Evaluation of a large scale pervasive embedded network for robot path planning

We investigate a technique that uses an embedded network deployed pervasively throughout an environment to aid robots in navigation. First, we show that the path computed by the network is useful for a simple mobile robot. The robot uses a network of 156 nodes to navigate through a complex, dynamic, environment. This is the largest embedded network used for navigation we are aware of. In our approach, the network nodes do not need to know their absolute or relative positions and the mobile robots do not build any kind of map. Second, the impact of specific network deployments on path quality is examined. Two types of arrangements, hexagonal and rectangular, in two different environments are considered. 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. Also, we find a slight advantage for the hexagonal arrangement