51 research outputs found
Abstracting Multidimensional Concepts for Multilevel Decision Making in Multirobot Systems
Multirobot control architectures often require robotic tasks to be well defined before allocation. In complex missions, it is often difficult to decompose an objective into a set of well defined tasks; human operators generate a simplified representation based on experience and estimation. The result is a set of robot roles, which are not best suited to accomplishing those objectives. This thesis presents an alternative approach to generating multirobot control algorithms using task abstraction. By carefully analysing data recorded from similar systems a multidimensional and multilevel representation of the mission can be abstracted, which can be subsequently converted into a robotic controller.
This work, which focuses on the control of a team of robots to play the complex game of football, is divided into three sections: In the first section we investigate the use of spatial structures in team games. Experimental results show that cooperative teams beat groups of individuals when competing for space and that controlling space is important in the game of robot football. In the second section, we generate a multilevel representation of robot football based on spatial structures measured in recorded matches. By differentiating between spatial configurations appearing in desirable and undesirable situations, we can abstract a strategy composed of the more desirable structures. In the third section, five partial strategies are generated, based on the abstracted structures, and a suitable controller is devised. A set of experiments shows the success of the method in reproducing those key structures in a multirobot system. Finally, we compile our methods into a formal architecture for task abstraction and control.
The thesis concludes that generating multirobot control algorithms using task abstraction is appropriate for problems which are complex, weakly-defined, multilevel, dynamic, competitive, unpredictable, and which display emergent properties
Design of an UAV swarm
This master thesis tries to give an overview on the general aspects involved in the design of an UAV swarm. UAV swarms are continuoulsy gaining popularity amongst researchers and UAV manufacturers, since they allow greater success rates in task accomplishing with reduced times. Appart from this, multiple UAVs cooperating between them opens a new field of missions that can only be carried in this way. All the topics explained within this master thesis will explain all the agents involved in the design of an UAV swarm, from the communication protocols between them, navigation and trajectory analysis and task allocation
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Cooperative Navigation for Low-bandwidth Mobile Acoustic Networks.
This thesis reports on the design and validation of estimation and planning algorithms for underwater vehicle cooperative localization. While attitude and depth are easily instrumented with bounded-error, autonomous underwater vehicles (AUVs) have no internal sensor that directly observes XY position. The global positioning system (GPS) and other radio-based navigation techniques are not available because of the strong attenuation of electromagnetic signals in seawater. The navigation algorithms presented herein fuse local body-frame rate and attitude measurements with range observations between vehicles within a decentralized architecture.
The acoustic communication channel is both unreliable and low bandwidth, precluding many state-of-the-art terrestrial cooperative navigation algorithms. We exploit the underlying structure of a post-process centralized estimator in order to derive two real-time decentralized estimation frameworks. First, the origin state method enables a client vehicle to exactly reproduce the corresponding centralized estimate within a server-to-client vehicle network. Second, a graph-based navigation framework produces an approximate reconstruction of the centralized estimate onboard each vehicle. Finally, we present a method to plan a locally optimal server path to localize a client vehicle along a desired nominal trajectory. The planning algorithm introduces a probabilistic channel model into prior Gaussian belief space planning frameworks.
In summary, cooperative localization reduces XY position error growth within underwater vehicle networks. Moreover, these methods remove the reliance on static beacon networks, which do not scale to large vehicle networks and limit the range of operations. Each proposed localization algorithm was validated in full-scale AUV field trials. The planning framework was evaluated through numerical simulation.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113428/1/jmwalls_1.pd
Dynamic obstacles avoidance algorithms for unmanned ground vehicles
En las últimas décadas, los vehÃculos terrestres no tripulados (UGVs) están siendo cada vez más empleados como robots de servicios. A diferencia de los robots industriales, situados en posiciones fijas y controladas, estos han de trabajar en entornos dinámicos, compartiendo su espacio con otros vehÃculos y personas. Los UGVs han de ser capaces de desplazarse sin colisionar con ningún obstáculo, de tal manera que puedan asegurar tanto su integridad como la del entorno.
En el estado del arte encontramos algoritmos de navegación autónoma diseñados para UGVs que son capaces de planificar rutas de forma segura con objetos estáticos y trabajando en entornos parcialmente controlados. Sin embargo, cuando estos entornos son dinámicos, se planifican rutas más peligrosas y que a menudo requieren de un mayor consumo de energÃa y recursos, e incluso pueden llegar a bloquear el UGV en un mÃnimo local.
En esta tesis, la adaptación de algunos algoritmos disponibles en el estado del arte para trabajar en entornos dinámicos han sido planteados. Estos algoritmos incluyen información temporal tales como los basados en arcos de curvatura (PCVM y DCVM) y los basados en ventanas dinámicas (DW4DO y DW4DOT). Además, se ha propuesto un planificador global basado en Lattice State Planner (DLP) que puede resolver situaciones donde los evitadores de obstáculos reactivos no funcionan.
Estos algoritmos han sido validados tanto en simulación como en entornos reales, utilizando distintas plataformas robóticas, entre las que se incluye un robot asistente (RoboShop) diseñado y construido en el marco de esta tesis
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Decentralized Collision-Free Control of Multiple Robots in 2D and 3D Spaces
Decentralized control of robots has attracted huge research interests.
However, some of the research used unrealistic assumptions without collision
avoidance. This report focuses on the collision-free control for multiple
robots in both complete coverage and search tasks in 2D and 3D areas which are
arbitrary unknown. All algorithms are decentralized as robots have limited
abilities and they are mathematically proved.
The report starts with the grid selection in the two tasks. Grid patterns
simplify the representation of the area and robots only need to move straightly
between neighbor vertices. For the 100% complete 2D coverage, the equilateral
triangular grid is proposed. For the complete coverage ignoring the boundary
effect, the grid with the fewest vertices is calculated in every situation for
both 2D and 3D areas.
The second part is for the complete coverage in 2D and 3D areas. A
decentralized collision-free algorithm with the above selected grid is
presented driving robots to sections which are furthest from the reference
point. The area can be static or expanding, and the algorithm is simulated in
MATLAB.
Thirdly, three grid-based decentralized random algorithms with collision
avoidance are provided to search targets in 2D or 3D areas. The number of
targets can be known or unknown. In the first algorithm, robots choose vacant
neighbors randomly with priorities on unvisited ones while the second one adds
the repulsive force to disperse robots if they are close. In the third
algorithm, if surrounded by visited vertices, the robot will use the
breadth-first search algorithm to go to one of the nearest unvisited vertices
via the grid. The second search algorithm is verified on Pioneer 3-DX robots.
The general way to generate the formula to estimate the search time is
demonstrated. Algorithms are compared with five other algorithms in MATLAB to
show their effectiveness
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