Hark! Who goes there? Concurrent association of communication channels for multiple mobile robots

Robots working in teams can benefit from recruiting the help of nearby robots. But, while robots are typically aware of their neighbors’ relative positions through information sensed locally (e.g., range and bearing), a robot does not necessarily know the network identifiers (IDs) of its neighbors directly from observation. In this work robots use a simple visual gesture, paired with wireless messages, to rapidly and effectively establish a one-to-one association between the relative positions (local, visual IDs) of neighboring robots and their network addresses (global, wireless IDs). We formalize the channel association problem and explore its structure from an information filter perspective. Under an idealized communication model, we investigate two simple probabilistic algorithms and contribute analyses of performance in terms of parameters, such as robot density, communication range, and movement speed. Branching Processes are used to predict the macroscopic performance of the algorithms, producing models that characterize the channel association behavior, given parameters that describe the multi-robot system. The approach also allows parameters to be fine-tuned when designing a system so that its performance meets some specified threshold.