-- Sign up for the new EW Daily Newsletter, for latest news and
products --
Hampshire-based lab Roke Manor
Research is developing an artificial
intelligence (AI) system that looks at clouds to allow autonomous
gliders to find lift in the sky.
"The primary objective is to harvest atmospheric energy to
prolong mission duration," said Roke. "This is achieved by
automated on-board energy aware planning, whereby areas of rising
air are identified and exploited, and regions of sinking air are
avoided."
Models are used to predict vertical air movement from two
sources: the wind blowing over physical land features, and the sun
warming land surfaces causing 'thermals'.
"At the core of the software lies a map-based 'blackboard'
knowledge management architecture," said Roke. "This is an
agent-based architecture, featuring decoupled knowledge source and
data processing agents."
The central blackboard is the synthetic equivalent to a World
War II air raid planning table.
Some agents post predicted vertical air velocity data onto its
surface, others look at the data to create a flight plan.
In trials, Roke used 3D atmospheric wind velocity data from the
Met Office, adding in the effect of local terrain on the lower
reaches of that data set using computational fluid dynamics.
Solar-induced vertical air movement was predicted by automating
a technique used by human glider pilots.
Thermals form over land which is warmer than surrounding land,
so pilots look for large areas likely to heat more quickly than
adjacent areas - a substantial car park next to a lake for
example.
Once formed, a cloud of a characteristic shape develops at the
top of the thermal.
Different surface types - for example: grassland, woodland,
tarmac and urban - were observed for two years to develop an
'energy balance model' which predicts how land heats the air above
it.
According to Roke, on average summer's day tarmac is 8°C hotter
than grass.
This knowledge was applied to surface type data from the trial
area to identify likely stating points for thermals.
To identify actual thermals above candidate surfaces "a novel
'cloudscaping' video image processing algorithm, detects thermal
columns of rising air from a live video feed, providing an
up-to-date picture of local conditions," said Roke.
Using data presented on the blackboard, further agents find a
route to the required destination that loses least height, based on
information provided by mission requirement agents.
"The 'planners' reason over the blackboard world model to
produce detailed waypoint sequences for the flight management
system to use," said Roke.
Further work may include having additional agents that can
communicate with similar nearby aircraft to share data.
Potential applications include extending the flight range of
powered military, communication, and surveying un-manned aerial
vehicles, said Roke.