13 research outputs found
Closed-loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search
In search applications, autonomous unmanned vehicles must be able to
efficiently reacquire and localize mobile targets that can remain out of view
for long periods of time in large spaces. As such, all available information
sources must be actively leveraged -- including imprecise but readily available
semantic observations provided by humans. To achieve this, this work develops
and validates a novel collaborative human-machine sensing solution for dynamic
target search. Our approach uses continuous partially observable Markov
decision process (CPOMDP) planning to generate vehicle trajectories that
optimally exploit imperfect detection data from onboard sensors, as well as
semantic natural language observations that can be specifically requested from
human sensors. The key innovation is a scalable hierarchical Gaussian mixture
model formulation for efficiently solving CPOMDPs with semantic observations in
continuous dynamic state spaces. The approach is demonstrated and validated
with a real human-robot team engaged in dynamic indoor target search and
capture scenarios on a custom testbed.Comment: Final version accepted and submitted to 2018 FUSION Conference
(Cambridge, UK, July 2018
Human-Centered Autonomy for UAS Target Search
Current methods of deploying robots that operate in dynamic, uncertain
environments, such as Uncrewed Aerial Systems in search \& rescue missions,
require nearly continuous human supervision for vehicle guidance and operation.
These methods do not consider high-level mission context resulting in
cumbersome manual operation or inefficient exhaustive search patterns. We
present a human-centered autonomous framework that infers geospatial mission
context through dynamic feature sets, which then guides a probabilistic target
search planner. Operators provide a set of diverse inputs, including priority
definition, spatial semantic information about ad-hoc geographical areas, and
reference waypoints, which are probabilistically fused with geographical
database information and condensed into a geospatial distribution representing
an operator's preferences over an area. An online, POMDP-based planner,
optimized for target searching, is augmented with this reward map to generate
an operator-constrained policy. Our results, simulated based on input from five
professional rescuers, display effective task mental model alignment, 18\% more
victim finds, and 15 times more efficient guidance plans then current
operational methods.Comment: Extended version to ICRA conference submission. 9 pages, 5 figure