Mario P. Brito

Risk analysis for autonomous underwater vehicle operations in extreme environments.

Autonomous underwater vehicles (AUVs) are used increasingly to explore hazardous marine environments. Risk assessment for such complex systems is based on subjective judgment and expert knowledge as much as on hard statistics. Here, we describe the use of a risk management process tailored to AUV operations, the implementation of which requires the elicitation of expert judgment. We conducted a formal judgment elicitation process where eight world experts in AUV design and operation were asked to assign a probability of AUV loss given the emergence of each fault or incident from the vehicles life history of 63 faults and incidents. After discussing methods of aggregation and analysis, we show how the aggregated risk estimates obtained from the expert judgments were used to create a risk model. To estimate AUV survival with mission distance, we adopted a statistical survival function based on the nonparametric Kaplan-Meier estimator. We present theoretical formulations for the estimator, its variance, and confidence limits. We also present a numerical example where the approach is applied to estimate the probability that the Autosub3 AUV would survive a set of missions under Pine Island Glacier, Antarctica in January-March 2009.

A Markov Chain State Transition Approach to Establishing Critical Phases for AUV Reliability

The deployment of complex autonomous underwater platforms for marine science comprises sequential steps each of which is critical to mission success. Here we present a state transition approach, in the form of a Markov chain, which models step sequence from prelaunch to operation to recovery. The aim is to identify states and state transitions presenting high risk to the vehicle and hence to the mission, based on evidence and judgment. Developing a Markov chain consists of two separate tasks. The first defines the structure that encodes event sequence. The second assigns probabilities to each possible transition. Our model comprises 11 discrete states, and includes distance-dependent underway survival statistics. Integration of the Markov model with underway survival statistics allows us to quantify success likelihood during each state and state transition, and consequently the likelihood of achieving desired mission goals. To illustrate this generic process, the fault history of the Autosub3 autonomous underwater vehicle (AUV) provides the information for different operation phases. In our proposed method, faults are discriminated according to the mission phase in which they took place.

A Behavioral Probabilistic Risk Assessment Framework for Managing Autonomous Underwater Vehicle Deployments

The deployment of a deep-diving long-range autonomous underwater vehicle (AUV) is a complex operation that requires the use of a risk informed decision-making process. Operational risk assessment is heavily dependent on expert subjective judgment. Expert judgments can be elicited either mathematically or behaviorally. During mathematical elicitation experts are kept separate and provide their assessment individually. These are then mathematically combined to create a judgment that represents the group view. The limitation with this approach is that experts do not have the opportunity to discuss different views and thus remove bias from their assessment. In this paper a Bayesian behavioral approach to estimate and manage AUV operational risk is proposed. At an initial workshop, behavioral aggregation, reaching agreement on distributions of risks for faults or incidents, is followed by an agreed initial estimate of the likelihood of success of proposed risk mitigation methods. Post-expedition, a second workshop assesses the new data, compares observed to predicted risk, thus updating the prior estimate using Bayes’ rule. This feedback further educates the experts and assesses the actual effectiveness of the mitigation measures. Applying this approach to an AUV campaign in ice-covered waters in the Arctic showed that maximum error between the predicted and the actual risk was 9% and that the experts’ assessments of the effectiveness of risk mitigation led to a maximum of 24% in risk reduction.

Underwater Glider Reliability and Implications for Survey Design

It has been 20 years since the concept of Autonomous Oceanographic Sampling Network (AOSN) was first introduced. This vision has been brought closer to reality with the introduction of undersea gliders. Whilst in terms of functionality the undersea glider has shown to be capable of meeting the AOSN vision, in terms of reliability there is no community-wide hard evidence on whether persistent presence is currently being achieved. This paper studies the reliability of undersea gliders in order to assess the feasibility of using these platforms for future AOSN. The data used is taken from 205 deployments of gliders by 12 European laboratories between 2008 and 2012. Risk profiles were calculated for two makes of deep underwater glider; there is no statistically significant difference between them. Regardless of make the probability of a deep undersea glider surviving a 90 day mission without pre-mature mission end is approximately 0.5. The probability of a shallow undersea glider surviving 30 day mission without premature mission end is 0.59. This implies that to date factors other than the energy available are preventing undersea gliders achieving their maximum capability. This reliability information was used to quantify the likelihood of two reported undersea glider surveys meeting the observation needs for a period of 6 months and to quantify the level of redundancy needed to in order to increase the likelihood of meeting the observation needs.

Predicting risk in missions under sea ice with Autonomous Underwater Vehicles

Autonomous Underwater Vehicles (AUVs) have a future as effective platforms for multi-disciplinary science research and monitoring in the polar oceans. However, operation under ice may involve significant risk to the vehicle. A risk assessment and management process that balances the risk appetite of the responsible owner with the reliability of the vehicle and the probability of loss has been proposed. A critical step in the process of assessing risk is based on expert judgment of the fault history of the vehicle, and what affect faults or incidents have on the probability of loss. However, this subjective expert judgment is sensitive to the nature of sea ice cover. In contrast to the simple, yet high risk, case of operation under an ice shelf, sea ice offers a complex risk environment. Furthermore, the risk is modified by the characteristics of the support vessel, especially its ice-breaking capability. We explore how the ASPeCt sea ice characterization protocol and probability distributions of ice thickness and concentration can be used within a rigorous process to quantify risk given a range of sea ice conditions and with ships of differing ice capabilities. A solution founded on a Bayesian Belief Network approach is proposed, where the results of the expert judgment elicitation is taken as a reference. The design of the network topology captures the causal effects of the environment separately on the vehicle and on the ship, and combines these to produce the output. Complementary expert knowledge is included within the conditional probability tables of the Bayesian Belief Network. Using expert judgment on the fault history of the Autosub3 vehicle and sea ice data gathered in the Arctic and Antarctic by its predecessor, Autosub2, examples are provided of how risk is modified by the sea ice environment.

A Bayesian approach for predicting risk of autonomous underwater vehicle loss during their missions

Autonomous Underwater Vehicles (AUVs) are effective platforms for science research and monitoring, and for military and commercial data-gathering purposes. However, there is an inevitable risk of loss during any mission. Quantifying the risk of loss is complex, due to the combination of vehicle reliability and environmental factors, and cannot be determined through analytical means alone. An alternative approach – formal expert judgment – is a time-consuming process; consequently a method is needed to broaden the applicability of judgments beyond the narrow confines of an elicitation for a defined environment. We propose and explore a solution founded on a Bayesian Belief Network (BBN), where the results of the expert judgment elicitation are taken as the initial prior probability of loss due to failure. The network topology captures the causal effects of the environment separately on the vehicle and on the support platform, and combines these to produce an updated probability of loss due to failure. An extended version of the Kaplan–Meier estimator is then used to update the mission risk profile with travelled distance. Sensitivity analysis of the BBN is presented and a case study of Autosub3 AUV deployment in the Amundsen Sea is discussed in detail.

The Role of adaptive mission planning and control in persistent autonomous underwater vehicles presence

The Autonomous Underwater Vehicle (AUV) community has for many years recognized the potential benefits made by adapting mission planning on-the-fly. Over the years there has been some degree of success in applying adaptive mission planning to very specific problems. Examples of applications include capabilities for a vehicle to search for, and then modify its trajectory to follow, a feature such as a plume or a thermocline, or to modify its trajectory to avoid an obstacle, or to find and follow a feature such as a pipeline. Despite an evident increase in the number of applications, the use of adaptive mission planning is still in its infancy. There is no doubt that adaptive mission planning will play a pivotal role in future AUV persistent presence. So what is delaying this technology from making the leap towards wider industry acceptance? This paper reviews the literature in adaptive mission planning and uses a failure analysis technique to identify key obstacles for the integration of this technique in wider AUV applications. We use our failure analysis to help devise recommendations for mitigating these obstacles. The complexity of the mathematical approaches used by adaptive techniques is one key obstacle. Perhaps of more importance is that the AUV community is increasingly requiring quantitative assessment of risk associated with the use of AUVs. We propose that probability is the appropriate measure for quantifying the risk of adaptive systems and their uncertainty. The work here presented is a collective endeavor of the Engineering Committee on Oceanic Resources Specialist Panel on Underwater Vehicles.

Estimating and managing blowout risk during access to subglacial Antarctic lakes

Abstract As Antarctic subglacial lake research progresses to in situ exploration an important topic is the lakes probable gas concentration. Depending on hydrological setting, subglacial lakes may contain large amounts of dissolved gas or gas trapped within clathrates. Consequently, access can be potentially dangerous due to the risk of blowout where depressurization could lead to high-speed ejection of water and gas from a borehole. We present a structured approach to assess the blowout risk in subglacial lake exploration. The approach integrates a generic event tree, applicable to open and closed hydrological systems, with site-specific expert judgment incorporating rigorous probabilistic formulations. The methodology is applied to a motivating example: Ellsworth Subglacial Lake. Judgments elicited through a formal process were provided by five experts with 88 years combined experience that, after aggregation, gave a median risk of blowout of 1 in 2186 with a lower quartile of 1 in 3433 and an upper quartile of 1 in 1341. This approach can be applied to any subglacial lake given a modicum of knowledge on its hydrological setting, as uncertainty can be captured through the elicited judgments. Additionally, the event tree analysis informs blowout mitigation strategies to reduce risk of injury or death.

Design considerations and solutions in rapid-prototyping an ultraviolet reactor for ice borehole disinfection

Abstract Antarctic subglacial lakes are of great interest to the science community. These systems are considered to be in pristine condition, potentially harbouring an environment containing undisturbed sedimentary sequences and ecosystems adapted to cold oligotrophic environments in the absence of sunlight. Gaining access to subglacial lakes presents major technological challenges. To comply with conventions covering the exploration of pristine Antarctic environments, access should be conducted so the lake is not contaminated in any way. Consequently, all equipment to enter the lake must be sterile and the entrance should isolate the lake from the external environment. Currently, clean access to these environments is achieved using a hot-water drilling system. Differences between the hydraulic pressure head of the lake and the glacial surface result in a section of the borehole being air-filled. It is imperative that this section is disinfected prior to introducing any sampling equipment. This paper describes the design process involved in rapid-prototyping an ultraviolet (UV) disinfection reactor for achieving this goal. Considerations such as UV output, physical constraints, temperature management, and deployment procedures are assessed. We present a design that addresses these considerations.

Updating Autonomous Underwater Vehicle Risk based on the Effectiveness of Failure Prevention and Correction

AbstractAutonomous underwater vehicles (AUVs) have proven to be feasible platforms for marine observations. Risk and reliability studies on the performance of these vehicles by different groups sho...