282 research outputs found
An Optimal State Dependent Haptic Guidance Controller via a Hard Rein
The aim of this paper is to improve the optimality
and accuracy of techniques to guide a human in limited visibility & auditory conditions such as in fire-fighting in warehouses or similar environments. At present, teams of breathing apparatus (BA) wearing fire-fighters move in teams following walls. Due to limited visibility and high noise in the oxygen masks, they predominantly depend on haptic communication through reins.
An intelligent agent (man/machine) with full environment perceptual capabilities is an alternative to enhance navigation in such unfavorable environments, just like a dog guiding a blind person. This paper proposes an optimal state-dependent control policy to guide a follower with limited environmental perception, by an intelligent and environmentally perceptive agent. Based on experimental systems identification and numerical simulations on
human demonstrations from eight pairs of participants, we show that the guiding agent and the follower experience learning for a optimal stable state-dependent a novel 3rd and 2nd order auto regressive predictive and reactive control policies respectively.
Our findings provide a novel theoretical basis to design advanced human-robot interaction algorithms in a variety of cases that require the assistance of a robot to perceive the environment by a human counterpart
Identification of Haptic Based Guiding Using Hard Reins
This paper presents identifications of human-human interaction in which one person with limited auditory and visual perception of the environment (a follower) is guided by an agent with full perceptual capabilities (a guider) via a hard rein along a given path. We investigate several identifications of the interaction between the guider and the follower such as computational models that map states of the follower to actions of the guider and the computational basis of the guider to modulate the force on the rein in response to the trust level of the follower. Based on experimental identification systems on human demonstrations show that the guider and the follower experience learning for an optimal stable state-dependent novel 3rd and 2nd order auto-regressive predictive and reactive control policies respectively. By modeling the follower's dynamics using a time varying virtual damped inertial system, we found that the coefficient of virtual damping is most appropriate to explain the trust level of the follower at any given time. Moreover, we present the stability of the extracted guiding policy when it was implemented on a planar 1-DoF robotic arm. Our findings provide a theoretical basis to design advanced human-robot interaction algorithms applicable to a variety of situations where a human requires the assistance of a robot to perceive the environment
A two party haptic guidance controller via a hard rein
In the case of human intervention in disaster
response operations like indoor firefighting, where the environment perception is limited due to thick smoke, noise in the oxygen masks and clutter, not only limit the environmental perception of the human responders, but also causes distress. An intelligent agent (man/machine) with full environment perceptual capabilities is an alternative to enhance navigation in such unfavorable environments. Since haptic communication is the least affected mode of communication in such cases, we consider human demonstrations to use a hard rein to guide blindfolded
followers with auditory distraction to be a good paradigm to
extract salient features of guiding using hard reins. Based on numerical simulations and experimental systems identification based on demonstrations from eight pairs of human subjects, we show that, the relationship between the orientation difference between the follower and the guider, and the lateral swing patterns of the hard rein by the guider can be explained by a novel 3rd order auto regressive predictive controller. Moreover,by modeling the two party voluntary movement dynamics using a virtual damped inertial model, we were able to model the
mutual trust between two parties. In the future, the novel
controller extracted based on human demonstrations can be
tested on a human-robot interaction scenario to guide a visually impaired person in various applications like fire fighting, search and rescue, medical surgery, etc
Salient Feature of Haptic-Based Guidance of People in Low Visibility Environments Using Hard Reins
This paper presents salient features of
human–human interaction where one person with limited
auditory and visual perception of the environment (a follower)
is guided by an agent with full perceptual capabilities (a guider)
via a hard rein along a given path. We investigate several salient
features of the interaction between the guider and follower such
as: 1) the order of an autoregressive (AR) control policy that
maps states of the follower to actions of the guider; 2) how
the guider may modulate the pulling force in response to the
trust level of the follower; and 3) how learning may successively
apportion the responsibility of control across different muscles
of the guider. Based on experimental systems identification on
human demonstrations from ten pairs of naive subjects, we
show that guiders tend to adopt a third-order AR predictive
control policy and followers tend to adopt second-order reactive
control policy. Moreover, the extracted guider’s control policy
was implemented and validated by human–robot interaction
experiments. By modeling the follower’s dynamics with a time
varying virtual damped inertial system, we found that it is the
coefficient of virtual damping which is most sensitive to the
trust level of the follower. We used these experimental insights
to derive a novel controller that integrates an optimal order
control policy with a push/pull force modulator in response to
the trust level of the follower monitored using a time varying
virtual damped inertial model
Development of an Intelligent Robotic Rein for Haptic Interaction with Mobile Machines
The haptic sense is often placed secondary when compared to vision and hearing in allowing navigation and autonomy. However, in certain conditions such as low or no visibility, the haptic senses can play a crucial role in aiding navigation and obtaining spatial knowledge of the environment and object properties. For example, search and rescue operations are often undertaken in complex and hazardous situations where factors such as limited or no visibility, noise and time constraints impede progress into an unknown environment. In these situations, the fire-fighter/rescue personnel could be aided by a machine (a mobile robot) that can sense and give the follower tactile and haptic information in a similar manner to the interaction between the visually impaired person and a guide dog. The visually impaired person follows the dog through the signs being transmitted to his hand and interprets them into information about the environment and how to navigate the route. The aim of this research is to investigate and build a prototype robotic rein to emulate the natural and adaptable relationship observed between a guide dog and human when traversing an unknown path. From the previous work, an investigation and evaluation of the design of the SHU prototype have been undertaken and its outcomes are used in this research to improve the new system prototypes, especially in the area of designing the feedback and adaptive control.
The new system has divided into four prototypes, each prototype has separated test to determine it is suitability. In the prototype (I) a set of sensors set in the front edge of the existing rein to know follower /robot location. The prototype (II), will implement to moving rein by installing an actuator. Prototype (III) has some sensors in the grip of the rein to calculate the strength of tensile which occur between the follower hand and the long axis of rein. In the prototype (IV), all previous prototypes will be connected to an integrated system which can move the follower to follow the robot path by sending messages from the robot to the follower in the form of rein movements
The resultant system is an intelligent robotic rein which continuously interacts with the user to optimize the guidance in terms of comfort, following accuracy and safety. The improved system has been tested, analysed and evaluated against previous designs and compared against the aspiration of the human - guide dog relationship.
A novel idea has been established in the use of the sensors to raise the level of the robot human interaction and achieving automatous robot/ follower safe navigation
Shared control strategies for automated vehicles
188 p.Los vehículos automatizados (AVs) han surgido como una solución tecnológica para compensar las deficiencias de la conducción manual. Sin embargo, esta tecnología aún no está lo suficientemente madura para reemplazar completamente al conductor, ya que esto plantea problemas técnicos, sociales y legales. Sin embargo, los accidentes siguen ocurriendo y se necesitan nuevas soluciones tecnológicas para mejorar la seguridad vial. En este contexto, el enfoque de control compartido, en el que el conductor permanece en el bucle de control y, junto con la automatización, forma un equipo bien coordinado que colabora continuamente en los niveles táctico y de control de la tarea de conducción, es una solución prometedora para mejorar el rendimiento de la conducción manual aprovechando los últimos avances en tecnología de conducción automatizada. Esta estrategia tiene como objetivo promover el desarrollo de sistemas de asistencia al conductor más avanzados y con mayor grade de cooperatición en comparación con los disponibles en los vehículos comerciales. En este sentido, los vehículos automatizados serán los supervisores que necesitan los conductores, y no al revés. La presente tesis aborda en profundidad el tema del control compartido en vehículos automatizados, tanto desde una perspectiva teórica como práctica. En primer lugar, se proporciona una revisión exhaustiva del estado del arte para brindar una descripción general de los conceptos y aplicaciones en los que los investigadores han estado trabajando durante lasúltimas dos décadas. Luego, se adopta un enfoque práctico mediante el desarrollo de un controlador para ayudar al conductor en el control lateral del vehículo. Este controlador y su sistema de toma de decisiones asociado (Módulo de Arbitraje) se integrarán en el marco general de conducción automatizada y se validarán en una plataforma de simulación con conductores reales. Finalmente, el controlador desarrollado se aplica a dos sistemas. El primero para asistir a un conductor distraído y el otro en la implementación de una función de seguridad para realizar maniobras de adelantamiento en carreteras de doble sentido. Al finalizar, se presentan las conclusiones más relevantes y las perspectivas de investigación futuras para el control compartido en la conducción automatizada
Prohibited Volume Avoidance for Aircraft
This thesis describes the development of a pilot override control system that prevents aircraft
entering critical regions of space, known as prohibited volumes. The aim is to prevent another
9/11 style terrorist attack, as well as act as a general safety system for transport aircraft.
The thesis presents the design and implementation of three core modules in the system; the
trajectory generation algorithm, the trigger mechanism for the pilot override and the trajectory
following element. The trajectory generation algorithm uses a direct multiple shooting strategy
to provide trajectories through online computation that avoid pre-defi ned prohibited volume
exclusion regions, whilst accounting for the manoeuvring capabilities of the aircraft. The trigger
mechanism incorporates the logic that decides the time at which it is suitable for the override to
be activated, an important consideration for ensuring that the system is not overly restrictive
for a pilot. A number of methods are introduced, and for safety purposes a composite trigger
that incorporates di fferent strategies is recommended. Trajectory following is best achieved via
a nonlinear guidance law. The guidance logic sends commands in pitch, roll and yaw to the
control surfaces of the aircraft, in order to closely follow the generated avoidance trajectory.
Testing and validation is performed using a full motion simulator, with volunteers
flying a
representative aircraft model and attempting to penetrate prohibited volumes.
The proof-of-concept system is shown to work well, provided that extreme aircraft manoeuvres
are prevented near the exclusion regions. These hard manoeuvring envelope constraints allow
the trajectory following controllers to follow avoidance trajectories accurately from an initial
state within the bounding set. In order to move the project closer to a commercial product,
operator and regulator input is necessary, particularly due to the radical nature of the pilot
override system
A Review on Robot Manipulation Methods in Human-Robot Interactions
Robot manipulation is an important part of human-robot interaction
technology. However, traditional pre-programmed methods can only accomplish
simple and repetitive tasks. To enable effective communication between robots
and humans, and to predict and adapt to uncertain environments, this paper
reviews recent autonomous and adaptive learning in robotic manipulation
algorithms. It includes typical applications and challenges of human-robot
interaction, fundamental tasks of robot manipulation and one of the most widely
used formulations of robot manipulation, Markov Decision Process. Recent
research focusing on robot manipulation is mainly based on Reinforcement
Learning and Imitation Learning. This review paper shows the importance of Deep
Reinforcement Learning, which plays an important role in manipulating robots to
complete complex tasks in disturbed and unfamiliar environments. With the
introduction of Imitation Learning, it is possible for robot manipulation to
get rid of reward function design and achieve a simple, stable and supervised
learning process. This paper reviews and compares the main features and popular
algorithms for both Reinforcement Learning and Imitation Learning
Programming by Demonstration on Riemannian Manifolds
This thesis presents a Riemannian approach to Programming by Demonstration (PbD).
It generalizes an existing PbD method from Euclidean manifolds to Riemannian manifolds.
In this abstract, we review the objectives, methods and contributions of the presented
approach.
OBJECTIVES
PbD aims at providing a user-friendly method for skill transfer between human and
robot. It enables a user to teach a robot new tasks using few demonstrations. In order
to surpass simple record-and-replay, methods for PbD need to \u2018understand\u2019 what to
imitate; they need to extract the functional goals of a task from the demonstration data.
This is typically achieved through the application of statisticalmethods.
The variety of data encountered in robotics is large. Typical manipulation tasks involve
position, orientation, stiffness, force and torque data. These data are not solely
Euclidean. Instead, they originate from a variety of manifolds, curved spaces that are
only locally Euclidean. Elementary operations, such as summation, are not defined on
manifolds. Consequently, standard statistical methods are not well suited to analyze
demonstration data that originate fromnon-Euclidean manifolds. In order to effectively
extract what-to-imitate, methods for PbD should take into account the underlying geometry
of the demonstration manifold; they should be geometry-aware.
Successful task execution does not solely depend on the control of individual task
variables. By controlling variables individually, a task might fail when one is perturbed
and the others do not respond. Task execution also relies on couplings among task variables.
These couplings describe functional relations which are often called synergies. In
order to understand what-to-imitate, PbDmethods should be able to extract and encode
synergies; they should be synergetic.
In unstructured environments, it is unlikely that tasks are found in the same scenario
twice. The circumstances under which a task is executed\u2014the task context\u2014are more
likely to differ each time it is executed. Task context does not only vary during task execution,
it also varies while learning and recognizing tasks. To be effective, a robot should
be able to learn, recognize and synthesize skills in a variety of familiar and unfamiliar
contexts; this can be achieved when its skill representation is context-adaptive.
THE RIEMANNIAN APPROACH
In this thesis, we present a skill representation that is geometry-aware, synergetic and
context-adaptive. The presented method is probabilistic; it assumes that demonstrations
are samples from an unknown probability distribution. This distribution is approximated
using a Riemannian GaussianMixtureModel (GMM).
Instead of using the \u2018standard\u2019 Euclidean Gaussian, we rely on the Riemannian Gaussian\u2014
a distribution akin the Gaussian, but defined on a Riemannian manifold. A Riev
mannian manifold is a manifold\u2014a curved space which is locally Euclidean\u2014that provides
a notion of distance. This notion is essential for statistical methods as such methods
rely on a distance measure. Examples of Riemannian manifolds in robotics are: the
Euclidean spacewhich is used for spatial data, forces or torques; the spherical manifolds,
which can be used for orientation data defined as unit quaternions; and Symmetric Positive
Definite (SPD) manifolds, which can be used to represent stiffness and manipulability.
The Riemannian Gaussian is intrinsically geometry-aware. Its definition is based on
the geometry of the manifold, and therefore takes into account the manifold curvature.
In robotics, the manifold structure is often known beforehand. In the case of PbD, it follows
from the structure of the demonstration data. Like the Gaussian distribution, the
Riemannian Gaussian is defined by a mean and covariance. The covariance describes
the variance and correlation among the state variables. These can be interpreted as local
functional couplings among state variables: synergies. This makes the Riemannian
Gaussian synergetic. Furthermore, information encoded in multiple Riemannian Gaussians
can be fused using the Riemannian product of Gaussians. This feature allows us to
construct a probabilistic context-adaptive task representation.
CONTRIBUTIONS
In particular, this thesis presents a generalization of existing methods of PbD, namely
GMM-GMR and TP-GMM. This generalization involves the definition ofMaximum Likelihood
Estimate (MLE), Gaussian conditioning and Gaussian product for the Riemannian
Gaussian, and the definition of ExpectationMaximization (EM) and GaussianMixture
Regression (GMR) for the Riemannian GMM. In this generalization, we contributed
by proposing to use parallel transport for Gaussian conditioning. Furthermore, we presented
a unified approach to solve the aforementioned operations using aGauss-Newton
algorithm. We demonstrated how synergies, encoded in a Riemannian Gaussian, can be
transformed into synergetic control policies using standard methods for LinearQuadratic
Regulator (LQR). This is achieved by formulating the LQR problem in a (Euclidean) tangent
space of the Riemannian manifold. Finally, we demonstrated how the contextadaptive
Task-Parameterized Gaussian Mixture Model (TP-GMM) can be used for context
inference\u2014the ability to extract context from demonstration data of known tasks.
Our approach is the first attempt of context inference in the light of TP-GMM. Although
effective, we showed that it requires further improvements in terms of speed and reliability.
The efficacy of the Riemannian approach is demonstrated in a variety of scenarios.
In shared control, the Riemannian Gaussian is used to represent control intentions of a
human operator and an assistive system. Doing so, the properties of the Gaussian can
be employed to mix their control intentions. This yields shared-control systems that
continuously re-evaluate and assign control authority based on input confidence. The
context-adaptive TP-GMMis demonstrated in a Pick & Place task with changing pick and
place locations, a box-taping task with changing box sizes, and a trajectory tracking task
typically found in industr
TRAINING AND ASSESSMENT OF HAND-EYE COORDINATION WITH ELECTROENCEPHALOGRAPHY
Ph.DDOCTOR OF PHILOSOPH
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