東華大學圖書館 |

Cooperative Terrain-Relative Navigation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Cooperative Terrain-Relative Navigation./
作者:	Wiktor, Adam Tadeusz.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	145 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
Contained By:	Dissertations Abstracts International83-09B.
標題:	Robots. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28971834
ISBN:	9798780652380

Cooperative Terrain-Relative Navigation.
Wiktor, Adam Tadeusz.

Cooperative Terrain-Relative Navigation. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 145 p.

Source: Dissertations Abstracts International, Volume: 83-09, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

Accurate localization is critical for many robotic systems. Localization is especially challenging for agents in environments without access to signals from a Global Navigation Satellite System (GNSS), such as underwater or in deep space. Terrain-Relative Navigation (TRN) is one approach to solving the GNSS-denied navigation problem, which works by matching sensor measurements of the environment with a known map to find the most likely position of the vehicle in map coordinates. A TRN system using a particle filter to compare sonar range measurements to the seafloor with a bathymetric map has been developed and tested on Autonomous Underwater Vehicles (AUVs) in Monterey Bay. This system has been shown to be highly effective at localizing vehicles over informative terrain, but fails to converge for vehicles over flat terrain.This thesis attempts to solve the problem of localization in flat terrain by fusing measurements between a team of vehicles acting cooperatively. Assuming vehicles can measure the relative positions of other agents and communicate in real time, vehicles over informative terrain can act as ad-hoc navigational aids to localize vehicles in flat terrain. However, the well-known problem of measurement correlation in cooperative navigation networks causes existing state estimation filters to become inconsistent and overconverge. No prior approach is able to meet the requirements of cooperative underwater navigation, specifically, a distributed algorithm with low-bandwidth communication that allows for multi-modal position estimates while remaining consistent. This thesis introduces a new filter architecture that meets these requirements for underwater cooperative TRN. By splitting the estimator into a bank of filters, with each filter uniquely assigned to a different information source, the architecture entirely eliminates one type of measurement correlation. The remaining correlation is handled using Covariance Intersection, a conservative technique that consistently fuses correlated measurements. When restricting the problem to linear, Gaussian systems, this new algorithm is referred to as Compartmentalized Covariance Intersection (CCI). CCI is shown to have substantially tighter convergence than existing methods. The covariance recovers 99% of the performance of an ideal centralized filter in some tests. Additionally, a proof is presented to guarantee that the algorithm is consistent under standard Kalman filter assumptions.The CCI algorithm is extended to apply to nonlinear measurements, losing the guarantee of consistency but still working well in practice. The algorithm is demonstrated with a laboratory experiment using six ultra-wideband tags collecting real-world measurements, and simulated navigation data from an OptiTrack camera system. This shows that the CCI approach is applicable to real-world nonlinear and partially observable range measurements.The CCI algorithm is again extended to use non-parametric particle filters to allow for full cooperative TRN with multi-modal position estimates. This is demonstrated in simulation, where cooperative TRN is shown to have a 63% reduction in localization error over standard single-vehicle TRN for one example mission, reducing the average error from 23.7m to 8.7m for a vehicle over flat terrain.Finally, the cooperative TRN algorithm is demonstrated using field data from a team of LongRange AUVs in Monterey Bay. The AUVs used sonar to perform TRN as usual, and collected range and bearing measurments to the other vehicles using an acoustic modem.

ISBN: 9798780652380Subjects--Topical Terms:

529507
Robots.

Cooperative Terrain-Relative Navigation.
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