Coupled Sensor Configuration and Planning in Unknown Dynamic Environments with Context-Relevant Mutual Information-based Sensor Placement

We address path-planning for a mobile agent to navigate in an unknown environment with minimum exposure to a spatially and temporally varying threat field. The threat field is estimated using pointwise noisy measurements from a sensor network separate from the mobile agent. For this problem, we present a new metric for optimal sensor placement that quantifies reduction in uncertainty in the path cost, rather than the environment state. This metric, which we call the context-relevant mutual information (CRMI), couples the sensor placement and path-planning problem. We propose also an iterative coupled sensor configuration and path planning (CSCP) algorithm. At each iteration, the algorithm places sensors to maximize CRMI, updates the threat estimate using new measurements, and recalculates the path with minimum expected exposure to the threat. The iterations converge when the path cost variance, which is an indicator of risk, reduces below a desired threshold. Through numerical simulations we demonstrate that the principal advantage of this algorithm is that near-optimal low-variance paths are achieved using far fewer sensor measurements as compared to a standard decoupled method.