24 research outputs found
Robot control based on qualitative representation of human trajectories
A major challenge for future social robots is the high-level interpretation of human motion, and the consequent generation of appropriate robot actions. This paper describes some fundamental steps towards the real-time implementation of a system that allows a mobile robot to transform quantitative information about human trajectories (i.e. coordinates and speed) into qualitative concepts, and from these to generate appropriate control commands. The problem is formulated using a simple version of qualitative trajectory calculus, then solved using an inference engine based on fuzzy temporal logic and situation graph trees. Preliminary results are discussed and future directions of the current research are drawn
Qualitative design and implementation of human-robot spatial interactions
Despite the large number of navigation algorithms available for mobile robots, in many social contexts they often exhibit inopportune motion behaviours in proximity of people, often with very "unnatural" movements due to the execution of segmented trajectories or the sudden activation of safety mechanisms (e.g., for obstacle avoidance). We argue that the reason of the problem is not only the difficulty of modelling human behaviours and generating opportune robot control policies, but also the way human-robot spatial interactions are represented and implemented.
In this paper we propose a new methodology based on a qualitative representation of spatial interactions, which is both flexible and compact, adopting the well-defined and coherent formalization of Qualitative Trajectory Calculus (QTC). We show the potential of a QTC-based approach to abstract and design complex robot behaviours, where the desired robot's behaviour is represented together with its actual performance in one coherent approach, focusing on spatial interactions rather than pure navigation problems
Exploring the Design Space of Robot Appearance and Behavior in an Attention-Seeking Living Room Scenario for a Robot Companion
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.---- Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. --DOI : 10.1109/ALIFE.2007.36781
Implementing human-acceptable navigational behavior and fuzzy controller for an autonomous robot
Robots are just starting to appear in peopled environments but in order to be accepted by humans,
they should obey basic peopleâs social rules. In particular, they have to be able to move around without
disturbing people. This means that they have to obey the social rules that manage the movement of people,
for example following virtual lanes when moving through corridors, not crossing in front of moving people,
etc. In this paper some of these aspects are explained, as well as the implementation of preliminaries
works to implement the proposed solutions are described. So, a slight modiïŹcation to the Lane-Curvature
Method is presented to improve the behavior of a mobile robot when crossing people in a corridor. Other
works needed to test this modiïŹcations in the robot Amelia of the Reliable Autonomous Systems Lab, as
the implementation of a fuzzy controller, are also described in this pape
Social-aware robot navigation in urban environments
In this paper we present a novel robot navigation approach based on the so-called Social Force Model (SFM). First, we construct a graph map with a set of destinations that completely describe the navigation environment. Second, we propose a robot navigation algorithm, called social-aware navigation, which is mainly driven by the social-forces centered at the robot. Third, we use a MCMC Metropolis-Hastings algorithm in order to learn the parameters values of the method. Finally, the validation of the model is accomplished throughout an extensive set of simulations and real-life experiments.Peer ReviewedPostprint (authorâs final draft
Robot social-aware navigation framework to accompany people walking side-by-side
The final publication is available at link.springer.comWe present a novel robot social-aware navigation framework to walk side-by-side with people in crowded urban areas in a safety and natural way. The new system includes the following key issues: to propose a new robot social-aware navigation model to accompany a person; to extend the Social Force Model,Peer ReviewedPostprint (author's final draft
Evaluation of Passing Distance for Social Robots
(c) 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.The 15th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN06); Hatfield, UK; September 6-8, 2006.Digital Object Identifier : 10.1109/ROMAN.2006.314436Casual encounters with mobile robots for nonexperts
can be a challenge due to lack of an interaction model.
The present work is based on the rules from proxemics which
are used to design a passing strategy. In narrow corridors the
lateral distance of passage is a key parameter to consider. An
implemented system has been used in a small study to verify the
basic parametric design for such a system. In total 10 subjects
evaluated variations in proxemics for encounters with a robot
in a corridor setting. The user feedback indicates that entering
the intimate sphere of people is less comfortable, however a too
significant avoidance is also considered unnecessary. Adequate
signaling of avoidance is a behaviour that must be carefully
tuned
Personalizing Human-Robot Dialogue Interactions using Face and Name Recognition
Task-oriented dialogue systems are computer systems that aim to provide an interaction
indistinguishable from ordinary human conversation with the goal of completing user-
defined tasks. They are achieving this by analyzing the intents of users and choosing
respective responses. Recent studies show that by personalizing the conversations with
this systems one can positevely affect their perception and long-term acceptance.
Personalised social robots have been widely applied in different fields to provide assistance.
In this thesis we are working on development of a scientific conference assistant. The goal
of this assistant is to provide the conference participants with conference information and
inform about the activities for their spare time during conference. Moreover, to increase
the engagement with the robot our team has worked on personalizing the human-robot
interaction by means of face and name recognition.
To achieve this personalisation, first the name recognition ability of available physical
robot was improved, next by the concent of the participants their pictures were taken
and used for memorization of returning users. As acquiring the consent for personal data
storage is not an optimal solution, an alternative method for participants recognition
using QR Codes on their badges was developed and compared to pre-trained model in
terms of speed. Lastly, the personal details of each participant, as unviversity, country of
origin, was acquired prior to conference or during the conversation and used in dialogues.
The developed robot, called DAGFINN was displayed at two conferences happened this
year in Stavanger, where the first time installment did not involve personalization feature.
Hence, we conclude this thesis by discussing the influence of personalisation on dialogues
with the robot and participants satisfaction with developed social robot
Social-aware drone navigation using social force model
Robotâs navigation is one of the hardest challenges to deal with, because
real environments imply highly dynamic objects moving in all directions.
The main ideal goal is to conduct a safe navigation within the environment,
avoiding obstacles and reaching the final proposed goal. Nowadays, with
the last advances in technology, we are able to see robots almost everywhere,
and this can lead us to think about the robotâs role in the future,
and where we would find them, and it is no exaggerated to say, that practically,
flying and land-based robots are going to live together with people,
interacting in our houses, streets and shopping centers. Moreover, we will
notice their presence, gradually inserted in our human societies, every time
doing more human tasks, which in the past years were unthinkable.
Therefore, if we think about robots moving or flying around us, we must
consider safety, the distance the robot should take to make the human feel
comfortable, and the different reactions people would have. The main goal
of this work is to accompany people making use of a flying robot. The term
social navigation gives us the path to follow when we talk about a social environment.
Robots must be able to navigate between humans, giving sense
of security to those who are walking close to them. In this work, we present
a model called Social Force Model, which states that the human social interaction
between persons and objects is inspired in the fluid dynamics de-
fined by Newtonâs equations, and also, we introduce the extended version
which complements the initial method with the human-robot interaction
force.
In the robotics field, the use of tools for helping the development and
the implementation part are crucial. The fast advances in technology allows
the international community to have access to cheaper and more compact
hardware and software than a decade ago. It is becoming more and
more usual to have access to more powerful technology which helps us to
run complex algorithms, and because of that, we can run bigger systems
in reduced space, making robots more intelligent, more compact and more
robust against failures. Our case was not an exception, in the next chapters
we will present the procedure we followed to implement the approaches,
supported by different simulation tools and software. Because of the nature
of the problem we were facing, we made use of Robotic Operating System
along with Gazebo, which help us to have a good outlook of how the code
will work in real-life experiments.
In this work, both real and simulated experiments are presented, in
which we expose the interaction conducted by the 3D Aerial Social Force
Model, between humans, objects and in this case the AR.Drone, a flying
drone property of the Instituto de RobĂłtica e InformĂĄtica Industrial. We
focus on making the drone navigation more socially acceptable by the humans
around; the main purpose of the drone is to accompany a person,
which we will call the "main" person in this work, who is going to try to
navigate side-by-side, with a behavior being dictated with some forces exerted
by the environment, and also is going to try to be the more socially
close acceptable possible to the remaining humans around. Also, it is presented
a comparison between the 3D Aerial Social Force Model and the
Artificial Potential Fields method, a well-known method and widely used
in robot navigation. We present both methods and the description of the
forces each one involves.
Along with these two models, there is also another important topic to
introduce. As we said, the robot must be able to accompany a pedestrian in
his way, and for that reason, the forecasting capacity is an important feature
since the robot does not know the final destination of the human to accompany.
It is essential to give it the ability to predict the human movements.
In this work, we used the differential values between the past position values
to know how much is changing through time. This gives us an accurate
idea of how the human would behave or which direction he/she would
take next.
Furthermore, we present a description of the human motion prediction
model based on linear regression. The motivation behind the idea of building
a Regression Model was the simplicity of the implementation, the robustness
and the very accurate results of the approach. The previous main
human positions are taken, in order to forecast the new position of the human,
the next seconds. This is done with the main purpose of letting the
drone know about the direction the human is taking, to move forward beside
the human, as if the drone was accompanying him. The optimization
for the linear regression model, to find the right weights for our model, was
carried out by gradient descent, implementing also de RMSprop variant in
order to reach convergence in a faster way. The strategy that was followed
to build the prediction model is explained with detail later in this work.
The presence of social robots has grown during the past years, many
researchers have contributed and many techniques are being used to give
them the capacity of interacting safely and effectively with the people, and
it is a hot topic which has matured a lot, but still there is many research to
be investigated