In future inspections of offshore assets utilizing robots, robots will not only be expected to collate new data from their payload of instruments, but they will also be expected to interact with the infrastructure being inspected, undertake remedial tasks and engage with embedded monitoring systems of the asset. This increasing level of interaction and deployment frequency of robot inspections requires an understanding of how we can embed safe and trusted operational architectures within robots. Currently, robots can undertake constrained semi-autonomous inspections, using predetermined tasks (missions) with minimum supervision. However, the challenge is that the state of the world changes with time as does the condition of the robot. Therefore, robots must be able to undertake adaptive measures to support optimal outcomes during autonomous missions. In this paper, we propose an initial architecture to the safe verification and validation of health condition and certification of robotic and autonomous inspection systems for offshore assets. Our first contribution relates to the verification and validation architecture, which takes into account risks associated with asset inspection, safety protocols, evolving ambient changes, as well as the inherent state of health of the robot. The second part of our paper looks to how prognostic analytics can be used to support robot resilience in terms of sensor drift and accurate state of health estimates of critical sub-systems. Initial results demonstrate that methods such as relevance vector machines and Bayesian networks can be used to accurately mitigate risks to autonomy.

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Nowadays, the complexity of high speed civil transport and highly-augmented rotorcraft, has led to an increase in the chances of encountering unwanted unstable phenomena, such as the so called Aircraft/Rotorcraft-Pilot Couplings (A/RPCs) or Pilot-Induced Oscillations (PIOs), whose unpredictability has given rise to a serious problem concerning the safety of a mission. When talking about PIOs, McRuer de- 1ned them as “inadvertent, sustained aircraft oscillations which are a consequence of an abnormal joint enterprise between the aircraft and the pilot”. However, A/RPCs, these undesirable events associated with the interaction between pilot and aircraft, have become diverse and more complex than those encountered in the past. At the moment, there are different methods available to prevent and detect Cat. I/II A/RPC, but particular interest has recently arisen in this topic for 2ight simulation applications as any enhancement of these tools in order to accurately and objectively predict, detect (in real-time) and alleviate RPCs will be greatly welcomed. One of the main questions to be answered through the efforts carried out within this work is related to the better detection in real-time of embedded tendencies to RPCs in modern aircraft. To answer this question, initially an assessment of the eZcacy of the Phase-Aggression Criterion (PAC), which has been designed a few years ago at the University of Liverpool, will be undertaken either: as a means of alerting the pilot to conditions likely to lead to the onset of a PIO; or, given that the time available for the pilot to counteract may be extremely limited, as a means to assist him/her in alleviating (automatically) the PIO condition itself. Preliminary results from 2ight simulation trials to explore how best to achieve this will be reported. Moreover, this work will report on the development of PAC boundaries for more highly augmented response types. Furthermore, as classi1ed by McRuer, Cat. III PIO, which is nonlinear in essence, is the most complex one. However, the researches on Cat. III PIO are rare. This paper will reveal some elementary results of Cat. III PIO. Since there is no existing method used for predicting and detecting Cat. III PIO, this paper utilized the characteristics of PIO, such as the amplitude, the oscillation frequency and ultimate tendency of key aircraft response states to judge Cat. III PIO preliminarily. By using this elementary judgment of PIO, we studied the following factors: time delay of pilot input and helicopter main body, actuator position saturation, actuator rate limit and SCAS control authority in triggering PIO. Results show that PIO induced by actuator position saturation, actuator rate limit and SCAS control authority can be regarded as Cat. III PIO as the variation of these factors can be viewed as a kind of transition of effective controlled vehicle dynamics. These kinds of transition can cause a mismatch between the effective controlled vehicle dynamics and pilot control strategy, which is the main cause of Cat. III PIO. Copyright Statement The authors con1rm that they, and/or their company or organization, hold copyright on all of the original material included in this paper. The authors also con1rm that they have obtained permission, from the copyright holder of any third party material included in this paper, to publish it as part of their paper. The authors con1rm that they give permission, or have obtained permission from the copyright holder of this paper, for the publication and distribution of this paper as part of the ERF proceedings or as individual offprints from the proceedings and for inclusion in a freely accessible web-based repository. @en