We study two new informative path planning problems motivated by the use of aerial and ground robots in precision agriculture. The first problem, termed Sampling Traveling Salesperson Problem with Neighborhoods (STSPN), is motivated by scenarios where Unmanned Ground Vehicles (UGVs) are used to obtain time-consuming soil measurements. The input in STSPN is a set of possibly overlapping disks. The objective is to choose a sampling location in each disk, and a tour to visit the set of sampling locations so as to minimize the sum of the travel and measurement times. The second problem concerns obtaining the maximum number of aerial measurements using an Unmanned Aerial Vehicle (UAV) with limited energy. We study the scenario where the two types of robots form a symbiotic system---the UAV lands on the UGV, and the UGV transports the UAV between deployment locations. This paper makes the following contributions: First, we present an O(rmax/rmin) approximation algorithm for STSPN, where rmin and rmax are the minimum and maximum radii of input disks. Second, we show how to model the UAV planning problem using a metric graph and formulate an orienteering instance to which a known approximation algorithm can be applied. Third, we apply the two algorithms to the problem of obtaining ground and aerial measurements in order to accurately estimate a nitrogen map of a plot. Along with theoretical results, we present results from simulations conducted using real soil data and preliminary field experiments with the UAV.

@article{ tokekar2016sensor, title = "Sensor Planning for a Symbiotic {UAV} and {UGV} System for Precision Agriculture", author = {Pratap Tokekar and Joshua {Vander Hook} and David Mulla and Volkan Isler}, journal = {IEEE Transactions on Robotics}, year = "2016", }