Mission planning

Mission planning
Single platform
Current mission plan solutions for underwater platforms
are procedural and static (Hagen 2001). If behaviors are
added (Pang et al. 2003), they are only to cope with possible
changes that are known a-priori by the operator. In order
to achieve higher levels of autonomy, plan adaptability
should be available not only to procedural waypoint-based
approaches on trajectory planning but also to a declarative
goal-based solution for adaptive mission planning.
On a goal-oriented command and control, decision tools
can be developed providing support for adaptive data sampling,
optimization of resources and fault recovery capabilities
of the platform (Bellingham, Kirkwood, and Rajan
2006).
The potential benefits of the planning capabilities for
adaptive data sampling have been studied by Rajan (Rajan et
al. 2007) and Fox (Fox et al. 2007). An example of this application
is the T-Rex system, an adaptive planning architecture
with notions of timing and onboard resource management
responsiveness to opportunistic science events (Mc-
Gann et al. 2007).

In a similar way, fault-tolerant mission planning adaptation
can also contribute towards providing more mission
flexibility and robustness. When active and passive measures
fail to protect the vehicle, or unexpected hardware failures
occur, the focus of the mission should shift to ’reconfigure’
itself to use alternative combinations of the remaining
resources (see Fig. 9). In the underwater environment, autonomous
embedded recoverability, is a key capability for
vehicle’s endurance. This can be achieved via adaptation of
the vehicle’s mission plan (Patr´on et al. 2008c). The aim
is to provide with a mission plan as close as possible to the
original. In such case, plan optimality during the adaptation
process can be sacrified for plan stability (Fox et al. 2006).
This level of control can also benefit from the use of
knowledge frameworks. Adaptation is limited to the quality
and scope of the available information. Therefore, to adapt
mission plans due to unforeseen and incipient faults, it is
required that accurate information is available, to recognize
that a fault has occurred and deduce the root cause of the
failure.
Current robotic solutions are generally equipped with
Failure Diagnosis and Detection (FDD) systems based on
damage control that results in the vehicle resurfacing in
the event of any fault in the system. But future operations
will require long-endurance unattended systems that operate
even while being partially damaged or while having constrained
capabilities. Hence, it is important to develop system
which not only detect failure in the component but also
provide mission plan modules capable of adapting and recovering
from these failures. This exchange of meaningful
and reliable knowledge between FDDs and mission planners
can leverage on the use of semantic knowledge frameworks.
Once the knowledge has been transferred from the FDD,
the adaptation approach combines robust execution and mission
plan repair in order to maximize system performance
and response time. Two levels of repair are possible. If the
fault coming from the FDD is incipient or non-critical, a repair
at the executive level is performed. This entails adapting
the intances and parameters of the action performing the
task without changing the action itself. If a critical fault on
a component is diagnosed, a repair at the plan level is performed
that changes the action requiring of the component

and the constraints on the action. During this decision making
process, a combination of refinement and unrefinement
phases of the constraints of a partial ordered mission plan
can be used (Patr´on et al. 2008b) (see Fig. 10).