Malgré le franc succès des techniques de planification de mouvement au cours de ces deux dernières décennies, leur adaptation à des scènes comprenant à la fois des obstacles statiques et des obstacles mobiles s'est avérée limitée jusqu'ici. Une des raisons en est le coût associé à la mise à jour des structures de données précalculées afin de capturer la connexité de l'espace libre. Notre contribution principale concerne la proposition d'un nouveau planificateur capable de traiter ces problèmes d'environnements partiellement dynamiques composés à la fois d'obstacles statiques et d'obstacles mobiles.

Fundação de Apoio a Pesquisa e à Inovação Tecnológica do Estado de Sergipe - FAPITEC/SE / The evolution of mobile robotics has directed research in this area to solve increasingly complex tasks. In these tasks, when optimized behaviors are specified, a deliberative process is required in order to determine the best action before executing it. In navigation architectures, the deliberation process is usually accomplished by a motion planning strategy. One of the motion planning techniques which has received much of the attention from the researches is the Rapidly-exploring Random Tree (RRT), because of its capacity to reduce representation dimension quickly. The vast majority of the research developed in this area, so far, is mainly focused on developing variants of the RRT for specific problems, not providing detailed analyzes regarding the influence of different variables in the classical algorithm. In this master’s work the focus is precisely to fill this gap by investigating the influence of different variables that compose the classic RRT algorithm, in other words, a detailed analysis of the RRT degrees of freedom and its influence on the final result. In addition, unlike most RRT papers, where the objective is to find the best path between two points, this dissertation presents a new approach in RRT searches by combining the search for a compact and complete representation of the configuration space with a low computational cost and knowledge of only the robot’s goal configuration. To validate and analyze the results obtained, tests by simulation are performed. / A evolução na área de robótica móvel tem direcionado as pesquisas nesse campo para a solução de tarefas cada vez mais complexas. Nessas tarefas, quando comportamentos otimizados são especificados, faz-se necessário um processo de deliberação para determinar a melhor ação a ser tomada antes de executá-la. Em arquiteturas de navegação, o processo de deliberação é normalmente realizado por uma estratégia de planejamento de movimento. Uma das técnicas de planejamento de movimento que tem recebido grande parte da atenção dos pesquisadores dessa área nos últimos tempos é a Rapidly-exploring Random Tree (RRT), pela sua capacidade de reduzir a dimensão da representação de forma rápida. A maioria dos trabalhos de pesquisa desenvolvidos utilizando RRT, até o momento, tem como foco principal desenvolver variantes dessa técnica para problemas específicos, sem apresentar análises aprofundadas quanto a influência das diferentes variáveis do algoritmo clássico. Neste trabalho de mestrado o foco é, justamente, suprir essa carência, investigando a influência das diferentes variáveis que compõem o algoritmo clássico da RRT, ou seja, uma análise detalhada dos graus de liberdade da RRT e suas influências no resultado final. Além disso, diferentemente da maioria dos trabalhos em RRT, em que o objetivo é encontrar o melhor caminho entre dois pontos, esta dissertação apresenta uma nova abordagem nas pesquisas em RRT ao combinar a busca por uma representação compacta e completa do espaço de configuração com um baixo custo computacional e com o conhecimento a priori apenas da configuração de destino do robô. Para validar e analisar os resultados obtidos, testes por simulação são realizados.

End stage renal disease (ESRD) patients have an excess cardiovascular risk, above that predicted by traditional risk factor models. Despite the advances in both Cardiovascular disease (CVD) management and renal replacement therapy (RRT), there still is a major burden of cardiovascular mortality and morbidity in the chronic kidney disease (CKD) population. Declining renal function itself represents a continuum of cardiovascular risk and in those individuals who survive to reach ESRD, the risk of suffering a cardiac event is uncomfortably and unacceptably high. Pro-thrombotic status may contribute to this increased risk. Global thrombotic status assessment, including measurement of occlusion time (OT) the time taken to form an occlusive platelet rich thrombus and thrombolytic status (time taken to lyse such thrombus) as assessed by measuring Lysis Time (LT), may identify vulnerable patients. The aim of this study was to assess overall thrombotic status in ESRD and relate this to cardiovascular and peripheral thrombotic risk. Small sub studies were also planned to establish the effect of RRT modality on the thrombotic status.

Les nanostructures périodiques induites par faisceau laser ont stimulé de nombreuses recherches en raison de leurs applications dans les domaines des technologies micros et nanométriques, telles que la lithographie, la mise en mémoire des données à haute densité, les systèmes nano et micro-électromécaniques (NEMS/MEMS). La dynamique de leur formation sur la surface des couches minces de cuivre (CMC) déposées sur les substrats de silicium et de verre est étudiée dans ce travail. Cette analyse est réalisée en utilisant deux approches de caractérisation : ex situ pour les analyses Microscopie Electronique à Balayage (MEB) et en transmission (MET), Microscopie à Force Atomique (AFM) et in situ pour les signaux de Réflectométrie en Temps Réel (RRT). Les processus de changement d’état (fusion, ablation, décollement…) et des modifications de la morphologie de surface à l’échelle nanométrique sont étudies en variant un nombre de paramètres clés, à savoir : le dose énergétique (la fluence et le nombre de tirs laser), l’épaisseur des CMC et la nature de substrat en régime d’interaction picoseconde et nanoseconde. En effet, les nanostructures avec une période spatiale de 266 nm (proche de la longueur d’onde laser (λ)), 130 nm (λ/2) et 60 nm (λ/4) sont obtenues. Ces différentes nanostructures périodiques ont été rassemblées dans des cartographies 2D et corrélés à la dose énergétique (fluence et nombre de tirs). Enfin, une tentative d’interprétation des mécanismes de formation des nanostructures périodiques sur les CMC générées en régime laser picoseconde, établie sur la base de nos données expérimentales, semble pertinente avec la théorie d’auto-organisation, notamment pour des nombres de tirs laser importants. / Periodic surface nanostructures induced by laser have attracted particular attention because of their applications in the domain of micro and nanotechnologies such as lithography, high density data storage, nano- and micro-electromechanical systems (NEMS/MEMS). The dynamic of their formation on the surface of copper thin film deposited on silicon and glass substrates was investigated in this present work. Two methods are used in this analysis: ex situ analyses by Scanning and Transmission Electron Microscopy (SEM/TEM), Atomic Force Microscopy (AFM) and in situ diagnostic by Time Resolved Reflectivity method (TRR). The process of phase change (melting, ablation, thin film removal …) and surface morphology modification at the nanoscale are studied with respect to irradiation dose (the fluence and the number of laser shots), the thickness of thin film and the substrate thermal conductivity in the pico- and nanosecond regime. Namely, nanostructures with a spatial period of 266 nm (close to the irradiation wavelength (λ)), 130 nm (λ/2) and 60 nm (λ/4) were successfully obtained. The global relationship between the laser parameters (i.e. fluence and number of laser shots) and nanostructure formation was established in the form of a 2D map. Lastly, an interpretation of the mechanism of periodic nanostructures formation on copper thin film induced by picosecond laser was established on the basis of our experimental data, seems relevant to the self-organization theory, particularly, in multi-pulses regime.

Motion planning is defined as the problem of computing a feasible trajectory for an agent to follow. It is a well-studied problem with applications in fields such as robotics, control theory and artificial intelligence. In the last decade there has been an increased interest in algorithms for motion planning under uncertainty where the agent does not know the state of the environment due to, e.g. motion and sensing uncertainties. One approach is to generate an initial feasible trajectory using for example an algorithm such as RRT* and then improve that initial trajectory using local optimization. This thesis proposes a new modification of the RRT* algorithm that can be used to generate initial paths from which initial trajectories for the local optimization step can be generated. Unlike standard RRT*, the modified RRT* generates multiple paths at the same time, all belonging to different families of solutions (homotopy classes). Algorithms for motion planning under uncertainty that rely on local optimization of trajectories can use trajectories generated from these paths as initial solutions. The modified RRT* is implemented and its performance with respect to computation time and number of paths found is evaluated on simple scenarios. The evaluations show that the modified RRT* successfully computes solutions in multiple homotopy classes. Two methods for motion planning under uncertainty, Trajectory-optimized LQG (T-LQG), and a belief space variant of iterative LQG (iLQG) are implemented and combined with the modified RRT*. The performance with respect to cost function improvement, computation time and success rate when following the optimized trajectories for the two methods are evaluated in a simulation study. The results from the simulation studies show that it is advantageous to generate multiple initial trajectories. Some initial trajectories, due to for example passing through narrow passages or through areas with high uncertainties, can only be slightly improved by trajectory optimization or results in trajectories that are hard to follow or with a high collision risk. If multiple initial trajectories are generated the probability is higher that at least one of them will result in an optimized trajectory that is easy to follow, with lower uncertainty and lower collision risk than the initial trajectory. The results also show that iLQG is much more computationally expensive than T-LQG, but that it is better at computing control policies to follow the optimized trajectories.

This thesis deals about design and realization software application "Mobile robot studio" for planning path mobile robot in pseudo 3D world. It contains several tools, witch most important are: simulation control, path planning, world editor and commands editor for CAN. Application was made by technology .NET 2.0 and for 3D design was used Microsoft DirectX 9 API. This thesis has been supported by the Czech Ministry of Education in the frame of MSM 0021630529 Research Intention Inteligent Systems in Automation.

The thesis aims to design and implement an Android application with ability to control the autopilot of the Skydog aircraft model using the wireless telemetry. The application shall receive data from an aircraft model gathered from various installed sensors. These data shall be then processed and corresponding instructions for autopilot shall be sent back. When collision with terrain or obstacle is detected, the application shall send instructions to autopilot to avoid such collision. RRT algorithm is used to find collision-free flight trajectory. Database of known obstacles and digital terrain model are provided to application in formats XML and GeoTIFF respectively.

Path planning that considers accurate vehicle dynamics and obstacle avoidance is an important problem in the area of autonomous driving. This paper describes a method of implementing trajectory planning for autonomous parking in conditions where the starting point and the position of fixed obstacles are uncertain. The narrow spaces and complicated manoeuvres required for parking demands a lot from the trajectory planning algorithm. It needs to have the ability to accurately model vehicle dynamics and find an efficient way around obstacles. Having obstacles in the way of the parking vehicle makes this a nonconvex problem the goal can usually not be reached by travelling in a straight line and finding a perfect trajectory around them is generally not computationally tractable. This paper reviews a two tiered approach to solving this problem. First a rough path is found using a modified Rapidly-exploring Random Tree (RRT) algorithm called Forward-Backward RRT, which runs two treebuilding processes in parallel and constructs a feasible path from where they intersect. Using optimisation this is then improved into a trajectory that is at least a local optimum. These methods will be demonstrated to produce efficient and feasible trajectories that respects the dynamic constraints of the vehicle and avoids collisions.

Robot path planning is a critical feature of autonomous systems. Rapidly-exploring Random Trees (RRT) is a path planning technique that randomly samples the robot configuration space to find a path between the start and end point. This thesis studies and compares the performance of four important RRT algorithms, namely, the original RRT, the optimal RRT (also termed RRT*), RRT*-Smart, and Informed RRT* for six different environments. The performance measures include the final path length (which is also the shortest path length found by each algorithm), time to find the first path, run time (of 1000 iterations) for each algorithm, total number of sampling nodes, and success rate (out of 100 runs). It is found that both RRT*-Smart and Informed RRT* algorithm result in shorter path lengths than the original RRT and RRT*. Typically, RRT*-Smart can find a suboptimal path in less number of iterations while the Informed RRT* is able to find the shortest path with increased number of iterations. On the other hand, the original RRT and RRT* are better suited for real-time applications as the Informed RRT* and RRT*-Smart have longer run time due to the additional steps in their processes.

Autonomous Unmanned Aerial Vehicles (UAV) havegreat potential in executing various complex tasks due to theirflexibility and relatively small size. The aim of this paper is todevelop a motion planner capable of generating a trajectory withaggressive maneuvers through narrow spaces without collision.The approach utilizes a framework using an optimized variantof the Rapidly-exploring Random Tree (RRT) algorithm, calledRRT*, with a Control Barrier Functions (CBF) based obstacleavoidance algorithm as well as a motion primitive generator. If amotion primitive collides with an obstacle, the obstacle avoidancealgorithm will attempt to reach the end state of a motion primitivein a collision free manner while complying with the actuationconstraints. From the collision free trajectories an optimal path iscontinuously searched for by RRT* by minimizing a cost in jerk.The performance of RRT* and the obstacle avoidance are testedin simulations independently and jointly, in several differentscenarios. The resulting motion planner successfully finds ahigh-level trajectory for the different scenarios. Limitations ofthe method as well as possible areas of improvements are alsodiscussed at the end of this paper. / Autonoma UAV har goda möjligheter för att utföra flera olika komplexa uppgifter tack vare deras flexibilitet och storlek. Denna rapport redogör för en rörelseplaneringsalgoritm som kombinerar manövrerbarheten hos en UAV för att skapa en kollisionsfri bana som innehåller aggressiva manövreringar genom trånga utrymmen. Tillvägagångssättet innefattar att kombinera Rapidly-exploring Random Tree (RRT*) med en algoritm för att undvika hinder baserad på Control Barrier Functions (CBF), samt att låta banan delas upp i segment, så kallade motion primitives, som genereras var för sig. Om en motion primitive kolliderar kommer den hinderundvikande algoritmen göra ett försök att nå dess målposition medan kollision undviks och manövreringsbegränsningarna uppfylls. Med en samling genomförbara motion primitives söker RRT* efter en kontinuerlig bana optimerad med hänsyn till en kostnad i ryck. Prestandan för RRT* och den hinderundvikande algoritmen simuleras både separat och tillsammans. Den resulterande rörelseplaneraren lyckas hitta en genomförbar bana för vardera scenario. Begränsningar av metoden samt potentiella förbättringsområden diskuteras i slutet av denna rapport. / Kandidatexjobb i elektroteknik 2021, KTH, Stockholm