Recent Advances in UAV Navigation

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 40587

Special Issue Editor


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Guest Editor
Institute for Aeronautics and Astronautics, Flight Guidance and Air Transport, Technical University of Berlin, Berlin, Germany
Interests: unmanned aircraft systems; integrated navigation; UAV traffic management; flight guidance

Special Issue Information

Dear Colleagues,

In recent years, the number of potential applications for Unmanned Aerial Vehicles (UAVs) has significantly increased. Current applications include environmental monitoring, surveillance, mapping, agriculture, aerial photography, infrastructure monitoring, search and rescue, and law enforcement, to name a few. For each of these applications, the UAVs operate in a unique environment (e.g., rural, urban, indoor) at various possible altitudes (e.g., high, low, very low). To support navigation and separation assurance during the various phases of flight knowledge regarding the UAV’s position, velocity, and attitude in the absolute sense (i.e., with respect to the geographic coordinate frame in which the routes and geofences are defined) and in the relative sense (i.e., with respect to other swarm members, other traffic, and objects in the environment), its performance must adhere to a set of requirements with respect to accuracy, integrity, availability, and continuity. This so-called required navigation performance depends on the operational environment and the phase of flight (e.g., en route, landing).

For many commercial UAVs, Global Navigation Satellite Systems (GNSS) such as the Global Positioning System (GPS) have become one of the most dependable solutions for position and navigation. However, in some operational environments such as the urban environment, GNSS may be unavailable, sparsely available, or significantly deteriorated due to shadowing and multipath of GNSS signals from objects and buildings, significant signal attenuation, or even intentional denial or deception. These effects could lead to hazardous misleading information that, if used for navigation, may result in an accident. Hence, monitors must be included to detect the occurrence of misleading information and mitigate its effects. In summary, to enable operation of UAVs at any time in any environment, a robust precision navigation that is not solely dependent on GPS is required.

To improve availability and guarantee continuity of service in, for example, GPS-challenged environments, GPS can be integrated with an IMU or have its sensitivity increased by using external data sources (i.e., assisted GPS). This integration strategy is successful in many cases but does not cover all possible scenarios. Alternative navigation technologies may include the integration of inertial sensors with imagery and laser scanners, beacon-based navigation (i.e., pseudolites, ultra-wideband), navigation using signals of opportunity (e.g., cellular signals, magnetic field), available information (e.g., terrain data urban maps, indoor maps), or novel integration approaches (e.g., application of explainable artificial intelligence). In addition to integrating onboard sensor information, UAVs may also collaborate with other UAVs operating in their vicinity to obtain a better navigation solution by exploiting the exchange of navigation-related information.

We would like to invite submissions on, but not limited to, the following subject areas:

  • Integrated navigation approaches for UAV operation in challenging environments;
  • Collaborative navigation and swarm navigation;
  • Biologically inspired and cognitive navigation;
  • Advances in vision- and laser-based navigation;
  • Use of cellular signals for UAV navigation;
  • Application of ultrawideband beacons;
  • New navigation sensor technologies;
  • Application of explainable artificial intelligence concepts in UAV navigation and how these methods address meeting required navigation performance;
  • Integrity-monitoring approaches;
  • Application of RNP concepts to UAV procedures;
  • Navigation aspects of a UAV traffic management system.

Prof. Dr. Maarten Uijt de Haag
Guest Editor

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Published Papers (5 papers)

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Research

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28 pages, 5305 KiB  
Article
Machine Learning-Based Environment-Aware GNSS Integrity Monitoring for Urban Air Mobility
by Oguz Kagan Isik, Ivan Petrunin and Antonios Tsourdos
Drones 2024, 8(11), 690; https://doi.org/10.3390/drones8110690 - 19 Nov 2024
Viewed by 552
Abstract
The increasing deployment of unmanned aerial vehicles (UAVs) in urban air mobility (UAM) necessitates robust Global Navigation Satellite System (GNSS) integrity monitoring that can adapt to the complexities of urban environments. The traditional integrity monitoring approaches struggle with the unique challenges posed by [...] Read more.
The increasing deployment of unmanned aerial vehicles (UAVs) in urban air mobility (UAM) necessitates robust Global Navigation Satellite System (GNSS) integrity monitoring that can adapt to the complexities of urban environments. The traditional integrity monitoring approaches struggle with the unique challenges posed by urban settings, such as frequent signal blockages, multipath reflections, and Non-Line-of-Sight (NLoS) receptions. This study introduces a novel machine learning-based GNSS integrity monitoring framework that incorporates environment recognition to create environment-specific error models. Using a comprehensive Hardware-in-the-Loop (HIL) simulation setup, extensive data were generated for suburban, urban, and urban canyon environments to train and validate the models. The proposed Natural Gradient Boosting Protection Level (NGB-PL) method, leveraging the uncertainty prediction capabilities of the NGB algorithm, demonstrated superior performance in estimating protection levels compared to the classical methods. The results indicated that environment-specific models significantly enhanced both accuracy and system availability, particularly in challenging urban scenarios. The integration of environment recognition into the integrity monitoring framework allows the dynamic adaptation to varying environmental conditions, thus substantially improving the reliability and safety of UAV operations in urban air mobility applications. This research offers a novel protection level (PL) estimation method and a framework tailored to GNSS integrity monitoring for UAM, which enhances the availability with narrower PL bound gaps without yielding higher integrity risks. Full article
(This article belongs to the Special Issue Recent Advances in UAV Navigation)
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22 pages, 6640 KiB  
Article
Efficient 3D Exploration with Distributed Multi-UAV Teams: Integrating Frontier-Based and Next-Best-View Planning
by André Ribeiro and Meysam Basiri
Drones 2024, 8(11), 630; https://doi.org/10.3390/drones8110630 - 31 Oct 2024
Viewed by 567
Abstract
Autonomous exploration of unknown environments poses many challenges in robotics, particularly when dealing with vast and complex landscapes. This paper presents a novel framework tailored for distributed multi-robot systems, harnessing the 3D mobility capabilities of Unmanned Aerial Vehicles (UAVs) equipped with advanced LiDAR [...] Read more.
Autonomous exploration of unknown environments poses many challenges in robotics, particularly when dealing with vast and complex landscapes. This paper presents a novel framework tailored for distributed multi-robot systems, harnessing the 3D mobility capabilities of Unmanned Aerial Vehicles (UAVs) equipped with advanced LiDAR sensors for the rapid and effective exploration of uncharted territories. The proposed approach uniquely integrates the robustness of frontier-based exploration with the precision of Next-Best-View (NBV) planning, supplemented by a distance-based assignment cooperative strategy, offering a comprehensive and adaptive strategy for these systems. Through extensive experiments conducted across distinct environments using up to three UAVs, the efficacy of the exploration planner and cooperative strategy is rigorously validated. Benchmarking against existing methods further underscores the superiority of the proposed approach. The results demonstrate successful navigation through complex 3D landscapes, showcasing the framework’s capability in both single- and multi-UAV scenarios. While the benefits of employing multiple UAVs are evident, exhibiting reductions in exploration time and individual travel distance, this study also reveals findings regarding the optimal number of UAVs, particularly in smaller and wider environments. Full article
(This article belongs to the Special Issue Recent Advances in UAV Navigation)
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14 pages, 4573 KiB  
Article
UAV Path Planning in Multi-Task Environments with Risks through Natural Language Understanding
by Chang Wang, Zhiwei Zhong, Xiaojia Xiang, Yi Zhu, Lizhen Wu, Dong Yin and Jie Li
Drones 2023, 7(3), 147; https://doi.org/10.3390/drones7030147 - 21 Feb 2023
Cited by 2 | Viewed by 2560
Abstract
Path planning using handcrafted waypoints is inefficient for a multi-task UAV operating in dynamic environments with potential risks such as bad weather, obstacles, or forbidden zones, among others. In this paper, we propose an automatic path planning method through natural language that instructs [...] Read more.
Path planning using handcrafted waypoints is inefficient for a multi-task UAV operating in dynamic environments with potential risks such as bad weather, obstacles, or forbidden zones, among others. In this paper, we propose an automatic path planning method through natural language that instructs the UAV with compound commands about the tasks and the corresponding regions in a given map. First, we analyze the characteristics of the tasks and we model each task with a parameterized zone. Then, we use deep neural networks to segment the natural language commands into a sequence of labeled words, from which the semantics are extracted to select the waypoints and trajectory patterns accordingly. Finally, paths between the waypoints are generated using rapidly exploring random trees (RRT) or Dubins curves based on the task requirements. We demonstrate the effectiveness of the proposed method using a simulated quadrotor UAV that follows sequential commands in four typical tasks with potential risks. Full article
(This article belongs to the Special Issue Recent Advances in UAV Navigation)
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18 pages, 1251 KiB  
Article
Adaptive Nonlinear Model Predictive Horizon Using Deep Reinforcement Learning for Optimal Trajectory Planning
by Younes Al Younes and Martin Barczyk
Drones 2022, 6(11), 323; https://doi.org/10.3390/drones6110323 - 27 Oct 2022
Cited by 3 | Viewed by 2231
Abstract
This paper presents an adaptive trajectory planning approach for nonlinear dynamical systems based on deep reinforcement learning (DRL). This methodology is applied to the authors’ recently published optimization-based trajectory planning approach named nonlinear model predictive horizon (NMPH). The resulting design, which we call [...] Read more.
This paper presents an adaptive trajectory planning approach for nonlinear dynamical systems based on deep reinforcement learning (DRL). This methodology is applied to the authors’ recently published optimization-based trajectory planning approach named nonlinear model predictive horizon (NMPH). The resulting design, which we call ‘adaptive NMPH’, generates optimal trajectories for an autonomous vehicle based on the system’s states and its environment. This is done by tuning the NMPH’s parameters online using two different actor-critic DRL-based algorithms, deep deterministic policy gradient (DDPG) and soft actor-critic (SAC). Both adaptive NMPH variants are trained and evaluated on an aerial drone inside a high-fidelity simulation environment. The results demonstrate the learning curves, sample complexity, and stability of the DRL-based adaptation scheme and show the superior performance of adaptive NMPH relative to our earlier designs. Full article
(This article belongs to the Special Issue Recent Advances in UAV Navigation)
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Review

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41 pages, 3112 KiB  
Review
Vision-Based Navigation Techniques for Unmanned Aerial Vehicles: Review and Challenges
by Muhammad Yeasir Arafat, Muhammad Morshed Alam and Sangman Moh
Drones 2023, 7(2), 89; https://doi.org/10.3390/drones7020089 - 27 Jan 2023
Cited by 71 | Viewed by 32297
Abstract
In recent years, unmanned aerial vehicles (UAVs), commonly known as drones, have gained increasing interest in both academia and industries. The evolution of UAV technologies, such as artificial intelligence, component miniaturization, and computer vision, has decreased their cost and increased availability for diverse [...] Read more.
In recent years, unmanned aerial vehicles (UAVs), commonly known as drones, have gained increasing interest in both academia and industries. The evolution of UAV technologies, such as artificial intelligence, component miniaturization, and computer vision, has decreased their cost and increased availability for diverse applications and services. Remarkably, the integration of computer vision with UAVs provides cutting-edge technology for visual navigation, localization, and obstacle avoidance, making them capable of autonomous operations. However, their limited capacity for autonomous navigation makes them unsuitable for global positioning system (GPS)-blind environments. Recently, vision-based approaches that use cheaper and more flexible visual sensors have shown considerable advantages in UAV navigation owing to the rapid development of computer vision. Visual localization and mapping, obstacle avoidance, and path planning are essential components of visual navigation. The goal of this study was to provide a comprehensive review of vision-based UAV navigation techniques. Existing techniques have been categorized and extensively reviewed with regard to their capabilities and characteristics. Then, they are qualitatively compared in terms of various aspects. We have also discussed open issues and research challenges in the design and implementation of vision-based navigation techniques for UAVs. Full article
(This article belongs to the Special Issue Recent Advances in UAV Navigation)
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