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The aim of the law is to maintain social order, peace, and justice in society, whereas the aim of ethics is to provide codes of ethics and conduct that help people to decide what is wrong, and how to act and behave. Laws provide a minimum set of standards for obtaining good human behavior. Ethics often provides standards that exceed the legal minimum. Therefore, for the best behavior, both law and ethics should be respected. The Internet of Things (IoT) involves a large number of objects and humans that are connected via the Internet ‘anytime’ and ‘anyplace’ to provide homogeneous communication and contextual services. Thus, it creates a new social, economic, political, and ethical landscape that needs new enhanced legal and ethical measures for privacy protection, data security, ownership protection, trust improvement, and the development of proper standards. This survey and opinion article is concerned with the ethics and legislation of the IoT and provides an overview of the following: definition and history of the IoT; general ethical principles and theories that are available for application in the IoT; the role of governments in the IoT; regulations in the European Union (EU) and United States for the IoT’ IoT characteristics that have the potential to create ethical problems; IoT ethical questions and principles; IoT security, privacy, and trust aspects; and the ethical culture of IoT-related companies.

The aim of this paper is to provide a global overview of mobile robot control and navigation methodologies developed over the last decades. Mobile robots have been a substantial contributor to the welfare of modern society over the years, including the industrial, service, medical, and socialization sectors. The paper starts with a list of books on autonomous mobile robots and an overview of survey papers that cover a wide range of decision, control and navigation areas. The organization of the material follows the structure of the author’s recent book on mobile robot control. Thus, the following aspects of wheeled mobile robots are considered: kinematic modeling, dynamic modeling, conventional control, affine model-based control, invariant manifold-based control, model reference adaptive control, sliding-mode control, fuzzy and neural control, vision-based control, path and motion planning, localization and mapping, and control and software architectures.

Most robotics and automation scientists believe that many new aspects currently emerging in robotics and automation R A and aspects that are expected to emerge in future call for the development of new cultural ethical and legal regulations that can face efficiently the most delicate issues that may arise in real practice Over the last two decades the subject of ethics in R A has received great attention and many important theoretical and practical results were derived in the direction of making robots and automation systems ethical The purpose of this paper is to discuss the issue of ethics in robotics and automation and outline major representative achievements in the field

Information is a basic element of life and society involved in all areas of human, scientific, technological, economic, and developmental activity. Information storage, flow, and processing are inherent processes in nature and living organisms. Information transmission and communication/networking techniques are contributing to the development of modern society, including social, economic, business, scientific, and technological operations and activities. This chapter covers at a conceptual level the following issues of information: definition, historical landmarks of its manifestations, communication models, modulation/demodulation, computer networks, multimedia, informatics/telematics, Shannon’s information entropy, source and channel coding/decoding, and theorems of information theory. The above set of information/communication models, techniques, and technologies are affecting, and will continue to increasingly affect the social, economic/business, and developmental activities of people in the short-term and long-term future.

This chapter is devoted to three modern subfields of information, namely information science, information technology, and information systems. These fields have an enormous impact on modern society and its development. Information science is generally concerned with the processes of storing and transferring information via the merging of concepts and methodologies of computer science, linguistics, and library science. Information technology (IT) or “infotech” covers all methodologies and technologies which are used for the production, storage, processing, transmission, and dissemination of information. Information systems use information science and information technology concepts and tools in the everyday operation of enterprises and organizations that needs the cooperation (symbiosis) of technology with human-controlled processes and actions. This chapter starts with a discussion of the fundamental general issues of information science including several classification schemes (knowledge maps), and continues with a guided tour to computer science, computer engineering, internet/www, and web-based multimedia. Finally, this chapter provides a general discussion of information systems which include their fundamental concepts, general structure, types, and development.

Modern control has decisively contributed to the human society development providing the means for successful control and efficient and safe operation of complex technological and non-technological systems such as computer-based systems, aircrafts, robots, automation systems, managerial systems, decision support systems, economic systems, etc. It is based on the concepts of “system state vector” and “state-space models” which are applicable to time-varying, multivariable, and nonlinear systems in both continuous-time and discrete-time representations. In this chapter, we present the fundamental concepts, principles, and methodologies covering most developments at an introductory level. Specifically, the following topics are considered: state-space modeling, Lyapunov stability, controllability and observability, optimal, stochastic, adaptive, predictive, robust, nonlinear, and intelligent control. Also, the following classes of dynamic models, that cover a wider range of natural and man-made systems, are briefly discussed: large-scale, distributed-parameter, time delay, finite state, and discrete event models. The field of modern control is still expanding offering new challenges in research and real-life bioengineering and technological applications.

Energy is the basis of everything. It is the dominant fundamental element of life and society. Its movement or transformation is always followed by a certain event, phenomenon, or dynamic process. Energy is used by humans to acquire useful minerals from earth, and construct technological creatures (buildings, transportation systems, factories, machines, etc). The energy used by end users in our society comes from exhaustible sources (coal, fuel oil, natural gas), non-exhaustible (renewable) sources (hydroelectric, wind, solar) or from alternative sources (bio-alcohol, biodiesel, liquid nitrogen, hydrogen). In this chapter, we provide a historical tour to the energy and thermodynamics studies and developments, accompanied by an exposition of the fundamental aspects of energy. These aspects include the energy concept itself, the energy types, the energy sources, and the impact of energy generation and use on the environment.

Information is present in all natural, living, and technological systems, and is recognized as the third basic universal quantity after energy and matter. For this reason, information manifestations in both natural and man-made systems have attracted the interest of humans through the historical evolution of the humankind. On the life and biological side of information there are two axes of study, namely: (i) the study of the underlying natural/biological mechanisms of storing, processing, and transmission of information from cells to entire organisms, and (ii) the use of biological mechanisms of computation in the design and implementation of new types of man-made computational systems. On the technological side, information and communication technology (ICT) is increasingly entering to the “heart” of large-scale competitive policies, due to its capacity as a key player in the ongoing human growth, development, and modernization. This chapter is concerned with the role and application of information to life and society. Regarding the life side the issues of the substantive role and the transmission sense of information in biology, the natural information principles, and biocomputation, are discussed. On the society side, the application of IT to office automation, power generation and distribution, computer-integrated manufacturing, robotics, business and electronic commerce, education, medicine, and transportation, is investigated. This chapter ends with a look at the issues of social networking, and ethics of IT (infoethics).

This work presents a field programmable gate array (FPGA)-based embedded software platform coupled with a software-based plant, forming a hardware-in-the-loop (HIL) that is used to validate a systematic sensor selection framework. The systematic sensor selection framework combines multi-objective optimization, linear-quadratic-Gaussian (LQG)-type control, and the nonlinear model of a maglev suspension. A robustness analysis of the closed-loop is followed (prior to implementation) supporting the appropriateness of the solution under parametric variation. The analysis also shows that quantization is robust under different controller gains. While the LQG controller is implemented on an FPGA, the physical process is realized in a high-level system modeling environment. FPGA technology enables rapid evaluation of the algorithms and test designs under realistic scenarios avoiding heavy time penalty associated with hardware description language (HDL) simulators. The HIL technique facilitates significant speed-up in the required execution time when compared to its software-based counterpart model.

The sophisticated method for mathematical modeling of humanoid robots formulated in Part 1 of this paper is applied here to the dynamic task of keeping a posture under disturbance, which is equally important to humans and humanoid robots. The idea of this work is to develop and realize a simulator tool for dynamic analysis of human-or-humanoid behavior under disturbances. To show the potentials and verify this tool, we comparatively analyze the robustness of some postures to external disturbance. At this stage of research we do not conduct real experiments with humans/humanoids but try to verify our simulation tool by relying on available experience. Therefore, the postures for comparison are taken from everyday life and from sports: upright standing, squat posture, and three karate postures. As the external disturbance we choose an impulse and a permanent force, both with variable direction and magnitude.

It is a well-known fact that the growth of technology has radically changed our approach to biosciences and medicine. What is interesting is that in the last decade we have witnessed a reverse influence--a trend towards "biologically inspired" solutions to technical problems. This leads to a true symbiosis between bio and technical sciences. A good example is the intersection and overlapping of three distinct fields: sports, medicine, and robotics. This paper intends to apply sophisticated methods developed for mathematical modeling of humanoid robots in real human motions, particularly in posture stabilization and selection of appropriate postures for different situation in sports and every day life. A general simulation system is realized: following a deductive principle, the algorithm considers particular human/humanoid motions (like those occurring in different sports) as being just special cases of a general motion and impact theory. Simulation includes the interaction with the environment. Simulating a human/humanoid dynamics in a given task, all relevant characteristics could be found: trajectories, velocities and accelerations, loads of joints, power requirements, energy consumption, contact forces including ground reactions, impact effects, etc. Simulation is used in solving a problem that is important for both humans and humanoid robots, namely, the behavior of a posture (keeping stability or collapsing) when subject to different disturbances. Although "posture" is mainly a static term, maintaining its balance in the presence of disturbances is a truly dynamic problem. Typical postures from every day life and sports are considered, such as: upright standing, squat (and partial squat), and three karate postures. Two sorts of disturbances are applied to eventually, compromise the posture: external impulse and permanent external force. This paper does not aim to suggest some new control strategy but to develop the dynamic model and simulation algorithm, and apply them to compare the robustness of different postures to external disturbances.

In this paper a switching fuzzy logic controller for mobile robots with a bounded curvature constraint is presented. The controller tracks piece-wise linear paths, which are an approximation of the feasible smooth reference path. The controller is constructed through the use of a map, which transforms the problem to a simpler one; namely the tracking of straight lines. This allows the use of an existing fuzzy tracker deployed in a previous work, and its simplification leading to a 70% rule reduction. Simulation results and a comparison analysis with existing trackers are also presented along with some stability considerations on the impulsive error dynamics which emerge.

Ethical and social issues of robotics have attracted increasing attention from the scientific and technical community over the years. These issues arise particularly in mental and sensitive robotic applications, such as robot-based rehabilitation, social robot (sociorobot) applications, and military robot applications. The purpose of launching this Special Issue was to publish high-quality papers addressing timely and important aspects of roboethics, and to serve as a dissemination source of novel ideas demonstrating the necessity of roboethics. The papers finally included in the Special Issue deal with fundamental aspects and address interesting deep questions in the roboethics and robophililosophy field.

This paper presents a System on Chip (SoC) for the path following task of autonomous non-holonomic mobile robots. The SoC consists of a parameterized Digital Fuzzy Logic Controller (DFLC) core and a flow control algorithm that runs under the Xilinx Microblaze soft processor core. The fuzzy controller supports a fuzzy path tracking algorithm introduced by the authors. The FPGA board hosting the SoC was attached to an actual differential-drive Pioneer 3-DX8 robot, which was used in field experiments in order to assess the overall performance of the tracking scheme. Moreover, quantization problems and limitations imposed by the system configuration are also discussed.

Many recent studies (e.g., IFR: International Federation of Robotics, 2016) predict that the number of robots (industrial, service/social, intelligent/autonomous) will increase enormously in the future. Robots are directly involved in human life. Industrial robots, household robots, medical robots, assistive robots, sociable/entertainment robots, and war robots all play important roles in human life and raise crucial ethical problems for our society. The purpose of this paper is to provide an overview of the fundamental concepts of robot ethics (roboethics) and some future prospects of robots and roboethics, as an introduction to the present Special Issue of the journal Information on “Roboethics”. We start with the question of what roboethics is, as well as a discussion of the methodologies of roboethics, including a brief look at the branches and theories of ethics in general. Then, we outline the major branches of roboethics, namely: medical roboethics, assistive roboethics, sociorobot ethics, war roboethics, autonomous car ethics, and cyborg ethics. Finally, we present the prospects for the future of robotics and roboethics.

In many robotic applications it is required to manipulate a specific rigid object whose CAD model is known a priori, but its position and orientation in space are unknown. This category of tasks includes piercing, painting or iron welding on some specific points of the rigid object's surface. For this kind of tasks to become feasible the CAD data of the rigid object should be registered into the robot's workspace frame, so that the robot arm becomes aware of the position and orientation of the rigid body with reference to its own flange or base coordinate frame. In order to achieve this goal several techniques from the fields of image processing, 3D modeling and robot kinematics should be combined. This paper provides a convenient combination of such methods that can be used for the successful on-line registration of a rigid object's CAD data to the robot's workspace frame.

A low computational cost method is proposed for detecting actuator/sensor faults. Typical model-based fault detection (FD) units for multiple sensor faults require a bank of estimators [i.e., conventional Kalman estimators or artificial intelligence (AI)-based ones]. The proposed FD scheme uses an AI approach for developing of a low computational power FD unit abbreviated as $i$ FD. In contrast to the bank-of-estimators approach, the proposed $i$ FD unit employs a single estimator for multiple actuator/sensor FD. The efficacy of the proposed FD scheme is illustrated through a rigorous analysis of the results for a number of sensor fault scenarios on an electromagnetic suspension system.

Extended Kalman Filtering (EKF) is proposed for: (i) the extraction of a fuzzy model from numerical data; and (ii) the localization of an autonomous vehicle. In the first case, the EKF algorithm is...

Roboethics (robot ethics) is the branch of applied ethics that studies the positive and negative implications of robots to society, aiming at inspiring the moral design, development and use of robots, especially of intelligent and autonomous robots. This chapter provides a preliminary discussion of some fundamental concepts of robot ethics and morality, a short literature review of roboethics literature, and an outline of the present book.

The design of sociorobots can be performed efficiently by exploiting some kind of structured framework, in order to integrate and implement the underlying perception, cognition, learning, control, and social interaction functions. This necessity has motivated the development of many different intelligent control architectures with particular features, advantages, and weaknesses. This chapter starts by providing a discussion of the basic functional design requirements, and an outline of the two early seminal behavior-based control architectures, namely the subsumption and motor schemas architectures, Then, the chapter describes three important newer architectures, namely a 4-layer architecture, the deliberative-reactive architecture, and the combined symbolic/ subsumption/ servo-control (SSS) architecture. A general discussion and categorization of the characteristics of the intelligent control architectures is also included. All these architectures were used successfully in many available sociorobots.

Walking bipedal anthropomorphic robots (also known as humanoids, androids or gynoids) are very complex mechanical creatures with very large numbers of motion degrees of freedom. In many cases they are designed to exhibit face and emotional expressions with the aid of a large number of motors inside their head. Typically, their present cost is prohibitive, and so many robotic companies are now turning their efforts toward producing cheap humanoids with open source software and modular design, in order to be usable and affordable by Universities and research institutes. This chapter starts with a discussion of some fundamental issues concerning the design of humanoid heads and faces. Then, the chapter outlines the specifications, structures, features, and capabilities of seven walking anthropomorphic robots, namely: Honda’s ASIMO, Fujitsu’s HOAP-3, Sony’s QRIO, Abdebaran Robotics’ NAO, Japan METI’s HRP-2, Tokyo University’s gynoid SAYA, and KAIST’s android Albert HUBO.

Wheeled mobile anthropomorphic robots (WMAR’s) are very popular due to their increased mobility and stability, and the relatively affordable cost. A WMAR has a mobile platform which very often accommodates inside it the required computer and other hardware equipment. The upper part or torso of WMAR’s may have any form from very simple to very complex depending on the application(s) for which they are designed. This chapter discusses the general structure of WMAR’s, and provides a conceptual description of two sophisticated WMAR’s, namely ARMAR III (Karlsruhe University), and Rollin Justin (DLR: German Aerospace Center). Then, the chapter describes seven available WMAR’s designed for social interaction with humans. These robots are: Maggie, Robovie, CosmoBot, Bandit, PEARL, Nexi, and Mobiserv.

In this work, a Field Programmable Gate Array (FPGA)-based embedded software platform coupled with a software-based plant, forming a Hardware-In-the-Loop (HIL), is used to validate a systematic sensor selection framework. The systematic sensor selection framework combines multi-objective optimization, Linear-Quadratic-Gaussian (LQG) control, and the nonlinear model of a maglev suspension. The physical process that represents the suspension plant is realized in a high-level system modeling environment, while the LQG controller is implemented on an FPGA. FPGAs allow to rapidly evaluate algorithms and test designs under real-world scenarios avoiding heavy time penalty associated with Hardware Description Language (HDL) simulators. Moreover, the HIL technique implemented shows a significant speed-up in the required execution time when compared to the software-based model.

Quantum information processing in neural structures results in an exponential increase of patterns storage capacity and can explain the extensive memorization and inferencing capabilities of humans. An example can be found in neural associative memories if the synaptic weights are taken to be fuzzy variables. In that case, the weights’ update is carried out with the use of a fuzzy learning algorithm which satisfies basic postulates of quantum mechanics. The resulting weight matrix can be decomposed into a superposition of associative memories. Thus, the fundamental memory patterns (attractors) can be mapped into different vector spaces which are related to each other via unitary rotations. Quantum learning increases the storage capacity of associative memories by a factor of 2 N , where N is the number of neurons.

The questions when and why one needs to use mathematical models, and especially the models of dynamics, represent a still unresolved issue. A general answer would be that dynamic modeling is needed as a tool when designing structure of the system and its control unit. In this case we talk about simulation. The other application is in on-line control of the system – the so-called dynamic control. While for simulation one should generally use the best available model, the control can be based on a reduced dynamics, depending on a particular task. This article considers humans and humanoid robots and addresses a rather important question: what are dynamic effects that one should take care of when modeling and simulating a human or a humanoid? The article suggests the key topics for work and tries to justify them. As a final result a General Human/Humanoid-Dynamics Simulator is seen.

As mobile robots are mostly designed to act autonomously, procedures that detect and isolate faults on the various parts of a robot are essential. The most powerful approaches in fault detection and isolation (FDI) are those using a process model, where quantitative and qualitative knowledge-based models, databased models, or combinations thereof are applied. This article suggests a model-free approach to the solution of the fault detection problem. One way to deal with the absence of a mathematical model is to build a model from input-output data. In this article, local model networks (LMNs) are used for plant modeling. The key to fault detection and diagnosis is the creation of residual signals. Although the way these signals are formed varies, in all cases the residuals change their value accordingly with the presence of faults. To avoid false alarms, the residuals must be affected by factors unrelated to faults (like modeling errors) as little as possible. Change-detection algorithms are therefore used for reliable residual generation. These algorithms are designed to detect changes in signals that include noise or other types of disorders. The combination of local model networks for modeling and change-detection algorithms for residual creation provides an efficient method for fault detection and diagnosis. The method is applied on the wheels subsystem of a mobile robot.

In this paper we investigate the use of the multi-layer perceptron (MLP) for system modelling. A new sigmoidal activation function is introduced and the study is focused at the utilization of this function on a MLP that performs modelling of dynamic, discrete time systems. The role of the activation function in the training process is investigated analytically, and it is proven that the shape of the activation function and it's derivative can affect the training outcome. The method is simulated at a well known benchmark, namely the three tank system, and is incorporated in a Fault Detection and Identification (FDI) method, also applied and simulated at the three tank system. Finally, a comparison is made with an approach that utilizes local model neural networks for system modeling.

This paper presents a new topological structure for use in the context of hierarchical radiosity combined with discontinuity meshing. This is most useful for a new strategy adopted for subdividing the elements of a scene consisting of convex polygons. The subdivision is done in a local optimization manner keeping the aspect ratios of produced polygons low. The generated meshes give high visual accuracy.

One of the ultimate goals of mobile robotics research is to build robots that can safely carry out missions in hazardous and populated environments. Most of today's commercial mobile devices scale poorly along this dimension. Their motion planning relies on accurate, static models of the environments, and therefore they often fail their mission if humans or other unpredictable obstacles block their path. To build autonomous mobile robots one has to build systems that can perceive their environments, react to unforeseen circumstances, and plan dynamically in order to achieve their mission. Thus, the objective of the motion planning and control problem is to find collision-free trajectories, in static or dynamic environments containing some obstacles, between a start and a goal configuration. It has attracted much research in recent years. In this context the term control has a broad meaning that includes many different controls, such as low-level motor control, and behaviour control, where behaviour represents many complicated tasks, like obstacle avoidance and goal seeking. This article describes an intelligent motion planning and navigation system for omnidirectional mobile robots based on fuzzy logic. Owing to its simplicity and hence its short response time, the fuzzy navigator is especially suitable for on-line applications with strong real-time requirements. On-line planning is an on-going activity. The planner receives a continuous flow of information about occurring events and generates new commands in response to the incoming events, while previously planned motions are being executed. The fuzzy-rule-base of the proposed system combines the repelling influence, which is related to the distance and the angle between the robot and nearby obstacles, with the attracting influence produced by the distance and the angular difference between the actual direction and position of the robot and the final configuration, to generate actuating commands for the mobile platform. It can be considered as an on-line local navigation method for omnidirectional mobile robots for the generation of instantaneous collision-free motions. This reactive system is especially suitable for real-time applications. The use of fuzzy logic leads to a transparent system which can be tuned by hand or by a set of learning rules. Furthermore, this approach allows obstacle avoidance and navigation in dynamic environments. The functioning of the fuzzy motion planner with respect to omnidirectional mobile robots and results of simulated experiments are presented.

Two approaches are proposed to model and control a human-like motion of robot arms. The first, which is based on the concept of distributed positioning (DP), is suggested as a good model of arm motion in the phase where fatigue does not appear. The prescribed motion of the end-effector is distributed to a redundant number of arm joints in accordance to their acceleration capabilities. For the phase where fatigue appears, the concept of virtual fatigue is proposed. This artificial variable, which is based on robot dynamics, emulates the progress of biological fatigue. The human handwriting task is chosen for the simulation. The DP concept is tested first by modeling nonfatigued motion. The justification of the usual inclination of letters is presented, and the relation between the inclination, legibility, and a secondary objective (finger involvement, energy consumption, motor thermal load) is discussed. It is found that, for some prescribed level of legibility, the individual optima of all the secondary cost functions are quite near to each other. Writing in the presence of fatigue is also analyzed, applying the method of the so-called "virtual fatigue".

This part of the paper examines numerically the possibility of modeling “robot fatigue” being representative of a human psychophysical state that can be applied to robots. Temperatures of driving motors are suggested as analogs to fatigue in muscles. Simulation of robot behavior is performed on a typical human task, namely handwriting. Three phases of task execution, characteristic for humans, are observed, i.e. regular motion, reconfiguration after symptoms of fatigue, and degeneration caused by the too long, hard work.

In this paper the theory of fuzzy logic and fuzzy reasoning is combined with the theory of Markov systems and the concept of a fuzzy non-homogeneous Markov system is introduced for the first time. This is an effort to deal with the uncertainty introduced in the estimation of the transition probabilities and the input probabilities in Markov systems. The asymptotic behaviour of the fuzzy Markov system and its asymptotic variability is considered and given in closed analytic form. Moreover, the asymptotically attainable structures of the system are estimated also in a closed analytic form under some realistic assumptions. The importance of this result lies in the fact that in most cases the traditional methods for estimating the probabilities can not be used due to lack of data and measurement errors. The introduction of fuzzy logic into Markov systems represents a powerful tool for taking advantage of the symbolic knowledge that the experts of the systems possess.

Man–machine communication had been recognized a long time ago as a significant issue in the implementation of automation. It influences the machine effectiveness through direct costs for operator training and through more or less comfortable working conditions. The solution for the increased effectiveness might be found in user-friendly human–machine interface. In robotics, the question of communication and its user-friendliness is becoming even more significant. It is no longer satisfactory that a communication can be called “human–machine interface”, since one must see robots as future collaborators, service workers, and probably personal helpers. So, a new way of communication is needed. This paper starts from the fact that a variety of unconventional ways of communication is being explored: speech communication, handwriting, and facial mimicry. Speech recognition and generation, apart from its potentially strong impact to human–robot interface, is out of the scope of this study, and the same holds for handwriting. The facial mimicry and the wider field of robot emotion lead to a new way of communication that can be called “gestural language”. We may say that our research matches such a wide notion. However, there is an important specificity of our approach. Previous works in this field looked at facial and gestural expressions of emotions and messages as actually voluntary activities. They were induced by some situations, but such situations had to be recognized after which the appropriate preprogrammed reaction followed. Thus, some expert system or other artificial intelligence system were applied. The current study explores the conditional reactions that are “chemically based”. Human psycho-physical states like fear, stress and especially fatigue follow from complex chemical processes. Such states cannot be voluntarily controlled. They are not expressed through some specific mimicry or body movement, but they influence all movements. Any motion intended to accomplish some task will be changed, thus sending a message to the neighborhood. This change can be observed, and so the message is received. For processes of this kind and their resembling, we prefer using the term modeling psycho-physical states. Among various states interesting for application in robots, the present study concentrates on fatigue, which is a good example for the initial research. The first part of the paper explains the concept and the background, while the second part provides numerical justification.

In this paper, occlusion culling techniques that appeared in the last decade are reviewed. Occlusion culling techniques are responsible for reducing the polygons rendered by the graphics hardware with the target of achieving real-time rendering. The various techniques are discussed in detail and a synopsis table with their main characteristics is given.

The authors claim that a careful examination of robotics science and technology from its origins in the 1950s to its current status reveals that such progress, albeit very important, profitable, and with a strong impact on society, was basically the "side activities" to the ever-existing and continuous desire of mankind to build and realize humanoid robots, artificial men equipped uith proper intelligence capable of operating autonomously, thus replacing trained individuals for dexterous jobs. They consider such a humanoid robot through the realistic tasks/jobs it will most probably do. It is postulated that this humanoid robot be considered as a personal helper (a home robot or personal robot). Given the present level of technology, the question is posed: are we ready to move towards personal robotics, and what might be the first step? A possible answer to this question is given through a discussion of the human-like characteristics a personal robot must have; namely, human-like motion, human-like intelligence, and human-like communication.

This two-part paper is concerned with the analysis and achievement of human-like behavior by robot arms (manipulators). The analysis involves three issues: (i) the resolution of the inverse kinematics problem of redundant robots, (ii) the separation of the end-effector's motion into two components, i.e. the smooth (low accelerated) component and the fast (accelerated) component, and (iii) the fatigue of the motors (actuators) of the robot joints. In the absence of the fatigue, the human-like performance is achieved by using the partitioning of the robot joints into “smooth” and “accelerated” ones (called distributed positioning—DP). The actuator fatigue is represented by the so-called “virtual fatigue” (VF) concept. When fatigue starts, the human-like performance is achieved by engaging more the joints (motors) that are less fatigued, as does the human arm. Part I of the paper provides the theoretical issues of the above approach, while Part II applies it to the handwriting task and provides extensive simulation results that support the theoretical expectations.

NeuroFAST is an on-line fuzzy modeling learning algorithm, featuring high function approximation accuracy and fast convergence. It is based on a first-order Takagi-Sugeno-Kang (TSK) model, where the consequence part of each fuzzy rule is a linear equation. Structure identification is performed by a fuzzy adaptive resonance theory (ART)-like mechanism, assisted by fuzzy rule splitting and adding procedures. The well known /spl delta/ rule continuously performs parameter identification on both premise and consequence parameters. Simulation results indicate the potential of the algorithm. It is worth noting that NeuroFAST achieves a remarkable performance in the Box and Jenkins gas furnace process, outperforming all previous approaches compared.

Electric load forecasting has received an increasing attention over the years by academic and industrial researchers and practitioners due to its major role for the effective and economic operation of power utilities. The aim of this paper is to provide a collective unified survey study on the application of computational intelligence (CI) model-free techniques to the short-term load forecasting of electric power plants. All four classes of CI methodologies, namely neural networks (NNs), fuzzy logic (FL), genetic algorithms (GAs) and chaos are addressed. The paper starts with some background material on model-based and knowledge-based forecasting methodologies revealing a number of key issues. Then, the pure NN-based and FL-based forecasting methodologies are presented in some detail. Next, the hybrid neurofuzzy forecasting methodology (ANFIS, GARIC and Fuzzy ART variations), and three other hybrid CI methodologies (KB-NN, Chaos-FL, Neurofuzzy-GA) are reviewed. The paper ends with eight representative case studies, which show the relative merits and performance that can be achieved by the various forecasting methodologies under a large repertory of geographic, weather and other peculiar conditions. An overall evaluation of the state-of-art of the field is provided in the conclusions.

This paper (Part II) investigates the motion of a redundant anthropomorphic arm during the writing task. Two approaches are applied. The first is based on the concept of distributed positioning which is suitable to model the “writing” task before the occurrence of fatigue symptoms. The second approach uses the concept of “virtual fatigue” (VF) which is a variable that dynamically behaves in a way analogous to the biological fatigue. VF enables the arm to reconfigure itself and take postures appropriate for the current level of fatigue. The study includes the analysis of legibility and inclination of handwriting, and a set of simulation results that show most practical aspects of robot human-like performance.

Robotic soccer is an interesting test bench for the field of self-organizing and cooperating multi-agent systems. This paper deals with learning of two basic low-level behaviors that will enable the robotic player to participate further in higher-level collaborative and adversarial learning situations. First, a ball interception and obstacle avoidance behavior is learned. Then the acquired skills are incorporated into a next higher-level multi-agent learning scenario, namely the shooting ball behavior. The proposed control scheme for these behaviors consists of a trajectory generator with a layered structure, which supplies data to a trajectory-tracking controller.

Total Quality Management (TQM) principles and methods try to improve the overall performance of an enterprise or organization. Knowledge Based Systems (KBS) can support Total Quality Management procedures by organizing, storing and processing TQM knowledge and both rule-based and object-oriented knowledge representation structures and reasoning can be used for these applications. Three interesting cases concerning the application of KB systems in TQM will be presented in this paper: a)Statistical process quality control reasoning using knowledge based systems, b)Knowledge based decision support systems for quality circles, and c)Knowledge based Business Process Reengineering (BPR) and software design.

The rapid increase of warehouse use demands automated management services in order to make decisions for all tasks concerned. These decisions must ensure optimised usage of resources, which leads to cost reduction and better customer service. A major consideration is the way the warehouse area, which consists of different storage types and similar product groups, is exploited. The optimisation of the warehouse's occupied area is the target of replenishment, which iS essentially a constrained placement problem. In this paper, a genetic algorithm with revised operators iS developed. This algorithm is applied to real warehouse data and results show that it produces successful replenishments in a complex environment where many criteria have to be considered and met to some user-defined extent.

This paper deals with the regulation of the thermal characteristics of gas metal arc welding (GMAW). A complete treatment of the welding control problem requires the regulation of both the geometrical and thermal characteristics of the weld. Both classes of characteristics are of critical importance, but the thermal ones have received less attention in the majority of previous work. The present paper proposes a hierarchical predictive control scheme for the metallurgical characteristics of GMAW. A previously developed model for the regulation of the heat-affected zone, the cooling rate and the nugget cross-sectional area is used for the open-loop predictions. At the first level of the hierarchy, a parameterized generalized predictive control (GPC) algorithm is selected, among other control techniques, due to the inherited difficulty of the welding thermal process and the robustness of this algorithm against modeling errors and parameter variations. The main drawback of GPC is that it does not guarantee stability. The second level of the control hierarchy, where a coordinator specifies a set of reliable values for the parameters of GPC, so that stability is assured, and transmits them to the controller of the first level overcomes this difficulty. This paper provides a representative set of simulation results obtained by the present hierarchical GPC scheme, including a comparison with the classical linear-quadratic optimal control scheme. These results show the superiority of the GPC scheme.

Fuzzy relational systems can represent symbolic knowledge in a formal numerical (subsymbolic) framework, with the aid of fuzzy relation equations. The disadvantage of this methodology is the need for a priori knowledge in order to construct the fuzzy relation equation. In this paper, a neural network model is proposed in order to represent fuzzy relational systems without the need of the construction of the fuzzy relation equation. The network ensures the ideal perfect recall of fuzzy associative memories when the a posteriori constructed fuzzy relation equation has a non-empty solution set. It is actually a single layer of generalized neurons (compositional neurons) that perform the sup-t-norm composition. An on-line learning algorithm adapting the weights of its interconnections is incorporated into the neural network. These weights are actually the elements of the fuzzy relation representing the fuzzy relational system. The algorithm is based on the knowledge about the topographic structure of the respective fuzzy relation.

This paper is concerned with the solution of the principal component analysis (PCA) problem with the aid of neural networks (NNs). After an overview of the basic NN-based PCA concepts and a listing of the available algorithms, two criteria for evaluating PCA NN algorithms are proposed. Then, a new criterion for the generation of improved PCA NN structures with reduced size is presented. Using this criterion, one can start with a small network and dynamically add new nodes at the hidden layer(s) during training, one at a time, until the desired performance is achieved. A simulation example is provided that shows the applicability and effectiveness of the methodology.

This paper is concerned with the trajectory definition in robot tasks. Although very often ignored, the specification of robot motion is not the first step in the definition of a robot task. The task definition starts with the description of the final outcome, i.e. with the specification of the job to be performed. When this is done, the proper robot kinematics (motion law) is defined. This step, from the outcome of the task to the robot motion, is sometimes straightforward and dictated by the technology applied. However, even if a multiple choice of motion is possible leading to the same outcome, this possibility is usually avoided by preassuming a certain optimality criterion for the quality of work. Clearly, such an approach does not leave the possibility for some additional optimization in the sense of a secondary objective. So, some potential benefits are lost. This paper starts from the observation that successful operation of a robot does not necessarily imply the maximum quality of its outcome. It is sufficient if the quality is kept at a given (lower) level. Such a suboptimal task execution offers the possibility of some additional secondary optimization. The kinematics of the task is modified according to a secondary objective function. The quality is then treated as a constraint in the minimization of this secondary objective function. This optimization can be based on biomechanical principles. Here the principle: “learn from humans” is adopted, i.e. the kinematics modification is done so as to resemble the behavior of a human worker. The benefits of the approach of the paper are illustrated by two specific examples, namely the handwriting and spray-coating tasks.

Fuzzy set theory has recently attracted much attention in the field of image classification, image understanding and image processing. One of the major topics in fuzzy image processing is the image classification problem. This paper presents a fast and accurate iterative fuzzy clustering (I.F.C.) method dynamically adapted to the classification process. This is used for high performance fuzzy segmentation which forms the basis for reliable image understanding. The proposed fuzzy segmentation scheme examines the image connectivity in the space and frequency domains. The detected fuzzy features are combined via a block synthesis and local correlation algorithmic procedure. Some results showing that the performance of the proposed I.F.C./clustering method is superior from that of the standard fuzzy c-means method are provided.