UGV-to-UAV cooperative ranging for robust navigation in GNSS-challenged environments

TLDR: It is shown that employing UGV-to-UAV cooperative navigation can reduce the positioning error of a UAV that is operating in a GNSS-challenged environment, from approximately 1-meter-level to approximately 10-cm-level 3D positioning error.

About: This article is published in Aerospace Science and Technology.The article was published on 2017-12-01 and is currently open access. It has received 55 citations till now. The article focuses on the topics: Unmanned ground vehicle & GNSS applications.

Figures

Figure 8.13: Block diagram of UAV systems

Table 7.1: Simulation parameters

Figure 9.8: PDOP reduction of nonlinear optimization vs Regionally Optimal approach

Table 9.2: Mean PDOP comparison for 50 data sets: nonlinear optimization cases versus Regionally Optimal

Figure 9.13: PDOP reduction of nonlinear optimization between UAV with no positioning error and UAV’s position with error and with weighting

Table 9.4: Mean sum PDOP comparison for 50 data sets: 10-step path planning nonlinear optimization cases

Frequently asked questions

Q1. What have the authors contributed in "Ugv-to-uav cooperative ranging for robust navigation in gnss-challenged environments" ?

The overall goal of this research is to reduce the positioning error of the UAV. To achieve this goal, the project was divided into three phases. During the first phase of the project, a Monte-Carlo simulation was employed to show that: UGVto-UAV cooperative navigation can reduce the positioning error of a UAV operating in a GNSS-challenged environment by more than tenfold ( i. e., 1-meter-level to 10cm-level 3D positioning error ) and the best approach to design the UGVs trajectory is a constrained nonlinear optimization that takes into account the GNSS satellites available to the UAV and the location of the UAV. During the second phase of the project, the algorithm is extended to consider the benefits of employing a UGV path planning approach that leverages prior knowledge of the UAVs planned mission and the ephemeris of the GNSS satellites, and to consider employing multiple UGVs to help a UAVs navigation solution. Finally, during the last phase of the project, a series of experimental flight tests were conducted using an instrumented UAV and an instrumented UGV in order to demonstrate the applicability of this research, specifically the cooperative strategies. 

Q2. What have the authors stated for future works in "Ugv-to-uav cooperative ranging for robust navigation in gnss-challenged environments" ?

There are many opportunities to further the research presented in this dissertation. Future work would include having a model of the city block to be able to have an accurate elevation mask and an accurate 117 multipath estimation. Currently the nonlinear optimization cooperative navigation strategy only evaluates the reduction of PDOP, and while this is an accurate evaluation, future work would include using the two estimation filters to evaluate the positioning error of the UAV. Future work would test out the other two cooperative algorithms of the UGV to show which was the most beneficial in experimental data. 

Q3. Who is the author of this article?

405.1 Reducing PDOP with a Single Ranging Source . . . . . . . . . . . . . 43 5.2 Undifferenced GNSS Extended Kalman Filter . . . . . . . . . . . . . 

Q4. What is the main topic of the paper?

374.5.1 Unconstrained Optimization . . . . . . . . . . . . . . . . . . . 37 4.5.2 Constrained Optimization . . . . . . . . . . . . . . . . . . . .