Abstract. In contrast to classic robot 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 location on the path using all information acquired so far. The goal is to minimize the robot's travel cost required to identify a true hypothesis. Adaptive IPP is NP-hard. This paper presents 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 efficient in practice and provides good approximate solutions for several distinct robot planning tasks. Although RAId is designed primarily for noiseless observations, a simple extension allows it to handle some tasks with noisy observations.

Abstract. In contrast to classic robot 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 location on the path using all information acquired so far. The goal is to minimize the robot's travel cost required to identify a true hypothesis. Adaptive IPP is NP-hard. This paper presents 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 efficient in practice and provides good approximate solutions for several distinct robot planning tasks. Although RAId is designed primarily for noiseless observations, a simple extension allows it to handle some tasks with noisy observations.