Trajectory Planning Algorithms in Two-Dimensional Environment with Obstacles

This article proposes algorithms for planning and controlling the movement of a mobile robot in a two-dimensional stationary environment with obstacles. The task is to reduce the length of the planned path, take into account the dynamic constraints of the robot and obtain a smooth trajectory. To take into account the dynamic constraints of the mobile robot, virtual obstacles are added to the map to cover the unfeasible sectors of the movement. This way of accounting for dynamic constraints allows the use of map-oriented methods without increasing their complexity. An improved version of the rapidly exploring random tree algorithm (multi-parent nodes RRT – MPN-RRT) is proposed as a global planning algorithm. Several parent nodes decrease the length of the planned path in comprise with the original one-node version of RRT. The shortest path on the constructed graph is found using the ant colony optimization algorithm. It is shown that the use of two-parent nodes can reduce the average path length for an urban environment with a low building density. To solve the problem of slow convergence of algorithms based on random search and path smoothing, the RRT algorithm is supplemented with a local optimization algorithm. The RRT algorithm searches for a global path, which is smoothed and optimized by an iterative local algorithm. The lower-level control algorithms developed in this article automatically decrease the robot’s velocity when approaching obstacles or turning. The overall efficiency of the developed algorithms is demonstrated by numerical simulation methods using a large number of experiments

Cinemática Composta de Manipuladores Móveis

Um dos desafios atuais dos manipuladores móveis é o planeamento integrado da sua trajetória. O objetivo deste trabalho é assim integrar ambas as cadeias cinemáticas de modo a que o sistema possa realizar os movimentos restritos do manipulador enquanto a plataforma se move. Para isto vão ser implementadas duas possiveis abordagens de controlo. Para complementar a dissertação , é ainda desenvolvido um planeador de trajetórias recorrendo ao algoritmo A-sta

Planeamento Global de Trajetórias com Desvio de Obstáculos Híbrido para Embarcações Autónomas

De modo a tornar o método de transporte marítimo mais eficiente, rentável e seguro foi desenvolvido e implementado um algoritmo de planeamento de trajetórias com desvio de obstáculos em tempo real para ser futuramente utilizado pelas embarcações.Com múltiplos pontos de destino definidos, este algoritmo permite em primeiro lugar o planeamento completo da missão tendo em consideração todos os obstáculos estáticos conhecidos no momento e de seguida a execução desta, atuando de forma reativa na ocorrência de obstáculos dinâmicos.Considerando a informação obtida por um sistema de perceção, principalmente composto por LIDAR, GPS e uma câmera estereoscópica, este método permite a estimação de uma velocidade livre de colisão ao ser atuada no ASV

An Analysis of Some Algorithms and Heuristics for Multiobjective Graph Search

Muchos problemas reales requieren examinar un número exponencial de alternativas para encontrar la elección óptima. A este tipo de problemas se les llama de optimización combinatoria. Además, en problemas reales normalmente se evalúan múltiples magnitudes que presentan conflicto entre ellas. Cuando se optimizan múltiples obje-tivos simultáneamente, generalmente no existe un valor óptimo que satisfaga al mismo tiempo los requisitos para todos los criterios. Solucionar estos problemas combinatorios multiobjetivo deriva comúnmente en un gran conjunto de soluciones Pareto-óptimas, que definen los balances óptimos entre los objetivos considerados. En esta tesis se considera uno de los problemas multiobjetivo más recurrentes: la búsqueda de caminos más cortos en un grafo, teniendo en cuenta múltiples objetivos al mismo tiempo. Se pueden señalar muchas aplicaciones prácticas de la búsqueda multiobjetivo en diferentes dominios: enrutamiento en redes multimedia (Clímaco et al., 2003), programación de satélites (Gabrel & Vanderpooten, 2002), problemas de transporte (Pallottino & Scutellà, 1998), enrutamiento en redes de ferrocarril (Müller-Hannemann & Weihe, 2006), planificación de rutas en redes de carreteras (Jozefowiez et al., 2008), vigilancia con robots (delle Fave et al., 2009) o planificación independiente del dominio (Refanidis & Vlahavas, 2003). La planificación de rutas multiobjetivo sobre mapas de carretera realistas ha sido considerada como un escenario de aplicación potencial para los algoritmos y heurísticos multiobjetivo considerados en esta tesis. El transporte de materias peligrosas (Erkut et al., 2007), otro problema de enrutamiento multiobjetivo relacionado, ha sido también considerado como un escenario de aplicación potencial interesante. Los métodos de optimización de un solo criterio son bien conocidos y han sido ampliamente estudiados. La Búsqueda Heurística permite la reducción de los requisitos de espacio y tiempo de estos métodos, explotando el uso de estimaciones de la distancia real al objetivo. Los problemas multiobjetivo son bastante más complejos que sus equivalentes de un solo objetivo y requieren métodos específicos. Éstos, van desde técnicas de solución exactas a otras aproximadas, que incluyen los métodos metaheurísticos aproximados comúnmente encontrados en la literatura. Esta tesis se ocupa de algoritmos exactos primero-el-mejor y, en particular, del uso de información heurística para mejorar su rendimiento. Esta tesis contribuye análisis tanto formales como empíricos de algoritmos y heurísticos para búsqueda multiobjetivo. La caracterización formal de estos algoritmos es importante para el campo. Sin embargo, la evaluación empírica es también de gran importancia para la aplicación real de estos métodos. Se han utilizado diversas clases de problemas bien conocidos para probar su rendimiento, incluyendo escenarios realistas como los descritos más arriba. Los resultados de esta tesis proporcionan una mejor comprensión de qué métodos de los disponibles sonmejores en situaciones prácticas. Se presentan explicaciones formales y empíricas acerca de su comportamiento. Se muestra que la búsqueda heurística reduce considerablemente los requisitos de espacio y tiempo en la mayoría de las ocasiones. En particular, se presentan los primeros resultados sistemáticos mostrando las ventajas de la aplicación de heurísticos multiobjetivo precalculados. Esta tesis también aporta un método mejorado para el precálculo de los heurísticos, y explora la conveniencia de heurísticos precalculados más informados.Many real problems require the examination of an exponential number of alternatives in order to find the best choice. They are the so-called combinatorial optimization problems. Besides, real problems usually involve the consideration of several conflicting magnitudes. When multiple objectives must be simultaneously optimized, there is generally not an optimal value satisfying the requirements for all the criteria at the same time. Solving these multiobjective combinatorial problems commonly results in a large set of Pareto-optimal solutions, which define the optimal tradeoffs between the objectives under consideration. One of most recurrent multiobjective problems is considered in this thesis: the search for shortest paths in a graph, taking into account several objectives at the same time. Many practical applications of multiobjective search in different domains can be pointed out: routing in multimedia networks (Clímaco et al., 2003), satellite scheduling (Gabrel & Vanderpooten, 2002), transportation problems (Pallottino & Scutellà, 1998), routing in railway networks (Müller-Hannemann & Weihe, 2006), route planning in road maps (Jozefowiez et al., 2008), robot surveillance (delle Fave et al., 2009) or domain independent planning (Refanidis & Vlahavas, 2003). Multiobjective route planning over realistic road maps has been considered as a potential application scenario for the multiobjective algorithms and heuristics considered in this thesis. Hazardous material transportation (Erkut et al., 2007), another related multiobjective routing problem, has also been considered as an interesting potential application scenario. Single criterion shortest path methods are well known and have been widely studied. Heuristic Search allows the reduction of the space and time requirements of these methods, exploiting estimates of the actual distance to the goal. Multiobjective problems are much more complex than their single-objective counterparts, and require specific methods. These range from exact solution techniques to approximate ones, including the metaheuristic approximate methods usually found in the literature. This thesis is concerned with exact best-first algorithms, and particularly, with the use of heuristic information to improve their performance. This thesis contributes both formal and empirical analysis of algorithms and heuristics for multiobjective search. The formal characterization of algorithms is important for the field. However, empirical evaluation is also of great importance for the real application of these methods. Several well known classes of problems have been used to test their performance, including some realistic scenarios as described above. The results of this thesis provide a better understanding of which of the available methods are better in practical situations. Formal and empirical explanations of their behaviour are presented. Heuristic search is shown to reduce considerably space and time requirements in most situations. In particular, the first systematic results showing the advantages of the application of precalculated multiobjective heuristics are presented. The thesis also contributes an improved method for heuristic precalculation, and explores the convenience of more informed precalculated heuristics.This work is partially funded by / Este trabajo está financiado por: Consejería de Economía, Innovación, Ciencia y Empresa. Junta de Andalucía (España) Referencia: P07-TIC-0301

A collaborative system for providing routes between locations

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University 2008.Thesis (Master's) -- Bilkent University, 2008.Includes bibliographical references leaves 55-57.Many systems, such as in-car GPS devices and airline company web sites, provide route information between locations. Although such systems are used widely and can provide route information successfully, users of these systems cannot contribute to the data entry process. In these systems, data is entered by the administrators and these systems cannot take advantage of the route expertise of their users. In this work, we present a collaborative system, which provides routes between locations upon user queries. The data in the system is entered by the users of the system. We present a model which is containing locations, links between locations and relationships between locations (containment, neighborhood and intersection) in order to store the data. For the route finding purpose, we present a customized version of the A* search algorithm. This customized version, named A*CD (A* for Collaborative Data), uses heuristics for estimating the cost remaining to the target location while processing the nodes. A*CD can also provide alternative routes, exclude certain link types in the searches according to user preferences and handle the problems associated with multiple stop transportation lines. As the cost models, we use duration and financial cost. We also present the intuitive connections concept. Even if a route does not exist between the selected locations, the system can provide a route with missing links. The gap(s) between the disconnected locations are filled by the help of the relationships between locations. In order to evaluate the performance of the A*CD algorithm, we present automated tests. These tests show that the costs of the routes that are provided by the A*CD algorithm are close to the actual shortest routes. In order to demonstrate the intuitive connections concept, we also present manual test queries.Uluğ, Kerem AliM.S

Unifying model-based programming and randomized path planning through optimal search

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 120-122).The deployment of robots at the World Trade Center (WTC) site after September 11, 2001, highlighted the potential for robots to aid in search and rescue missions that pose great threats and challenges to humans. However, robots that are tele-operated and tethered for power and communication are restricted in terms of their operational area. Thus, rescue robots must be equipped with onboard autonomy that enables them to select feasible plans on their own, within their physical and computational limitations. There are three main characteristics that a rescue robot's onboard system must posses. First, the system must be able to generate plans for mobile systems, that is, plans with activities and paths. Second, in order to operate as efficiently as possible, particularly in emergency situations, the system must be globally optimal. Third, the system must be able to generate plans quickly. This thesis introduces a novel autonomous control system that interleaves methods for spatial and activity planning, by merging model-based programming with roadmap-based path planning. The primary contributions are threefold. The first contribution is a model that represents possible mission strategies with activities that have cost and are constrained to a location. The second is an optimal pre-planner that reasons through the possible mission strategies in order to quickly find the optimal feasible strategy. The third contribution is a unified, global activity and path planning system. The system unifies the optimal pre-planner with a randomized roadmap-based path planner, in order to find the optimal feasible strategy to achieve a mission. The impact of these contributions is highlighted in the context of an urban search and rescue (USAR) mission.by Aisha Walcott.S.M

Planeamento cooperativo de tarefas e trajectórias em múltiplos robôs

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Universidade do Porto. Faculdade de Engenharia. 201