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In robotics, path planning refers to the process of establishing paths for robots to move from initial positions to goal positions without colliding into any obstacle within specified environments. Constructing roadmaps and searching for paths in the roadmaps is one of the most commonly used methodologies adopted in path planning. However, most sampling-based path planners focus on improving the speed of constructing roadmaps without taking into account the quality. Therefore, they often produce poor-quality roadmaps. Poor-quality roadmaps can cause problems, such as time-consuming path searches, poor quality path production, and even failure of the searching. This research aims to develop a novel sampling-based path planning algorithm which is able to incrementally construct high-quality roadmaps while answering path queries for robots with many degrees of freedom. A novel K-order surrounding roadmap (KSR) concept is proposed in this research based on a thorough investigation into the criteria of high-quality roadmaps, including the criteria themselves and the relationships between them. A KSR contains K useful cycles. There exist a value T for which we can say, with confidence, that the KSR is a high quality roadmap when K=T. A new sampling-based path planning algorithm, known as the KSR path planner that is able to construct a roadmap incrementally while answering path queries, is also developed. The KSR path planner can be employed to answer path queries without requiring any pre-processing. The planner grows trees from the initial and goal III configurations of a path query and connects these two trees to obtain a path. The path planner retains useful vertices of the trees and uses these to construct the roadmap and adds useful cycles to the existing roadmap in order to improve the quality. The roadmap constructed can be used to answer further queries. With the KSR path planner algorithm, there is no need to calculate the value of K to construct a high quality roadmap in advance. The quality of the roadmap improves as the KSR path planner answer queries until the roadmap is able to answer any path queries and no further useful cycles can be added into the roadmap. If the number of path queries is infinite, a high quality KSR can be constructed. The novelty of this KSR path planner is twofold. Firstly, it employs a vertex category classifier to understand local environments where roadmap vertices reside. The classifier is developed using a decision tree method. The classifier is able to classify vertices in a roadmap based on the region information stored in the vertices and their neighbours within a certain distance. The region information stored in the vertices is obtained while the edges connecting the vertices are added to the roadmap. Therefore, employing the vertex category classifier does not require much additional execution time. Secondly, the KSR path planner selects suitable developed strategies to prune the existing roadmap and add useful cycles according to the identified local environments where the vertices reside to improve the quality of the existing roadmap. Experimental results show that the KSR path planner can construct a roadmap and improve the quality of the roadmap incrementally while answering path queries until the roadmap can answer all the path queries without any pre-processing stage. The roadmap constructed by the KSR path planner then achieves better quality than the roadmaps constructed by Reconfigurable Random Forest (RRF) path planner and traditional probabilistic roadmap (PRM) path planner.

This work is dedicated to robot path planning with using principles of dynamic programing in discrete state space. Theoretical part is dedicated to actual situation in this field and to principle of applying Markov decission process to path planning. Practical part is dedicated to implementation of two algorithms based on MDP principles.

This diploma´s thesis analyzes methods for robot path planning by means of algorithms AO*. The practical part focuses on the implementation of selected methods AO*, which are designed for planning under uncertainty environment. There was created the simulation program in this work. Simulation program enables testing the methods, that were implemented.

Yes / To solve the multi-objective mobile robot path planning in a dangerous environment with dynamic obstacles, this paper proposes a modified membraneinspired algorithm based on particle swarm optimization (mMPSO), which combines membrane systems with particle swarm optimization. In mMPSO, a dynamic double one-level membrane structure is introduced to arrange the particles with various dimensions and perform the communications between particles in different membranes; a point repair algorithm is presented to change an infeasible path into a feasible path; a smoothness algorithm is proposed to remove the redundant information of a feasible path; inspired by the idea of tightening the fishing line, a moving direction adjustment for each node of a path is introduced to enhance the algorithm performance. Extensive experiments conducted in different environments with three kinds of grid models and five kinds of obstacles show the effectiveness and practicality of mMPSO. / National Natural Science Foundation of China (61170016, 61373047), the Program for New Century Excellent Talents in University (NCET-11-0715) and SWJTU supported project (SWJTU12CX008); grant of the Romanian National Authority for Scientific Research, CNCSUEFISCDI, project number PN-II-ID-PCE- 2011-3-0688.

presente trabalho consolida um estudo para o planejamento de trajetória livre de colisão para um robô pneumático com 5 graus de liberdade aplicando três algoritmos metaheurísticos: algoritmos metaheurísticos por vagalumes, algoritmos metaheurísticos por enxames de partículas e algoritmos genéticos. No que se refere à aplicação de algoritmos metaheurísticos ao estudo de planejamento de trajetória de robôs manipuladores na presença de obstáculos, existem diferentes tipos de técnicas para evitar colisões que consideram os efeitos cinemáticos e dinâmicos na obtenção de trajetórias com o menor tempo, torque, etc. Neste estudo, são propostas contribuições à aplicação dessas técnicas especificamente a robôs manipuladores pneumáticos, sobretudo, no que diz respeito às características específicas dos servoposicionadores pneumáticos, como, por exemplo, a modelagem do atrito desses sistemas, o cálculo da massa equivalente, etc. A metodologia utilizada é definida em duas etapas. A primeira delas consiste na obtenção de pontos intermediários, adquiridos considerando a menor distância entre os mesmos e o ponto final, gerados considerando a presença de obstáculos (cilindros, cubos e esferas) Esses obstáculos são mapeados em regiões de colisão, que constituem restrições para o problema de otimização. A segunda etapa baseia-se no estudo do planejamento de trajetórias: aplicam-se b-splines de 5º e 7º grau na interpolação dos pontos intermediários, com vistas à obtenção de trajetórias que considerem, de um lado, a menor força dos atuadores associada à dinâmica do manipulador em estudo e, de outro, restrições cinemáticas e dinâmicas, determinadas por meio das características operacionais dos servoposicionadores pneumáticos. Os resultados mostram que a metodologia proposta é adequada para tarefas de manipulação de peças na presença de obstáculos, uma vez que os pontos intermediários situam-se fora da região de colisão nos três casos aqui apresentados. Além disso, quanto à segunda etapa, observou-se que as trajetórias de 5º e 7º grau apresentaram resultados similares, de maneira que os erros obtidos poderiam ser melhorados analisando aspectos associados ao controlador do robô em estudo. / The thesis presents a study for collision-free trajectory planning for a pneumatic robot with 5 degrees of freedom applying three metaheuristic algorithms: firefly metaheuristic algorithm, particle swarm optimization and genetic algorithms. As regards the application of metaheuristic algorithms to the study of the trajectory planning of manipulating robots in the presence of obstacles, there are different types of techniques to avoid collisions that consider the kinematic and dynamic effects, obtaining trajectories with the optimal time, torque, etc. In this study, contributions are made to the application of these techniques specifically to pneumatic manipulator robots, particularly with regard to the specific characteristics of pneumatic servo-actuators, such as friction modeling of these systems, calculation of equivalent mass, etc. The methodology used is defined in two steps. The first one consists of obtaining intermediate points, acquired considering the smallest distance between the intermediate points and the final point, generated considering the presence of obstacles (cylinders, cubes and spheres) These obstacles are mapped in collision regions, which are constraints to the optimization problem. The second step is based on the study of the trajectory planning: 5th and 7th degree b-splines are applied in the interpolation of the intermediate points, in order to obtain trajectories that consider the smallest actuator force associated to the dynamics of the manipulator and the kinematic and dynamic constraints, determined by the operational characteristics of pneumatic servo-positioners. The results show that the proposed methodology is suitable for tasks of manipulating parts in the presence of obstacles because the intermediate points are outside the collision region in the three cases presented here. In addition, it was observed that the trajectories of 5th and 7th degree presented similar results, so that the errors obtained could be improved by analyzing aspects associated to the controller of the robot.

presente trabalho consolida um estudo para o planejamento de trajetória livre de colisão para um robô pneumático com 5 graus de liberdade aplicando três algoritmos metaheurísticos: algoritmos metaheurísticos por vagalumes, algoritmos metaheurísticos por enxames de partículas e algoritmos genéticos. No que se refere à aplicação de algoritmos metaheurísticos ao estudo de planejamento de trajetória de robôs manipuladores na presença de obstáculos, existem diferentes tipos de técnicas para evitar colisões que consideram os efeitos cinemáticos e dinâmicos na obtenção de trajetórias com o menor tempo, torque, etc. Neste estudo, são propostas contribuições à aplicação dessas técnicas especificamente a robôs manipuladores pneumáticos, sobretudo, no que diz respeito às características específicas dos servoposicionadores pneumáticos, como, por exemplo, a modelagem do atrito desses sistemas, o cálculo da massa equivalente, etc. A metodologia utilizada é definida em duas etapas. A primeira delas consiste na obtenção de pontos intermediários, adquiridos considerando a menor distância entre os mesmos e o ponto final, gerados considerando a presença de obstáculos (cilindros, cubos e esferas) Esses obstáculos são mapeados em regiões de colisão, que constituem restrições para o problema de otimização. A segunda etapa baseia-se no estudo do planejamento de trajetórias: aplicam-se b-splines de 5º e 7º grau na interpolação dos pontos intermediários, com vistas à obtenção de trajetórias que considerem, de um lado, a menor força dos atuadores associada à dinâmica do manipulador em estudo e, de outro, restrições cinemáticas e dinâmicas, determinadas por meio das características operacionais dos servoposicionadores pneumáticos. Os resultados mostram que a metodologia proposta é adequada para tarefas de manipulação de peças na presença de obstáculos, uma vez que os pontos intermediários situam-se fora da região de colisão nos três casos aqui apresentados. Além disso, quanto à segunda etapa, observou-se que as trajetórias de 5º e 7º grau apresentaram resultados similares, de maneira que os erros obtidos poderiam ser melhorados analisando aspectos associados ao controlador do robô em estudo. / The thesis presents a study for collision-free trajectory planning for a pneumatic robot with 5 degrees of freedom applying three metaheuristic algorithms: firefly metaheuristic algorithm, particle swarm optimization and genetic algorithms. As regards the application of metaheuristic algorithms to the study of the trajectory planning of manipulating robots in the presence of obstacles, there are different types of techniques to avoid collisions that consider the kinematic and dynamic effects, obtaining trajectories with the optimal time, torque, etc. In this study, contributions are made to the application of these techniques specifically to pneumatic manipulator robots, particularly with regard to the specific characteristics of pneumatic servo-actuators, such as friction modeling of these systems, calculation of equivalent mass, etc. The methodology used is defined in two steps. The first one consists of obtaining intermediate points, acquired considering the smallest distance between the intermediate points and the final point, generated considering the presence of obstacles (cylinders, cubes and spheres) These obstacles are mapped in collision regions, which are constraints to the optimization problem. The second step is based on the study of the trajectory planning: 5th and 7th degree b-splines are applied in the interpolation of the intermediate points, in order to obtain trajectories that consider the smallest actuator force associated to the dynamics of the manipulator and the kinematic and dynamic constraints, determined by the operational characteristics of pneumatic servo-positioners. The results show that the proposed methodology is suitable for tasks of manipulating parts in the presence of obstacles because the intermediate points are outside the collision region in the three cases presented here. In addition, it was observed that the trajectories of 5th and 7th degree presented similar results, so that the errors obtained could be improved by analyzing aspects associated to the controller of the robot.

presente trabalho consolida um estudo para o planejamento de trajetória livre de colisão para um robô pneumático com 5 graus de liberdade aplicando três algoritmos metaheurísticos: algoritmos metaheurísticos por vagalumes, algoritmos metaheurísticos por enxames de partículas e algoritmos genéticos. No que se refere à aplicação de algoritmos metaheurísticos ao estudo de planejamento de trajetória de robôs manipuladores na presença de obstáculos, existem diferentes tipos de técnicas para evitar colisões que consideram os efeitos cinemáticos e dinâmicos na obtenção de trajetórias com o menor tempo, torque, etc. Neste estudo, são propostas contribuições à aplicação dessas técnicas especificamente a robôs manipuladores pneumáticos, sobretudo, no que diz respeito às características específicas dos servoposicionadores pneumáticos, como, por exemplo, a modelagem do atrito desses sistemas, o cálculo da massa equivalente, etc. A metodologia utilizada é definida em duas etapas. A primeira delas consiste na obtenção de pontos intermediários, adquiridos considerando a menor distância entre os mesmos e o ponto final, gerados considerando a presença de obstáculos (cilindros, cubos e esferas) Esses obstáculos são mapeados em regiões de colisão, que constituem restrições para o problema de otimização. A segunda etapa baseia-se no estudo do planejamento de trajetórias: aplicam-se b-splines de 5º e 7º grau na interpolação dos pontos intermediários, com vistas à obtenção de trajetórias que considerem, de um lado, a menor força dos atuadores associada à dinâmica do manipulador em estudo e, de outro, restrições cinemáticas e dinâmicas, determinadas por meio das características operacionais dos servoposicionadores pneumáticos. Os resultados mostram que a metodologia proposta é adequada para tarefas de manipulação de peças na presença de obstáculos, uma vez que os pontos intermediários situam-se fora da região de colisão nos três casos aqui apresentados. Além disso, quanto à segunda etapa, observou-se que as trajetórias de 5º e 7º grau apresentaram resultados similares, de maneira que os erros obtidos poderiam ser melhorados analisando aspectos associados ao controlador do robô em estudo. / The thesis presents a study for collision-free trajectory planning for a pneumatic robot with 5 degrees of freedom applying three metaheuristic algorithms: firefly metaheuristic algorithm, particle swarm optimization and genetic algorithms. As regards the application of metaheuristic algorithms to the study of the trajectory planning of manipulating robots in the presence of obstacles, there are different types of techniques to avoid collisions that consider the kinematic and dynamic effects, obtaining trajectories with the optimal time, torque, etc. In this study, contributions are made to the application of these techniques specifically to pneumatic manipulator robots, particularly with regard to the specific characteristics of pneumatic servo-actuators, such as friction modeling of these systems, calculation of equivalent mass, etc. The methodology used is defined in two steps. The first one consists of obtaining intermediate points, acquired considering the smallest distance between the intermediate points and the final point, generated considering the presence of obstacles (cylinders, cubes and spheres) These obstacles are mapped in collision regions, which are constraints to the optimization problem. The second step is based on the study of the trajectory planning: 5th and 7th degree b-splines are applied in the interpolation of the intermediate points, in order to obtain trajectories that consider the smallest actuator force associated to the dynamics of the manipulator and the kinematic and dynamic constraints, determined by the operational characteristics of pneumatic servo-positioners. The results show that the proposed methodology is suitable for tasks of manipulating parts in the presence of obstacles because the intermediate points are outside the collision region in the three cases presented here. In addition, it was observed that the trajectories of 5th and 7th degree presented similar results, so that the errors obtained could be improved by analyzing aspects associated to the controller of the robot.

As already clear from the title, this dissertation deals with autonomous locomotive robot path planning, based on machine learning. Robot path planning task is to find a path from initial to target position without collision with obstacles so that the cost of the path is minimized. Autonomous robot is such a machine which is able to perform tasks completely independently even in environments with dynamic changes. Path planning in dynamic partially known environment is a difficult problem. Autonomous robot ability to adapt its behavior to changes in the environment can be ensured by using machine learning methods. In the field of path planning the mostly used methods of machine learning are case based reasoning, neural networks, reinforcement learning, swarm intelligence and genetic algorithms. The first part of this thesis introduces the current state of research in the field of path planning. Overview of methods is focused on basic omnidirectional robots and robots with differential constraints. In the thesis, several methods of path planning for omnidirectional robot and robot with differential constraints are proposed. These methods are mainly based on case-based reasoning and genetic algorithms. All proposed methods were implemented in simulation applications. Results of experiments carried out in these applications are part of this work. For each experiment, the results are analyzed. The experiments show that the proposed methods are able to compete with commonly used methods, because they perform better in most cases.

This dissertation models human intent for a robot navigation task, managed by a human and undertaken by a robot in a dynamic, multi-objective environment. Intent is expressed by a human through a user interface and then translated into a robot trajectory that satisfies a set of human-specified objectives and constraints. For a goal-based robot navigation task in a dynamic environment, intent includes expectations about a path in terms of objectives and constraints to be met. If the planned path drifts from the human's intent as the environment changes, a new path needs to be planned. The intent framework has four elements: (a) a mathematical representation of human intent within a multi-objective optimization problem; (b) design of an interactive graphical user interface that enables a human to communicate intent to the robot and then to subsequently monitor intent execution; (c) integration and adoption of a fast online path-planning algorithms that generate solutions/trajectories conforming to the given intent; and (d) design of metric-based triggers that provide a human the opportunity to correct or adapt a planned path to keep it aligned with intent as the environment changes. Key contributions of the dissertation are: (i) design and evaluation of different user interfaces to express intent, (ii) use of two different metrics, cosine similarity and intent threshold margin, that help quantify intent, and (iii) application of the metrics in path (re)planning to detect intent mismatches for a robot navigating in a dynamic environment. A set of user studies including both controlled laboratory experiments and Amazon Mechanical Turk studies were conducted to evaluate each of these dissertation components.