The Unmanned Aerial Vehicle (UAV) has over the last years become an increasingly prevalent technology in several sectors of modern society. Many UAVs are today used in a wide series of applications, from disaster relief to surveillance. A recent initiative by the Swedish Sea Rescue Society (SSRS) aims to implement UAVs in their emergency response. By quickly deploying drones to an area of interest, an assessment can be made, prior to personnel getting there, thus saving time and increasing the likelihood of a successful rescue operation. An aircraft like this, that will travel great distances, have to rely on a navigation system that does not require an operator to continuously see the vehicle. To travel to its goal, or search an area, the operator should be able to define a travel route that the UAV follows, by feeding it a series of waypoints. As an initial step towards that kind of system, this thesis has developed and tested the concept of waypoint navigation on a small and slow airship/blimp, in a simulated indoor environment. Mainly, two commonly used navigation algorithms were tested and compared. One is inspired by a sub-category of machine learning: reinforcement learning (RL), and the other one is based on the rapidly exploring random tree (RRT) algorithm. Four experiments were conducted to compare the two methods in terms of travel distance, average speed, energy efficiency, as well as robustness towards changes in the waypoint configurations. Results show that when the blimp was controlled by the best performing RL-based version, it generally travelled a more optimal (distance-wise) path than the RRT-based method. It also, in most cases, proved to be more robust against changes in the test tracks, and performed more consistently over different waypoint configurations. However, the RRT approach usually resulted in a higher average speed and energy efficiency. Also, the RL algorithm had some trouble navigating tracks where a physical obstacle was present. To sum up, the choice of algorithm depends on which parameters are prioritized by the blimp operator for a certain track. If a high velocity and energy efficiency is desirable, the RRT-based method is recommended. However, if it is important that the blimp travels as short a distance as possible between waypoints, and a higher degree of consistency in its performance is wanted, then the RL-method should be used. Moving forward from this report, toward the future implementation of both methods in rescue operations, it would be reasonable to analyze their performance under more realistic conditions. This can be done using a real indoor airship. Looking at how hardware that do not exceed the payload of the blimp can execute both methods and how the blimp will determine its position and orientation is recommended. It would also be interesting to see how different reward function affect the performance of the blimp. / Den obemannade luftfarkosten (UAV) har under de senaste åren blivit en teknik vars användning blivit allt vanligare i flera sektorer av det moderna samhället. Olika sorters UAV robotar associeras idag med en omfattande serie användningsområden, från katastrofhjälp till övervakning. Ett nyligen påbörjat initiativ från svenska sjöräddningssällskapet (SSRS) syftar till att implementera drönare i deras utryckningar. Genom att snabbt sända drönare till platsen i fråga, kan en bedömning göras innan personal kommer dit, vilket sparar tid och ökar sannolikheten för en framgångsrik räddningsaktion. En farkost som denna, som kommer att resa långa sträckor, måste förlita sig på ett navigationssystem som inte kräver att en operatör kontinuerligt ser farkosten. För att resa till sitt mål, eller söka av ett område, bör operatören kunna definiera en resväg som drönaren följer genom att ge den en serie vägpunkter. Som ett inledande steg mot den typen av system har denna uppsats utvecklat och testat begreppet vägpunktsnavigering på ett litet och långsamt luftskepp/blimp, i en simulerad inomhusmiljö. Huvudsakligen testades och jämfördes två vanligt förekommande navigationsalgoritmer. En inspirerad av en underkategori till maskininlärning: förstärkningsinlärning (RL), och den andra baserad på rapidly exploring random tree (RRT) algoritmen. Fyra experiment utfördes för jämföra båda metoderna med avseende på färdsträcka, medelhastighet, energieffektivitet samt robusthet gentemot ändringar i färdpunktskonfigurationerna. Resultaten visar att när blimpen kontrollerades av den bästa RL-baserade versionen åkte den generellt en mer avståndsmässigt optimal väg än när den RRT-baserade metoden användes. I de flesta fallen visade sig även RL-metoden vara mer robust mot förändringar i testbanorna, och presterade mer konsekvent över olika vägpunktskonfigurationer. RRT-metoden resulterade dock vanligtvis i en högre medelhastighet och energieffektivitet. RL-algoritmen hade också problem med att navigera banor där den behövde ta sig runt ett hinder. Sammanfattningsvis beror valet av algoritm på vilka parametrar som prioriteras av blimpoperatören för en viss bana. Om en hög hastighet och energieffektivitet är önskvärd rekommenderas den RRT-baserade metoden. Men om det är viktigt att blimpen reser så kort avstånd som möjligt mellan färdpunkterna, och har en jämnare prestanda, bör RL-metoden användas. För att ta nästa steg, mot en framtida implementering av båda metoder i räddningsoperationer, vore det rimligt att analysera deras prestanda under mer realistiska förhållanden. Detta skulle kunna göras inomhus med ett riktigt luftskepp. Författarna rekommenderar att undersöka om hårdvara som inte överstiger blimpens maxlast kan utföra båda metodernas beräkningar och hur blimpen bestämmer sin position och orientering. Det skulle också vara intressant att se hur olika belöningsfunktioner påverkar blimpens prestanda.

Approved for public release, distribution is unlimited / This thesis is part of an ongoing research conducted at the Naval Postgraduate School to achieve the autonomous shipboard landing of Unmanned Aerial Vehicles (UAV). Two main problems are addressed in this thesis. The first is to establish communication between the UAV's ground station and the Autonomous Landing Flight Control Computer effectively. The second addresses the design and implementation of an autonomous landing controller using classical control techniques. Device drivers for the sensors and the communications protocol were developed in ANSI C. The overall system was implemented in a PC104 computer running a real-time operating system developed by The Mathworks, Inc. Computer and hardware in the loop (HIL) simulation, as well as ground test results show the feasibility of the algorithm proposed here. Flight tests are scheduled to be performed in the near future. / Lieutenant Junior Grade, Mexican Navy

Alors que l’utilisation d’un fauteuil roulant permet aux personnes en situation de handicap de compenser une perte de la mobilité, certaines personnes se voient privées de l’utilisation d’un fauteuil roulant électrique. En effet, la présence de troubles cognitifs ou de la perception visuelle altère la capacité à conduire sans danger. Dans ce contexte, l’accès à la mobilité peut être amélioré par l’apport d’aides techniques adaptées permettant de compenser la perte de mobilité dans tous types d’environnements. Alors que les premiers travaux sur les fauteuils roulants intelligents datent du début des années 80, aucune solution n’est à ce jour sur le marché ou dans les centres de rééducation. Ce travail vise à proposer un ensemble de solutions d’aide à la conduite de fauteuil roulant électrique conçu en collaboration. Le développement de telles aides techniques constitue de multiples défis robotiques mêlant techniques de détection innovantes et méthodes de contrôle partagé avec l’utilisateur. Dans ce travail, un simulateur de conduite visant à appuyer la recherche et le développement de nouvelles solutions robotiques est proposé. Puis des solutions bas coût d’assistance semiautonome à la conduite en intérieur et en extérieur sont détaillées. L’évaluation avec des participants sains nous permet de valider les méthodes mathématiques mises en oeuvre et de fournir des preuves de concept des solutions proposées. Enfin, les premières évaluations cliniques avec des usagers au Pôle MPR Saint Hélier montrent la validation de de la méthode proposée en termes de satisfaction des utilisateurs. / While the use of a wheelchair allows people with disabilities to compensate for a loss of mobility, people with severe disabilities are denied the use of a power wheelchair. Indeed, cognitive or visual perception impairments can affect the ability to drive safely. In this context, access to mobility can be improved by providing appropriate assistive technologies to compensate for loss of mobility in all types of environments. While the first research on smart wheelchairs dates back to the early 1980s, no solutions have yet been proposed on the market or in rehabilitation centers and other specialized structures. This work aims to propose a set of solutions for power wheelchair navigation assistance designed in close collaboration with users and therapists. The development of such assistive solutions faces multiple robotic challenges combining innovative detection techniques, shared control with the user. In this work, a driving simulator supporting research and development of new robotic solutions for wheelchair navigation assistance is proposed. Then low-cost semi-autonomous assistance solutions for navigation assistance in indoor and outdoor environments are detailed. The evaluation with able-bodied participants allows to validate the mathematical methods and provide proof of concept of the proposed solutions. Finally, the first clinical evaluations with regular users at Pôle MPR Saint Hélier show the validation of the proposed framework in terms of user satisfaction.

In this research work, we develop an obstacle detection and emergency exit sign recognition system on a mobile phone by extending the feature from accelerated segment test detector with Harris corner filter. The first step often required for many vision based applications is the detection of objects of interest in an image. Hence, in this research work, we introduce emergency exit sign detection method using colour histogram. The hue and saturation component of an HSV colour model are processed into features to build a 2D colour histogram. We backproject a 2D colour histogram to detect emergency exit sign from a captured image as the first task required before performing emergency exit sign recognition. The result of classification shows that the 2D histogram is fast and can discriminate between objects and background with accuracy. One of the challenges confronting object recognition methods is the type of image feature to compute. In this work therefore, we present two feature detectors and descriptor methods based on the feature from accelerated segment test detector with Harris corner filter. The first method is called Upright FAST-Harris and binary detector (U-FaHB), while the second method Scale Interpolated FAST-Harris and Binary (SIFaHB). In both methods, feature points are extracted using the accelerated segment test detectors and Harris filter to return the strongest corner points as features. However, in the case of SIFaHB, the extraction of feature points is done across the image plane and along the scale-space. The modular design of these detectors allows for the integration of descriptors of any kind. Therefore, we combine these detectors with binary test descriptor like BRIEF to compute feature regions. These detectors and the combined descriptor are evaluated using different images observed under various geometric and photometric transformations and the performance is compared with other detectors and descriptors. The results obtained show that our proposed feature detector and descriptor method is fast and performs better compared with other methods like SIFT, SURF, ORB, BRISK, CenSurE. Based on the potential of U-FaHB detector and descriptor, we extended it for use in optical flow computation, which we termed the Nearest-flow method. This method has the potential of computing flow vectors for use in obstacle detection. Just like any other new methods, we evaluated the Nearest flow method using real and synthetic image sequences. We compare the performance of the Nearest-flow with other methods like the Lucas and Kanade, Farneback and SIFT-flow. The results obtained show that our Nearest-flow method is faster to compute and performs better on real scene images compared with the other methods. In the final part of this research, we demonstrate the application potential of our proposed methods by developing an obstacle detection and exit sign recognition system on a camera phone and the result obtained shows that the methods have the potential to solve this vision based object detection and recognition problem.

Ce mémoire décrit mes travaux de thèse de doctorat menés au sein de l’équipe ComSee (Computers that See) rattachée à l’axe ISPR (Image, Systèmes de Perception et Robotique) de l’Institut Pascal. Celle-ci a été financée par la Région Auvergne et le Fonds Européen de Développement Régional. Les travaux présentés s’inscrivent dans le cadre d’applications de localisation pour la robotique mobile et la Réalité Augmentée. Le framework réalisé au cours de cette thèse est une approche générique pour l’implémentation d’applications de SLAM : Simultaneous Localization And Mapping (algorithme de localisation par rapport à un modèle simultanément reconstruit). L’approche intègre une multitude de contraintes dans les processus de localisation et de reconstruction. Ces contraintes proviennent de données capteurs mais également d’a priori liés au contexte applicatif. Chaque contrainte est utilisée au sein d’un même algorithme d’optimisation afin d’améliorer l’estimation du mouvement ainsi que la précision du modèle reconstruit. Trois problèmes ont été abordés au cours de ce travail. Le premier concerne l’utilisation de contraintes sur le modèle reconstruit pour l’estimation précise d’objets 3D partiellement connus et présents dans l’environnement. La seconde problématique traite de la fusion de données multi-capteurs, donc hétérogènes et asynchrones, en utilisant un unique algorithme d’optimisation. La dernière problématique concerne la génération automatique et efficace d’algorithmes d’optimisation à contraintes multiples. L’objectif est de proposer une solution temps réel 1 aux problèmes de SLAM à contraintes multiples. Une approche générique est utilisée pour concevoir le framework afin de gérer une multitude de configurations liées aux différentes contraintes des problèmes de SLAM. Un intérêt tout particulier a été porté à la faible consommation de ressources (mémoire et CPU) tout en conservant une grande portabilité. De plus, la méta-programmation est utilisée pour générer automatiquement et spécifiquement les parties les plus complexes du code en fonction du problème à résoudre. La bibliothèque d’optimisation LMA qui a été développée au cours de cette thèse est mise à disposition de la communauté en open-source. Des expérimentations sont présentées à la fois sur des données de synthèse et des données réelles. Un comparatif exhaustif met en évidence les performances de la bibliothèque LMA face aux alternatives les plus utilisées de l’état de l’art. De plus, le framework de SLAM est utilisé sur des problèmes impliquant une difficulté et une quantité de contraintes croissantes. Les applications de robotique mobile et de Réalité Augmentée mettent en évidence des performances temps réel et un niveau de précision qui croît avec le nombre de contraintes utilisées. / This report describes my thesis work conducted within the ComSee (Computers That See) team related to the ISPR axis (ImageS, Perception Systems and Robotics) of Institut Pascal. It was financed by the Auvergne Région and the European Fund of Regional Development. The thesis was motivated by localization issues related to Augmented Reality and autonomous navigation. The framework developed during this thesis is a generic approach to implement SLAM algorithms : Simultaneous Localization And Mapping. The proposed approach use multiple constraints in the localization and mapping processes. Those constraints come from sensors data and also from knowledge given by the application context. Each constraint is used into one optimization algorithm in order to improve the estimation of the motion and the accuracy of the map. Three problems have been tackled. The first deals with constraints on the map to accurately estimate the pose of 3D objects partially known in the environment. The second problem is about merging multiple heterogeneous and asynchronous data coming from different sensors using an optimization algorithm. The last problem is to write an efficient and real-time implementation of the SLAM problem using multiple constraints. A generic approach is used to design the framework and to generate different configurations, according to the constraints, of each SLAM problem. A particular interest has been put in the low computational requirement (in term of memory and CPU) while offering a high portability. Moreover, meta-programming techniques have been used to automatically and specifically generate the more complex parts of the code according to the given problem. The optimization library LMA, developed during this thesis, is made available of the community in open-source. Several experiments were done on synthesis and real data. An exhaustive benchmark shows the performances of the LMA library compared to the most used alternatives of the state of the art. Moreover, the SLAM framework is used on different problems with an increasing difficulty and amount of constraints. Augmented Reality and autonomous navigation applications show the good performances and accuracies in multiple constraints context.

Les robots modernes sont appelés à effectuer des opérations ou tâches complexes et la capacité de navigation autonome dans un environnement dynamique est un besoin essentiel pour les robots mobiles. Dans l’objectif de soulager de la fastidieuse tâche de préprogrammer un robot manuellement, cette thèse contribue à la conception de commande intelligente afin de réaliser l’apprentissage des robots mobiles durant la navigation autonome. D’abord, nous considérons l’apprentissage des robots via des démonstrations d’experts. Nous proposons d’utiliser un réseau de neurones pour apprendre hors-ligne une politique de commande à partir de données utiles extraites d’expertises. Ensuite, nous nous intéressons à l’apprentissage sans démonstrations d’experts. Nous utilisons l’apprentissage par renforcement afin que le robot puisse optimiser une stratégie de commande pendant le processus d’interaction avec l’environnement inconnu. Un réseau de neurones est également incorporé et une généralisation rapide permet à l’apprentissage de converger en un certain nombre d’épisodes inférieur à la littérature. Enfin, nous étudions l’apprentissage par fonction de récompenses potentielles compte rendu des démonstrations d’experts optimaux ou non-optimaux. Nous proposons un algorithme basé sur l’apprentissage inverse par renforcement. Une représentation non-linéaire de la politique est désignée et la méthode du max-margin est appliquée permettant d’affiner les récompenses et de générer la politique de commande. Les trois méthodes proposées sont évaluées sur des robots mobiles afin de leurs permettre d’acquérir les compétences de navigation autonome dans des environnements dynamiques et inconnus / Modern robots are designed for assisting or replacing human beings to perform complicated planning and control operations, and the capability of autonomous navigation in a dynamic environment is an essential requirement for mobile robots. In order to alleviate the tedious task of manually programming a robot, this dissertation contributes to the design of intelligent robot control to endow mobile robots with a learning ability in autonomous navigation tasks. First, we consider the robot learning from expert demonstrations. A neural network framework is proposed as the inference mechanism to learn a policy offline from the dataset extracted from experts. Then we are interested in the robot self-learning ability without expert demonstrations. We apply reinforcement learning techniques to acquire and optimize a control strategy during the interaction process between the learning robot and the unknown environment. A neural network is also incorporated to allow a fast generalization, and it helps the learning to converge in a number of episodes that is greatly smaller than the traditional methods. Finally, we study the robot learning of the potential rewards underneath the states from optimal or suboptimal expert demonstrations. We propose an algorithm based on inverse reinforcement learning. A nonlinear policy representation is designed and the max-margin method is applied to refine the rewards and generate an optimal control policy. The three proposed methods have been successfully implemented on the autonomous navigation tasks for mobile robots in unknown and dynamic environments.

Autonomous navigation is the determination of satellites position and velocity vectors onboard the satellite, using the measurements available onboard. The orbital information of a satellite needs to be obtained to support different house keeping operations such as routine tracking for health monitoring, payload data processing and annotation, orbit manoeuver planning, and prediction of intrusion in various sensors' field of view by celestial bodies like Sun, Moon etc. Determination of the satellites orbital parameters is done in a number of ways using a variety of measurements. These measurements may originate from ground based systems as range and range rate measurements, or from another satellite as in the case of GPS (Global Positioning System) and TDUSS (Tracking Data Relay Satellite Systems), or from the same satellite by using sensors like horizon sensor^ sun sensor, star tracker, landmark tracker etc. Depending upon the measurement errors, sampling rates, and adequacy of the estimation scheme, the navigation accuracy can be anywhere in the range of 10m - 10 kms in absolute location. A wide variety of tracking sensors have been proposed in the literature for autonomous navigation. They are broadly classified as (1) Satellite-satellite tracking, (2) Ground- satellite tracking, (3) fully autonomous tracking. Of the various navigation sensors, it may be cost effective to use existing onboard sensors which are well proven in space. Hence, in the current thesis, the Horizon scanner is employed as the primary navigation sensor-. It has been shown in the literature that by using horizon sensors and gyros, a high accuracy pointing of the order of .01 - .03 deg can be achieved in the case of low earth orbits. Motivated by such a fact, the current thesis deals with autonomous orbit determination using measurements from the horizon sensors with the assumption that the attitude is known to the above quoted accuracies. The horizon scanners are mounted on either side of the yaw axis in the pitch yaw plane at an angle of 70 deg with respect to the yaw axis. The Field Of View (FOV) moves about the scanner axis on a cone of 45 deg half cone angle. During each scan, the FOV generates two horizon points, one at the space-Earth entry and the other at the Earth-space exit. The horizon points, therefore, lie• on the edge of the Earth disc seen by the satellite. For a spherical earth, a minimum of three such horizon points are needed to estimate the angular radius and the center of the circular horizon disc. Since a total of four horizon points are available from a pair of scanners, they can be used to extract the satellite-earth distance and direction.These horizon points are corrupted by noise due to uncertainties in the Earth's radiation pattern, detector mechanism, the truncation and roundoff errors due to digitisation of the measurements. Owing to the finite spin rate of the scanning mechanism, the measurements are available at discrete time intervals. Thus a filtering algorithm with appropriate state dynamics becomes essential to handle the •noise in the measurements, to obtain the best estimate and to propagate the state between the measurements. The orbit of a low earth satellite can be represented by either a state vector (position and velocity vectors in inertial frame) or Keplerian elements. The choice depends upon the available processors, functions and the end use of the estimated orbit information. It is shown in the thesis that position and velocity vectors in inertial frame or the position vector in local reference frame, do result in a simplified, state representation. By using the f and g series method for inertial position and velocity, the state propagation is achieved in linear form. i.e. Xk+1 = AXK where X is the state (position, velocity) and A the state transition matrix derived from 'f' and 'g' series. The configuration of a 3 axis stabilised spacecraft with two horizon scanners is used to simulate the measurements. As a step towards establishing the feasibility of extracting the orbital parameters, the governing equations are formulated to compute the satellite-earth vector from the four horizon points generated by a pair of Horizon Scanners in the presence of measurement noise. Using these derived satellite-earth vectors as measurements, Kalman filter equations are developed, where both the state and measurements equations are linear. Based on simulations, it is shown that a position accuracy of about 2 kms can be achieved. Additionally, the effect of sudden disturbances like substantial slewing of the solar panels prior and after the payload operations are also analysed. It is shown that a relatively simple Low Pass Filter (LPF) in the measurements loop with a cut-off frequency of 10 Wo (Wo = orbital frequency) effectively suppresses the high frequency effects from sudden disturbances which otherwise camouflage the navigational information content of the signal. Then Kalman filter can continue to estimate the orbit with the same kind of accuracy as before without recourse to re-tuning of covariance matrices. Having established the feasibility of extracting the orbit information, the next step is to treat the measurements in its original form, namely, the non-linear form. The entry or exit timing pulses generated by the scanner when multiplied by the scan rate yield entry or exit azimuth angles in the scanner frame of reference, which in turn represents an effective measurement variable. These azimuth angles are obtained as inverse trigonometric functions of the satellite-earth vector. Thus the horizon scanner measurements are non-linear functions of the orbital state. The analytical equations for the horizon points as seen in the body frame are derived, first for a spherical earth case. To account for the oblate shape of the earth, a simple one step correction algorithm is developed to calculate the horizon points. The horizon points calculated from this simple algorithm matches well with the ones from accurate model within a bound of 5%. Since the horizon points (measurements) are non-linear functions of the state, an Extended Kalman Filter (EKF) is employed for state estimation. Through various simulation runs, it is observed that the along track state has got poor observability when the four horizon points are treated as measurements in their original form, as against the derived satellite-earth vector in the earlier strategy. This is also substantiated by means of condition number of the observability matrix. In order to examine this problem in detail, the observability of the three modes such as along-track, radial, and cross-track components (i.e. the local orbit frame of reference) are analysed. This difficulty in observability is obviated when an additional sensor is used in the roll-yaw plane. Subsequently the simulation studies are carried out with two scanners in pitch-yaw plane and one scanner in the roll-yaw plane (ie. a total of 6 horizon points at each time). Based on the simulations, it is shown that the achievable accuracy in absolute position is about 2 kms.- Since the scanner in the roll-yaw plane is susceptible to dazzling by Sun, the effect of data breaks due to sensor inhibition is also analysed. It is further established that such data breaks do not improve the accuracy of the estimates of the along-track component during the transient phase. However, filter does not diverge during this period. Following the analysis of the' filter performance, influence of Earth's oblateness on the measurement model studied. It is observed that the error in horizon points, due to spherical Earth approximation behave like a sinusoid of twice the orbital frequency alongwith a bias of about 0.21° in the case of a 900 kms sun synchronous orbit. The error in the 6 horizon points is shown to give rise to 6 sinusoids. Since the measurement model for a spherical earth is the simplest one, the feasibility of estimating these sinusoids along with the orbital state forms the next part of the thesis. Each sinusoid along with the bias is represented as a 3 state recursive equation in the following form where i refers to the ith sinusoid and T the sampling interval. The augmented or composite state variable X consists of bias, Sine and Cosine components of the sinusoids. The 6 sinusoids together with the three dimensional orbital position vector in local coordinate frame then lead to a 21 state augmented Kalman Filter. With the 21 state filter, observability problems are experienced. Hence the magnetic field strength, which is a function of radial distance as measured by an onboard magnetometer is proposed as additional measurement. Subsequently, on using 6 horizon point measurements and the radial distance measurements obtained from a magnetometer and taking advantage of relationships between sinusoids, it is shown that a ten state filter (ie. 3 local orbital states, one bias and 3 zero mean sinusoids) can effectively function as an onboard orbit filter. The filter performance is investigated for circular as well as low eccentricity orbits. The 10-state filter is shown to exhibit a lag while following the radial component in case of low eccentricity orbits. This deficiency is overcome by introducing two more states, namely the radial velocity and acceleration thus resulting in a 12-state filter. Simulation studies reveal that the 12-state filter performance is very good for low eccentricity orbits. The lag observed in 10-state filter is totally removed. Besides, the 12-state filter is able to follow the changes in orbit due to orbital manoeuvers which are part of orbit acquisition plans for any mission.

Orientadores: Fernando Jose Von Zuben, Mauricio Fernandes Figueiredo / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-06T19:35:31Z (GMT). No. of bitstreams: 1 Michelan_Roberto_M.pdf: 4495515 bytes, checksum: aed72feefc89070579190e862ea0f740 (MD5) Previous issue date: 2006 / Resumo: A concepção de sistemas autônomos de navegação para robôs móveis, havendo múltiplos objetivos simultâneos a serem atendidos, como a coleta de lixo com manutenção da integridade, requer a adoção de técnicas refinadas de coordenação de módulos de comportamento elementar. Modelos de redes imunológicas artificiais podem então ser empregados na proposição de um controlador concebido com base em um processo de mapeamento dinâmico. Os anticorpos da rede são responsáveis pelos módulos de comportamento elementar, na forma de regras do tipo <condição>-<ação>, e as conexões são responsáveis pelos mecanismos de estímulo e supressão entre os anticorpos. A rede iniciará uma resposta imunológica sempre que lhe forem apresentados os antígenos. Estes antígenos representam a situação atual capturada pelos sensores do robô. A dinâmica da rede é baseada no nível de concentração dos anticorpos, definida com base na interação dos anticorpos e dos anticorpos com os antígenos. De acordo com o nível de concentração, um anticorpo é escolhido para definir a ação do robô. Um processo evolutivo é então responsável por definir um padrão de conexões para a rede imunológica, a partir de uma população de redes candidatas, capaz de maximizar o atendimento dos objetivos durante a navegação. Resulta então um sistema híbrido que tem a rede imunológica como responsável por introduzir um processo dinâmico de tomada de decisão e tem agora a computação evolutiva como responsável por definir a estrutura da rede. Para que fosse possível avaliar os controladores (redes imunológicas) a cada geração do processo evolutivo, um ambiente virtual foi desenvolvido para simulação computacional, com base nas características do problema de navegação. As redes imunológicas obtidas através do processo evolutivo foram analisadas e testadas em novas situações, apresentando capacidade de coordenação em tarefas simples e complexas. Os experimentos preliminares com um robô real do tipo Khepera II indicaram a eficácia da ferramenta de navegação / Abstract: The design of an autonomous navigation system for mobile robots, with simultaneous objectives to be satisfied, as garbage collection with maintenance of integrity, requires refined coordination mechanisms to deal with modules of elementary behavior. Models of artificial immune networks can then be applied to produce a controller based on dynamic mapping. The antibodies of the immune network are responsible for the modules of elementary behavior, in the form of <condition>-<action> rules, and the connections are responsible for the mechanisms of stimulation and suppression of antibodies. The network will always start an immune response when antigens are presented. These antigens represent the current output of the robot sensors. The network dynamics is based on the levels of antibody concentration, provided by interaction among antibodies, and among antibodies and antigens. Based on its concentration level, an antibody is chosen to define the robot action. An evolutionary process is then used to define the connection pattern of the immune network, from a population of candidate networks, capable of maximizing the objectives during navigation. As a consequence, a hybrid system is conceived, with an immune network implementing a dynamic process of decision-making, and an evolutionary algorithm defining the network structure. To be able to evaluate the controllers (immune networks) at each iteration of the evolutionary process, a virtual environment was developed for computer simulation, based on the characteristics of the navigation problem. The immune networks obtained by evolution were analyzed and tested in new situations and presented coordination capability in simple and complex tasks. The preliminary experiments on a real Khepera II robot indicated the efficacy of the navigation tool / Mestrado / Engenharia de Computação / Mestre em Engenharia Elétrica

L'objectif de cette thèse est de réaliser des missions diverses de navigation autonome en environnement intérieur et encombré avec des robots terrestres. La perception de l'environnement est assurée par un banc stéréo embarqué sur le robot et permet entre autres de calculer la localisation de l'engin grâce à un algorithme d'odométrie visuelle. Mais quand la qualité de la scène perçue par les caméras est faible, la localisation visuelle ne peut pas être calculée de façon précise. Deux solutions sont proposées pour remédier à ce problème. La première solution est l'utilisation d'une méthode de fusion de données multi-capteurs pour obtenir un calcul robuste de la localisation. La deuxième solution est la prédiction de la qualité de scène future afin d'adapter la trajectoire du robot pour s'assurer que la localisation reste précise. Dans les deux cas, la boucle de commande est basée sur l'utilisation de la commande prédictive afin de prendre en compte les différents objectifs de la mission : ralliement de points, exploration, évitement d'obstacles. Une deuxième problématique étudiée est la navigation par points de passage avec évitement d'obstacles mobiles à partir des informations visuelles uniquement. Les obstacles mobiles sont détectés dans les images puis leur position et vitesse sont estimées afin de prédire leur trajectoire future et ainsi de pouvoir anticiper leur déplacement dans la stratégie de commande. De nombreuses expériences ont été réalisées en situation réelle et ont permis de montrer l'efficacité des solutions proposées. / The aim of this thesis is to perform various autonomous navigation missions in indoor and cluttered environments with mobile robots. The environment perception is ensured by an embedded stereo-rig and a visual odometry algorithm which computes the localization of the robot. However, when the quality of the scene perceived by the cameras is poor, the visual localization cannot be computed with a high precision. Two solutions are proposed to tackle this problem. The first one is the data fusion from multiple sensors to perform a robust computation of the localization. The second solution is the prediction of the future scene quality in order to adapt the robot's trajectory to ensure that the localization remains accurate. In the two cases, the control loop is based on model predictive control, which offers the possibility to consider simultaneously the different objectives of the mission : waypoint navigation, exploration, obstacle avoidance. A second issue studied is waypoint navigation with avoidance of mobile obstacles using only the visual information. The mobile obstacles are detected in the images and their position and velocity are estimated in order to predict their future trajectory and consider it in the control strategy. Numerous experiments were carried out and demonstrated the effectiveness of the proposed solutions.

In this thesis I deal with the problem of navigation and autonomous driving using convolutional neural networks. I focus on the main approaches utilizing sensory inputs described in literature and the theory of neural networks, imitation and reinforcement learning. I also discuss the tools and methods applicable to driving systems. I created two deep learning models for autonomous driving in simulated environment. These models use the Dataset Aggregation and Deep Deterministic Policy Gradient algorithms. I tested the created models in the TORCS car racing simulator and compared the result with available sources.