**1. Introduction**

26 Will-be-set-by-IN-TECH

198 Autonomous Underwater Vehicles

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Oceanographic observatories, year-round energy industry subsea field inspections and continuous homeland security coast patrolling now all require the routine and permanent presence of underwater sensing tools.

These applications require underwater networks of fixed sensors that collaborate with fleets of unmanned underwater vehicles (UUVs). Technological challenges related to the underwater domain, such as power source limitations, communication and perception noise, navigation uncertainties and lack of user delegation, are limiting their current development and establishment. In order to overcome these problems, more evolved embedded tools are needed that can raise the platform's autonomy levels while maintaining the trust of the operator.

Embedded decision making agents that contain reasoning and planning algorithms can optimize the long term management of heterogeneous assets and provide fast dynamic response to events by autonomously coupling global mission requirements and resource capabilities in real time. The problem, however, is that, at present, applications are mono-domain: Mission targets are simply mono-platform, and missions are generally static procedural list of commands described *a-priori* by the operator. All this, leaves the platforms in isolation and limits the potential of multiple coordinated actions between adaptive collaborative agents.

In a standard mission flow, operators describe the mission to each specific platform, data is collected during mission and then post-processed off-line. Consequently, the main use for underwater platforms is to gather information from sensor data on missions that are static and incapable to cope with the long term environmental challenges or resource changes.

In order for embedded service agents to make decisions and interoperate, it is necessary that they have the capability of dealing with and understanding the highly dynamic and complex environments where these networks are going to operate. These decision making tools are constrained to the quality and scope of the available information.

Shared knowledge representation between embedded service-oriented agents is therefore necessary to provide them with the required common situation awareness. Two sources can provide this type of information: the domain knowledge extracted from the expert (orientation) and the inferred knowledge from the processed sensor data (observation). In both cases, it will be necessary for the information to be stored, accessed and shared efficiently

• Σ is the mission domain model containing information about domain, i.e. the platform

<sup>201</sup> Embedded Knowledge and Autonomous Planning:

• Ω is the mission problem model containing information about the problem, i.e. mission

The set of all possible mission environments for a given domain is defined as the *domain space* (e.g., the domain space of the underwater domain). It is denoted by Θ. A mission environment

From this model, a mission plan *π* that tries to accomplish the mission objectives can be produced. However, this mission environment evolves over time *t* as new observations of

The decision making process to calculate a mission plan *π<sup>t</sup>* for a given mission environment Π*<sup>t</sup>* occurs in a cycle of observe-orient-decide-act. This process was termed by Boyd (1992) as the OODA-loop, and it was modelled on human behaviour. Inside this loop, the *Orientation* phase contains the previously acquired knowledge and initial understanding of the situation of the mission environment (Π*t*−1). The *Observation* phase corresponds to new perceptions of the mission domain model (Σ*t*) and the mission problem model (Ω*t*) that modify the mission environment. The *Decision* component represents the level of comprehension and projection, the central mechanism enabling adaptation before closing the loop with the *Action* stage. Note that it is possible to make decisions by looking only at orientation inputs without making any use of observations. In this case, Eq. 1 becomes Π*<sup>t</sup>* ← Π*t*−1. In the same way, it is also possible to make decisions by looking only at the observation inputs without making use of

In current UUVs implementations, the human operator constitutes the decision phase. See Figure 1 for a schematic representation of the control loop. When high bandwidth communication links exist, the operator remains in the OODA-loop during the mission execution taking the decisions. For each update of the mission environment Π*<sup>t</sup>* received, the operator decides on the correspondent mission plan *π<sup>t</sup>* to be performed. From the list of actions in this mission plan, the mission executive issues the correspondent commands to the platform. Examples of the implementation of this architecture are existing Remotely Operated

However, when communication is unreliable or unavailable, the operator must attempt to include all possible if-then-else cases to cope with execution alternatives before the mission starts. This is the case of current UUVs implementations that follow an orientation-only model. Figure 2 shows this model, where the OODA-loop is broken because observations

We will now discuss a few recent UUV implementations which show where the state-of-the-art is currently positioned. Most implementations rely on pre-scripted mission

Commands

Observations

Executive Platform Environment

Events

Mission

Fig. 1. Observation, Orientation, Decision and Action (OODA) loop for unmanned vehicle

Π*<sup>t</sup>* ← Π*t*−<sup>1</sup> ∪ Σ*<sup>t</sup>* ∪ Ω*<sup>t</sup>* (1)

the domain model Σ*<sup>t</sup>* and the problem model Ω*<sup>t</sup>* continuously modify it:

available prior knowledge. In this case, Eq. 1 becomes Π*<sup>t</sup>* ← Σ*<sup>t</sup>* ∪ Ω*t*.

and the environment of execution, and

The Path Towards Permanent Presence of Underwater Networks

status, requirements, and objectives.

Π is an element of one and only one Θ.

Vehicles (ROVs).

are not reported to the human operator.

Π<sup>0</sup>

Human Operator πt

Πt

systems with decision making provided by the human operator.

by the deliberative agents while performing a mission. These agents, providing different capabilities and working in collaboration, might even be distributed among the different platforms or sharing some limited resources.
