Advanced UAV Task Verification: Trajectory Generation, Planning, Control and Guidance

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 6338

Special Issue Editor


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Guest Editor
School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Glasgow, UK
Interests: parallel robotics; UAVs; drones; quadcruisers; trajectory certification; path planning; robot controls; advanced controls; aircraft designs; mechanism design

Special Issue Information

Dear Colleagues,

In the area of unmanned aerial vehicles, with the ever-increasing advent of more powerful digital electronics, controllers, sensors, and technologies as well as the access to cost reduction, advanced controls, and measurement strategies are becoming feasible and affordable for the civil operation managed by SMEs. This improves market penetration of their robotics version where tasks can be achieved autonomously. As we can appreciate the drone's ever-increasing application in more areas of civil operations, research has continued to examine the control methods to generate advanced trajectory generation. It is become essential to contextualize and specialize the control strategies in order to target and achieve the specifics of tasks requiring very specialized performance criteria. This leads to the very significant question of task-centered approaches which will require to be evaluated, verified, and certified.

From the robotics and controls point of view, trajectory generation requires examining and studying formal methods which allow for generating proper path planning, path control and path guidance. This will include and integrate a complete mechatronics emphasis where engineering meets instrumentation, computing, computer science and mathematics. This exemplifies the necessity for systemic approaches encompassing analysis including the outcome as a bland between hardware and software with theoretical and conceptual control.

This Special Issue aims at collecting new developments and methodologies, best practices and applications of UAVs in task verification related to trajectory or path analysis where generation includes.

We welcome submissions that provide the community with the most recent advancements in all aspects of UAV controls targetting effective trajectory production as applied to achieve improved tasks evaluated in terms of the required identified performance.

We are therefore inviting submissions on, but not limited to, the following subject areas:

  • Navigation and position/orientation determination;
  • UAV control, obstacle sense and avoidance;
  • Autonomous flight and exploration;
  • Trajectory optimization;
  • Platforms and new sensors on board;
  • UAV control methods issues;
  • Classical and advanced control approaches for drones;
  • Trajectory planning;
  • Task specification, verification and certification;
  • Advances in vision- and laser-based navigation;
  • Sensor-based controls;
  • Navigation aspects of a UAV traffic management system;
  • Integrated navigation approaches for UAV operation in challenging environments;
  • Intelligent control application;
  • Pilot modeling and human-aircraft interaction.

Dr. Luc Rolland
Guest Editor

Manuscript Submission Information

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Keywords

  • drones
  • unmanned aerial vehicles
  • autonomy
  • trajectory generation, planning, optimization
  • path control
  • advanced controls
  • navigation
  • task certification

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Published Papers (2 papers)

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Research

17 pages, 11329 KiB  
Article
A Benchmarking of Commercial Small Fixed-Wing Electric UAVs and RGB Cameras for Photogrammetry Monitoring in Intertidal Multi-Regions
by Gabriel Fontenla-Carrera, Enrique Aldao, Fernando Veiga and Higinio González-Jorge
Drones 2023, 7(10), 642; https://doi.org/10.3390/drones7100642 - 20 Oct 2023
Viewed by 2419
Abstract
Small fixed-wing electric Unmanned Aerial Vehicles (UAVs) are perfect candidates to perform tasks in wide areas, such as photogrammetry, surveillance, monitoring, or search and rescue, among others. They are easy to transport and assemble, have much greater range and autonomy, and reach higher [...] Read more.
Small fixed-wing electric Unmanned Aerial Vehicles (UAVs) are perfect candidates to perform tasks in wide areas, such as photogrammetry, surveillance, monitoring, or search and rescue, among others. They are easy to transport and assemble, have much greater range and autonomy, and reach higher speeds than rotatory-wing UAVs. Aiming to contribute towards their future implementation, the objective of this article is to benchmark commercial, small, fixed-wing, electric UAVs and compatible RGB cameras to find the best combination for photogrammetry and data acquisition of mussel seeds and goose barnacles in a multi-region intertidal zone of the south coast of Galicia (NW of Spain). To compare all the options, a Coverage Path Planning (CPP) algorithm enhanced for fixed-wing UAVs to cover long areas with sharp corners was posed, followed by a Traveling Salesman Problem (TSP) to find the best route between regions. Results show that two options stand out from the rest: the Delair DT26 Open Payload with a PhaseOne iXM-100 camera (shortest path, minimum number of pictures and turns) and the Heliplane LRS 340 PRO with the Sony Alpha 7R IV sensor, finishing the task in the minimum time. Full article
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24 pages, 14889 KiB  
Article
Systematically Improving the Efficiency of Grid-Based Coverage Path Planning Methodologies in Real-World UAVs’ Operations
by Savvas D. Apostolidis, Georgios Vougiatzis, Athanasios Ch. Kapoutsis, Savvas A. Chatzichristofis and Elias B. Kosmatopoulos
Drones 2023, 7(6), 399; https://doi.org/10.3390/drones7060399 - 15 Jun 2023
Cited by 5 | Viewed by 3146
Abstract
This work focuses on the efficiency improvement of grid-based Coverage Path Planning (CPP) methodologies in real-world applications with UAVs. While several sophisticated approaches are met in literature, grid-based methods are not commonly used in real-life operations. This happens mostly due to the error [...] Read more.
This work focuses on the efficiency improvement of grid-based Coverage Path Planning (CPP) methodologies in real-world applications with UAVs. While several sophisticated approaches are met in literature, grid-based methods are not commonly used in real-life operations. This happens mostly due to the error that is introduced during the region’s representation on the grid, a step mandatory for such methods, that can have a great negative impact on their overall coverage efficiency. A previous work on UAVs’ coverage operations for remote sensing, has introduced a novel optimization procedure for finding the optimal relative placement between the region of interest and the grid, improving the coverage and resource utilization efficiency of the generated trajectories, but still, incorporating flaws that can affect certain aspects of the method’s effectiveness. This work goes one step forward and introduces a CPP method, that provides three different ad-hoc coverage modes: the Geo-fenced Coverage Mode, the Better Coverage Mode and the Complete Coverage Mode, each incorporating features suitable for specific types of vehicles and real-world applications. For the design of the coverage trajectories, user-defined percentages of overlap (sidelap and frontlap) are taken into consideration, so that the collected data will be appropriate for applications like orthomosaicing and 3D mapping. The newly introduced modes are evaluated through simulations, using 20 publicly available benchmark regions as testbed, demonstrating their stenghts and weaknesses in terms of coverage and efficiency. The proposed method with its ad-hoc modes can handle even the most complex-shaped, concave regions with obstacles, ensuring complete coverage, no-sharp-turns, non-overlapping trajectories and strict geo-fencing. The achieved results demonstrate that the common issues encountered in grid-based methods can be overcome by considering the appropriate parameters, so that such methods can provide robust solutions in the CPP domain. Full article
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