Information-Optimal Selective Data Return for Autonomous Rover Traverse Science and Survey

Selective data return leverages onboard data analysis to allocate limited bandwidth resources during remote exploration. Here we present an adaptive method to subsample image sequences for downlink. We treat selective data return as a compression problem in which the explorer agent transmits the subset of measurements that are most informative with respect to the complete dataset. Experiments demonstrate selective downlink of navigation imagery by a rover during autonomous geologic investigations in the Atacama desert of Chile. Here automatic analysis identifies informative images using classifications based on natural image statistics. Image texture analysis, together with a context-sensitive Hidden Markov Model representation, permits adaptive downlink in response to geologic unit boundaries. Selective data return improves the science content of returned data for this geologic mapping task.</p

A cooperative architecture for target localization using underwater vehicles

Nous nous intéressons à l'architecture de robots marins et sous-marins autonomes dans le cadre de missions nécessitant leur coopération. Cette coopération s'avère difficile du fait que la communication (acoustique) est très contrainte en termes de débit et de portée.  Notre travail se place dans le contexte de missions d'exploration pour détecter des éléments particuliers sur les fonds marins, et en particulier des sources d'eau chaude. Pour cela, le véhicule sous-marin parcours des segments de droite pré-planifiés et rejoint des points de rendez-vous (points de communication). Ces derniers permettent d'assurer le suivi de bon déroulement de la mission, mais surtout de mettre en oeuvre des schémas de coopération entre les véhicules sous-marins. Au fur et à mesure de l'exploration, les sous-marins construisent et mettent à jour une représentation de l'environnement qui décrit les probabilités de localisation de sources. Une approche adaptative exploite ces informations et permet de dévier les sous-marins de leurs plan initial pour augmenter la quantité d'information, tout en respectant les contraintes du plan initial, et en particulier les rendez-vous de communication. Lors des rendez-vous, chaque véhicule échange ses données avec les autres, en ne transmettant que les informations nécessaires à la mise en place de schémas de coopération. L'ensemble de ces fonctions sont intégrées au sein de l'architecture existante T-REX, pour laquelle nous proposons des composants supplémentaires qui permettent la cartographie des fonds et la définition de schémas de coopération. Différentes simulations permettent d'évaluer les travaux proposés. ABSTRACT : There is a growing research interest in Autonomous Underwater Vehicles (AUV), due to the need for increasing our knowledge about the deep sea and understanding the effects the human way of life has on it. This need has pushed the development of new technologies to design more efficient and more autonomous underwater vehicles. Autonomy refers, in the context of this thesis, to the “decisional autonomy”, i.e. the capability of taking decisions, in uncertain, varying and unknown environments. A more recent concern in AUV area is to consider a fleet of vehicles (AUV, ASV, etc). Indeed, multiple vehicles with heterogeneous capabilities have several advantages over a single vehicle system, and in particular the potential to accomplish tasks faster and better than a single vehicle. Underwater target localization using several AUVs (Autonomous Underwater Vehicles) is a challenging issue. A systematic and exhaustive coverage strategy is not efficient in term of exploration time: it can be improved by making the AUVs share their information and cooperate to optimize their motions. The contribution of this thesis is the definition of an architecture that integrates such a strategy that adapts each vehicle motions according to its and others’ sensory information. Communication points are required to make underwater vehicles exchange information : for that purpose the system involves one ASV (Autonomous Surface Vehicle), that helps the AUVs re-localize and exchange data, and two AUVs that adapt their strategy according to gathered information, while satisfying the associated communication constraints. Each AUV is endowed with a sensor that estimates its distance with respect to targets, and cooperates with others to explore an area with the help of an ASV. To provide the required autonomy to these vehicles, we build upon an existing system (T-REX) with additional components, which provides an embedded planning and execution control framework. Simulation results are carried out to evaluate the proposed architecture and adaptive exploration strategy

An Unsupervised Approach to Modelling Visual Data

For very large visual datasets, producing expert ground-truth data for training supervised algorithms can represent a substantial human effort. In these situations there is scope for the use of unsupervised approaches that can model collections of images and automatically summarise their content. The primary motivation for this thesis comes from the problem of labelling large visual datasets of the seafloor obtained by an Autonomous Underwater Vehicle (AUV) for ecological analysis. It is expensive to label this data, as taxonomical experts for the specific region are required, whereas automatically generated summaries can be used to focus the efforts of experts, and inform decisions on additional sampling. The contributions in this thesis arise from modelling this visual data in entirely unsupervised ways to obtain comprehensive visual summaries. Firstly, popular unsupervised image feature learning approaches are adapted to work with large datasets and unsupervised clustering algorithms. Next, using Bayesian models the performance of rudimentary scene clustering is boosted by sharing clusters between multiple related datasets, such as regular photo albums or AUV surveys. These Bayesian scene clustering models are extended to simultaneously cluster sub-image segments to form unsupervised notions of “objects” within scenes. The frequency distribution of these objects within scenes is used as the scene descriptor for simultaneous scene clustering. Finally, this simultaneous clustering model is extended to make use of whole image descriptors, which encode rudimentary spatial information, as well as object frequency distributions to describe scenes. This is achieved by unifying the previously presented Bayesian clustering models, and in so doing rectifies some of their weaknesses and limitations. Hence, the final contribution of this thesis is a practical unsupervised algorithm for modelling images from the super-pixel to album levels, and is applicable to large datasets