13 research outputs found
A Realistic Simulation for Swarm UAVs and Performance Metrics for Operator User Interfaces
Robots have been utilized to support disaster mitigation missions through exploration of areas that are either unreachable or hazardous for human rescuers [1]. The great potential for robotics in disaster mitigation has been recognized by the research community and during the last decade, a lot of research has been focused on developing robotic systems for this purpose. In this thesis, we present a description of the usage and classification of UAVs and performance metrics that affect controlling of UAVs. We also present new contributions to the UAV simulator developed by ECSL and RRL: the integration of flight dynamics of Hummingbird quadcopter, and distance optimization using a Genetic algorithm
Sharing a work team with robots : The negative effect of robot co-workers on in-group identification with the work team
This study investigated whether the introduction of robots as teammates has an impact on in-group identification. We used two samples from the United States (N = 1003, N = 969). Participants were asked to imagine a hypothetical situation in which they were assigned to a work team at a new job. The number of robot teammates was manipulated, and the control group included only humans. Two studies examined perceived in-group identification with variance analysis and individual differences with regression analysis. Having a robot on the work team had a negative impact on in-group identification. The results suggest that when humans are members of minority subgroup within a work team, their subgroup identity is threatened. Identification with a work team including robot members is associated with individual factors such as attitude towards robots, technological expertise, and personality. Our findings indicate that introducing a robot as a teammate may affect in-group identification process negatively with some individual differences.acceptedVersionPeer reviewe
FABRIC: A Framework for the Design and Evaluation of Collaborative Robots with Extended Human Adaptation
A limitation for collaborative robots (cobots) is their lack of ability to
adapt to human partners, who typically exhibit an immense diversity of
behaviors. We present an autonomous framework as a cobot's real-time
decision-making mechanism to anticipate a variety of human characteristics and
behaviors, including human errors, toward a personalized collaboration. Our
framework handles such behaviors in two levels: 1) short-term human behaviors
are adapted through our novel Anticipatory Partially Observable Markov Decision
Process (A-POMDP) models, covering a human's changing intent (motivation),
availability, and capability; 2) long-term changing human characteristics are
adapted by our novel Adaptive Bayesian Policy Selection (ABPS) mechanism that
selects a short-term decision model, e.g., an A-POMDP, according to an estimate
of a human's workplace characteristics, such as her expertise and collaboration
preferences. To design and evaluate our framework over a diversity of human
behaviors, we propose a pipeline where we first train and rigorously test the
framework in simulation over novel human models. Then, we deploy and evaluate
it on our novel physical experiment setup that induces cognitive load on humans
to observe their dynamic behaviors, including their mistakes, and their
changing characteristics such as their expertise. We conduct user studies and
show that our framework effectively collaborates non-stop for hours and adapts
to various changing human behaviors and characteristics in real-time. That
increases the efficiency and naturalness of the collaboration with a higher
perceived collaboration, positive teammate traits, and human trust. We believe
that such an extended human adaptation is key to the long-term use of cobots.Comment: The article is in review for publication in International Journal of
Robotics Researc