ICAPS05 WS6 - icaps 2005


ICAPS05 WS6 - icaps 2005

Figure 2: Naval Postgraduate School’s ARIES AUV.

main in terms of the high degree of uncertainty and incomplete

knowledge, its dynamic nature, and its complexity—

given the state of the art in planning technology, most planners

are simply not up to the task. Part, too, is due to the

needs of the users of the AUVs. Scientists want assurance

that data is collected where they specify, when they specify,

not at the whim of an AUV controller with too much

autonomy. The military, too, has often been opposed to

on-board planners, since it is critical for most of their missions

that an AUV’s behavior be predictable: one does not

want a weapon, for example, with too much of a mind of its

own. 1 And, finally, part is due to the fact that current nonplanning

control software is adequate to the rather simple

uses to which AUVs have so far been put.

For more advanced missions, however, this will have to

change. Missions in which the AUV must exhibit a high

degree of autonomy, long-duration missions, missions in

highly dynamic environments, and complex missions involving

multiple vehicles all will require capabilities far beyond

the current state of the art in AUV control. In particular,

such missions will require AUVs that are capable of

replanning or repairing downloaded missions or of planning

their own missions in the first place.

Here, I will briefly discuss some causes of the uncertainty

that, in large part, makes planning in the AUV domain difficult.

In the accompanying talk, I will survey some of the past

and current approaches to planning in the AUV domain, discuss

the state of the art, and conclude with a look at what the

future may hold for planner-based AUV mission controllers.

The most basic reason to use planning technology for

AUVs is, of course, the same as for any other agent: to correctly

sequence actions to accomplish users’ goals. If there

is no uncertainty involved, then off-line planning is sufficient.

This means that the agent must be operating a wellknown,

static environment, and it must have certain knowledge

about the environment and itself as well as accurate

1 For a humorous treatment of this idea, see the 1974 movie

“Dark Star”.

ICAPS 2005

Figure 3: The Autonomous Undersea Systems Institute’s

Solar AUV.


If there is uncertainty, however, then the AUV needs to be

able at least to modify its plan or even replan completely.

Uncertainty undermines the assumptions upon which the

off-line plan was based. For example, if the AUV is following

a plan to retrieve the black box from a downed aircraft

from location x, but the AUV’s knowledge is imprecise so

that the black box could be anywhere in an area of radius ɛ

around that point, then there is a good chance that AUV will

not be able to find the target simply by following its plan.

An AUV will encounter uncertainty to one degree or another

on almost any mission. This is due to a variety of factors

in the AUV’s environment, the mission, and itself. One

factor is the inherent incompleteness of the AUV’s knowledge

about its environment. In many respects, relatively

little is known about the ocean, especially the deep ocean.

Indeed, it was noted some time ago that we know less about

Neptune’s realm than we do about the planet Neptune (Blidberg,

Turner, & Chappell 1991); this is still true to a large

extent. Consequently, predications about the environment

upon which any a priori plan is based will often be violated

during the plan’s execution.

A dynamic environment can also lead to uncertainty. If

the world were completely deterministic, complete knowledge

of the world would allow predictions to be made with

complete certainty about how it will change. However, this

is not the case. In general, stochastic processes, as well as

uncertain or incomplete knowledge of processes operating

in the world, will lead to changes occurring that a planner

cannot predicate ahead of time. This, too, can undermine

the plan at execution time.

Imprecision in the AUV’s sensors also contributes to uncertainty.

For example, sonar is notoriously undependable,

especially underwater, being susceptible to such things a

bouncing off thermoclines or other density changes in the

water. Even localization is uncertain. Except in very shallow

water, AUVs cannot make use of GPS on a regular basis

to determine their position. Unless it is feasible to have

transponders placed for long-baseline navigation, they have

to rely on such things as inertial guidance or dead reckoning

6 Workshop on Planning under Uncertainty for Autonomous Systems

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