Algorithms for Control of Welding Robotic-Manipulators Based on a Statistical Model of a Configuration Space

Предложены новые эффективные алгоритмы управления сварочными роботами-манипуляторами на основе статистической модели конфигурационного пространства. Предложенные алгоритмы, в отличие от известных, позволяют эффективно учесть ограничения на ориентацию технологического инструмента, сложную форму элементов роботизированного комплекса, а также ограничения, накладываемые на угловые скорости движения звеньев манипулятора. Эффективность разработанных алгоритмов подтверждается результатами тестирования в экспериментальной среде моделирования роботов.= The new effective algorithms were proposed for control of welding robotic-manipulators. The proposed algorithms are based on a statistical model of a configuration space. In contrast to known they effectively take into account complex shape of the elements of the robotic technological cell, the limits for the orientation of technological tool, as well as limits for the angular velocity of the manipulator links. The effectiveness of the proposed algorithms is confirmed by the tests in the experimental space of robot modeling

Programming assignments to learn how to build a Probabilistic Roadmap Planner

Research in robot motion planning requires the availability of a simulation environment where to test and validate the theoretic contributions. Nevertheless, PhD programs and graduate studies that include robot motion planning courses usually cover the subject only from a theoretical perspective. Therefore, students aiming to focus their research in this line, face the big challenge of developing their own simulation environment. To cope with this problem, this paper proposes the use of a programming framework based on multi-platform open source code, and presents a set of twelve programming assignments to allow students of any robot motion planning course a quick mastering of the basic skills involved, covering from graphical rendering issues to collision detection or graph representations and algorithms

On the performance of sampling-based optimal motion planners

Sampling based algorithms provide efficient methods of solving robot motion planning problem. The advantage of these approaches is the ease of their implementation and their computational efficiency. These algorithms are probabilistically complete i.e. they will find a solution if one exists, given a suitable run time. The drawback of sampling based planners is that there is no guarantee of the quality of their solutions. In fact, it was proven that their probability of reaching an optimal solution approaches zero. A breakthrough in sampling planning was the proposal of optimal based sampling planners. Current optimal planners are characterized with asymptotic optimality i.e. they reach an optimal solutions as time approaches infinity. Motivated by the slow convergence of optimal planners, post-processing and heuristic approach have been suggested. Due to the nature of the sampling based planners, their implementation requires tuning and selection of a large number of parameters that are often overlooked. This paper presents the performance study of an optimal planner under different parameters and heuristics. We also propose a modification in the algorithm to improve the convergence rate towards an optimal solution

Планирование траекторий роботов-манипуляторов в технологическом процессе точечной контактной сварки

Предложен новый метод планирования траектории роботов-манипуляторов для процесса точечной контактной сварки, который в отличие от известных позволяет эффективно учесть технологические ограничения на ориентацию сварочных клещей, а также сложную форму элементов роботизированного комплекса. Эффективность разработанного метода подтверждается результатами тестирования.A new method of designing the path of the robot-manipulator for the process of resistance spot welding is proposed which unlike others efficiently allows for technological limitations for welding guns orientation and also for intricate shape of the robot complex elements. Effi­ciency of the method developed is confirmed by the results of testing

A novel path planning proposal based on the combination of deterministic sampling and harmonic functions

The sampling-based approach is currently the most successful and yet more promising approach to path planning problems. Sampling-based methods are demonstrated to be probabilistic complete, being their performance reliant on the generation of samples. To obtain a good set of samples, this paper proposes a new sampling paradigm based on deterministic sampling paradigm based on a deterministic sampling sequence guided by an harmonic potential function computed on a hierarchical cell decomposition of C-space. In the proposed method, known as Kautham sampler, samples are not isolated configurations but parts of a whole. As samples are generated they are dynamically grouped into cells that capture the C-space structure. This allows the use of harmonic functions to share information and guide further sampling towards more promising regions of C-space. Finally, using the samples obtained, a roadmap is easily built taking advantage of the known neighbourhood relationships

Optimistic Motion Planning Using Recursive Sub- Sampling: A New Approach to Sampling-Based Motion Planning

Sampling-based motion planning in the field of robot motion planning has provided an effective approach to finding path for even high dimensional configuration space and with the motivation from the concepts of sampling based-motion planners, this paper presents a new sampling-based planning strategy called Optimistic Motion Planning using Recursive Sub-Sampling (OMPRSS), for finding a path from a source to a destination sanguinely without having to construct a roadmap or a tree. The random sample points are generated recursively and connected by straight lines. Generating sample points is limited to a range and edge connectivity is prioritized based on their distances from the line connecting through the parent samples with the intention to shorten the path. The planner is analysed and compared with some sampling strategies of probabilistic roadmap method (PRM) and the experimental results show agile planning with early convergence

Planificación de movimientos para conjuntos mano-brazo con numerosos grados de libertad

Con el objetivo de avanzar hacia la emulaci ´on de las capacidades y destreza de la mano humana se est ´an desarrollando manos rob´oticas con prestaciones muy superiores a las tradicionales pinzas usadas en la rob´otica industrial. El uso de estas manos rob´oticas conlleva asociados nuevos retos, y entre ellos est ´a el de la planificaci ´on eficiente de sus movimientos, ya que aunque algunos m´etodos de planificaci ´on tradiciones podr´ıan ser v´alidos, su aplicaci ´on pr´actica es limitada debido sobre todo al elevado n´umero de grados de libertad que tiene un sistema rob´otico que incluya una mano antropomorfa montada sobre un brazo manipulador. Este art´ıculo presenta un enfoque que permite soluciones suficientemente eficientes para ser utilizadas en la pr´actica. El enfoque propuesto est ´a basado en la reducci ´on de la dimensi ´on del espacio de b´usqueda y en la utilizaci ´on de t´ecnicas de muestreo para generar movimientos del conjunto mano-brazo en entornos con obst ´aculos que pueden imponer fuertes restricciones en la b´usqueda de movimientos sin colisi ´on, como sucede normalmente cuando la mano se acerca a un objeto para su sujeci ´on y manipulaci ´on. Los desarrollos te´oricos han sido implementados tanto en simulaci ´on como en un sistema f´ısico real, por lo que se incluyen resultados experimentales reales.Postprint (published version

Local Randomization in Neighbor Selection Improves PRM Roadmap Quality

Probabilistic Roadmap Methods (PRMs) are one of the most used classes of motion planning methods. These sampling-based methods generate robot configurations (nodes) and then connect them to form a graph (roadmap) containing representative feasible pathways. A key step in PRM roadmap construction involves identifying a set of candidate neighbors for each node. Traditionally, these candidates are chosen to be the k-closest nodes based on a given distance metric. This work proposes a new neighbor selection policy called LocalRand(k, k'), that first computes the k' closest nodes to a specified node and then selects k of those nodes at random. Intuitively, LocalRand attempts to benefit from random sampling while maintaining the higher levels of local planner success inherent to selecting more local neighbors. A methodology for selecting the parameters k and k' is provided, and an experimental comparison for both rigid and articulated robots show that LocalRand results in roadmaps that are better connected than the traditional k-closest or a purely random neighbor selection policy. The cost required to achieve these results is shown to be comparable to the cost of k-closest

Sistema de guiado asistido para la ejecución de tareas virtuales con dispositivos hapticos

El empleo de dispositivos hápticos en sistemas de teleoperación, permite al operador ejecutar las tareas con mayor efectividad, esto junto al desarrollo de nuevas tecnologías logra que la eficiencia en las actividades de teleoperación sea cada día mayor. En este proyecto se establece una arquitectura para el desarrollo de aplicaciones de guiado háptico para ayuda a la teleoperación o ejecución de tareas virtuales, basada en una sola librería (H3D) con la cual se pueden realizar las tareas de representación gráfica en tres dimensiones, manejo de colisiones, representación del dispositivo háptico y creación de la interfaz gráfica para el usuario. Esta arquitectura es de software libre y multiplataforma. Empleando esta arquitectura se ha implementado una aplicación, para realizar la planificación de una ruta a seguir desde un punto inicial hacia un punto final, dentro de un espacio virtual tridimensional. La ruta es seguida mediante el dispositivo háptico y haciendo uso de las funciones de fuerza de éste, se logra hacer que el usuario se mantenga dentro de la ruta sugerida. Esta aplicación constituye un módulo de asistencia al operador en las tareas de teleoperación o tareas virtuales, que le permitirá simular el recorrido de una ruta para la ejecución de una tarea. En la planificación de la ruta se emplea el método de los árboles de exploración rápida, y se implementan básicamente dos de sus algoritmos, la aplicación presenta de forma gráfica los arboles empleados y muestra el camino a seguir. Para la interacción entre el usuario y la aplicación a través del dispositivo háptico, se crea un nodo adicional que permite usar el dispositivo háptico para mover el móvil en el espacio virtual. Se propone una variación al algoritmo de planificación basado en los árboles de exploración rápida, para permitir al usuario establecer a través del dispositivo háptico un conjunto de puntos por los cuales se pretende pase la ruta calculada. En el final de proyecto se sugieren las mejoras posibles, y las recomendaciones para crear nuevas aplicaciones más eficientes y útiles para el operador