Coverage Strategy for Target Location in Marine Environments Using Fixed-Wing UAVs

Muñoz, Javier; López, Blanca Muñoz; Quevedo, Fernando; Monje, Concepción A.; Garrido, Santiago; Moreno, Luís

doi:10.3390/drones5040120

Cited by 7 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Robotics Lab research group at the University Carlos III of Madrid (UC3M) developed the algorithms to find a feasible path, which was returned as a four-dimensional (4D) trajectory. The details of these algorithms have already been described in other articles [14][15][16][17][18][19][20][21], where their potential can be better discovered, since only part of their possibilities was used in the real flights during the project. The task of the algorithms was to provide the drone operators with a deconflicted trajectory based on their flight intentions, capabilities and some existing constraints:…”

Section: The Scope Of Labyrinthmentioning

confidence: 99%

Implementing and Testing a U-Space System: Lessons Learnt

Fas-Millán,

Pick,

Río

et al. 2024

Aerospace

View full text Add to dashboard Cite

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.

show abstract

Section: The Scope Of Labyrinthmentioning

confidence: 99%

Implementing and Testing a U-Space System: Lessons Learnt

Fas-Millán,

Pick,

Río

et al. 2024

Aerospace

View full text Add to dashboard Cite

show abstract

“…A single UAV is no longer sufficient to meet the quality requirements of military and civilian needs. The development of UAVs that can perform extensive and sophisticated tasks such as border management [4], disaster response [5,6], freight distribution [7], and target search [8] is moving towards the use of clusters of UAVs. Compared to single aircraft, UAV clusters outperform them in terms of search range, efficiency, and robustness.…”

Section: Introductionmentioning

confidence: 99%

A Study of Collaborative Trajectory Planning Method Based on Starling Swarm Bionic Algorithm for Multi-Unmanned Aerial Vehicle

Chen

Tang

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

This academic paper addresses the challenges associated with trajectory planning for affordable and light-weight Unmanned Aerial Vehicle (UAV) swarms, despite limited computing resources and extensive cooperation requirements. Specifically, an imitation-based starling cluster cooperative trajectory planning technique is proposed for a fixed-wing model of a six-degree-of-freedom UAV cluster. To achieve this, dynamic trajectory prediction of the rapid random search tree is utilized to generate a track solution adapted to the terrain environment. Additionally, the Dubins aircraft path solution is applied as it is suitable for executing input track commands by the UAV model. Computational simulations on different cluster sizes show the approach can maintain the cluster state while navigating diverse terrains, with the track solution complying with the UAV’s physical model properties.

show abstract

“…More recent publications present different approaches for planning CPP missions. Muñoz et al [ 38 ] propose a Differential Evolution-based CPP method for search and rescue tasks in marine environments. Chen et al [ 39 ] propose a clustering-based CPP method, which consists in dividing the target area into clusters and assign each area to a different UAV based on the area properties and the UAV’s scanning width and flying speed.…”

Section: Introductionmentioning

confidence: 99%

Multi UAV Coverage Path Planning in Urban Environments

Muñoz

López

Quevedo

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

Coverage path planning (CPP) is a field of study which objective is to find a path that covers every point of a certain area of interest. Recently, the use of Unmanned Aerial Vehicles (UAVs) has become more proficient in various applications such as surveillance, terrain coverage, mapping, natural disaster tracking, transport, and others. The aim of this paper is to design efficient coverage path planning collision-avoidance capable algorithms for single or multi UAV systems in cluttered urban environments. Two algorithms are developed and explored: one of them plans paths to cover a target zone delimited by a given perimeter with predefined coverage height and bandwidth, using a boustrophedon flight pattern, while the other proposed algorithm follows a set of predefined viewpoints, calculating a smooth path that ensures that the UAVs pass over the objectives. Both algorithms have been developed for a scalable number of UAVs, which fly in a triangular deformable leader-follower formation with the leader at its front. In the case of an even number of UAVs, there is no leader at the front of the formation and a virtual leader is used to plan the paths of the followers. The presented algorithms also have collision avoidance capabilities, powered by the Fast Marching Square algorithm. These algorithms are tested in various simulated urban and cluttered environments, and they prove capable of providing safe and smooth paths for the UAV formation in urban environments.

show abstract

Coverage Strategy for Target Location in Marine Environments Using Fixed-Wing UAVs

Cited by 7 publications

References 19 publications

Implementing and Testing a U-Space System: Lessons Learnt

Implementing and Testing a U-Space System: Lessons Learnt

A Study of Collaborative Trajectory Planning Method Based on Starling Swarm Bionic Algorithm for Multi-Unmanned Aerial Vehicle

Multi UAV Coverage Path Planning in Urban Environments

Contact Info

Product

Resources

About