A review of GNSS-independent UAV navigation techniques

Gyagenda, Nasser; Hatilima, Jasper V.; Roth, Hubert; Zhmud, Vadim

doi:10.1016/j.robot.2022.104069

Cited by 92 publications

(42 citation statements)

References 87 publications

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“…In recent years, many Reinforcement Learning (RL) and imitation learning methods and approaches based on self-supervised learning [146][147][148][149] have been applied in mobile robot navigation policy design and for training supervision. Many classical modular and deep learning-based approaches have been utilised in the navigation modules of outdoor mobile robots [150]. RL uses strategies to learn optimal robot decisions from experiences.…”

Section: Mobile Robot Local Path Planningmentioning

confidence: 99%

Challenges and Solutions for Autonomous Ground Robot Scene Understanding and Navigation in Unstructured Outdoor Environments: A Review

Wijayathunga¹,

Rassau²,

Chai³

2023

Preprint

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The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, autonomous robots have been able to achieve relatively high levels of autonomy. In more unstructured environments, however, the realisation of autonomous mobile robots remains challenging due to limitations in the robots’ external environment understanding. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. The classical navigation approach typically includes robot perception, localisation, environmental mapping, path planning and motion control stages. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots need to be able to understand their external environments at a sophisticated level according to their intended applications. Therefore, in addition to robot perception, scene analysis and higher-level scene understanding (e.g., traversable/non-traversable, and rough or smooth terrain) are required for autonomous robot navigation in unstructured outdoor environments. A wide number of alternative approaches have been proposed in recent years to attempt to address these scene understanding requirements. This paper provides a comprehensive review and critical analysis of these methods in the context of their applications to the problems of robot perception and scene understanding in unstructured environments, and the related problems of localisation, environment mapping and path planning. State-of-the-art sensor fusion methods and multimodal scene understanding approaches are also discussed and evaluated within this context. The paper concludes with an in-depth discussion regarding the current state of the autonomous ground robot navigation challenge in unstructured outdoor environments and the most promising future research directions to overcome these challenges.

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Section: Mobile Robot Local Path Planningmentioning

confidence: 99%

Challenges and Solutions for Autonomous Ground Robot Scene Understanding and Navigation in Unstructured Outdoor Environments: A Review

Wijayathunga¹,

Rassau²,

Chai³

2023

Preprint

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“…At this time there are no comprehensive solutions to the operation of autonomous UAVs in GNSS-Denied scenarios, for which the permanent loss of the GNSS signals is equivalent to losing the airframe in an uncontrolled way. A summary of the challenges of GNSS-Denied navigation and the research efforts intended to improve its performance are provided by [5,24,25]. Two promising techniques for completely eliminating the position drift are the use of signals of opportunity 23 [26, 27, 28], and georegistration 24 [29,30,31,32], also known as image registration.…”

Section: A Gnss-denied Navigation and Visual Inertial Odometrymentioning

confidence: 99%

Reduction of GNSS-Denied inertial navigation errors for fixed wing autonomous unmanned air vehicles

Gallo

Barrientos

2022

Aerospace Science and Technology

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“…UAV navigation using GNSSs is the classical method in which single-frequency receivers are most commonly used [ 19 , 20 ]. A popular and important topic is navigation in an environment where a GNSS is difficult to access or unavailable [ 21 ]. In this case, inertial systems can be used, which are often combined with GNSS technology [ 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of GPS/EGNOS Positioning Quality Using Different Ionospheric Models in UAV Navigation

Grunwald

Ciećko

Kozakiewicz

et al. 2023

Sensors

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Unmanned aerial vehicles (UAVs) have become very popular tools for geoinformation acquisition in recent years. They have also been applied in many other areas of life. Their navigation is highly dependent on global navigation satellite systems (GNSS). The European Geostationary Navigation Overlay Service (EGNOS) is intended to support GNSSs during positioning, mainly for aeronautical applications. The research presented in this paper concerns the analysis of the positioning quality of a modified GPS/EGNOS algorithm. The calculations focus on the source of ionospheric delay data as well as on the aspect of smoothing code observations with phase measurements. The modifications to the algorithm concerned the application of different ionospheric models for position calculation. Consideration was given to the EGNOS ionospheric model, the Klobuchar model applied to the GPS system, the Klobuchar model applied to the BeiDou system, and the NeQuick model applied to the Galileo system. The effect of removing ionospherical corrections from GPS/EGNOS positioning on the results of the determination of positioning quality was also analysed. The results showed that the original EGNOS ionospheric model maintains the best accuracy results and a better correlation between horizontal and vertical results than the other models examined. The additional use of phase-smoothing of code observations resulted in maximum horizontal errors of approximately 1.3 m and vertical errors of approximately 2.2 m. It should be noted that the results obtained have local characteristics related to the area of north-eastern Poland.

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A review of GNSS-independent UAV navigation techniques

Cited by 92 publications

References 87 publications

Challenges and Solutions for Autonomous Ground Robot Scene Understanding and Navigation in Unstructured Outdoor Environments: A Review

Challenges and Solutions for Autonomous Ground Robot Scene Understanding and Navigation in Unstructured Outdoor Environments: A Review

Reduction of GNSS-Denied inertial navigation errors for fixed wing autonomous unmanned air vehicles

Analysis of GPS/EGNOS Positioning Quality Using Different Ionospheric Models in UAV Navigation

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