Semantic-based adaptive mission planning for unmanned underwater vehicles

Current underwater robotic platforms rely upon waypoint-based scripted missions which are described by the operator a-priori. This renders systems incapable of reacting to the unexpected. In this thesis, we claim that the ability to autonomously adapt the decision making process is the key to facilitating the change over from human intervention to intelligent autonomy. We identify goal-based declarative mission planning as an attractive solution to autonomous adaptability because it combines autonomous decision making with higher levels of human interaction. Goal-based mission planning requires the use of abstract knowledge representation and situation awareness to link the prior knowledge provided by the operator with the information coming from the processed sensor data. To achieve this, we propose a semantic-based knowledge representation framework that allows this integration of prior and processed information among all different agents available in the platform. In order to evaluate adaptive mission planning techniques, we also introduce a novel metric which measures the proximity between plans. We demonstrate that this metric is better informed than previous metrics for measuring the adaptation process. In this thesis we implement three different approaches to goal-based mission planning in order to investigate which approach is most appropriate under different circumstances. The first approach, continuous mission planning, focusses on long-term deployment. This approach is based on a continuous re-assessment of the status of the mission environment. Using our proximity metric, we evaluated this approach and show that there is a high degree of similarity between our approach and the humanly driven adaptation, both in a known static environment and in a partially-known dynamic discoverable environment. The second, service-oriented mission planning, makes use of the semantic framework to provide autonomous mission planning for the dynamic discovery of the services published by the different agents in the system. This allows platform independence, easing the manual creation of mission plans, and robustness to changes. We show that this approach produces the same plans as the baseline which was explicitly provided with the platform configuration. The last approach, mission plan repair, handles the scenario where small changes occur in the mission environment and there are limited resources for planning. We develop and deploy a mission plan repair approach within a semantic-based autonomous planning system in a real underwater vehicle. Experiments demonstrate that the integrated system is capable of providing mission adaptation for maintaining the operability of the host platform in the face of unexpected events

MIRIAM: A Multimodal Chat-Based Interface for Autonomous Systems

We present MIRIAM (Multimodal Intelligent inteRactIon for Autonomous systeMs), a multimodal interface to support situation awareness of autonomous vehicles through chat-based interaction. The user is able to chat about the vehicle's plan, objectives, previous activities and mission progress. The system is mixed initiative in that it pro-actively sends messages about key events, such as fault warnings. We will demonstrate MIRIAM using SeeByte's SeeTrack command and control interface and Neptune autonomy simulator.Comment: 2 pages, ICMI'17, 19th ACM International Conference on Multimodal Interaction, November 13-17 2017, Glasgow, U

On noise, uncertainty and inference for computational diffusion MRI

Diffusion Magnetic Resonance Imaging (dMRI) has revolutionised the way brain microstructure and connectivity can be studied. Despite its unique potential in mapping the whole brain, biophysical properties are inferred from measurements rather than being directly observed. This indirect mapping from noisy data creates challenges and introduces uncertainty in the estimated properties. Hence, dMRI frameworks capable to deal with noise and uncertainty quantification are of great importance and are the topic of this thesis. First, we look into approaches for reducing uncertainty, by de-noising the dMRI signal. Thermal noise can have detrimental effects for modalities where the information resides in the signal attenuation, such as dMRI, that has inherently low-SNR data. We highlight the dual effect of noise, both in increasing variance, but also introducing bias. We then design a framework for evaluating denoising approaches in a principled manner. By setting objective criteria based on what a well-behaved denoising algorithm should offer, we provide a bespoke dataset and a set of evaluations. We demonstrate that common magnitude-based denoising approaches usually reduce noise-related variance from the signal, but do not address the bias effects introduced by the noise floor. Our framework also allows to better characterise scenarios where denoising can be beneficial (e.g. when done in complex domain) and can open new opportunities, such as pushing spatio-temporal resolution boundaries. Subsequently, we look into approaches for mapping uncertainty and design two inference frameworks for dMRI models, one using classical Bayesian methods and another using more recent data-driven algorithms. In the first approach, we build upon the univariate random-walk Metropolis-Hastings MCMC, an extensively used sampling method to sample from the posterior distribution of model parameters given the data. We devise an efficient adaptive multivariate MCMC scheme, relying upon the assumption that groups of model parameters can be jointly estimated if a proper covariance matrix is defined. In doing so, our algorithm increases the sampling efficiency, while preserving accuracy and precision of estimates. We show results using both synthetic and in-vivo dMRI data. In the second approach, we resort to Simulation-Based Inference (SBI), a data-driven approach that avoids the need for iterative model inversions. This is achieved by using neural density estimators to learn the inverse mapping from the forward generative process (simulations) to the parameters of interest that have generated those simulations. By addressing the problem via learning approaches offers the opportunity to achieve inference amortisation, boosting efficiency by avoiding the necessity of repeating the inference process for each new unseen dataset. It also allows inversion of forward processes (i.e. a series of processing steps) rather than only models. We explore different neural network architectures to perform conditional density estimation of the posterior distribution of parameters. Results and comparisons obtained against MCMC suggest speed-ups of 2-3 orders of magnitude in the inference process while keeping the accuracy in the estimates

On noise, uncertainty and inference for computational diffusion MRI

Diffusion Magnetic Resonance Imaging (dMRI) has revolutionised the way brain microstructure and connectivity can be studied. Despite its unique potential in mapping the whole brain, biophysical properties are inferred from measurements rather than being directly observed. This indirect mapping from noisy data creates challenges and introduces uncertainty in the estimated properties. Hence, dMRI frameworks capable to deal with noise and uncertainty quantification are of great importance and are the topic of this thesis. First, we look into approaches for reducing uncertainty, by de-noising the dMRI signal. Thermal noise can have detrimental effects for modalities where the information resides in the signal attenuation, such as dMRI, that has inherently low-SNR data. We highlight the dual effect of noise, both in increasing variance, but also introducing bias. We then design a framework for evaluating denoising approaches in a principled manner. By setting objective criteria based on what a well-behaved denoising algorithm should offer, we provide a bespoke dataset and a set of evaluations. We demonstrate that common magnitude-based denoising approaches usually reduce noise-related variance from the signal, but do not address the bias effects introduced by the noise floor. Our framework also allows to better characterise scenarios where denoising can be beneficial (e.g. when done in complex domain) and can open new opportunities, such as pushing spatio-temporal resolution boundaries. Subsequently, we look into approaches for mapping uncertainty and design two inference frameworks for dMRI models, one using classical Bayesian methods and another using more recent data-driven algorithms. In the first approach, we build upon the univariate random-walk Metropolis-Hastings MCMC, an extensively used sampling method to sample from the posterior distribution of model parameters given the data. We devise an efficient adaptive multivariate MCMC scheme, relying upon the assumption that groups of model parameters can be jointly estimated if a proper covariance matrix is defined. In doing so, our algorithm increases the sampling efficiency, while preserving accuracy and precision of estimates. We show results using both synthetic and in-vivo dMRI data. In the second approach, we resort to Simulation-Based Inference (SBI), a data-driven approach that avoids the need for iterative model inversions. This is achieved by using neural density estimators to learn the inverse mapping from the forward generative process (simulations) to the parameters of interest that have generated those simulations. By addressing the problem via learning approaches offers the opportunity to achieve inference amortisation, boosting efficiency by avoiding the necessity of repeating the inference process for each new unseen dataset. It also allows inversion of forward processes (i.e. a series of processing steps) rather than only models. We explore different neural network architectures to perform conditional density estimation of the posterior distribution of parameters. Results and comparisons obtained against MCMC suggest speed-ups of 2-3 orders of magnitude in the inference process while keeping the accuracy in the estimates

Necesario carácter vinculante de las recomendaciones efectuadas por la Contraloría General de la República y los órganos de control institucional en los servicios de control interno preventivos y simultáneos

The main focus of this article is to contribute to a more efficient, effective and transparent public management. For this, the author states that the recommendations made to public entities by the Comptroller General of the Republic and the Institutional Control Bodies, through internal control services, preventive and simultaneous, have vinculant character for the purpouse of strengthen in order to strengthen the National Control System, in the fight against corruption.El presente artículo tiene como enfoque principal coadyuvar a una gestión pública más eficiente, eficaz y transparente. Para ello, el autor plantea que las recomendaciones realizadas a las entidades públicas por parte de la Contraloría General de la República y los Órganos de Control Institucional, a través de los servicios de control interno preventivos y simultáneos, tengan carácter vinculante, a fin de fortalecer el Sistema Nacional de Control, en la lucha contra la corrupción