http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Solving the Communication Channel Association Problem for Mobile Robots
Robots working in teams can benefit from recruiting the help of conveniently located nearby robots. To do this an initiating robot needs to be aware of the network addresses of its neighbors. However, a robot is typically aware of its neighbors' relative positions through locally sensed information, such as range and bearing, which does not include the network ID of the neighbor. In this work, robots use a simple visual gesture, such as a light being turned on or off, paired with wireless messages to rapidly and effectively establish a one-to-one association between the relative positions (visual IDs) of neighboring robots and their network addresses (wireless IDs). We identify and formalize the problem of associating the two types of IDs - the association problem, and explore its structure in detail. We also identify that the visual gesture along with the sensor which detects it form a second communication channel that is used by the robots to transmit information. We present two deterministic and one probabilistic algorithm which solve the association problem for stationary robots. Furthermore, we introduce modifications to the probabilistic algorithm in order to tackle the harder, mobile robot version of the problem where robot motion results in changing connectivity between robots. Our algorithmic approach exploits the physically situated properties of the visual IDs to help solve the association problem. We identify key parameters, such as robot density, communication range and movement speed, and study their effect on the performance of the probabilistic algorithm. We use a population growth modeling framework, called Branching Processes, as part of a set of models for the association process which can be used to predict the macroscopic performance of a multi-robot system running the probabilistic algorithms. This set of models can be used to determine how successful the probabilistic algorithm is at solving the association problem in any multi-robot system based on the above mentioned key parameters. The framework can also be used to fine-tune parameters when designing a system so that its performance achieves some desired threshold. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/155503
Master of Science Texas A & M University 2015 http://hdl.handle.net/1969.1/155503