Planning Aims for a Network of Horizontal and Overhead Sensors

The ever increasing capabilities and complexity of sensor networks have led to an increased interest in sensor placement and observation planning problems. Many sensor placement and planning problems, however, lead to instances of the intractable classical planning problems or (similarly intractable) partially observable Markov decision processes. We consider the problem of planning sensor actions for a network of overhead sensors which will resolve ambiguities in the output of a horizontal sensor network. More specifically, we address the problem of counting the number of objects detected by the horizontal sensor network, using the overhead network to aim at specific areas to improve the count. The main theme of our results is that, even though sensor planning is intractable for such a network, a simple, greedy algorithm for controlling the overhead sensors guarantees performance with bounded and reasonable suboptimality. Our results are very general and make few assumptions about the specific sensors used. As such, the techniques described in this paper can be used to plan sensor aims for a wide variety of sensor types and counting problems.