Multi-UAV Cooperative Patrol Task Planning Novel Method Based on Improved PFIH Algorithm

Qiao, Yaojun; Yang, Jie; Zhang, Qi; Xi, Jianxiang; Kong, Lingjiang

doi:10.1109/access.2019.2952877

Cited by 11 publications

(7 citation statements)

References 23 publications

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“…This enhances the safety of the UAVs working together on a mission. The spatial synergy performance of multiple UAVs can be expressed as the sum of the minimum distances between multiple UAV path points within n path segments [25]. The spatial synergy index f space can be expressed as follows:…”

Section: ) Spatial Synergy Indexmentioning

confidence: 99%

An Adaptive Evolutionary Multi-Objective Estimation of Distribution Algorithm and Its Application to Multi-UAV Path Planning

Yuhang

Zhang

2023

IEEE Access

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This paper concerns the multi-UAV cooperative path planning problem, which is solved by multi-objective optimization and by an adaptive evolutionary multi-objective estimation of distribution algorithm (AEMO-EDA). Since the traditional multi-objective optimization algorithms tend to fall into local optimum solutions when dealing with optimization problems in three dimensions, we suggest an advanced estimation of distribution algorithm. The main idea of this algorithm is to integrate the adaptive deflation of the selection rate, adaptive evolution of the covariance matrix, comprehensive evaluation of individual convergence and diversity, and reference point-based non-dominated ranking. A multi-UAV path planning model involving multi-objective optimization is established, and the designed algorithm is simulated and compared with other three high-dimensional multi-objective optimization algorithms. The results show that the AEMO-EDA proposed in this paper has stronger convergence and wider population distribution diversity in applying to the multi-UAV cooperative path planning model, as well as better global convergence. The algorithm can provide an stable path for each UAV and promote the intelligent operation of the UAV system.

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Section: ) Spatial Synergy Indexmentioning

confidence: 99%

An Adaptive Evolutionary Multi-Objective Estimation of Distribution Algorithm and Its Application to Multi-UAV Path Planning

Yuhang

Zhang

2023

IEEE Access

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“…There are static, dynamic solutions for boundary surveillance. Dynamic solutions include different patrol mechanisms [ 25 , 26 ]. Generally, short—a few hours—flight times are typical for drones [ 27 ], but developments aiming at flight times of several hours [ 28 ] also exist.…”

Section: Related Literaturementioning

confidence: 99%

Placement of Optical Sensors in 3D Terrain Using a Bacterial Evolutionary Algorithm

Kovács

Bolemányi

Botzheim

2022

Sensors

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This paper proposes an optimization framework for terrain large scale optical sensor placement to improve border protection. Compared to the often used, maximal coverage of an area approach, this method minimizes the undetected passages in the monitored area. Border protection is one of the most critical areas for sensor placement. Unlike traditional border protection solutions, we do not optimize for 2D but for 3D to prevent transit. Additionally, we consider both natural and built environmental coverings. The applied environmental model creates a highly inhomogeneous sensing area for sensors instead of the previously used homogeneous one. The detection of each sensor was provided by a line-of-sight model supplemented with inhomogeneous probabilities. The optimization was performed using a bacterial evolutionary algorithm. In addition to maximizing detection, minimizing the number of the applied sensors played a crucial role in design. These two cost components are built on each other hierarchically. The developed simulation framework based on ray tracing provided an excellent opportunity to optimize large areas. The presented simulation results prove the efficiency of this method. The results were evaluated by testing on a large number of intruders. Using sensors with different quantities and layouts in the tested 1×1×1 km environment, we reduced the probability of undetected intrusion to below 0.1% and increased the probability of acceptable classification to 99%.

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“…Besides, many optimization criteria have been designed to evaluate the performance of patrolling policies, such as minimum total patrolling path length [19], minimum total voyage time [20], the revisit time interval between two consecutive visits to each target [21], minimum the commute time between all pairs of patrolling points [22], and defined reward function [23,24].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Partition-Based Patrolling Scheme for Maritime Area Patrol with Multiple Cooperative Unmanned Surface Vehicles

Xie

Zhou

Luo

et al. 2020

JMSE

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Multi-robot cooperative patrolling systems have been extensively employed in the civilian and military fields, including monitoring forest fires, marine search-and-rescue, and area patrol. Multi-robot area patrol problems refer to the activity that a team of robots works cooperatively and regularly to visit the key targets in the given area for security. Following consideration of the low cost and high safety of unmanned surface vehicles (USV), a team of USVs is organized to perform area patrol in a sophisticated maritime environment. In this paper, we establish a mathematical model considering the characteristics of the cooperative patrol task and the limited conditions of USVs. A hybrid partition-based patrolling scheme is proposed for a multi-USV system to visit targets with different importance levels in a maritime area. Firstly, a centralized area partition algorithm is utilized to partition the patrolling area according to the number of USVs. Secondly, a distributed path planning algorithm is applied to planning the patrolling path for each USV to visit the targets in a maritime environment to minimize the length of the patrolling path for the USV team. Finally, comparative experiments between the proposed scheme and other methods are carried out to validate the performance of the hybrid partition-based patrolling scheme. Simulation results and experimental analysis show the efficiency of the proposed hybrid partition-based patrolling scheme compared to several previous patrolling algorithms.

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Multi-UAV Cooperative Patrol Task Planning Novel Method Based on Improved PFIH Algorithm

Cited by 11 publications

References 23 publications

An Adaptive Evolutionary Multi-Objective Estimation of Distribution Algorithm and Its Application to Multi-UAV Path Planning

An Adaptive Evolutionary Multi-Objective Estimation of Distribution Algorithm and Its Application to Multi-UAV Path Planning

Placement of Optical Sensors in 3D Terrain Using a Bacterial Evolutionary Algorithm

Hybrid Partition-Based Patrolling Scheme for Maritime Area Patrol with Multiple Cooperative Unmanned Surface Vehicles

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