Flight-testing of a cooperative UGV-to-UAV strategy for improved positioning in challenging GNSS environments

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Abstract

We present an experimental flight test evaluation of a cooperative navigation strategy in which an Unmanned Aerial Vehicle (UAV) that is subjected to very poor GNSS satellite geometry is provided ranging updates from Unmanned Ground Vehicle (UGV). Central to the design of this approach, the UGV's motion planning is designed to provide the most favorable positioning geometry for the UAV. During a set of field tests, the positioning error of a UAV that is confronted with unfavorable GNSS satellite geometry is shown to be reduced by more than five-fold through the use of ranging updates from a UGV.

Introduction

One of the many technologies that has greatly benefited from the availability of precise Global Navigation Satellite System (GNSS) positioning is Unmanned Aerial Vehicles (UAVs). However, it is well known that there are many situations where GNSS is not reliable for UAV applications due to environmental conditions. For example, in urban settings, urban canyons often lead to extreme multipath, complete GNSS blockages, and/or the reception of non-Line of Sight (NLOS) signals, which can lead to position errors as large as hundreds of meters [1].

Errors due to lack of GNSS signals and/or tracked NLOS GNSS signals can be alleviated to a large extent by fusing GNSS with additional navigation sensors (e.g., optical/infrared cameras [2], LIDAR [3], cellular signals [4], etc.). However, given the fact that UAVs are typically subjected to stringent mass and power constraints, adding additional sensor packages and their associated processing needs may be unfavorable and could limit the length of available UAV flight duration. As such, another popular approach to address this problem is to employ cooperative or collaborative navigation amongst multiple vehicles [5], [6], [7], [8], [9], [10]. The approach presented in this article follows this line of reasoning, and seeks to mitigate poor GNSS positioning of a UAV with the aid of a cooperative Unmanned Ground Vehicle (UGV).

The algorithm that is tested herein is one of the formulations that have been presented and tested in a simulation environment within our prior work [11], and as part of the first author's graduate dissertation [12]. As such, the primary contributions of this article are to demonstrate the practicality of the approach through an experimental test campaign and to document its expected performance when using actual GNSS data collected on-board a remotely piloted quad-rotor UAV and autonomous UGV. In order avoid replication from [11], in this article, it is assumed that the reader has access to [11], and has referred to in order to obtain context with respect to the high-level motivation of the cooperative algorithm and its concept of operations. In this article, multiple figures and results tables, along with their associated description and discussions have been reproduced or paraphrased directly from the first author's dissertation [12].

This article is organized as follows. First, in Section 2, the cooperative navigation algorithm being evaluated is summarized by walking through some of the details discussed already in [11]. Then, in Section 3, the experimental set-up is described. Finally, the experimental results are reported in Section 4, and conclusions are drawn in Section 5.

Section snippets

Technical approach

The cooperative navigation approach presented in this paper is detailed in our prior work [11], and consists of a differential GNSS Extended Kalman Filter (EKF) that operates between a UGV and UAV and incorporates a relative ranging measurement between the two vehicles. In this scenario, the UGV effectively plays the role of a mobile differential GNSS reference station, while the UAV is the roving receiver. Furthermore, the UGV also acts as an additional GNSS satellite by providing a

Experimental set-up

The premise of the experimental design is to flight-test the experiment and collect data in an environment that has favorable GNSS positioning with access to many signals. Then, to evaluate the potential of the approach, in post-processing, harsh elevation and azimuth masks were applied to the recorded data in order to limit the available GPS signals available for the UAV positioning and to therefore severely degraded its position solution. Note that, to execute the cooperative strategies in

Results

The results from the experimental testing are from four data sets of having the UAV flying and the UGV moving based on the locally greedy cooperative strategy and the differential GNSS EKF with cooperative ranging updates, as outlined above. The results compare the UAV's positioning accuracy with respect to the reference solutions when having the cooperative UGV ranging and without the aid of the UGV.

Fig. 10 shows the path for one experimental test of both the UAV and the UGV, using the

Conclusions

The purpose of this article has been to present the result of experimental flight demonstration of a cooperative navigation between a UAV and UGV that have been presented using only simulated data in our prior work [11], [12]. In particular, a cooperative navigation algorithm using a locally greedy strategy was tested on a UGV in concert with a UAV, and a differential GNSS EKF was then used alleviate the impact of poor GPS positioning geometry for the UAV. This experimental test showed that

Conflict of interest statement

The authors declare that there is no conflict of interest in regard to the publication of this manuscript.

Acknowledgement

This research supported in part by the U.S. National Geospatial-Intelligence Agency Academic Research Program (NARP) grant # HM0476-15-1-0004. Approved for public release, 18-638.

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