Towards adaptive autonomy

Autonomous adaptation can release the operator from decision

making tasks at the trajectory and mission planning levels.

These, in consequence, can require less communication

with the consequent power saving. Adaptation plays an important

role in providing autonomy. The aim is to be effective

and efficient and a plan costs time to prepare. This time

has been already invested once (to compute the plan that is

now failing), so it might be more efficient to try to reuse previous

efforts by repairing it. Also, commitments might have

been made to the current plan: trajectory reported to other

intelligent agents, assignment of resources or assignment of

part of mission plan to executors, etc. Repairing an existing

plan ensures that as few commitments as possible are invalidated.

Finally, several planners (usually autonomous and

human planners combined) could be performing together to

achieve the goals. In such cases, it is more likely that a similar

mission plan will be accepted by the operator than one

that is potentially completely different.

Autonomous adaptation requires an autonomous understanding

of the environment. The human capability of dealing

and understanding highly dynamic and complex environments

is known as situation awareness (SAH). SAH

breaks down into perception of the environment, comprehension

of the situation and projection of the future status.

Decision making occurs in a cycle of observe-orient-decideact

(OODA) (Boyd 1995). The Observation component

corresponds to the perception level of SAH. The Orientation

component contains the previously adquired knowledge

and understanding of the situation. The Decision component

represents the SAH levels of comprehension and projection.

This last stage is the central mechanism enabling adaptation

before closing the loop with the final Action stage. Note that

it is possible to take decisions by looking only at orientation

inputs without making any use of observations.

Based on the autonomy levels and environmental characteristics,

SAH definitions can be directly applied to

the notion of unmanned vehicle situation awareness

(SAV ) (Adams 2007). Increasing the levels of situation

awareness for individual unmanned vehicle systems (SAS)

can help the transfer from current full human control to fully

autonomous unmanned capabilities (see Fig. 4). This knowledge

representation is the focus of the next section.