Adaptive informative path planning in metric spaces

In contrast to classic geometric motion planning, informative path planning (IPP) seeks a path for a robot to sense the world and gain information. In adaptive IPP, the robot chooses the next sensing location conditioned on all information acquired so far, and the robot’s goal is to minimize the travel cost required for identifying a true hypothesis. Adaptive IPP is NP-hard, because the robot must trade-off information gain and travel cost optimally. In this paper we present Recursive Adaptive Identification (RAId), a new polynomial-time approximation algorithm for adaptive IPP. We prove a polylogarithmic approximation bound when the robot travels in a metric space. Furthermore, our experiments suggest that RAId is practical and provides good approximate solutions for two distinct robot planning tasks. Although RAId is designed primarily for noiseless observations, a simple extension allows it to handle some tasks with noisy observations.