Using a mobile robot to test a theory of cognitive mapping

This paper describes using a mobile robot, equipped with some sonar sensors and an odometer, to test navigation through the use of a cognitive map. The robot explores an office environment, computes a cognitive map, which is a network of ASRs [36, 35], and attempts to find its way home. Ten trials were conducted and the robot found its way home each time. From four random positions in two trials, the robot estimated the home position relative to its current position reasonably accurately. Our robot does not solve the simultaneous localization and mapping problem and the map computed is fuzzy and inaccurate with much of the details missing. In each homeward journey, it computes a new cognitive map of the same part of the environment, as seen from the perspective of the homeward journey. We show how the robot uses distance information from both maps to find its way home. © 2007 Springer-Verlag Berlin Heidelberg

Collision-free motion of two robot arms in a common workspace

Collision-free motion of two robot arms in a common workspace is investigated. A collision-free motion is obtained by detecting collisions along the preplanned trajectories using a sphere model for the wrist of each robot and then modifying the paths and/or trajectories of one or both robots to avoid the collision. Detecting and avoiding collisions are based on the premise that: preplanned trajectories of the robots follow a straight line; collisions are restricted to between the wrists of the two robots (which corresponds to the upper three links of PUMA manipulators); and collisions never occur between the beginning points or end points on the straight line paths. The collision detection algorithm is described and some approaches to collision avoidance are discussed

Motion-based stereovision model with potential utility in robot navigation.

Autonomous robot guidance in dynamic environments requires, on the one hand, the study of relative motion of the objects of the environment with respect to the robot, and on the other hand, the analysis of the depth towards those objects. In this paper, a stereo vision method, which combines both topics with potential utility in robot navigation, is proposed. The goal of the stereo vision model is to calculate depth of surrounding objects by measuring the disparity between the two-dimensional imaged positions of the object points in a stereo pair of images. The simulated robot guidance algorithm proposed starts from the motion analysis that occurs in the scene and then establishes correspondences and analyzes the depth of the objects. Once these steps have been performed, the next step is to induce the robot to take the direction where objects are more distant in order to avoid obstacles

Towards building a team of intelligent robots

Topics addressed include: collision-free motion planning of multiple robot arms; two-dimensional object recognition; and pictorial databases (storage and sharing of the representations of three-dimensional objects)

Proceedings of the NASA Conference on Space Telerobotics, volume 4

Papers presented at the NASA Conference on Space Telerobotics are compiled. The theme of the conference was man-machine collaboration in space. The conference provided a forum for researchers and engineers to exchange ideas on the research and development required for the application of telerobotic technology to the space systems planned for the 1990's and beyond. Volume 4 contains papers related to the following subject areas: manipulator control; telemanipulation; flight experiments (systems and simulators); sensor-based planning; robot kinematics, dynamics, and control; robot task planning and assembly; and research activities at the NASA Langley Research Center

Planificación de movimientos en entornos dinámicos o inciertos mediante la coordinación de métodos aleatorios de búsqueda y funciones armónicas

En los métodos planificadores de trayectorias basados en funciones potenciales, la utilización de las funciones armónicas tiene la importante propiedad de no presentar mínimos locales. Sin embargo, la creación de planificadores basados en estas funciones armónicas se ha encontrado con serias dificultades, sobre todo cuando el número de grados de libertad es elevado. Por este motivo, esta tesis realiza inicialmente un estudio de las propiedades más relevantes de dichas funciones armónicas; destacando aquellas que han sido la causa de su reducida aplicación en la generación de trayectorias. Al mismo tiempo, el resultado de este estudio sirve de base para la proposición de métodos compensatorios que permitan reducir las propiedades negativas de las funciones armónicas, como funciones potenciales aplicables a la generación de movimientos en robótica. Después se considera los métodos numéricos de cálculo de las funciones armónicas, así como el coste computacional de los mismos. Con el objetivo de reducir el tiempo de cálculo, esta tesis propone una discretización jerárquica y un método eficiente de etiquetado de celdas. Por su parte, dicha discretización jerárquica, se va realizando progresivamente mediante muestreo aleatorio y descomposición de celdas, lo que genera un escenario parcialmente conocido que, sin embargo, permitirá en cierto número de casos encontrar la trayectoria buscada. Por lo tanto, esta propuesta reduce drásticamente el número de puntos de cálculo y, por consiguiente, el tiempo de computación. La tesis completa la propuesta de un planificador combinando las técnicas de muestreo con el cálculo de funciones armónicas mediante un método de exploración aleatorio conducido (PHM), aplicado a un espacio de configuraciones discretizado jerárquicamente sobre el que se va recalculando la función armónica. De esta forma la exploración se guía hacia zonas más prometedoras, intentando obtener la solución por fases.In methods based trajectories planners potential functions, the use of harmonic functions has the important property of not presenting local minima. However, the creation of planners based on these harmonic functions has met with serious difficulties, especially when the number of degrees of freedom is high. For this reason, this thesis makes an initial study of the properties most relevant of these harmonic functions, highlighting those that have been the cause of their limited application in the generation of trajectories. At the same time, the result of this study provides a basis for proposing compensatory methods to reduce the negative properties of harmonic functions as potential functions applicable to the generation of robotic movements. Then we consider numerical methods for calculating the harmonic functions and the computational cost of the same. In order to reduce computation time, this thesis proposes a hierarchical discretization and an efficient method of labeling cells. Meanwhile, this discretization hierarchical be made gradually by random sampling and decomposition of cells, generating a scene partially known, however, allow a number of cases in finding the trajectory sought. Therefore, this proposal drastically reduces the number of calculation points, and hence the computation time. The thesis, complete a proposed planner combining sampling techniques to the calculation of harmonic functions by a method of random exploration conducted (PHM), applied to a hierarchically discretized configuration space on which the harmonic function is recalculated. In this way the exploration is guided to more promising, trying to obtain the solution phases

Proceedings of EVA London 2024

The Electronic Visualisation and the Arts London 2024 Conference (EVA London 2024) is co-sponsored by the Computer Arts Society (CAS) and BCS, the Chartered Institute for IT, of which the CAS is a Specialist Group. As for 2022, the EVA London 2023 Conference is a physical and online “hybrid” conference. We continue with publishing the proceedings, both online, with open access via ScienceOpen, and also in our traditional printed form, in full colour. The main conference presentations run during 10–13 July 2023, with workshops and other activities, especially for students, on 14 July 2023