Coupled Sensor Configuration and Path-Planning in Unknown Static Environments
Chase St. Laurent, Raghvendra V. Cowlagi · 2021
We consider path-planning for a mobile agent in an unknown environment to be mapped by a sensor network, where the location and field of view of each sensor can be configured. To solve this problem we propose a coupled sensor configuration and path-planning (CSCP) iterative method, which finds an optimal sensor configuration (location and FoV) at each iteration, applies Gaussian process regression to construct a threat field estimate, and then finds a candidate optimal path with minimum expected threat exposure. We define a so-called task-driven information gain (TDIG) metric, the maximization of which provides sensor configurations. The TDIG quantifies the notion of acquiring sensor data of “most relevance” to path-planning. The CSCP iterations terminate when the path cost variance reduces below a prespecified threshold. Through numerical simulations we demonstrate that the CSCP algorithm finds near-optimal paths with significantly fewer sensor measurements compared to traditional methods.