146 research outputs found
Adaptive Shared Autonomy between Human and Robot to Assist Mobile Robot Teleoperation
Die Teleoperation vom mobilen Roboter wird in großem Umfang eingesetzt, wenn es für Mensch unpraktisch oder undurchführbar ist, anwesend zu sein, aber die Entscheidung von Mensch wird dennoch verlangt. Es ist für Mensch stressig und fehleranfällig wegen Zeitverzögerung und Abwesenheit des Situationsbewusstseins, ohne Unterstützung den Roboter zu steuern einerseits, andererseits kann der völlig autonome Roboter, trotz jüngsten Errungenschaften, noch keine Aufgabe basiert auf die aktuellen Modelle der Wahrnehmung und Steuerung unabhängig ausführen. Deswegen müssen beide der Mensch und der Roboter in der Regelschleife bleiben, um gleichzeitig Intelligenz zur Durchführung von Aufgaben beizutragen. Das bedeut, dass der Mensch die Autonomie mit dem Roboter während des Betriebes zusammenhaben sollte. Allerdings besteht die Herausforderung darin, die beiden Quellen der Intelligenz vom Mensch und dem Roboter am besten zu koordinieren, um eine sichere und effiziente Aufgabenausführung in der Fernbedienung zu gewährleisten.
Daher wird in dieser Arbeit eine neuartige Strategie vorgeschlagen. Sie modelliert die Benutzerabsicht als eine kontextuelle Aufgabe, um eine Aktionsprimitive zu vervollständigen, und stellt dem Bediener eine angemessene Bewegungshilfe bei der Erkennung der Aufgabe zur Verfügung. Auf diese Weise bewältigt der Roboter intelligent mit den laufenden Aufgaben auf der Grundlage der kontextuellen Informationen, entlastet die Arbeitsbelastung des Bedieners und verbessert die Aufgabenleistung. Um diese Strategie umzusetzen und die Unsicherheiten bei der Erfassung und Verarbeitung von Umgebungsinformationen und Benutzereingaben (i.e. der Kontextinformationen) zu berücksichtigen, wird ein probabilistischer Rahmen von Shared Autonomy eingeführt, um die kontextuelle Aufgabe mit Unsicherheitsmessungen zu erkennen, die der Bediener mit dem Roboter durchführt, und dem Bediener die angemesse Unterstützung der Aufgabenausführung nach diesen Messungen anzubieten. Da die Weise, wie der Bediener eine Aufgabe ausführt, implizit ist, ist es nicht trivial, das Bewegungsmuster der Aufgabenausführung manuell zu modellieren, so dass eine Reihe von der datengesteuerten Ansätzen verwendet wird, um das Muster der verschiedenen Aufgabenausführungen von menschlichen Demonstrationen abzuleiten, sich an die Bedürfnisse des Bedieners in einer intuitiven Weise über lange Zeit anzupassen. Die Praxistauglichkeit und Skalierbarkeit der vorgeschlagenen Ansätze wird durch umfangreiche Experimente sowohl in der Simulation als auch auf dem realen Roboter demonstriert. Mit den vorgeschlagenen Ansätzen kann der Bediener aktiv und angemessen unterstützt werden, indem die Kognitionsfähigkeit und Autonomieflexibilität des Roboters zu erhöhen
Overcoming barriers and increasing independence: service robots for elderly and disabled people
This paper discusses the potential for service robots to overcome barriers and increase independence of
elderly and disabled people. It includes a brief overview of the existing uses of service robots by disabled and elderly
people and advances in technology which will make new uses possible and provides suggestions for some of these new
applications. The paper also considers the design and other conditions to be met for user acceptance. It also discusses
the complementarity of assistive service robots and personal assistance and considers the types of applications and
users for which service robots are and are not suitable
Trajectory online adaption based on human motion prediction for teleoperation
In this work, a human motion intention prediction method based on an autoregressive (AR) model for teleoperation is developed. Based on this method, the robot's motion trajectory can be updated in real time through updating the parameters of the AR model. In the teleoperated robot's control loop, a virtual force model is defined to describe the interaction profile and to correct the robot's motion trajectory in real time. The proposed human motion prediction algorithm acts as a feedforward model to update the robot's motion and to revise this motion in the process of human-robot interaction (HRI). The convergence of this method is analyzed theoretically. Comparative studies demonstrate the enhanced performance of the proposed approach
Human-Machine Interfaces for Service Robotics
L'abstract è presente nell'allegato / the abstract is in the attachmen
The classification and new trends of shared control strategies in telerobotic systems: A survey
Shared control, which permits a human operator and an autonomous controller to share the control of a telerobotic system, can reduce the operator's workload and/or improve performances during the execution of tasks. Due to the great benefits of combining the human intelligence with the higher power/precision abilities of robots, the shared control architecture occupies a wide spectrum among telerobotic systems. Although various shared control strategies have been proposed, a systematic overview to tease out the relation among different strategies is still absent. This survey, therefore, aims to provide a big picture for existing shared control strategies. To achieve this, we propose a categorization method and classify the shared control strategies into 3 categories: Semi-Autonomous control (SAC), State-Guidance Shared Control (SGSC), and State-Fusion Shared Control (SFSC), according to the different sharing ways between human operators and autonomous controllers. The typical scenarios in using each category are listed and the advantages/disadvantages and open issues of each category are discussed. Then, based on the overview of the existing strategies, new trends in shared control strategies, including the “autonomy from learning” and the “autonomy-levels adaptation,” are summarized and discussed
An optimization-based formalism for shared autonomy in dynamic environments
Teleoperation is an integral component of various industrial processes. For
example, concrete spraying, assisted welding, plastering, inspection, and
maintenance. Often these systems implement direct control that maps interface
signals onto robot motions. Successful completion of tasks typically requires
high levels of manual dexterity and cognitive load. In addition, the operator is
often present nearby dangerous machinery. Consequently, safety is of critical
importance and training is expensive and prolonged -- in some cases taking
several months or even years.
An autonomous robot replacement would be an ideal solution since the human could
be removed from danger and training costs significantly reduced. However, this
is currently not possible due to the complexity and unpredictability of the
environments, and the levels of situational and contextual awareness required to
successfully complete these tasks.
In this thesis, the limitations of direct control are addressed by developing
methods for shared autonomy. A shared autonomous approach combines
human input with autonomy to generate optimal robot motions. The approach taken
in this thesis is to formulate shared autonomy within an optimization framework
that finds optimized states and controls by minimizing a cost function, modeling
task objectives, given a set of (changing) physical and operational constraints.
Online shared autonomy requires the human to be continuously interacting with
the system via an interface (akin to direct control). The key challenges
addressed in this thesis are: 1) ensuring computational feasibility (such a
method should be able to find solutions fast enough to achieve a sampling
frequency bound below by 40Hz), 2) being reactive to changes in the
environment and operator intention, 3) knowing how to appropriately blend
operator input and autonomy, and 4) allowing the operator to supply input in an
intuitive manner that is conducive to high task performance.
Various operator interfaces are investigated with regards to the control space,
called a mode of teleoperation. Extensive evaluations were carried out
to determine for which modes are most intuitive and lead to highest performance
in target acquisition tasks (e.g. spraying/welding/etc). Our performance metrics
quantified task difficulty based on Fitts' law, as well as a measure of how well
constraints affecting the task performance were met. The experimental
evaluations indicate that higher performance is achieved when humans submit
commands in low-dimensional task spaces as opposed to joint space manipulations.
In addition, our multivariate analysis indicated that those with regular
exposure to computer games achieved higher performance.
Shared autonomy aims to relieve human operators of the burden of precise motor
control, tracking, and localization. An optimization-based representation for
shared autonomy in dynamic environments was developed. Real-time tractability is
ensured by modulating the human input with information of the changing
environment within the same task space, instead of adding it to the optimization
cost or constraints. The method was illustrated with two real world
applications: grasping objects in cluttered environments and spraying tasks
requiring sprayed linings with greater homogeneity.
Maintaining motion patterns -- referred to as skills -- is often an
integral part of teleoperation for various industrial processes (e.g. spraying,
welding, plastering). We develop a novel model-based shared autonomous framework
for incorporating the notion of skill assistance to aid operators to sustain
these motion patterns whilst adhering to environment constraints. In order to
achieve computational feasibility, we introduce a novel parameterization for
state and control that combines skill and underlying trajectory models,
leveraging a special type of curve known as Clothoids. This new parameterization
allows for efficient computation of skill-based short term horizon plans,
enabling the use of a model predictive control loop. Our hardware realization
validates the effectiveness of our method to recognize a change of intended
skill, and showing an improved quality of output motion, even under dynamically
changing obstacles.
In addition, extensions of the work to supervisory control are described. An
exploratory study presents an approach that improves computational feasibility
for complex tasks with minimal interactive effort on the part of the human.
Adaptations are theorized which might allow such a method to be applicable and
beneficial to high degree of freedom systems. Finally, a system developed in our
lab is described that implements sliding autonomy and shown to complete
multi-objective tasks in complex environments with minimal interaction from the
human
Trust in Robots
Robots are increasingly becoming prevalent in our daily lives within our living or working spaces. We hope that robots will take up tedious, mundane or dirty chores and make our lives more comfortable, easy and enjoyable by providing companionship and care. However, robots may pose a threat to human privacy, safety and autonomy; therefore, it is necessary to have constant control over the developing technology to ensure the benevolent intentions and safety of autonomous systems. Building trust in (autonomous) robotic systems is thus necessary. The title of this book highlights this challenge: “Trust in robots—Trusting robots”. Herein, various notions and research areas associated with robots are unified. The theme “Trust in robots” addresses the development of technology that is trustworthy for users; “Trusting robots” focuses on building a trusting relationship with robots, furthering previous research. These themes and topics are at the core of the PhD program “Trust Robots” at TU Wien, Austria
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