Autonomous, wireless sensor network-assisted target search and mapping

Abstract

The requirements of wireless sensor networks for localization applications are largely dictated by the need to estimate node positions and to establish routes to dedicated gateways for user communication and control. These requirements add significantly to the cost and complexity of such networks. In some applications, such as autonomous exploration or search and rescue, which may benefit greatly from the capabilities of wireless sensor networks, it is necessary to guide an autonomous sensor and actuator platform to a target, for example to acquire a large data payload from a sensor node, or to retrieve the target outright. We consider the scenario of a mobile platform capable of directly interrogating individual, nearby sensor nodes. Assuming that a broadcast message originates from a source node and propagates through the network by flooding, we study applications of autonomous target search and mapping, using observations of the message hop count alone. Complex computational and communication tasks are offloaded from the sensor nodes, leading to significant simplifications of the node hardware and software. This introduces the need to model the hop count observations made by the mobile platform to infer node locations. Using results from first-passage percolation theory and a maximum entropy argument, we formulate a stochastic jump process which approximates the message hop count at distance r from the source. We show that the marginal distribution of this process has a simple analytic form whose parameters can be learned by maximum likelihood estimation. Target search involving an autonomous mobile platform is modeled as a stochastic planning problem, solved approximately through policy rollout. The cost-to-go at the rollout horizon is approximated by an open-loop search plan in which path constraints and assumptions about future information gains are relaxed. It is shown that the performance is improved over typical information-driven approaches. Finally, the hop count observation model is applied to an autonomous mapping problem. The platform is guided under a myopic utility function which quantifies the expected information gain of the inferred map. Utility function parameters are adapted heuristically such that map inference improves, without the cost penalty of true non-myopic planning.