A future for intelligent autonomous ocean observing systems

Ocean scientists have dreamed of and recently started to realize an ocean observing revolution with autonomous observing platforms and sensors. Critical questions to be answered by such autonomous systems are where, when, and what to sample for optimal information, and how to optimally reach the sampling locations. Definitions, concepts, and progress towards answering these questions using quantitative predictions and fundamental principles are presented. Results in reachability and path planning, adaptive sampling, machine learning, and teaming machines with scientists are overviewed. The integrated use of differential equations and theory from varied disciplines is emphasized. The results provide an inference engine and knowledge base for expert autonomous observing systems. They are showcased using a set of recent at-sea campaigns and realistic simulations. Real-time experiments with identical autonomous underwater vehicles (AUVs) in the Buzzards Bay and Vineyard Sound region first show that our predicted time-optimal paths were faster than shortest distance paths. Deterministic and probabilistic reachability and path forecasts issued and validated for gliders and floats in the northern Arabian Sea are then presented. Novel Bayesian adaptive sampling for hypothesis testing and optimal learning are finally shown to forecast the observations most informative to estimate the accuracy of model formulations, the values of ecosystem parameters and dynamic fields, and the presence of Lagrangian Coherent Structures

Increasing the robustness of autonomous systems to hardware degradation using machine learning

Autonomous systems perform predetermined tasks (missions) with minimum supervision. In most applications, the state of the world changes with time. Sensors are employed to measure part or whole of the world’s state. However, sensors often fail amidst operation; feeding as such decision-making with wrong information about the world. Moreover, hardware degradation may alter dynamic behaviour, and subsequently the capabilities, of an autonomous system; rendering the original mission infeasible. This thesis applies machine learning to yield powerful and robust tools that can facilitate autonomy in modern systems. Incremental kernel regression is used for dynamic modelling. Algorithms of this sort are easy to train and are highly adaptive. Adaptivity allows for model adjustments, whenever the environment of operation changes. Bayesian reasoning provides a rigorous framework for addressing uncertainty. Moreover, using Bayesian Networks, complex inference regarding hardware degradation can be answered. Specifically, adaptive modelling is combined with Bayesian reasoning to yield recursive estimation algorithms that are robust to sensor failures. Two solutions are presented by extending existing recursive estimation algorithms from the robotics literature. The algorithms are deployed on an underwater vehicle and the performance is assessed in real-world experiments. A comparison against standard filters is also provided. Next, the previous algorithms are extended to consider sensor and actuator failures jointly. An algorithm that can detect thruster failures in an Autonomous Underwater Vehicle has been developed. Moreover, the algorithm adapts the dynamic model online to compensate for the detected fault. The performance of this algorithm was also tested in a real-world application. One step further than hardware fault detection, prognostics predict how much longer can a particular hardware component operate normally. Ubiquitous sensors in modern systems render data-driven prognostics a viable solution. However, training is based on skewed datasets; datasets where the samples from the faulty region of operation are much fewer than the ones from the healthy region of operation. This thesis presents a prognostic algorithm that tackles the problem of imbalanced (skewed) datasets

Hierarchical Classification of Scientific Taxonomies with Autonomous Underwater Vehicles

Autonomous Underwater Vehicles (AUVs) have catalysed a significant shift in the way marine habitats are studied. It is now possible to deploy an AUV from a ship, and capture tens of thousands of georeferenced images in a matter of hours. There is a growing body of research investigating ways to automatically apply semantic labels to this data, with two goals. The task of manually labelling a large number of images is time consuming and error prone. Further, there is the potential to change AUV surveys from being geographically defined (based on a pre-planned route), to permitting the AUV to adapt the mission plan in response to semantic observations. This thesis focusses on frameworks that permit a unified machine learning approach with applicability to a wide range of geographic areas, and diverse areas of interest for marine scientists. This can be addressed through the use of hierarchical classification; in which machine learning algorithms are trained to predict not just a binary or multi-class outcome, but a hierarchy of related output labels which are not mutually exclusive, such as a scientific taxonomy. In order to investigate classification on larger hierarchies with greater geographic diversity, the BENTHOZ-2015 data set was assembled as part of a collaboration between five Australian research groups. Existing labelled data was re-mapped to the CATAMI hierarchy, in total more than 400,000 point labels, conforming to a hierarchy of around 150 classes. The common hierarchical classification approach of building a network of binary classifiers was applied to the BENTHOZ-2015 data set, and a novel application of Bayesian Network theory and probability calibration was used as a theoretical foundation for the approach, resulting in improved classifier performance. This was extended to a more complex hidden node Bayesian Network structure, which permits inclusion of additional sensor modalities, and tuning for better performance in particular geographic regions

Study of Mobile Robot Operations Related to Lunar Exploration

Mobile robots extend the reach of exploration in environments unsuitable, or unreachable, by humans. Far-reaching environments, such as the south lunar pole, exhibit lighting conditions that are challenging for optical imagery required for mobile robot navigation. Terrain conditions also impact the operation of mobile robots; distinguishing terrain types prior to physical contact can improve hazard avoidance. This thesis presents the conclusions of a trade-off that uses the results from two studies related to operating mobile robots at the lunar south pole. The lunar south pole presents engineering design challenges for both tele-operation and lidar-based autonomous navigation in the context of a near-term, low-cost, short-duration lunar prospecting mission. The conclusion is that direct-drive tele-operation may result in improved science data return. The first study is on demonstrating lidar reflectance intensity, and near-infrared spectroscopy, can improve terrain classification over optical imagery alone. Two classification techniques, Naive Bayes and multi-class SVM, were compared for classification errors. Eight terrain types, including aggregate, loose sand and compacted sand, are classified using wavelet-transformed optical images, and statistical values of lidar reflectance intensity. The addition of lidar reflectance intensity was shown to reduce classification errors for both classifiers. Four types of aggregate material are classified using statistical values of spectral reflectance. The addition of spectral reflectance was shown to reduce classification errors for both classifiers. The second study is on human performance in tele-operating a mobile robot over time-delay and in lighting conditions analogous to the south lunar pole. Round-trip time delay between operator and mobile robot leads to an increase in time to turn the mobile robot around obstacles or corners as operators tend to implement a `wait and see\u27 approach. A study on completion time for a cornering task through varying corridor widths shows that time-delayed performance fits a previously established cornering law, and that varying lighting conditions did not adversely affect human performance. The results of the cornering law are interpreted to quantify the additional time required to negotiate a corner under differing conditions, and this increase in time can be interpreted to be predictive when operating a mobile robot through a driving circuit

A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning

Machine Learning (ML) techniques have gained significant traction as a means of improving the autonomy of marine vehicles over the last few years. This article surveys the recent ML approaches utilised for ship collision avoidance (COLAV) and mission planning. Following an overview of the ever-expanding ML exploitation for maritime vehicles, key topics in the mission planning of ships are outlined. Notable papers with direct and indirect applications to the COLAV subject are technically reviewed and compared. Critiques, challenges, and future directions are also identified. The outcome clearly demonstrates the thriving research in this field, even though commercial marine ships incorporating machine intelligence able to perform autonomously under all operating conditions are still a long way off

A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning

Machine Learning (ML) techniques have gained significant traction as a means of improving the autonomy of marine vehicles over the last few years. This article surveys the recent ML approaches utilised for ship collision avoidance (COLAV) and mission planning. Following an overview of the ever-expanding ML exploitation for maritime vehicles, key topics in the mission planning of ships are outlined. Notable papers with direct and indirect applications to the COLAV subject are technically reviewed and compared. Critiques, challenges, and future directions are also identified. The outcome clearly demonstrates the thriving research in this field, even though commercial marine ships incorporating machine intelligence able to perform autonomously under all operating conditions are still a long way off.Peer reviewe

A review of the use of artificial intelligence methods in infrastructure systems

The artificial intelligence (AI) revolution offers significant opportunities to capitalise on the growth of digitalisation and has the potential to enable the ‘system of systems’ approach required in increasingly complex infrastructure systems. This paper reviews the extent to which research in economic infrastructure sectors has engaged with fields of AI, to investigate the specific AI methods chosen and the purposes to which they have been applied both within and across sectors. Machine learning is found to dominate the research in this field, with methods such as artificial neural networks, support vector machines, and random forests among the most popular. The automated reasoning technique of fuzzy logic has also seen widespread use, due to its ability to incorporate uncertainties in input variables. Across the infrastructure sectors of energy, water and wastewater, transport, and telecommunications, the main purposes to which AI has been applied are network provision, forecasting, routing, maintenance and security, and network quality management. The data-driven nature of AI offers significant flexibility, and work has been conducted across a range of network sizes and at different temporal and geographic scales. However, there remains a lack of integration of planning and policy concerns, such as stakeholder engagement and quantitative feasibility assessment, and the majority of research focuses on a specific type of infrastructure, with an absence of work beyond individual economic sectors. To enable solutions to be implemented into real-world infrastructure systems, research will need to move away from a siloed perspective and adopt a more interdisciplinary perspective that considers the increasing interconnectedness of these systems

An energy-aware architecture : a practical implementation for autonomous underwater vehicles

Energy awareness, fault tolerance and performance estimation are important aspects for extending the autonomy levels of today’s autonomous vehicles. Those are related to the concepts of survivability and reliability, two important factors that often limit the trust of end users in conducting large-scale deployments of such vehicles. With the aim of preparing the way for persistent autonomous operations this work focuses its efforts on investigating those effects on underwater vehicles capable of long-term missions. A novel energy-aware architecture for autonomous underwater vehicles (AUVs) is presented. This, by monitoring at runtime the vehicle’s energy usage, is capable of detecting and mitigating failures in the propulsion subsystem, one of the most common sources of mission-time problems. Furthermore it estimates the vehicle’s performance when operating in unknown environments and in the presence of external disturbances. These capabilities are a great contribution for reducing the operational uncertainty that most underwater platforms face during their deployment. Using knowledge collected while conducting real missions the proposed architecture allows the optimisation of on-board resource usage. This improves the vehicle’s effectiveness when operating in unknown stochastic scenarios or when facing the problem of resource scarcity. The architecture has been implemented on a real vehicle, Nessie AUV, used for real sea experiments as part of multiple research projects. These gave the opportunity of evaluating the improvements of the proposed system when considering more complex autonomous tasks. Together with Nessie AUV, the commercial platform IVER3 AUV has been involved in the evaluating the feasibility of this approach. Results and operational experience, gathered both in real sea scenarios and in controlled environment experiments, are discussed in detail showing the benefits and the operational constraints of the introduced architecture, alongside suggestions for future research directions