Path planning for moving target tracking by fixed-wing UAV

Liao, Sian; Zhu, Ruijie; Wu, Naiqi; Shaikh, Tauqeer Ahmed; Sharaf, Mohamed A.; Mostafa, Almetwally M.

doi:10.1016/j.dt.2019.10.010

Cited by 35 publications

(16 citation statements)

References 20 publications

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“…Method for drone flight planning [11] is more efficiency, cheaper and faster than a method of taking thermal images in entering points, these enter point of the public area must cover all area, and this cannot be completely guaranteed. Even though the drone flight planning method is fast, it is not fast enough to scan the whole area, and there is a redundancy of data (persons moving in the area).…”

Section: Methodsmentioning

confidence: 99%

Prevention of the Spread of Viral Disease Using Artificial Intelligence from Data Obtained by UAVs

Prihodova

Jech

2021

SHS Web of Conf.

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Research background: One of the significant globalization consequences is a threat of rapidly spreading communicable diseases. In recent months, COVID-19 has spread worldwide. It is a highly infectious disease, which is manifested mainly by fever, respiratory problems, muscle pain and fatigue. Therefore, there is a need to reliable monitor people’s body temperature. If the monitoring process takes places in enclosed spaces, the procedure may be performed at the entrance to the building. However, the problem occurs in public spaces. Therefore, to solve this problem, we propose the use of a drone with a thermal camera for scanning people in public spaces and subsequent evaluation using classification methods. Purpose of the article: The aim of this article is to create a model for sensing and measuring the body temperature of people in public spaces so that the global impacts of COVID-19 on the economy and society are reduced. Methods: To monitor large areas, it is necessary to have suitable methods for obtaining quality data. One of the methods for obtaining data with the high spatial resolution is the use of UAVs with a planned flight. Artificial intelligence methods will be used for the classification of persons; their representatives are, e.g. convolutional neural networks. Findings & Value added: The proposed model of sensing and subsequent classification of people into groups (normal body temperature, elevated body temperature). The output of the model will help to monitor the spread of infectious diseases (the condition is a symptom - increased body temperature) in today’s globalized world.

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Section: Methodsmentioning

confidence: 99%

Prevention of the Spread of Viral Disease Using Artificial Intelligence from Data Obtained by UAVs

Prihodova

Jech

2021

SHS Web of Conf.

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“…Path planning is one of the most important problems that can take place during the autonomous navigation flight of UAVs [2,3], which is usually defined as autonomously finding an optimal path from the start node to the end node [4]. Optimal path selection needs to be determined based on the flight performance constraints, the specific mission requirements, and the flight environment constraints [5,6]. Scholars have conducted a lot of research on the UAV path planning problem and proposed a series of algorithms, such as graph-based optimization methods, including the visibility graph (VG) algorithm [7] and Voronoi diagrams [8]; the searching-based methods, including the Dijkstra [9] algorithm, A* algorithm [10] and D* algorithm [11]; the sampling-based methods, such as PRM algorithm [12] and RRT algorithm [13]; the nature-inspired methods, such as genetic algorithm (GA) [14], ant colony optimization (ACO) [15], artificial potential field algorithm [16], particle swarm optimization (PSO) [17] and fluid-based algorithm [18]; and other methods, such as control theory-based methods [19].…”

Section: Introductionmentioning

confidence: 99%

FC-RRT*: An Improved Path Planning Algorithm for UAV in 3D Complex Environment

Guo

Liu

et al. 2022

IJGI

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In complex environments, path planning is the key for unmanned aerial vehicles (UAVs) to perform military missions autonomously. This paper proposes a novel algorithm called flight cost-based Rapidly-exploring Random Tree star (FC-RRT*) extending the standard Rapidly-exploring Random Tree star (RRT*) to deal with the safety requirements and flight constraints of UAVs in a complex 3D environment. First, a flight cost function that includes threat strength and path length was designed to comprehensively evaluate the connection between two path nodes. Second, in order to solve the UAV path planning problem from the front-end, the flight cost function and flight constraints were used to inspire the expansion of new nodes. Third, the designed cost function was used to guide the update of the parent node to allow the algorithm to consider both the threat and the length of the path when generating the path. The simulation and comparison results show that FC-RRT* effectively overcomes the shortcomings of standard RRT*. FC-RRT* is able to plan an optimal path that significantly improves path safety as well as maintains has the shortest distance while satisfying flight constraints in the complex environment. This paper has application value in UAV 3D global path planning.

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“…They have been extensively deployed for assisting in military missions such as reconnaissance [ 1 ], surveillance [ 2 ], and combat operations [ 3 ]. Recently, UAVs have been utilized in other sectors supporting different commercial [ 4 , 5 , 6 , 7 , 8 ], environmental [ 9 ], and leisure [ 4 ] applications. Examples include monitoring of construction sites [ 10 ], inspection of civil infrastructures [ 10 ], surveying powerlines [ 7 ], mapping gas pipelines [ 11 ], counting agriculture livestock [ 5 ], assisting with forest fires [ 9 ], and mostly in cinema and photography for professional and leisure purposes [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…To date, UAVs have shown to be efficient and effective at following MGVs for tracking purposes [ 7 , 8 , 11 ]; however, delivering immediate and instant reconnaissance and look-ahead coverage when needed is another feasible, effective, and vital application for multirotor UAVs that has not been explored yet. In scenarios such as a vehicle moving in a dynamic and unknown terrain, a fire truck approaching a fire area, and a law enforcement vehicle exploring unattended areas, a multirotor UAV can be launched to autonomously follow the moving vehicle, at a standoff distance, to collect and relay in real-time additional aerial mapping information and visual coverage of the vehicle’s routes and areas ahead, thus enhancing the MGVs’ situational awareness level beyond their onboard sensors’ coverage abilities.…”

Section: Introductionmentioning

confidence: 99%

“…In such a case, the path planning technique needs to satisfy three objectives, namely: (a) follow the MGV reliably; (b) provide a sufficient amount of coverage; and (c) provide the necessary coverage quality for the MGV’s route ahead. Existing path planning techniques for following MGVs have been mainly developed for fixed-wing UAVs [ 8 , 12 , 13 ]. They heavily rely on using gimbal cameras to aim at specific areas for coverage and tracking purposes [ 14 , 15 , 16 ], therefore, relaxing the complexities imposed by the path planning technique.…”

Section: Introductionmentioning

confidence: 99%

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UAV Path Planning for Reconnaissance and Look-Ahead Coverage Support for Mobile Ground Vehicles

Jayaweera

Hanoun

2021

Sensors

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Path planning of unmanned aerial vehicles (UAVs) for reconnaissance and look-ahead coverage support for mobile ground vehicles (MGVs) is a challenging task due to many unknowns being imposed by the MGVs’ variable velocity profiles, change in heading, and structural differences between the ground and air environments. Few path planning techniques have been reported in the literature for multirotor UAVs that autonomously follow and support MGVs in reconnaissance missions. These techniques formulate the path planning problem as a tracking problem utilizing gimbal sensors to overcome the coverage and reconnaissance complexities. Despite their lack of considering additional objectives such as reconnaissance coverage and dynamic environments, they retain several drawbacks, including high computational requirements, hardware dependency, and low performance when the MGV has varying velocities. In this study, a novel 3D path planning technique for multirotor UAVs is presented, the enhanced dynamic artificial potential field (ED-APF), where path planning is formulated as both a follow and cover problem with nongimbal sensors. The proposed technique adopts a vertical sinusoidal path for the UAV that adapts relative to the MGV’s position and velocity, guided by the MGV’s heading for reconnaissance and exploration of areas and routes ahead beyond the MGV sensors’ range, thus extending the MGV’s reconnaissance capabilities. The amplitude and frequency of the sinusoidal path are determined to maximize the required look-ahead visual coverage quality in terms of pixel density and quantity pertaining to the area covered. The ED-APF was tested and validated against the general artificial potential field techniques for various simulation scenarios using Robot Operating System (ROS) and Gazebo-supported PX4-SITL. It demonstrated superior performance and showed its suitability for reconnaissance and look-ahead support to MGVs in dynamic and obstacle-populated environments.

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Path planning for moving target tracking by fixed-wing UAV

Cited by 35 publications

References 20 publications

Prevention of the Spread of Viral Disease Using Artificial Intelligence from Data Obtained by UAVs

Prevention of the Spread of Viral Disease Using Artificial Intelligence from Data Obtained by UAVs

FC-RRT*: An Improved Path Planning Algorithm for UAV in 3D Complex Environment

UAV Path Planning for Reconnaissance and Look-Ahead Coverage Support for Mobile Ground Vehicles

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