Sensor Planning for a Symbiotic UAV and UGV system for Precision Agriculture

Abstract

We study the problem of coordinating an Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) to collect data for a precision agriculture application. In this application, the ground and aerial measurements collected by the system are used for estimating Nitrogen~(N) levels across a field. These estimates in turn guide fertilizer application. The capability to apply the right amount of fertilizer at the right time can drastically reduce fertilizer usage which is desirable from an environmental and economic standpoint.

We propose to use a symbiotic UAV and UGV system in which the UGV is capable of muling the UAV to a deployment location and picking it up later. This would allow the system to overcome the short battery life of a typical UAV. Toward building such a system, the paper makes the following contributions: We start with a prior N-distribution (which can be obtained from a satellite image) over the field. The goal is to classify each point into a fixed number of classes indicating N-stress levels. First, we present a method to identify Potentially Mislabeled (PML) points which are points whose probability of being mislabeled is above a threshold. Second, we study the problem of planning the UAV path so that it can visit the maximum number of PML points subject to its energy budget. The novelty of the formulation is the capability of the UGV to mule the UAV to deployment points. Third, we introduce a new path planning problem in which the UGV must take a measurement near each PML point visited by the UAV. The goal is to minimize the total time spent in traveling and taking measurements. For both problems, we present constant-factor approximation algorithms. Finally, we demonstrate the utility of the system and our algorithms with simulations which use manually collected data from the field as well as realistic energy models for the UAV and the UGV.