UAV Path Planning for Target Coverage Task in Dynamic Environment

Li, Jing; Xiong, Yonghua; She, Jinhua

doi:10.1109/jiot.2023.3277850

Cited by 25 publications

(3 citation statements)

References 33 publications

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“…Another potential application is search and rescue missions in post-disaster scenarios. Research has shown that the effective deployment of USVs can significantly enhance the success rate of rescue missions by reducing response times and improving overall effectiveness [6].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Task Allocation and Planning for Unmanned Surface Vehicle (USV) Using Self-Attention Mechanism and Locking Sweeping Method

Luo,

Zhang,

Zhuang

et al. 2024

JMSE

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The development of intelligent task allocation and path planning algorithms for unmanned surface vehicles (USVs) is gaining significant interest, particularly in supporting complex ocean operations. This paper proposes an intelligent hybrid algorithm that combines task allocation and path planning to improve mission efficiency. The algorithm introduces a novel approach based on a self-attention mechanism (SAM) for intelligent task allocation. The key contribution lies in the integration of an adaptive distance field, created using the locking sweeping method (LSM), into the SAM. This integration enables the algorithm to determine the minimum practical sailing distance in obstacle-filled environments. The algorithm efficiently generates task execution sequences in cluttered maritime environments with numerous obstacles. By incorporating a safety parameter, the enhanced SAM algorithm adapts the dimensional influence of obstacles and generates paths that ensure the safety of the USV. The algorithms have been thoroughly evaluated and validated through extensive computer-based simulations, demonstrating their effectiveness in both simulated and practical maritime environments. The results of the simulations verify the algorithm’s capability to optimize task allocation and path planning, leading to improved performance in complex and obstacle-laden scenarios.

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Section: Introductionmentioning

confidence: 99%

Intelligent Task Allocation and Planning for Unmanned Surface Vehicle (USV) Using Self-Attention Mechanism and Locking Sweeping Method

Luo,

Zhang,

Zhuang

et al. 2024

JMSE

View full text Add to dashboard Cite

show abstract

“…Evolutionary-algorithm-based methods have become increasingly popular in the path planning domain. In particular, these methods are capable of finding high-quality satisfactory solutions in complex scenarios, such as genetic algorithm (GE) [11][12][13], differential evolution (DE) [14,15], ant colony optimization (ACO) [16], and particle swarm optimization (PSO) [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

RGG-PSO+: Random Geometric Graphs Based Particle Swarm Optimization Method for UAV Path Planning

Liu,

Zhu,

Zhang

et al. 2024

Int J Comput Intell Syst

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Evolutionary algorithms, such as particle swarm optimization (PSO), are widely applied to UAV path planning problems. However, the fixed particle length of PSO, which may not be suitable for the scenario, will compromise the search efficiency. This paper proposes the RGG-PSO+ method, which adapts to scenarios by dynamically adjusting the number of waypoints. Random geometric graphs (RGG) and the divide-and-conquer paradigm are involved in improving the proposed method. Comparative analyses with established heuristic methods demonstrate RGG-PSO+’s superior performance in complex environments, particularly in terms of convergence speed and path length. The implementation of RGG significantly improves the F-Measure, indicating a shift from exploration to exploitation of PSO’s iterations, and the implementation of the divide-and-conquer paradigm is evident in the improved mean and variance of normalized path lengths.

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“…Task assignment and path planning constitute two vital elements in the mission planning of UAVs [2]. Intriguingly, both of these elements fall under the category of nondeterministic polynomial problems [3][4][5][6][7][8], renowned for their complexity and difficulty to resolve. At present, the leading approaches to addressing the task assignment of UAV clusters encompass the centralized algorithm, the distributed algorithm, and the hybrid solving algorithm [9].…”

Section: Introduction 1research Backgroundmentioning

confidence: 99%

Multi-Mission Oriented Joint Optimization of Task Assignment and Flight Path Planning for Heterogeneous UAV Cluster

Dong,

Shi,

Wen

et al. 2023

Remote Sensing

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This paper puts forward a joint optimization algorithm of task assignment and flight path planning for a heterogeneous unmanned aerial vehicle (UAV) cluster in a multi-mission scenario (MMS). The basis of the proposed algorithm is to establish constraint and threat models of a heterogeneous UAV cluster to simultaneously minimize range and maximize value gain and survival probability in an MMS under the constraints of task payload, range, and task requirement. On one hand, the objective function for the heterogeneous UAV cluster within an MMS is derived and it is adopted as a metric for assessing the performance of the joint optimization in task assignment and flight path planning. On the other hand, since the formulated joint optimization problem is a multi-objective, non-linear, and non-convex optimization model due to its multiple decision variables and constraints, the roulette wheel selection (RWS) principle and the elite strategy (ES) are introduced in an ant colony optimization (ACO) to solve the complex optimization model. The simulation results indicate that the proposed algorithm is superior and more efficient compared to other approaches.

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UAV Path Planning for Target Coverage Task in Dynamic Environment

Cited by 25 publications

References 33 publications

Intelligent Task Allocation and Planning for Unmanned Surface Vehicle (USV) Using Self-Attention Mechanism and Locking Sweeping Method

Intelligent Task Allocation and Planning for Unmanned Surface Vehicle (USV) Using Self-Attention Mechanism and Locking Sweeping Method

RGG-PSO+: Random Geometric Graphs Based Particle Swarm Optimization Method for UAV Path Planning

Multi-Mission Oriented Joint Optimization of Task Assignment and Flight Path Planning for Heterogeneous UAV Cluster

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