion coefficient \(\epsilon \). It is known that the exact solution may have large gradient in some regions and thus a very fine mesh is needed. For convection dominated problems, that is, when \(\epsilon \) is small, exact solutions may contain sharp layers. In these cases, adaptive mesh refinement is crucial in order to reduce the computational cost. In this paper, a new SDG method is proposed and the proof of its stability is provided. In order to construct an adaptive mesh refinement strategy for this new SDG method, we derive an a-posteriori error estimator and prove its efficiency and reliability under a boundedness assumption on \(h/\epsilon \), where h is the mesh size. Moreover, we will present some numerical results with singularities and sharp layers to show the good performance of the proposed error estimator as well as the adaptive mesh refinement strategy.     

Development of an Intelligent Pneumatic Cylinder for Distributed Physical Human–Machine Interaction

Pneumatic systems are well known for their advantages and simplicity, and have been applied in various applications. This paper presents the development and experimental evaluation of an intelligent pneumatic cylinder and its control system. The cylinder is designed to have an optical encoder, pressure sensor, valve and a Programmable System on a Chip (PSoC) as the central processing unit. The PSoC will handle I²C communication, input and output data from the analogue to digital converter, counter program and pulse width modulation (PWM) duty cycle. An application tool for a distributed physical human–machine interaction is proposed using an intelligent pneumatic cylinder. The system applied 36 links of the actuator to form an Intelligent Chair Tool (ICT). The control methodology presented contains an inner force loop and an outer position loop implemented using a unified control system driven by PWM to an on/off valve. In this research, four control approaches, i.e., position control, force control, compliance control and viscosity control, were constructed and experimented. The physical properties of various objects were also detected by the intelligent cylinder through the detecting function experiment. Finally, an emulation experiment using mass was carried out and the results clearly show the ability of the intelligent cylinder, and the control approaches towards realization of the future ICT application.