This thesis is concerned with the task of autonomous selection of objects to remove (unload) them from a pile in robotic manipulation systems. Applications such as the automation of logistics processes and service robots require an ability to autonomously manipulate objects in the environment. A collapse of a pile of objects due to an inappropriate choice of the object to be removed from the pile cannot be afforded for an autonomous robotic manipulation system. This dissertation presents an indepth analysis of the problem and proposes methods and algorithms to empower robotic manipulation systems to select a safe object from a pile elaborately and autonomously. The contributions presented in this thesis are three-fold. First, a set of algorithms is proposed for extracting a minimal set of high level symbolic relations, namely, gravitational act and support relations, of physical interactions between objects composing a pile. The symbolic relations, extracted by a geometrical reasoning method and a static equilibrium analysis can be readily used by AI paradigms to analyze the stability of a pile and reason about the safest set of objects to be removed. Considering the problem of undetected objects and the uncertainty in the estimated poses as they exist in realistic perception systems, a probabilistic approach is proposed to extract the support relations and to make a probabilistic decision about the set of safest objects using notions from machine learning and decision theory. Second, an efficient search based algorithm is proposed in an internal representation to automatically resolve the inter-penetrations between the shapes of objects due to errors in the poses estimated by an existing object detection module. Refining the poses by resolving the inter-penetrations results in a geometrically consistent model of the environment, and was found to reduce the overall pose error of the objects. This dissertation presents the concept of minimum translation search for object pose refinement and discusses a discrete search paradigm based on the concept of depth of penetration between two polyhedrons. Third, an application centric evaluation of ranging sensors for selecting a set of appropriate sensors for the task of object detection in the design process of a real-world robotics manipulation system is presented. The performance of the proposed algorithms are tested on data sets generated in simulation and from real-world scenarios.

Cette thèse, réalisée en coopération avec l'Institut Pascal et Renault, s'inscrit dans le domaine des applications d'aide à la conduite, la plupart de ces systèmes visant à améliorer la sécurité des passagers du véhicule. La fusion de différents capteurs permet de rendre plus fiable la prise de décision. L'objectif des travaux de cette thèse a été de développer un système de fusion entre un radar et une caméra intelligente pour la détection des obstacles frontaux au véhicule. Nous avons proposé une architecture modulaire de fusion temps réel utilisant des données asynchrones provenant des capteurs sans a priori applicatif. Notre système de fusion de capteurs est basé sur des méthodes de suivi de plusieurs cibles. Des méthodes probabilistes de suivi de cibles ont été envisagées et une méthode particulière, basée sur la modélisation des obstacles par un ensemble fini de variables aléatoires a été choisie et testée en temps réel. Cette méthode, appelée CPHD (Cardinalized Probability Hypothesis Density) permet de gérer les différents défauts des capteurs (non détections, fausses alarmes, imprécision de positions et de vitesses mesurées) et les incertitudes liées à l’environnement (nombre inconnu d'obstacles à détecter). Ce système a été amélioré par la gestion de différents types d'obstacles : piéton, voiture, camion, vélo. Nous avons proposé aussi une méthode permettant de résoudre le problème des occultations avec une caméra de manière explicite par une méthode probabiliste en prenant en compte les imprécisions de ce capteur. L'utilisation de capteurs intelligents a introduit un problème de corrélation des mesures (dues à un prétraitement des données) que nous avons réussi à gérer grâce à une analyse de l'estimation des performances de détection de ces capteurs. Afin de compléter ce système de fusion, nous avons mis en place un outil permettant de déterminer rapidement les paramètres de fusion à utiliser pour les différents capteurs. Notre système a été testé en situation réelle lors de nombreuses expérimentations. Nous avons ainsi validé chacune des contributions de manière qualitative et quantitative. / This PhD work, carried out in collaboration with Institut Pascal and Renault, is in the field of the Advanced Driving Assisted Systems, most of these systems aiming to improve passenger security. Sensors fusion makes the system decision more reliable. The goal of this PhD work was to develop a fusion system between a radar and a smart camera, improving obstacles detection in front of the vehicle. Our approach proposes a real-time flexible fusion architecture system using asynchronous data from the sensors without any prior knowledge about the application. Our fusion system is based on a multi targets tracking method. Probabilistic multi target tracking was considered, and one based on random finite sets (modelling targets) was selected and tested in real-time computation. The filter, named CPHD (Cardinalized Probability Hypothesis Density), succeed in taking into account and correcting all sensor defaults (non detections, false alarms and imprecision on position and speed estimated by sensors) and uncertainty about the environment (unknown number of targets). This system was improved by introducing the management of the type of the target: pedestrian, car, truck and bicycle. A new system was proposed, solving explicitly camera occlusions issues by a probabilistic method taking into account this sensor imprecision. Smart sensors use induces data correlation (due to pre-processed data). This issue was solved by correcting the estimation of sensor detection performance. A new tool was set up to complete fusion system: it allows the estimation of all sensors parameters used by fusion filter. Our system was tested in real situations with several experimentations. Every contribution was qualitatively and quantitatively validated.

To better manage deteriorating infrastructure, quantitative data about the performance of infrastructure assets is required. Rayleigh based distributed fibre optic strain sensing (FOS) is a technology that has the potential to offer this type of data and unlike traditional strain sensors it can measure the strain along the full length of the structure. A series of experiments were undertaken to develop installation techniques and evaluate sensor accuracy for typical civil engineering materials: steel, concrete and reinforced concrete. The results of these experiments showed that the choice of sensing fibre and adhesive was dependent on the material being monitored. When the sensing fibre and adhesive are chosen correctly, the Rayleigh system can provide the same accuracy as a strain gauge for steel and concrete, and useful measurements can be obtained even in areas of concrete cracking. The FOS technique was utilized to determine whether distributed strain measurements could be used to detect and quantify localized deterioration of the steel reinforcement (localized area reductions of 0-30%) at service loads. A series of specimens was tested, the sensing system was able to detect the presence of localized deterioration with embedded nylon and polyimide fibres, but the nylon fibre cannot quantify large strain gradients due to slip within the sensing fibre. The strain profiles gave insights to the failure mechanism occurring in the reinforced concrete specimens. The strain profiles for both test series indicated that the tension reinforcement was acting as a tension tie and the strain profiles suggested the presence of compressive struts indicative of an arching mechanism in the specimens. The Black River bridge in Madoc, Ontario was instrumented with fibre optics sensors to determine whether the use of FOS is both practical and beneficial for reinforced concrete bridge assessment when compared to conventional instrumentation. The FOS showed reasonably good agreement with conventional sensors. The fibre optic strain results are used to calculate curvature, slope and displacement but careful consideration of the boundary conditions is required. The results from the fibre optic sensors can be used to show the bridge load distribution and give insights into the support conditions of the beams. / Thesis (Master, Civil Engineering) -- Queen's University, 2013-08-21 11:56:53.276