Physical Path Planning Using a Pervasive Embedded Network

We evaluate a technique that uses an embedded network deployed pervasively throughout an environment to aid robots in navigation. The embedded nodes do not know their absolute or relative positions and the mobile robots do not perform localization or mapping. Yet, the mobile robot is able to navigate through complex environments effectively. First, we present an algorithm for physical path planning and its implementation on the Gnats, a novel embedded network platform. Next, we investigate the quality of the computed paths. We present quantitative results collected from a real-world embedded network of 60 nodes. Experimentally, we find that, on average, the path computed by the network is only 24% longer than the optimal path. Finally, we show that the paths computed by the network are useful for a simple mobile robot. Results from a network of 156 nodes in a static environment and a network of 60 nodes in a dynamic environment are presented.