Contributions to automated realtime underwater navigation

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2012This dissertation presents three separate–but related–contributions to the art of underwater navigation. These methods may be used in postprocessing with a human in the loop, but the overarching goal is to enhance vehicle autonomy, so the emphasis is on automated approaches that can be used in realtime. The three research threads are: i) in situ navigation sensor alignment, ii) dead reckoning through the water column, and iii) model-driven delayed measurement fusion. Contributions to each of these areas have been demonstrated in simulation, with laboratory data, or in the field–some have been demonstrated in all three arenas. The solution to the in situ navigation sensor alignment problem is an asymptotically stable adaptive identifier formulated using rotors in Geometric Algebra. This identifier is applied to precisely estimate the unknown alignment between a gyrocompass and Doppler velocity log, with the goal of improving realtime dead reckoning navigation. Laboratory and field results show the identifier performs comparably to previously reported methods using rotation matrices, providing an alignment estimate that reduces the position residuals between dead reckoning and an external acoustic positioning system. The Geometric Algebra formulation also encourages a straightforward interpretation of the identifier as a proportional feedback regulator on the observable output error. Future applications of the identifier may include alignment between inertial, visual, and acoustic sensors. The ability to link the Global Positioning System at the surface to precision dead reckoning near the seafloor might enable new kinds of missions for autonomous underwater vehicles. This research introduces a method for dead reckoning through the water column using water current profile data collected by an onboard acoustic Doppler current profiler. Overlapping relative current profiles provide information to simultaneously estimate the vehicle velocity and local ocean current–the vehicle velocity is then integrated to estimate position. The method is applied to field data using online bin average, weighted least squares, and recursive least squares implementations. This demonstrates an autonomous navigation link between the surface and the seafloor without any dependence on a ship or external acoustic tracking systems. Finally, in many state estimation applications, delayed measurements present an interesting challenge. Underwater navigation is a particularly compelling case because of the relatively long delays inherent in all available position measurements. This research develops a flexible, model-driven approach to delayed measurement fusion in realtime Kalman filters. Using a priori estimates of delayed measurements as augmented states minimizes the computational cost of the delay treatment. Managing the augmented states with time-varying conditional process and measurement models ensures the approach works within the proven Kalman filter framework–without altering the filter structure or requiring any ad-hoc adjustments. The end result is a mathematically principled treatment of the delay that leads to more consistent estimates with lower error and uncertainty. Field results from dead reckoning aided by acoustic positioning systems demonstrate the applicability of this approach to real-world problems in underwater navigation.I have been financially supported by: the National Defense Science and Engineering Graduate (NDSEG) Fellowship administered by the American Society for Engineering Education, the Edwin A. Link Foundation Ocean Engineering and Instrumentation Fellowship, and WHOI Academic Programs office

Investigation of risk-aware MDP and POMDP contingency management autonomy for UAS

Unmanned aircraft systems (UAS) are being increasingly adopted for various applications. The risk UAS poses to people and property must be kept to acceptable levels. This paper proposes risk-aware contingency management autonomy to prevent an accident in the event of component malfunction, specifically propulsion unit failure and/or battery degradation. The proposed autonomy is modeled as a Markov Decision Process (MDP) whose solution is a contingency management policy that appropriately executes emergency landing, flight termination or continuation of planned flight actions. Motivated by the potential for errors in fault/failure indicators, partial observability of the MDP state space is investigated. The performance of optimal policies is analyzed over varying observability conditions in a high-fidelity simulator. Results indicate that both partially observable MDP (POMDP) and maximum a posteriori MDP policies performed similarly over different state observability criteria, given the nearly deterministic state transition model

Interactive generation and learning of semantic-driven robot behaviors

The generation of adaptive and reflexive behavior is a challenging task in artificial intelligence and robotics. In this thesis, we develop a framework for knowledge representation, acquisition, and behavior generation that explicitly incorporates semantics, adaptive reasoning and knowledge revision. By using our model, semantic information can be exploited by traditional planning and decision making frameworks to generate empirically effective and adaptive robot behaviors, as well as to enable complex but natural human-robot interactions. In our work, we introduce a model of semantic mapping, we connect it with the notion of affordances, and we use those concepts to develop semantic-driven algorithms for knowledge acquisition, update, learning and robot behavior generation. In particular, we apply such models within existing planning and decision making frameworks to achieve semantic-driven and adaptive robot behaviors in a generic environment. On the one hand, this work generalizes existing semantic mapping models and extends them to include the notion of affordances. On the other hand, this work integrates semantic information within well-defined long-term planning and situated action frameworks to effectively generate adaptive robot behaviors. We validate our approach by evaluating it on a number of problems and robot tasks. In particular, we consider service robots deployed in interactive and social domains, such as offices and domestic environments. To this end, we also develop prototype applications that are useful for evaluation purposes

Variations on the Kalman filter for enhanced performance monitoring of gas turbine engines

Since their advent in the 1940's, gas turbines have been used in a wide range of land, sea and air applications due to their high power density and reliability. In today's competitive market, gas turbine operators need to optimise the dispatch availability (it i.e., minimise operational issues such as aborted take-offs or in-flight shutdowns) as well as the direct operating costs of their assets. Besides improvements in the design and manufacture processes, proactive maintenance practices, based on the actual condition of the turbine, enable the achievement of these objectives. Generating dependable information about the health condition of the gas turbine is a requisite for a successful implementation of condition-based maintenance. In this thesis, we focus on the assessment of the performance of the thermodynamic cycle, also known as Module Performance Analysis. The purpose of module performance analysis is to detect, isolate and quantify changes in engine module performance, described by so-called health parameters, on the basis of measurements collected along the gas-path of the engine. Generally, the health parameters are correcting factors on the efficiency and the flow capacity of the modules while the measurements are inter-component temperatures, pressures, shaft speeds and fuel flow. Module performance analysis can be cast as an estimation problem that is characterised by a number of difficulties such as non-linearity of the system and noise and bias in the measurements. Moreover the number of health parameters usually exceeds the number of gas-path measurements, making the estimation problem underdetermined. This thesis starts with a survey of the state-of-the-art in module performance analysis. We then propose enhancements to a monitoring tool for steady-state data developed by Dr. P. Dewallef during his thesis at the Turbomachinery Group. Specifically, the improvements concern the fault detection and isolation tasks, respectively handled by a hypothesis testing and a sparse estimator. As a complement, we define metrics for the selection and analysis of sensor--health parameter suites based on the Information Theory. In a second step, we investigate the feasibility and the benefit that could be expected from the processing of data collected during transient operation of a gas turbine. We also discuss the impact of modelling errors on the estimation procedure and propose a solution that makes the health assessment robust with respect to modelling errors. The theoretical developments are evaluated on the basis of simulated test-cases through a series of metrics that gauge the estimation accuracy and the performance of the fault detection and isolation modules

Variations on the Kalman filter for enhanced performance monitoring of gas turbine engines

Since their advent in the 1940's, gas turbines have been used in a wide range of land, sea and air applications due to their high power density and reliability. In today's competitive market, gas turbine operators need to optimise the dispatch availability (it i.e., minimise operational issues such as aborted take-offs or in-flight shutdowns) as well as the direct operating costs of their assets. Besides improvements in the design and manufacture processes, proactive maintenance practices, based on the actual condition of the turbine, enable the achievement of these objectives. Generating dependable information about the health condition of the gas turbine is a requisite for a successful implementation of condition-based maintenance. In this thesis, we focus on the assessment of the performance of the thermodynamic cycle, also known as Module Performance Analysis. The purpose of module performance analysis is to detect, isolate and quantify changes in engine module performance, described by so-called health parameters, on the basis of measurements collected along the gas-path of the engine. Generally, the health parameters are correcting factors on the efficiency and the flow capacity of the modules while the measurements are inter-component temperatures, pressures, shaft speeds and fuel flow. Module performance analysis can be cast as an estimation problem that is characterised by a number of difficulties such as non-linearity of the system and noise and bias in the measurements. Moreover the number of health parameters usually exceeds the number of gas-path measurements, making the estimation problem underdetermined. This thesis starts with a survey of the state-of-the-art in module performance analysis. We then propose enhancements to a monitoring tool for steady-state data developed by Dr. P. Dewallef during his thesis at the Turbomachinery Group. Specifically, the improvements concern the fault detection and isolation tasks, respectively handled by a hypothesis testing and a sparse estimator. As a complement, we define metrics for the selection and analysis of sensor--health parameter suites based on the Information Theory. In a second step, we investigate the feasibility and the benefit that could be expected from the processing of data collected during transient operation of a gas turbine. We also discuss the impact of modelling errors on the estimation procedure and propose a solution that makes the health assessment robust with respect to modelling errors. The theoretical developments are evaluated on the basis of simulated test-cases through a series of metrics that gauge the estimation accuracy and the performance of the fault detection and isolation modules

Data Science: Measuring Uncertainties

With the increase in data processing and storage capacity, a large amount of data is available. Data without analysis does not have much value. Thus, the demand for data analysis is increasing daily, and the consequence is the appearance of a large number of jobs and published articles. Data science has emerged as a multidisciplinary field to support data-driven activities, integrating and developing ideas, methods, and processes to extract information from data. This includes methods built from different knowledge areas: Statistics, Computer Science, Mathematics, Physics, Information Science, and Engineering. This mixture of areas has given rise to what we call Data Science. New solutions to the new problems are reproducing rapidly to generate large volumes of data. Current and future challenges require greater care in creating new solutions that satisfy the rationality for each type of problem. Labels such as Big Data, Data Science, Machine Learning, Statistical Learning, and Artificial Intelligence are demanding more sophistication in the foundations and how they are being applied. This point highlights the importance of building the foundations of Data Science. This book is dedicated to solutions and discussions of measuring uncertainties in data analysis problems

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018