**7. Towards a full life cycle ontology**

Ontologies are commonly used to store information within a domain. For this reason, they are a valuable asset to model the shared understanding of the system and its concepts at different stages of its life cycle. Here, we propose to take a further step and use that information model at run-time to provide the system with knowledge-driven self-adaptation.

standards. That information also includes discarded ideas and the reason why they are not included in the final design, as can become part of a contingency solution. To select the best possible design available in case of contingency, knowledge bases should include metrics regarding the cost (in terms of time, energy consumption, reliability) of the resulting system after applying reconfiguration. This selec-

Ontologies provide a baseline for shared information between systems, subsystems, and external agents. But that information can also be a tool for augmenting the autonomy levels of systems. Ontologies provide a formal conceptualization of entities and their relationships. The knowledge stored can be used along architectural models in system engineering to provide a general framework for autonomous

A concrete realization of this general framework has been tested with a mobile

The contingencies are solved by reasoning about the status of the components in

robot navigating to a point in a cluttered environment. The proof-of-concept address two contingency types, (i) a component-level failure, as the case when the laser becomes unavailable, or (ii) not reaching the mission requirements in terms of

use and the status of the objective and its quality attributes associated. Once a contingency is encountered, the reasoner provides the most suitable design

tion must preserve the mission fulfillment with optimal features.

*Going back in the system life cycle from an operational failure.*

*Using Ontologies in Autonomous Robots Engineering DOI: http://dx.doi.org/10.5772/intechopen.97357*

**8. Conclusions**

safety or energy consumption.

systems.

**85**

**Figure 4.**

The proof-of-concept presented here addresses reconfiguration to ensure the mission fulfillment within a set of predefined operational requirements. The main limitation is imposed by the set of alternative designs predefined for the system and its possible contingencies. Here, ontologies have been used to orchestrate the deployment of different design solutions at run-time.

To increase the autonomy levels of systems, particularly in the case of robots, we propose to take a further step and include all early phases of system life cycles, from the need identification to fulfill a mission to the verification of the system (see **Figure 4**).

When the system is deployed, each time a contingency is detected and it requires reconfiguration, the system should return to the needs evaluation and analyze which tools it has available to satisfy the need. According to that, it can adopt some requirements and come across with a design by itself. That design may be tested in simulation with a digital twin or just deployed if it has been evaluated in previous operations.

To reach such an elevated autonomy level, a massive effort in ontology standardization in systems engineering is required<sup>9</sup> . Moreover, the design engineers must encode all the information they have about the system in terms of those

<sup>9</sup> See for example the efforts of the IEEE 1872.1 working group on robot task standardization or the IOF group on systems engineering.

*Using Ontologies in Autonomous Robots Engineering DOI: http://dx.doi.org/10.5772/intechopen.97357*

reduction of the probability of faults during system operation but it also includes the possibility of better diagnostic features. having explicit knowledge on relationships among system entities allows the system to trace the source of faults and take

Besides, the system may adapt itself as the case presented in Section 5 to reduce damage or can even take preventive action based on deep reasoning. When the system is able to adapt to overcome problems derived from environmental changes or malfunctioning components, the system becomes more autonomous and trust-

Design decisions have always associated risks. Usually, the final design is commonly the solution with less risk in the context where the system is deployed. The use of ontologies provides a tool for risk management as design decisions may be justified in the knowledge base. Furthermore, this information can be used at runtime to apply the most suitable solutions if the operational environment changes. In the proof-of-concept presented here, quality attributes are used to select among design alternatives. When risk augments because of not meeting the security standards of the mission, other designs can be used. This may affect the performance of the mission but ensures its fulfillment. The system engineer must coordinate adequately those quality attributes to ensure a trade-off between performance and security. In this context, risk ceases to be a collateral effect of system design and

operation to become a first-level citizen in the explicit design of the system.

Ontologies are commonly used to store information within a domain. For this reason, they are a valuable asset to model the shared understanding of the system and its concepts at different stages of its life cycle. Here, we propose to take a further step and use that information model at run-time to provide the system with

The proof-of-concept presented here addresses reconfiguration to ensure the mission fulfillment within a set of predefined operational requirements. The main limitation is imposed by the set of alternative designs predefined for the system and its possible contingencies. Here, ontologies have been used to orchestrate the

To increase the autonomy levels of systems, particularly in the case of robots, we propose to take a further step and include all early phases of system life cycles, from the need identification to fulfill a mission to the verification of the system (see

When the system is deployed, each time a contingency is detected and it requires reconfiguration, the system should return to the needs evaluation and analyze which tools it has available to satisfy the need. According to that, it can adopt some requirements and come across with a design by itself. That design may be tested in simulation with a digital twin or just deployed if it has been evaluated in

To reach such an elevated autonomy level, a massive effort in ontology stan-

<sup>9</sup> See for example the efforts of the IEEE 1872.1 working group on robot task standardization or the IOF

must encode all the information they have about the system in terms of those

. Moreover, the design engineers

action in the implicated parts.

*Robotics Software Design and Engineering*

**7. Towards a full life cycle ontology**

deployment of different design solutions at run-time.

dardization in systems engineering is required<sup>9</sup>

knowledge-driven self-adaptation.

**Figure 4**).

previous operations.

group on systems engineering.

**84**

worthy.

**Figure 4.** *Going back in the system life cycle from an operational failure.*

standards. That information also includes discarded ideas and the reason why they are not included in the final design, as can become part of a contingency solution.

To select the best possible design available in case of contingency, knowledge bases should include metrics regarding the cost (in terms of time, energy consumption, reliability) of the resulting system after applying reconfiguration. This selection must preserve the mission fulfillment with optimal features.

## **8. Conclusions**

Ontologies provide a baseline for shared information between systems, subsystems, and external agents. But that information can also be a tool for augmenting the autonomy levels of systems. Ontologies provide a formal conceptualization of entities and their relationships. The knowledge stored can be used along architectural models in system engineering to provide a general framework for autonomous systems.

A concrete realization of this general framework has been tested with a mobile robot navigating to a point in a cluttered environment. The proof-of-concept address two contingency types, (i) a component-level failure, as the case when the laser becomes unavailable, or (ii) not reaching the mission requirements in terms of safety or energy consumption.

The contingencies are solved by reasoning about the status of the components in use and the status of the objective and its quality attributes associated. Once a contingency is encountered, the reasoner provides the most suitable design

alternative to overcome it according to the component availability and the estimated quality values in terms of energy consumption, safety and performance.

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*Using Ontologies in Autonomous Robots Engineering DOI: http://dx.doi.org/10.5772/intechopen.97357*

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very Large Knowledge bases:

However, this proof-of-concept focus on the selection of alternatives. To provide a complete module of self-adaptation and reconfiguration in the whole life cycle of systems, we need to take a larger perspective. To reach complete autonomy, the system needs to have access to knowledge from the needs for which it is designed, besides the design alternatives and the restrictions the engineers have faced. With this information, in case of failure, the system can have a wider picture of its context and take corrective action at different levels of its architecture to adapt to run-time situations.

To achieve this vision a *full system life cycle ontology for autonomous systems* is needed. Current efforts in ontology development for systems engineering, robots, and autonomous systems are quite valuable but they shall be (i) based on a general systems foundation; (ii) harmonized, and (iii) built with a modular ontology approach.
