2 research outputs found
Tethered Aerial Visual Assistance
In this paper, an autonomous tethered Unmanned Aerial Vehicle (UAV) is
developed into a visual assistant in a marsupial co-robots team, collaborating
with a tele-operated Unmanned Ground Vehicle (UGV) for robot operations in
unstructured or confined environments. These environments pose extreme
challenges to the remote tele-operator due to the lack of sufficient
situational awareness, mostly caused by the unstructuredness and confinement,
stationary and limited field-of-view and lack of depth perception from the
robot's onboard cameras. To overcome these problems, a secondary tele-operated
robot is used in current practices, who acts as a visual assistant and provides
external viewpoints to overcome the perceptual limitations of the primary
robot's onboard sensors. However, a second tele-operated robot requires extra
manpower and teamwork demand between primary and secondary operators. The
manually chosen viewpoints tend to be subjective and sub-optimal. Considering
these intricacies, we develop an autonomous tethered aerial visual assistant in
place of the secondary tele-operated robot and operator, to reduce human robot
ratio from 2:2 to 1:2. Using a fundamental viewpoint quality theory, a formal
risk reasoning framework, and a newly developed tethered motion suite, our
visual assistant is able to autonomously navigate to good-quality viewpoints in
a risk-aware manner through unstructured or confined spaces with a tether. The
developed marsupial co-robots team could improve tele-operation efficiency in
nuclear operations, bomb squad, disaster robots, and other domains with novel
tasks or highly occluded environments, by reducing manpower and teamwork
demand, and achieving better visual assistance quality with trustworthy
risk-aware motion.Comment: Submitted to special issue of "Field and Service Robotics" of the
Journal of Field Robotics (JFR). arXiv admin note: text overlap with
arXiv:1904.0007
Best Viewpoints for External Robots or Sensors Assisting Other Robots
This work creates a model of the value of different external viewpoints of a
robot performing tasks. The current state of the practice is to use a
teleoperated assistant robot to provide a view of a task being performed by a
primary robot; however, the choice of viewpoints is ad hoc and does not always
lead to improved performance. This research applies a psychomotor approach to
develop a model of the relative quality of external viewpoints using Gibsonian
affordances. In this approach, viewpoints for the affordances are rated based
on the psychomotor behavior of human operators and clustered into manifolds of
viewpoints with the equivalent value. The value of 30 viewpoints is quantified
in a study with 31 expert robot operators for 4 affordances (Reachability,
Passability, Manipulability, and Traversability) using a computer-based
simulator of two robots. The adjacent viewpoints with similar values are
clustered into ranked manifolds using agglomerative hierarchical clustering.
The results show the validity of the affordance-based approach by confirming
that there are manifolds of statistically significantly different viewpoint
values, viewpoint values are statistically significantly dependent on the
affordances, and viewpoint values are independent of a robot. Furthermore, the
best manifold for each affordance provides a statistically significant
improvement with a large Cohen's d effect size (1.1-2.3) in performance
(improving time by 14%-59% and reducing errors by 87%-100%) and improvement in
performance variation over the worst manifold. This model will enable
autonomous selection of the best possible viewpoint and path planning for the
assistant robot.Comment: Submitted to the IEEE Transactions on Human-Machine System