Aprendiendo simulación de eventos discretos con JaamSim

[Resumen] El aprendizaje, en particular de materias técnicas como la simulación de sistemas dinámicos, requiere que los estudiantes desarrollen casos de diverso nivel de complejidad, desde pequeños ejemplos didácticos hasta proyectos de cierta envergadura. Para ello es ineludible el uso de herramientas profesionales accesibles, lo que es prácticamente sinónimo de código abierto. En este artículo informamos de nuestra experiencia con JaamSim, un paquete de simulación que incluye una interfaz “arrastrar-y-soltar”, gráficos interactivos, procesamiento de entradas y salidas, y herramientas de desarrollo de modelos. El grado de madurez de la herramienta, y su comunidad de usuarios, nos parece más que suficiente para sustituir con ventaja otras opciones propietarias o con licencias de estudiante limitadas, si bien JaamSim todavía debe seguir evolucionando, sobre todo en aspectos de usabilidad, para lo que la contribución de los usuarios es fundamental. Una ventaja de algunas populares herramientas propietarias es la existencia de material didáctico, pero consideramos que puede suplirse ventajosamente compartiendo en abierto material didáctico análogo, especialmente ejemplos y casos desarrollados, por lo que contribuimos con el material de nuestro curso, que esperamos aumentar y perfeccionar en sucesivas ediciones.Universidad de Zaragoza; PIIDUZ-16-03

Simultaneous Deployment and Tracking Multi-Robot Strategies with Connectivity Maintenance

Multi robot teams composed by ground and aerial vehicles have gained attention during the last years. We present a scenario where both types of robots must monitor the same area from different view points. In this paper we propose two Lloyd-based tracking strategies to allow the ground robots (agents) follow the aerial ones (targets), keeping the connectivity between the agents. The first strategy establishes density functions on the environment so that the targets acquire more importance than other zones, while the second one iteratively modifies the virtual limits of the working area depending on the positions of the targets. We consider the connectivity maintenance due to the fact that coverage tasks tend to spread the agents as much as possible, which is addressed by restricting their motions so that they keep the links of a Minimum Spanning Tree of the communication graph. We provide a thorough parametric study of the performance of the proposed strategies under several simulated scenarios. In addition, the methods are implemented and tested using realistic robotic simulation environments and real experiments

Parallel and distributed map merging and localization: algorithms, tools and strategies for robotic networks

International audienc

Feature-Based Map Merging with Dynamic Consensus on Information Increments

Abstract — We study the feature-based map merging problem in robot networks. Each robot observes the environment and builds a local map. Simultaneously, robots communicate and compute the global map of the environment; this communication is range-limited. We propose a dynamic strategy based on consensus algorithms that is fully distributed and does not rely on any particular communication topology. Robots reach consensus on the latest global map, using the increments between their previous and current local maps. Under mild connectivity conditions, our merging algorithm asymptotically converges to the global map. We give proofs of unbiasedness of this global map, at each step and robot. Our approach has been validated using real RGB-D images. I

Distributed map merging with consensus on the common part

Abstract-Sensor fusion methods combine noisy measurements of common variables observed by several sensors, typically by averaging information matrices and vectors of the measurements. Some sensors may have also observed exclusive variables on their own. Examples include robots exploring different areas or cameras observing different parts of the scene in map merging or multi-target tracking scenarios. Iteratively averaging exclusive information is not efficient, since only one sensor provides the data, and the remaining ones echo this information. This paper proposes a method to average the information matrices and vectors associated only to the common variables. Sensors use this averaged common information to locally estimate the exclusive variables. Our estimates are equivalent to the ones obtained by averaging the complete information matrices and vectors. The proposed method preserves properties of convergence, unbiased mean, and consistency, and improves the memory, communication, and computation costs

Noisy Range Network Localization based on Distributed Multidimensional Scaling

This paper considers the noisy range-only network localization problem, in which, measurements of relative distances between agents are used to estimate their positions in networked systems. When distance information is noisy, existence and uniqueness of location solution usually are not guaranteed. It is well known that in presence of distance measurement noise, a node may have discontinuous deformations (e.g. flip ambiguities and discontinuous flex ambiguities). Thus there are two issues that we consider in noisy localization problem. The first one is the location estimate error propagated from distance measurement noise. We compare two kinds of analytical location error computation methods by assuming that each distance is corrupted with independent Gaussian random noise. These analytical results help us to understand effects of the measurement noises on the position estimation accuracy. After that, based on multidimensional scaling theory, we propose a distributed localization algorithm to solve the noisy range network localization problem. Our approach is robust to distance measurement noise, and it can be implemented in any random case without considering the network setup constraints. Moreover, a refined version of distributed noisy range localization method is developed, which achieves a good trade-off between computational effort and global convergence especially in large-scale network

A Linear Approximation for Graph-based Simultaneous Localization and Mapping

Abstract—This article investigates the problem of Simultaneous Localization and Mapping (SLAM) from the perspective of linear estimation theory. The problem is first formulated in terms of graph embedding: a graph describing robot poses at subsequent instants of time needs be embedded in a three-dimensional space, assuring that the estimated configuration maximizes measurement likelihood. Combining tools belonging to linear estimation and graph theory, a closed-form approximation to the full SLAM problem is proposed, under the assumption that the relative position and the relative orientation measurements are independent. The approach needs no initial guess for optimization and is formally proven to admit solution under the SLAM setup. The resulting estimate can be used as an approximation of the actual nonlinear solution or can be further refined by using it as an initial guess for nonlinear optimization techniques. Finally, the experimental analysis demonstrates that such refinement is often unnecessary, since the linear estimate is already accurate. I