Anytime planning of optimal schedules for a mobile sensing robot
Jingjin Yu, Javed Aslam, Sertac Karaman, Daniela Rus
Abstract
We study the problem in which a mobile sensing robot is tasked to travel among and gather intelligence at a set of spatially distributed points-of-interest (POIs). The quality of the information collected at a POI is characterized by some sensory (reward) function of time. With limited fuel, the robot must balance between spending time traveling to more POIs and performing time-consuming sensing activities at POIs to maximize the overall reward. In a dual formulation, the robot is required to acquire a minimum amount of reward with the least amount of time. We propose an anytime planning algorithm for solving these two NP-hard problems to arbitrary precision for arbitrary reward functions. The algorithm is effective on large instances with tens to hundreds of POIs, as demonstrated with an extensive set of computational experiments. Besides mobile sensor scheduling, our algorithm also applies to automation scenarios such as intelligent and optimal itinerary planning.
BibTeX
@inproceedings{iros2015_anytimeplanningo,
title = {Anytime planning of optimal schedules for a mobile sensing robot},
author = {Jingjin Yu and Javed Aslam and Sertac Karaman and Daniela Rus},
booktitle = {IROS 2015},
year = {2015}
}