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Aerospace
Special Issues
UAV Path Planning and Navigation
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UAV Path Planning and Navigation

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Aeronautics".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 13057

Special Issue Editors

Dr. Flavia Causa

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy
Interests: UAV navigation; UAV path planning; integrated navigation; cooperative navigation; UAM; urban traffic; GNSS
Special Issues, Collections and Topics in MDPI journals
Dr. Giancarmine Fasano

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Naples "Federico II", P.le Tecchio 80, 80125 Naples, Italy
Interests: unmanned aircraft systems; vision-based applications; avionics; guidance and navigation; detect and avoid; target tracking; path planning; data fusion; swarms; distributed space systems; formation flying; in orbit proximity operations; space surveillance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last decade, unmanned aerial vehicles (UAVs) have gained popularity in many application fields, due to their flexibility, level of automation/autonomy and relatively low cost. Their potential can be exploited to perform several missions, reducing the need for human efforts in risky operations. Nowadays, UAVs are key tools in several applications, such as monitoring, inspection and surveillance. Moreover, brand-new applications are envisaged to be carried out autonomously by drones in the near future, including the transportation of people for relatively short distances and in environments not served by traditional aviation.

Mission safety and effectiveness are key to fully unleashing UAVs’ potential. Several solutions and technological advances are being developed by the scientific community in this direction to expand UAVs’ capabilities and enable missions to be autonomously carried out by these platforms. To this aim, both autonomous planning and navigation functionality should be guaranteed. The first allows a UAV to design its trajectory and confers decision-making capabilities to help UAVs counteract any unexpected event. The latter is required to enable the UAV to localize itself in any environment. Even if planning and navigation problems are usually taken into account separately, several areas use planning capability to fulfill navigation requirements. This Special Issue aims to collect papers on the state of the art and future trends in UAVs techniques enabling reliable navigation and safe and effective path planning. Papers are solicited on all areas directly related to these topics, including, but not limited to, the following:

  • Autonomous UAV architectures;
  • UAV navigation and localization in outdoor environments;
  • UAV navigation and localization in outdoor, indoor and/or GNSS-denied environments, urban and/or GNSS challenged areas;
  • Usage of exteroceptive sensors (camera, Lidar, UWB radar) aiding navigation;
  • GNSS integrity monitoring and fault detection and exclusion;
  • Sensor fusion for improving UAV navigation;
  • Cooperative navigation for swarms of heterogenous and/or homogeneous UAVs;
  • UAV cooperative navigation in GNSS challenged or denied environments;
  • UAV trajectory design for urban environments and UAM;
  • UAV multi-optimization trajectory design;
  • UAV navigation-aware path planning;
  • UAV coverage path planning problem and modeling of payload sensor;
  • Path planning and task assignment for swarms of heterogenous and/or homogeneous UAVs.

Dr. Flavia Causa
Dr. Giancarmine Fasano
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • UAV path planning
  • UAV localization
  • challenging/GNSS-denied environment
  • GNSS integrity monitoring
  • path optimization
  • cooperative path planning
  • cooperative localization
  • task assignment
  • UAV swarm

Published Papers (8 papers)

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Research

30 pages, 16286 KiB  
Article
Implementing and Testing a U-Space System: Lessons Learnt
by Miguel-Ángel Fas-Millán, Andreas Pick, Daniel González del Río, Alejandro Paniagua Tineo and Rubén García García
Aerospace 2024, 11(3), 178; https://doi.org/10.3390/aerospace11030178 - 23 Feb 2024
Viewed by 3736
Abstract
Within the framework of the European Union’s Horizon 2020 research and innovation program, one of the main goals of the Labyrinth project was to develop and test the Conflict Management services of a U-space-based Unmanned Traffic Management (UTM) system. The U-space concept of [...] Read more.
Within the framework of the European Union’s Horizon 2020 research and innovation program, one of the main goals of the Labyrinth project was to develop and test the Conflict Management services of a U-space-based Unmanned Traffic Management (UTM) system. The U-space concept of operations (ConOps) provides a high-level description of the architecture, requirements and functionalities of these systems, but the implementer has a certain degree of freedom in aspects like the techniques used or some policies and procedures. The current document describes some of those implementation decisions. The prototype included part of the services defined by the ConOps, namely e-identification, Tracking, Geo-awareness, Drone Aeronautical Information Management, Geo-fence Provision, Operation Plan Preparation/Optimization, Operation Plan Processing, Strategic Conflict Resolution, Tactical Conflict Resolution, Emergency Management, Monitoring, Traffic Information and Legal Recording. Moreover, a Web app interface was developed for the operator/pilot. The system was tested in simulations and real visual line of sight (VLOS) and beyond VLOS (BVLOS) flights, with both vertical take-off and landing (VTOL) and fixed-wing platforms, while assisting final users interested in incorporating drones to support their tasks. The development and testing of the environment provided lessons at different levels: functionalities, compatibility, procedures, information, usability, ground control station (GCS) integration and aircrew roles. Full article
(This article belongs to the Special Issue UAV Path Planning and Navigation)
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21 pages, 6357 KiB  
Article
Evaluation of the Success of Simulation of the Unmanned Aerial Vehicle Precision Landing Provided by a Newly Designed System for Precision Landing in a Mountainous Area
by Pavol Kurdel, Natália Gecejová, Marek Češkovič and Anna Yakovlieva
Aerospace 2024, 11(1), 82; https://doi.org/10.3390/aerospace11010082 - 16 Jan 2024
Viewed by 923
Abstract
Unmanned aerial vehicle technology is the most advanced and helpful in almost every area of interest in human work. These devices become autonomous and can fulfil a variety of tasks, from simple imaging and obtaining data to search and rescue operations. The most [...] Read more.
Unmanned aerial vehicle technology is the most advanced and helpful in almost every area of interest in human work. These devices become autonomous and can fulfil a variety of tasks, from simple imaging and obtaining data to search and rescue operations. The most challenging environment for search and rescue operations is the mountainous area. This article is devoted to the theoretical description and simulation tests of a prototype method of landing the light and the medium-weight UAVs used as supplementary devices for SAR (search and rescue) and HEMS (helicopter emergency medical service) in hard-to-reach mountainous terrains. The autonomous flight of a UAV in mountainous terrain has many specifics, and it is usually performed according to predetermined map points (pins) uploaded directly into the control software of the UAV. It is necessary to characterise each point flown on the chosen flight route line in advance and therefore to know its exact geographical coordinates (longitude, latitude and height of the point above the terrain), and the control system of UAV must react to the change in the weather and other conditions in real time. Usually, it is difficult to make this forecast with sufficient time in advance, mainly when UAVs are used as supplementary devices for the needs of HEMS or MRS (mountain rescue service). The most challenging phase is the final approach and landing of the UAV, especially if a loss of GNSS (global navigation satellite system) signal occurs, like in the determined area of the Little Cold Valley in the Slovak High Tatras—which is infamous for the widespread loss of GNSS signals or communication/controlling connection between the UAV and the pilot-operator at the operational station. To solve the loss of guidance, a new method for guiding and controlling the UAV in its final approach and landing in a determined area is tested. An alternative landing navigation system for UAVs in a specific mountainous environment—the authors’ designed frequency Doppler landing system (FDLS)—is briefly described but thoroughly tested with the help of artificial intelligence. An estimation of dynamic stability is used based on the time recording of the current position of the UAV, with the help of a frequency-modulated or amplitude-modulated signal based on the author’s prototype of a precision landing system designed for mountainous terrain. This solution could overcome the problems of GNSS signal loss. The presented research primarily evaluates the success of the simulation flights for the supplementary UAV. The success of navigating the UAV to land in the mountainous environment at an exact landing point using the navigation signals from the FDLS was evaluated at more than 95%. Full article
(This article belongs to the Special Issue UAV Path Planning and Navigation)
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24 pages, 2448 KiB  
Article
Fixed-Wing UAV Formation Path Planning Based on Formation Control: Theory and Application
by Chenglou Liu, Fangfang Xie and Tingwei Ji
Aerospace 2024, 11(1), 1; https://doi.org/10.3390/aerospace11010001 - 19 Dec 2023
Viewed by 1133
Abstract
Formation path planning is a significant cornerstone for unmanned aerial vehicle (UAV) swarm intelligence. Previous methods were not suitable for large-scale UAV formation, which suffered from poor formation maintenance and low planning efficiency. To this end, this paper proposes a novel millisecond-level path [...] Read more.
Formation path planning is a significant cornerstone for unmanned aerial vehicle (UAV) swarm intelligence. Previous methods were not suitable for large-scale UAV formation, which suffered from poor formation maintenance and low planning efficiency. To this end, this paper proposes a novel millisecond-level path planning method appropriate for large-scale fixed-wing UAV formation, which consists of two parts. Instead of directly planning paths independently for each UAV in the formation, the proposed method first introduces a formation control strategy. It controls the chaotic UAV swarm to move as a single rigid body, so that only one planning can obtain the feasible path of the entire formation. Then, a computationally lightweight Dubins path generation method with a closed-form expression is employed to plan feasible paths for the formation. During flight, the aforementioned formation control strategy maintains the geometric features of the formation and avoids internal collisions within the formation. Finally, the effectiveness of the proposed framework is exemplified through several simulations. The results show that the proposed method can not only achieve millisecond-level path planning for the entire formation but also excellently maintain formation during the flight. Furthermore, simple formation obstacle avoidance in a special case also highlights the application potential of the proposed method. Full article
(This article belongs to the Special Issue UAV Path Planning and Navigation)
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21 pages, 5542 KiB  
Article
Path Planning with Multiple UAVs Considering the Sensing Range and Improved K-Means Clustering in WSNs
by Sejeong Kim and Jongho Park
Aerospace 2023, 10(11), 939; https://doi.org/10.3390/aerospace10110939 - 02 Nov 2023
Cited by 1 | Viewed by 1069
Abstract
Recently, an Unmanned Aerial Vehicle (UAV)-based Wireless Sensor Network (WSN) for data collection was proposed. Multiple UAVs are more effective than a single UAV in wide WSNs. However, in this scenario, many factors must be considered, such as collision avoidance, the appropriate flight [...] Read more.
Recently, an Unmanned Aerial Vehicle (UAV)-based Wireless Sensor Network (WSN) for data collection was proposed. Multiple UAVs are more effective than a single UAV in wide WSNs. However, in this scenario, many factors must be considered, such as collision avoidance, the appropriate flight path, and the task time. Therefore, it is important to effectively divide the mission areas of the UAVs. In this paper, we propose an improved k-means clustering algorithm that effectively distributes sensors with various densities and fairly assigns mission areas to UAVs with comparable performance. The proposed algorithm distributes mission areas more effectively than conventional methods using cluster head selection and improved k-means clustering. In addition, a postprocessing procedure for reducing the path length during UAV path planning for each mission area is important. Thus, a waypoint refinement algorithm that considers the sensing ranges of the sensor node and the UAV is proposed to effectively improve the flight path of the UAV. The task completion time is determined by evaluating how the UAV collects data through communication with the cluster head node. The simulation results show that the mission area distribution by the improved k-means clustering algorithm and postprocessing by the waypoint refinement algorithm improve the performance and the UAV flight path during data collection. Full article
(This article belongs to the Special Issue UAV Path Planning and Navigation)
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27 pages, 7516 KiB  
Article
Integrating GRU with a Kalman Filter to Enhance Visual Inertial Odometry Performance in Complex Environments
by Tarafder Elmi Tabassum, Zhengjia Xu, Ivan Petrunin and Zeeshan A. Rana
Aerospace 2023, 10(11), 923; https://doi.org/10.3390/aerospace10110923 - 29 Oct 2023
Viewed by 1649
Abstract
To enhance system reliability and mitigate the vulnerabilities of the Global Navigation Satellite Systems (GNSS), it is common to fuse the Inertial Measurement Unit (IMU) and visual sensors with the GNSS receiver in the navigation system design, effectively enabling compensations with absolute positions [...] Read more.
To enhance system reliability and mitigate the vulnerabilities of the Global Navigation Satellite Systems (GNSS), it is common to fuse the Inertial Measurement Unit (IMU) and visual sensors with the GNSS receiver in the navigation system design, effectively enabling compensations with absolute positions and reducing data gaps. To address the shortcomings of a traditional Kalman Filter (KF), such as sensor errors, an imperfect non-linear system model, and KF estimation errors, a GRU-aided ESKF architecture is proposed to enhance the positioning performance. This study conducts Failure Mode and Effect Analysis (FMEA) to prioritize and identify the potential faults in the urban environment, facilitating the design of improved fault-tolerant system architecture. The identified primary fault events are data association errors and navigation environment errors during fault conditions of feature mismatch, especially in the presence of multiple failure modes. A hybrid federated navigation system architecture is employed using a Gated Recurrent Unit (GRU) to predict state increments for updating the state vector in the Error Estate Kalman Filter (ESKF) measurement step. The proposed algorithm’s performance is evaluated in a simulation environment in MATLAB under multiple visually degraded conditions. Comparative results provide evidence that the GRU-aided ESKF outperforms standard ESKF and state-of-the-art solutions like VINS-Mono, End-to-End VIO, and Self-Supervised VIO, exhibiting accuracy improvement in complex environments in terms of root mean square errors (RMSEs) and maximum errors. Full article
(This article belongs to the Special Issue UAV Path Planning and Navigation)
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19 pages, 7530 KiB  
Article
Energy-Harvesting Strategy Investigation for Glider Autonomous Soaring Using Reinforcement Learning
by Jiachi Zhao, Jun Li and Lifang Zeng
Aerospace 2023, 10(10), 895; https://doi.org/10.3390/aerospace10100895 - 19 Oct 2023
Viewed by 1217
Abstract
Birds and experienced glider pilots frequently use atmospheric updrafts for long-distance flight and energy conservation, with harvested energy from updrafts serving as the foundation. Inspired by their common characteristics in autonomous soaring, a reinforcement learning algorithm, the Twin Delayed Deep Deterministic policy gradient, [...] Read more.
Birds and experienced glider pilots frequently use atmospheric updrafts for long-distance flight and energy conservation, with harvested energy from updrafts serving as the foundation. Inspired by their common characteristics in autonomous soaring, a reinforcement learning algorithm, the Twin Delayed Deep Deterministic policy gradient, is used to investigate the optimal strategy for an unpowered glider to harvest energy from thermal updrafts. A round updraft model is utilized to characterize updrafts with varied strengths. A high-fidelity six-degree-of-glider model is used in the dynamic modeling of a glider. The results for various flight initial positions and updraft strengths demonstrate the effectiveness of the strategy learned via reinforcement learning. To enhance the updraft perception ability and expand the applicability of the trained glider agent, an extra wind velocity differential correction module is introduced to the algorithm, and a strategy symmetry method is applied. Comparison experiments regarding round updraft, the Gedeon thermal model, and Dryden continuous turbulence indicate the crucial role of the further optimized methods in improving the updraft-sensing ability of the reinforcement learning glider agent. With optimized methods, a glider trained in a simplified thermal updraft with a simple training method can have more effective flight strategies. Full article
(This article belongs to the Special Issue UAV Path Planning and Navigation)
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17 pages, 24047 KiB  
Article
Bridging the Gap between Simulation and Real Autonomous UAV Flights in Industrial Applications
by Rafael Perez-Segui, Pedro Arias-Perez, Javier Melero-Deza, Miguel Fernandez-Cortizas, David Perez-Saura and Pascual Campoy
Aerospace 2023, 10(9), 814; https://doi.org/10.3390/aerospace10090814 - 17 Sep 2023
Cited by 1 | Viewed by 1175
Abstract
The utilization of autonomous unmanned aerial vehicles (UAVs) has increased rapidly due to their ability to perform a variety of tasks, including industrial inspection. Conducting testing with actual flights within industrial facilities proves to be both expensive and hazardous, posing risks to the [...] Read more.
The utilization of autonomous unmanned aerial vehicles (UAVs) has increased rapidly due to their ability to perform a variety of tasks, including industrial inspection. Conducting testing with actual flights within industrial facilities proves to be both expensive and hazardous, posing risks to the system, the facilities, and their personnel. This paper presents an innovative and reliable methodology for developing such applications, ensuring safety and efficiency throughout the process. It involves a staged transition from simulation to reality, wherein various components are validated at each stage. This iterative approach facilitates error identification and resolution, enabling subsequent real flights to be conducted with enhanced safety after validating the remainder of the system. Furthermore, this article showcases two use cases: wind turbine inspection and photovoltaic plant inspection. By implementing the suggested methodology, these applications were successfully developed in an efficient and secure manner. Full article
(This article belongs to the Special Issue UAV Path Planning and Navigation)
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30 pages, 12016 KiB  
Article
Path Planning of an Unmanned Combat Aerial Vehicle with an Extended-Treatment-Approach-Based Immune Plasma Algorithm
by Selcuk Aslan and Tugrul Oktay
Aerospace 2023, 10(5), 487; https://doi.org/10.3390/aerospace10050487 - 21 May 2023
Cited by 1 | Viewed by 1259
Abstract
The increasing usage of unmanned aerial vehicles (UAVs) and their variants carrying complex weapon systems, known as unmanned combat aerial vehicles (UCAVs), has triggered a global revolution in complex military and commercial operations and has attracted researcher attention from different engineering disciplines in [...] Read more.
The increasing usage of unmanned aerial vehicles (UAVs) and their variants carrying complex weapon systems, known as unmanned combat aerial vehicles (UCAVs), has triggered a global revolution in complex military and commercial operations and has attracted researcher attention from different engineering disciplines in order to solve challenging problems regarding these modern vehicles. Path planning is a challenging problem for UAV and UCAV systems that requires the calculation of an optimal solution by considering enemy threats, total flight length, fuel or battery consumption, and some kinematic properties such as turning or climbing angles. In this study, the immune plasma (IP or IPA) algorithm, one of the most recent nature-inspired intelligent optimization methods, was modified by changing the default plasma transfer operations with a newly proposed technique called the extended treatment approach; extended IPA (ExtIPA) was then introduced as a path planner. To analyze the solving capabilities of the ExtIPA, 16 cases from five battlefield scenarios were tested by assigning different values to the algorithm-specific control parameters. The paths calculated with ExtIPA were compared with the paths found by planners on the basis of other intelligent optimization techniques. Comparative studies between ExtIPA and other techniques allowed for stating that the extended treatment approach significantly contributes to both the convergence speed and qualities of the obtained solutions and helps ExtIPA in performing better than its rivals in most cases. Full article
(This article belongs to the Special Issue UAV Path Planning and Navigation)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Integrating GRU with Kalman Filter to enhance Visual Inertial Odometry Performance in Complex Environment
Authors: Ivan Petrunin
Affiliation: School of Aerospace, Transport and Manufacturing Building 105, Cranfield University, Cranfield, Bedfordshire MK43 0AL
Abstract: Integrating multiple sensors while designing autonomous navigation systems has become vital for precise positioning to enhance navigation performance in complex situations, e.g. in urban areas. To address the vulnerability of Global Navigation Satellite Systems (GNSS), the navigation solution fuses other sensor data from IMU and stereo camera with GNSS to bridge navigation data gaps and improve reliability. Kalman Filter (KF) is frequently used for sensor fusion approaches with drawbacks of sensor error, imperfect nonlinear system model, and KF estimation error, which rapidly degrades navigation performance. To overcome the shortcomings of traditional KF-based fusion methods, we propose AI-based hybrid Visual Inertial Odometry (VIO) to enhance positioning performance. To improve the accuracy of the VIO this work has undertaken Fault Tree Analysis (FTA) for visual odometry (VO) to identify the potential faults in the urban environment, which helps to design improved system architecture. The main contribution to Visual Odometry performance degradation errors was identified from data association errors while matching 2D feature locations to the 3D coordinates in complex environments. To mitigate this hybrid federated navigation system architecture has been employed using Gated Recurrent Unit (GRU) to reduce Kalman Filter errors under fault conditions. GRU, which is well-known for its estimation accuracy, has been used to predict state increments to update the state vector in the EKF measurement step to enhance the performance of the VIO fusion algorithm. The proposed GRU-based Adaptive EKF (AEKF) algorithm has been implemented in the GNSS/IMU/VO reference system to improve the overall performance. We have validated the proposed algorithm in a simulated urban environment using UAV Toolbox in MATLAB covering complex scenes such as open-sky, dense semi-structured urban and GNSS unavailable. The comparison of results between standard KF and GRU-based AEKF has been performed indicating that the AI-based approach performs better in complex environments. Furthermore, it is concluded that the hybrid VIO algorithm in the multi-sensor navigation system has better efficiency than other state-of-the-art solutions.

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