On-line Path Generation for Small Unmanned Aerial Vehicles using B-Spline Path Templates

Copyright © 2008 by D. Jung and P. Tsiotras. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.In this study we investigate the problem of generating a smooth, planar reference path, given a family of discrete optimal paths. In conjunction with a path representation by a finite sequence of square cells, the generated path is supposed to stay inside a feasible channel, while minimizing certain performance criteria. Constrained optimization problems are formulated subject to geometric (linear) constraints, as well as boundary conditions in order to generate a library of B-spline path templates. As an application to the vehicle motion planning, the path templates are incorporated to represent local segments of the entire path as geometrically smooth curves, which are then joined with one another to generate a reference path to be followed by a closed-loop tracking controller. The on-line path generation algorithm incorporates the path templates such that continuity and smoothness are preserved when switching from one template to another along the path. Combined with the D∗-lite path planning algorithm, the proposed algorithm provides a complete solution to the obstacle-free path generation problem in a computationally efficient manner, suitable for real-time implementation

Методы планирования пути в среде с препятствиями (обзор)

Planning the path is the most important task in the mobile robot navigation. This task involves basically three aspects. First, the planned path must run from a given starting point to a given endpoint. Secondly, it should ensure robot’s collision-free movement. Thirdly, among all the possible paths that meet the first two requirements it must be, in a certain sense, optimal.Methods of path planning can be classified according to different characteristics. In the context of using intelligent technologies, they can be divided into traditional methods and heuristic ones. By the nature of the environment, it is possible to divide planning methods into planning methods in a static environment and in a dynamic one (it should be noted, however, that a static environment is rare). Methods can also be divided according to the completeness of information about the environment, namely methods with complete information (in this case the issue is a global path planning) and methods with incomplete information (usually, this refers to the situational awareness in the immediate vicinity of the robot, in this case it is a local path planning). Note that incomplete information about the environment can be a consequence of the changing environment, i.e. in a dynamic environment, there is, usually, a local path planning.Literature offers a great deal of methods for path planning where various heuristic techniques are used, which, as a rule, result from the denotative meaning of the problem being solved. This review discusses the main approaches to the problem solution. Here we can distinguish five classes of basic methods: graph-based methods, methods based on cell decomposition, use of potential fields, optimization methods, фтв methods based on intelligent technologies.Many methods of path planning, as a result, give a chain of reference points (waypoints) connecting the beginning and end of the path. This should be seen as an intermediate result. The problem to route the reference points along the constructed chain arises. It is called the task of smoothing the path, and the review addresses this problem as well.Планирование пути — важнейшая задача в области навигации мобильных роботов. Эта задача включает в основном три аспекта. Во-первых, спланированный путь должен пролегать от заданной начальной точки к заданной конечной точке. Во-вторых, этот путь должен обеспечивать движение робота с обходом возможных препятствий. В-третьих, путь должен среди всех возможных путей, удовлетворяющих первым двум требованиям, быть в определенном смысле оптимальным.Методы планирования пути можно классифицировать по разным признакам. В контексте использования интеллектуальных технологий их можно разделить на традиционные методы и эвристические методы. По характеру окружающей обстановки можно разделить методы планирования на методы планирования в статической окружающей среде и в динамической среде (следует, однако, отметить, что статическая окружающая среда редко встречается на практике). Методы также можно разделить по полноте информации об окружающей среде: методы с полной информацией (в таком случае говорят о глобальном планировании пути) и методы с неполной информацией (обычно речь идет о знании обстановки в непосредственной близости от робота, в этом случае речь идет о локальном планировании пути). Отметим, что неполная информация об окружающей среде может быть следствием меняющейся обстановки, т.е. в условиях динамической среды планирование пути, как правило, локальное.В литературе предложено большое количество методов планирования пути, в которых используются различные эвристические приемы, вытекающие, как правило, из содержательного смысла решаемой задачи. В настоящем обзоре  рассматриваются основные подходы к решению задачи. Здесь можно выделить пять классов основных методов: методы на основе графов, методы на основе клеточной декомпозиции, использование потенциальных полей, оптими­зационные методы, методы на основе интеллектуальных технологий.Многие методы планирования пути в качестве результата дают цепь опорных точек (путевых точек), соединяющую начало и конец пути. Это следует рассматривать как промежуточный результат. Возникает задача прокладки пути вдоль построенной цепи опорных точек, называемая задачей сглаживания пути. Этой задаче в обзоре также уделено внимание

Planification de chemin pour un robot mobile dans un environnement partiellement connu

Méthodes par décomposition cellulaire -- Les méthodes Roadmap -- Méthode de champ potentiel -- Méthodes de planification optimale et algorithmes de recherche de graphe -- Environnement partiellement connu -- Définitions de termes -- Le modèle générique -- Mise en oeuvre

SURVEILLANCE MISSION PLANNING FOR UAVS IN GPS-DENIED URBAN ENVIRONMENT

Ph.DDOCTOR OF PHILOSOPH

Mobile robotic devices simulation with emphasis in trajectory planning for navigation

Orientador: João Maurício RosárioDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: Neste trabalho é proposto um sistema de navegação autônomo para dispositivos robóticos móveis capaz de operar e se adaptar a diferentes ambientes e condições, contribuindo para o desenvolvimento de uma navegação robusta e confiável. O sistema é baseado na arquitetura híbrida AuRA, assim, foi separado em quatro componentes: percepção do ambiente, localização e mapeamento, planejamento de movimento e execução da trajetória. A percepção do ambiente é o componente responsável em converter as leituras dos sensores em informações sobre o ambiente. Considerando os sensores usadas da plataforma robótica móvel ASURO, este componente baseia-se na informações obtidas através da odometria e dos sensores seguidores de linha, informando ao sistema a distância percorrida e a posição do robô em relação a pista a ser seguida. O mapeamento do sistema baseou-se em mapas topológicos devido ao baixo custo computacional necessário e à semelhança com a maneira humana de localizar-se, utilizando a odometria como sistema de localização do robô e sensores seguidores de linha para determinação de seu posicionamento. O planejamento de movimentos foi dividido em duas fases. No planejamento de caminho utilizou-se o algoritmo de Dijkstra para determinar por quais nós ele deve passar para atingir seu objetivo; e para o planejamento de trajetória utilizou-se uma abordagem baseada no caminho de Dubins. A execução da trajetória baseou-se no método de Motor-Schemas, onde as respostas dos atuadores são determinadas pela soma vetorial dos vetores resultantes de cada comportamento. Foram estudadas duas formas de comportamento: o de seguir o objetivo que utiliza o planejamento de movimento para determinar as velocidades dos atuadores; e o de seguir uma linha, que utiliza a percepção do ambiente para determinar as velocidades dos atuadores. As implementações experimentais foram realizadas a partir do ambiente de simulação DD&GP desenvolvido para o ambiente MATLABSimulink®, que permitiu a avaliação do sistema a partir de duas aplicações (transporte e inspeção) efetuada em três ambientes diferentes (fábrica, escritório e sistema de tubulação). Além disso, utilizou-se a plataforma robótica móvel ASURO para verificar a percepção do ambiente e validar os resultados encontrados nas simulações. Os resultados obtidos nas implementações experimentais foram satisfatórios e mostram que o sistema apresentado é promissorAbstract: In this work is proposed an autonomous navigation system for mobile robotic devices able to operate and adapt to different environments and conditions, contributing to the development of a robust and reliable navigation system. The system is based on hybrid architecture AuRA, thus, it was separated into four components: Perceptions of the environment, Localization and Mapping, Motion planning and Trajectory execution. The perception of the environment is the component responsible for converting the readings in sensors in environmental information. Considering the sensors used in mobile robotics platform ASURO, this component is based on information obtained from odometry and line following sensors, informing the system the distance traveled and the robot's position in relation to the track to be followed. The mapping of the system is based on topological maps due low computational cost required and its resemblance to the human way of locating themselves and the use of little computer memory, using the odometry as robot's localization system and line following sensors to determine their placement. The Motion planning was divided into two phases. In path planning was used Dijkstra's algorithm to determine for which node the robot must pass to achieve your goal; and for trajectory planning was used an approach based on Dubins path. The trajectory execution is based on the method of motor-schemas, where the responses of the actuators are determined by the vector sum of the resulting vectors from each behavior. Were studied two forms of behaviors: follow the goal, which uses the motion planning to determine the velocity of actuators; and follow a line, which uses the perception of the environment to determine the velocity of actuators. The experimental implementations were realized from the simulation environment DD&GP developed for the MATLAB-Simulink ®, which allowed the evaluation of the system after two applications (transport and inspection) performed in three different environments (factory, office and piping system). In addition, was used the platform for mobile robotics ASURO to verify the perception of the environment and validate the results found in the simulations. The results obtained in experimental implementations were satisfactory and showed that the system presented is promisingMestradoMecanica dos Sólidos e Projeto MecanicoMestre em Engenharia Mecânic