52 research outputs found
An assistive robot to support dressing-strategies for planning and error handling
© 2016 IEEE. Assistive robots are emerging to address a social need due to changing demographic trends such as an ageing population. The main emphasis is to offer independence to those in need and to fill a potential labour gap in response to the increasing demand for caregiving. This paper presents work undertaken as part of a dressing task using a compliant robotic arm on a mannequin. Several strategies are explored on how to undertake this task with minimal complexity and a mix of sensors. A Vicon tracking system is used to determine the arm position of the mannequin for trajectory planning by means of waypoints. Methods of failure detection were explored through torque feedback and sensor tag data. A fixed vocabulary of recognised speech commands was implemented allowing the user to successfully correct detected dressing errors. This work indicates that low cost sensors and simple HRI strategies, without complex learning algorithms, could be used successfully in a robot assisted dressing task
An Agency-Directed Approach to Test Generation for Simulation-based Autonomous Vehicle Verification
Simulation-based verification is beneficial for assessing otherwise dangerous
or costly on-road testing of autonomous vehicles (AV). This paper addresses the
challenge of efficiently generating effective tests for simulation-based AV
verification using software testing agents. The multi-agent system (MAS)
programming paradigm offers rational agency, causality and strategic planning
between multiple agents. We exploit these aspects for test generation, focusing
in particular on the generation of tests that trigger the precondition of an
assertion. On the example of a key assertion we show that, by encoding a
variety of different behaviours respondent to the agent's perceptions of the
test environment, the agency-directed approach generates twice as many
effective tests than pseudo-random test generation, while being both efficient
and robust. Moreover, agents can be encoded to behave naturally without
compromising the effectiveness of test generation. Our results suggest that
generating tests using agency-directed testing significantly improves upon
random and simultaneously provides more realistic driving scenarios.Comment: 18 pages, 8 figure
An Agency-Directed Approach to Test Generation for Simulation-based Autonomous Vehicle Verification
Simulation-based verification is beneficial for assessing otherwise dangerous
or costly on-road testing of autonomous vehicles (AV). This paper addresses the
challenge of efficiently generating effective tests for simulation-based AV
verification using software testing agents. The multi-agent system (MAS)
programming paradigm offers rational agency, causality and strategic planning
between multiple agents. We exploit these aspects for test generation, focusing
in particular on the generation of tests that trigger the precondition of an
assertion. On the example of a key assertion we show that, by encoding a
variety of different behaviours respondent to the agent's perceptions of the
test environment, the agency-directed approach generates twice as many
effective tests than pseudo-random test generation, while being both efficient
and robust. Moreover, agents can be encoded to behave naturally without
compromising the effectiveness of test generation. Our results suggest that
generating tests using agency-directed testing significantly improves upon
random and simultaneously provides more realistic driving scenarios.Comment: 18 pages, 8 figure
On Determinism of Game Engines used for Simulation-based Autonomous Vehicle Verification
Game engines are increasingly used as simulation platforms by the autonomous
vehicle (AV) community to develop vehicle control systems and test
environments. A key requirement for simulation-based development and
verification is determinism, since a deterministic process will always produce
the same output given the same initial conditions and event history. Thus, in a
deterministic simulation environment, tests are rendered repeatable and yield
simulation results that are trustworthy and straightforward to debug. However,
game engines are seldom deterministic. This paper reviews and identifies the
potential causes of non-deterministic behaviours in game engines. A case study
using CARLA, an open-source autonomous driving simulation environment powered
by Unreal Engine, is presented to highlight its inherent shortcomings in
providing sufficient precision in experimental results. Different
configurations and utilisations of the software and hardware are explored to
determine an operational domain where the simulation precision is sufficiently
low i.e.\ variance between repeated executions becomes negligible for
development and testing work. Finally, a method of a general nature is
proposed, that can be used to find the domains of permissible variance in game
engine simulations for any given system configuration.Comment: 17 pages, 9 figures, 1 tabl
Personalized robot assistant for support in dressing
Robot-assisted dressing is performed in close physical interaction with users who may have a wide range of physical characteristics and abilities. Design of user adaptive and personalized robots in this context is still indicating limited, or no consideration, of specific user-related issues. This paper describes the development of a multi-modal robotic system for a specific dressing scenario - putting on a shoe, where users’ personalized inputs contribute to a much improved task success rate. We have developed: 1) user tracking, gesture recognition andposturerecognitionalgorithmsrelyingonimagesprovidedby a depth camera; 2) a shoe recognition algorithm from RGB and depthimages;3)speechrecognitionandtext-to-speechalgorithms implemented to allow verbal interaction between the robot and user. The interaction is further enhanced by calibrated recognition of the users’ pointing gestures and adjusted robot’s shoe delivery position. A series of shoe fitting experiments have been performed on two groups of users, with and without previous robot personalization, to assess how it affects the interaction performance. Our results show that the shoe fitting task with the personalized robot is completed in shorter time, with a smaller number of user commands and reduced workload
AERoS: Assurance of Emergent Behaviour in Autonomous Robotic Swarms
The behaviours of a swarm are not explicitly engineered. Instead, they are an
emergent consequence of the interactions of individual agents with each other
and their environment. This emergent functionality poses a challenge to safety
assurance. The main contribution of this paper is a process for the safety
assurance of emergent behaviour in autonomous robotic swarms called AERoS,
following the guidance on the Assurance of Machine Learning for use in
Autonomous Systems (AMLAS). We explore our proposed process using a case study
centred on a robot swarm operating a public cloakroom.Comment: 12 pages, 11 figure
Soft Gripping: Specifying for Trustworthiness
Soft robotics is an emerging technology in which engineers create flexible
devices for use in a variety of applications. In order to advance the wide
adoption of soft robots, ensuring their trustworthiness is essential; if soft
robots are not trusted, they will not be used to their full potential. In order
to demonstrate trustworthiness, a specification needs to be formulated to
define what is trustworthy. However, even for soft robotic grippers, which is
one of the most mature areas in soft robotics, the soft robotics community has
so far given very little attention to formulating specifications. In this work,
we discuss the importance of developing specifications during development of
soft robotic systems, and present an extensive example specification for a soft
gripper for pick-and-place tasks for grocery items. The proposed specification
covers both functional and non-functional requirements, such as reliability,
safety, adaptability, predictability, ethics, and regulations. We also
highlight the need to promote verifiability as a first-class objective in the
design of a soft gripper.Comment: Updated the Standards subsection of paper. 9 pages, 2 figures, 1
table, 34 reference
On Specifying for Trustworthiness
As autonomous systems (AS) increasingly become part of our daily lives,
ensuring their trustworthiness is crucial. In order to demonstrate the
trustworthiness of an AS, we first need to specify what is required for an AS
to be considered trustworthy. This roadmap paper identifies key challenges for
specifying for trustworthiness in AS, as identified during the "Specifying for
Trustworthiness" workshop held as part of the UK Research and Innovation (UKRI)
Trustworthy Autonomous Systems (TAS) programme. We look across a range of AS
domains with consideration of the resilience, trust, functionality,
verifiability, security, and governance and regulation of AS and identify some
of the key specification challenges in these domains. We then highlight the
intellectual challenges that are involved with specifying for trustworthiness
in AS that cut across domains and are exacerbated by the inherent uncertainty
involved with the environments in which AS need to operate.Comment: Accepted version of paper. 13 pages, 1 table, 1 figur
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