Breadth-First Coupled Sensor Configuration and Path-Planning in Unknown Environments

Abstract

We present a breadth-first sensor configuration strategy to find near-optimal placement and sensor field of view (FoV). The strategy couples the sensor configuration procedure directly with the decision making task of planning a path for an agent in an unknown static environment comprised of threats. This coupled sensor configuration and path-planning (CSCP) strategy iteratively uses Gaussian Process Regression to construct a threat field estimate and find a candidate optimal path with minimum threat exposure. The strategy utilizes a unique task-driven information gain (TDIG) metric, which yields the sensor configurations when maximized. Due to the non-convex and non-submodular nature of the problem, we present an approximation for the optimization of the TDIG metric. Finally, we discuss the performance of the breadth-first strategy in contrast to a standard and depth-first strategy as well as traditional information-maximization.