Autonomous robotic systems are an indispensable component of work in many industries that are on the brink of entering many other areas of people's lives such as transportation or healthcare. Because attitudes towards new technologies shape consumers' decision to adopt these innovations, the present study examines the public opinion toward emergent robotic systems in Austria and Germany. The results showed that, in general, attitudes seemed rather positive in both countries. However, for Austria a rather ambivalent picture emerged: although Austrians exhibited the largest adoption rate of robotic technologies in Europe, at the same time they evaluated robots most unfavorably as compared to other European countries. Thus, experiences with robots seemed to have intensified potential downsides of automation that resulted in more concerns regarding the widespread use of robots.
In 'Artificial Intelligence: Robot Law, Policy and Ethics', Nathalie Rébé discusses the legal and contemporary issues in relation to creating conscious robots. She argues that AI?s physical and decision-making capacities to act on its own means having to grant it a juridical personality.0The advancement in new technologies forces us to reconsider the role Artificial Intelligence (AI) will have in our society. Sectors such as education, transportation, jobs, sex, business, the military, medical and security will be particularly affected by the development of AI.0This work provides an analysis of cases and existing regulatory tools, which could be used by lawyers in future trials. Rébé also offers a new comprehensive framework to regulate Strong AI so that ?it? can safely live among humans.0This book is a response to two questions: first, should we ban or prohibit AI; and, secondly, if not, what should be the salient features of a legal or regulatory framework for AI?
"This book provides a forum for the cybernetics field in critical emerging technologies, including research into design, engineering, and technological aspects of cyborg creation and existence alongside humankind for issues in their potential acceptance, participation, policy, governance, and requisite socialization between individualization and corporate, global, networked, mechanized human and humanized machine experiences"--
Images of killer robots are the stuff of science fiction - but also, increasingly, of scientific fact on the battlefield. Should we be worried, or is this a normal development in the technology of war? In this accessible volume ethicist Deane Baker cuts through the confusion over whether lethal autonomous weapons - so-called killer robots - should be banned. Setting aside unhelpful analogies taken from science fiction, Baker looks instead to our understanding of mercenaries (the metaphorical 'dogs of war') and weaponized animals (the literal dogs of war) to better understand the ethical challenges raised by the employment of lethal autonomous weapons (the robot dogs of war). These ethical challenges include questions of trust and reliability, control and accountability, motivation and dignity. Baker argues that, while each of these challenges is significant, they do not - even when considered together - justify a ban on this emerging class of weapon systems. This book offers a clear point of entry into the debate over lethal autonomous weapons - for students, researchers, policy makers and interested general readers.
As the capabilities of the mobile robots as well as their abilities to perform more tasks in an autonomous manner are increased, we need to think about the interactions that humans will have with these robots. Human-robot interaction (HRI) has recently received considerable attention in the academic community, government labs, technology companies, and through the media. The interdisciplinary nature of HRI requires researchers in the field to understand their research within a broader context. Since natural language is the easiest and most natural mode of communication for humans, it is desirable to use it to instruct the robot and to generate easy-to-understand messages for the user. Using natural language to teach a navigation task to a robot is an application of a more general instruction-based learning methodology. It can be used to instruct the robot with higher-level goals or to handle certain behaviors and modify their execution. One effective way is to describe the route to the robot in a multimodal way. On the other hand, significant progress has been made towards stable robotic bipedal walking in the last few years. This is creating an increased research interest in developing autonomous navigation strategies which are tailored specifically to humanoid robots. Efficient approaches to perception and motion planning, which are suited to the unique characteristics of bipedal humanoid robots and their typical operating environments, are receiving special interest. One important area of research involves the design of algorithms to compute robust navigation strategies for humanoid robots in human environments. Therefore, autonomous robot navigation based on route instruction is becoming an increasingly important research topic with regard to both humanoid and other mobile robots. In this dissertation, the problem of humanoid robot navigation in indoor environments is addressed. A complete framework is presented for humanoid robot navigation based on a multimodal cognitive interface. First, a spatial language to describe route-based navigation tasks for a mobile robot is proposed. This language is implemented to present an intuitive interface that enables novice users to easily and naturally describe a route to a mobile robot in indoor environments. An instruction interpreter is implemented to analyze the user's route to generate its equivalent symbolic and topological map representations which are used as an initial path estimation for the humanoid robot. Second, a robust lightweight object processing system with a high detection rate is developed. It can actually be used by mobile robots and meet their hard constraints to recognize landmarks during navigation. A landmark processing system is developed to detect, identify, and localize different types of landmarks during robot navigation in indoor or miniature city environments. The system is based on a two-step classification stage which is robust and invariant towards scaling and translations. By combining the strengths of appearance-based and model-based object classification techniques, it provides a good balance between fast processing time and high detection accuracy. Finally, a time-efficient hybrid motion planning system for a humanoid robot in indoor environments is implemented. The proposed technique is a combination of sampling-based planner and D* Lite search to generate dynamic footstep placements in unknown environments. A modified cylinder model is used to approximate the trajectory for the robot's body-center during navigation. It calculates the actual distances required to execute different actions of the robot and compares them to the distances from the nearest obstacles. D* Lite search is then used to find dynamic and low-cost footstep placements within the resulting configuration space. ; Da die Fähigkeiten von mobilen Robotern einschließlich ihrer Möglichkeiten, Aufgaben autonom durchzuführen, erweitert wurden, muss die Interaktion zwischen Mensch und Roboter neu betrachtet werden. Human-Robot-Interaction (HRI) ist ein aktuelles Thema in der Forschung, in Technologie-Unternehmen und in den Medien. Der interdisziplinäre Charakter des HRI-Bereiches erfordert Forschung innerhalb eines breiten Themenkomplexes. Da natürliche Sprache das einfachste und natürlichste Mittel der Kommunikation für Menschen ist, ist es wünschenswert, diese Form der Kommunikation auch bei der HRI zu nutzen, um einem Roboter Anweisungen zu geben und leicht verständliche Botschaften für den Benutzer zu generieren. Die Verwendung natürlicher Sprache zur Instruierung bei Navigations-Aufgaben ist eine Anwendung einer allgemeineren instruktions-basierten Lernmethodologie. Dem Roboter können so übergeordnete Ziele mitgeteilt werden, bestimmte Verhaltensweisen geändert oder auch die Ausführung einzelner Aktionen modifiziert werden. Eine effiziente Methode zur Beschreibung der Route ist die Verwendung multimodaler Anweisungen. Weil die vergangenen Jahre einen bedeutenden Fortschritt auf dem Gebiet der humanoiden Roboter und des stabilen zweibeinigen Gehens gebracht haben, besteht ein verstärktes Forschungsinteresse an der Entwicklung autonomer Navigationsstrategien, die speziell auf humanoide Roboter zugeschnitten sind. Von besonderem Interesse sind effiziente Ansätze zur kombinierten Perzeptions- und Aktionsplanung, die an die speziellen Eigenschaften von zweibeinigen humanoiden Robotern und ihre typischen Betriebsumgebungen angepasst sind. Ein wichtiges Gebiet der Forschung ist der Entwurf von Algorithmen zur Berechnung von robusten Navigations-Strategien für humanoide Roboter in menschlicher Umgebung. Aus diesem Grunde ist die auf Routen-Instruktion beruhende autonome Roboter-Navigation ein zunehmend interessantes Thema im Hinblick auf humanoide und andere mobile Roboter. Diese Dissertation befasst sich mit dem Problem der humanoiden Roboter-Navigation in Innenräumen. Es wird ein komplettes Framework für humanoide Roboter-Navigation basierend auf einer multimodalen Schnittstelle vorgestellt. Zunächst wird eine formale Sprache eingeführt, mit der die routen-basierten Navigationsaufgaben beschrieben werden können. Diese Sprache stellt eine intuitive Schnittstelle bereit, mit der auch unerfahrene Anwender leicht einen mobilen Roboter in einer Route in Innenräumen instruieren können. Ein Befehls-Interpreter analysiert die Benutzer-Eingabe und generiert entsprechende symbolische und topologische Darstellungen, die als erste Pfad-Schätzung für den humanoiden Roboter verwendet werden. Des Weiteren wird in dieser Arbeit ein robustes und effizientes Objekterkennungssystem mit einer hohen Erkennungsrate entwickelt. Es kann bei mobilen Robotern eingesetzt werden und erfüllt die Anforderung, Landmarken während der Navigation zu erkennen. Das Landmarken-Detektions-System ist in der Lage, während der Roboter-Navigation in einer Miniatur-Stadt verschiedene Typen von Landmarken zu detektieren, identifizieren und zu lokalisieren. Das System basiert auf einem zweistufigen Klassifikations-Prozess, der robust und invariant gegenüber Skalierung und Translation ist. Durch die Kombination der Stärken der erscheinungs-basierten und modell-basierten Objekt-Klassifikation bietet es einen guten Kompromiss zwischen schnellen Bearbeitungszeiten und hoher Erkennungsgenauigkeit. Ebenfalls Bestandteil dieser Arbeit ist die Implementierung eines zeiteffizienten hybriden Bewegungs-Planungs-Systems für humanoide Roboter in einer Innenraum-Umgebung. Die vorgeschlagene Technik ist eine Kombination aus Sampling-basierter Planung und "D * Lite"-Suche, die ermöglicht, dynamisch Tritt-Platzierungen in unbekannten Umgebungen zu erzeugen. Ein modifiziertes Zylinder-Modell wird verwendet, um die Trajektorie des Roboters während der Navigation näherungsweise zu bestimmen. Die Planungskomponente berechnet die benötigten Freiräume, um verschiedene Aktionen des Roboters auszuführen und vergleicht sie mit der aktuellen Entfernung zu den nächstgelegenen Hindernissen. "D* Lite"-Suche wird dann verwendet, um eine dynamische und effiziente Platzierung der Schritte innerhalb des resultierenden Konfigurations-Raumes zu finden.
The principles of adaptation to various environments have not yet been clarified. Furthermore, autonomous adaptation remains unsolved and a seriously difficult problem in robotics. Apparently, the adaptation ability shown by animals and that needed by robots in the real world cannot be explained or realized by one single function in a control system and mechanism. That is, adaptation in motion is induced at every level in a wide spectrum from the central neural system to the musculoskeletal system. This book contains the papers selected carefully from the symposium ANIAM2003, particularly concerning adaptation in locomotion from the viewpoint of robotics and neurophysiology. Due to this restriction in topics adopted, we expect that this book will efficiently provide good information for scientists and engineers, which is useful to discuss the principles and mechanisms underlying animals' adaptation under unstructured environment . TOC:Higher Level Control of Locomotion.- Analysis and Control of Locomotion.- Control Principles from Biologically Inspired Machine.- Rhythmic Motion Analysis and Implementation.- Dynamic and Adaptive Locomotion.- Neural Control and Learning.- Modeling and Analysis of Locomotion.- Biologically Inspired Machine Design and Control Architecture.- Robot Brain.- Mobiligence.
Robotter vil om få år blive en del af vores daglige liv. Inden for produktionsindustrien har det allerede være tilfældet i mange år, men anvendelsen af mobile robotter har hidtil været henvist til isolerede områder som græsslåning, overvågning, landbrugsproduktion og militære funktioner. Fremskridt i forskningen gør, at robotter vil kunne assistere os i mange af vore daglige gøremål i en ikke så fjern fremtid. En af de teknologier, der skal gøre dette muligt, er navigation, og navigation er emnet for denne afhandling. Navigation for autonome robotter handler om robottens evne til autonomt at manøvrere fra den nuværende position til et ønsket bestemmelsessted. Denne afhandling præsenterer og validerer eksperimentelt løsninger til detektering af farbar vej, omgåelse af forhindringer og gennemførelse af missioner. Vejklassifikationen er baseret på laserscanner-målinger og assisteret med vision for længere rækkevidde. Vejklassifikationen er tilstrækkelig selektiv til at kunne adskille selv flade vejrabatter fra selve vejen og kan isolere asfaltveje fra grusveje. Vejgenkendelse ud fra kamerabilleder tager udgangspunkt i klassifikationen fra laserscanneren og bruger en kombination af farve og kantdetektering til at estimere farbar vej på længere afstande. Resultatet af disse to sensorer anvendes under planlægning af en farbar rute, og her er det især robottens evne til at følge vejens kanter, der er undersøgt. Navigationen i en mission er styret af et sekventielt manuskript. Manuskriptsproget giver mulighed for detektering af vejkryds, for beregninger til brug under passagen af disse kryds og til valg a styringsmetode i øvrigt. Det samlede system er testet på en kombination af asfalt- og grusveje, med et antal forgreninger og vejkryds. Missioner på op til 3km længde er gennemkørt autonomt med det beskrevne system. Fokus i afhandlingen har været den eksperimentelle validering af de implementerede metoder og metodernes evne til at løse problemer i en virkelig verden. Der skal en betydelig mængde software til for at styre en autonom robot, emner som software genbrug og distribueret udvikling er derfor essentielle. Denne afhandling beskriver yderligere en komponentbaseret arkitektur for robotter, som kan fremme software genbrug og distribueret udvikling og vedligeholdelse. ; Abstract Robots will soon take part in everyone's daily life. In industrial production this has been the case for many years, but up to now the use of mobile robots has been limited to a few and isolated applications like lawn mowing, surveillance, agricultural production and military applications. The research is now progressing towards autonomous robots which will be able to assist us in our daily life. One of the enabling technologies is navigation, and navigation is the subject of this thesis. Navigation of an autonomous robot is concerned with the ability of the robot to direct itself from the current position to a desired destination. This thesis presents and experimentally validates solutions for road classification, obstacle avoidance and mission execution. The road classification is based on laser scanner measurements and supported at longer ranges by vision. The road classification is sufficiently sensitive to separate the road from flat roadsides, and to distinguish asphalt roads from gravelled roads. The vision-based road detection uses a combination of chromaticity and edge detection to outline the traversable part of the road based on a laser scanner classified sample area. The perception of these two sensors are utilised by a path planner to allow a number of drive modes, and especially the ability to follow road edges are investigated. The navigation mission is controlled by a script language. The navigation script controls route sequencing, junction detection, junction crossing calculations and drive mode selection. The entire system is tested on a combination of narrow asphalt and gravelled roads connected by a number of junctions. Missions of up to 3km in length have been successfully completed using the described system. The main focus of the thesis has been the experimental validation of the implemented solutions and the ability of the methods to solve real world problems. The amount of software needed by an autonomous robot can be overwhelming. Software reuse and distributed development are therefore important issues. The thesis describes a new component architecture for robotics software that promotes software reuse and distributed development and maintenance.