Multi-UAV Unbalanced Targets Coordinated Dynamic Task Allocation in Phases

Wang, Wenfei; Yu, Wenwu; Ru, Le; Lü, Bo; Hu, Shiguang; Chang, Xinlong

doi:10.3390/aerospace9090491

Cited by 6 publications

(5 citation statements)

References 35 publications

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“…The decomposed tasks are made available for bidding via open tendering or auctioning, with UAVs bidding in accordance with their actual task situation and flight status. Eventually, tasks are assigned based on the actual auction outcome [43,44]. Figure 3 provides an illustration of the distributed task assignment method.…”

Section: Distributed Task Assignment Methodsmentioning

confidence: 99%

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Comparative Analysis of Different UAV Swarm Control Methods on Unmanned Farms

Ming,

Jiang,

Luo

et al. 2023

Agronomy

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Unmanned farms employ a variety of sensors, automated systems, and data analysis techniques to enable fully automated and intelligent management. This not only heightens agricultural production efficiency but also reduces the costs associated with human resources. As integral components of unmanned farms’ automation systems, agricultural UAVs have been widely adopted across various operational stages due to their precision, high efficiency, environmental sustainability, and simplicity of operation. However, present-day technological advancement levels and relevant policy regulations pose significant restrictions on UAVs in terms of payload and endurance, leading to diminished task efficiency when a single UAV is deployed over large areas. Accordingly, this paper aggregates and analyzes research pertaining to UAV swarms from databases such as Google Scholar, ScienceDirect, Scopus, IEEE Xplorer, and Wiley over the past decade. An initial overview presents the current control methods for UAV swarms, incorporating a summary and analysis of the features, merits, and drawbacks of diverse control techniques. Subsequently, drawing from the four main stages of agricultural production (cultivation, planting, management, and harvesting), we evaluate the application of UAV swarms in each stage and provide an overview of the most advanced UAV swarm technologies utilized therein. Finally, we scrutinize and analyze the challenges and concerns associated with UAV swarm applications on unmanned farms and provide forward-looking insights into the future developmental trajectory of UAV swarm technology in unmanned farming, with the objective of bolstering swarm performance, scalability, and adoption rates in such settings.

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Section: Distributed Task Assignment Methodsmentioning

confidence: 99%

“…Agronomy 2023, 13, x FOR PEER REVIEW bidding in accordance with their actual task situation and flight status. Eventual are assigned based on the actual auction outcome [43,44]. Figure 3 provides an illu of the distributed task assignment method.…”

Section: Distributed Task Assignment Methodsmentioning

confidence: 99%

Comparative Analysis of Different UAV Swarm Control Methods on Unmanned Farms

Ming,

Jiang,

Luo

et al. 2023

Agronomy

View full text Add to dashboard Cite

show abstract

“…Particularly, there is a vast potential for the application of UAV systems in the domain of multi-UAV cooperative task allocation. Multi-UAV cooperative task allocation involves the rational distribution of various tasks to different UAVs within a multi-UAV system, based on task requirements and system resources, to achieve efficient task execution and optimal resource utilization [1,2]. However, due to the complex and diverse nature of tasks, which may include tasks such as surveillance, attack, and reconnaissance, and considering constraints such as the number of UAV resources and on-site operational conditions, the problem of multi-UAV cooperative task allocation becomes highly intricate.…”

Section: Introductionmentioning

confidence: 99%

Cooperative task allocation method for multi‐unmanned aerial vehicles based on the modified genetic algorithm

Tan,

Zhou,

Qian

2024

IET Intelligent Trans Sys

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Unmanned aerial vehicles (UAVs) play a crucial role in various domains such as military, civil, industrial, and so on. However, the coordination and task allocation of multiple UAVs in engineering practices face numerous challenges. As the scale of battlefields expands, and the diversity of UAV missions and constraints increases, the existing task allocation methods suffer from issues such as a mismatch between theoretical models and real‐world applications, low task execution efficiency, and poor responsiveness in dynamic environments. To address these challenges, this paper proposes an improved genetic algorithm (GA)‐based approach for multi‐UAV cooperative task allocation. By collecting battlefield information, decomposing tasks, and considering UAV resource types, an optimization model for multi‐UAV cooperative task allocation is constructed. The proposed method, using an improved GA, generates a set of Pareto‐optimal task solutions for decision‐makers. Case studies demonstrate that this approach effectively enhances task execution efficiency and reduces the total flight distance cost of UAVs.

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“…It has been widely studied in several specialized fields. In the field of machine planning, the problem of imbalance between multiple drones [4] and tasks, the linear task allocation problem of distributed multiple robots * Corresponding author. E-mail address: baojing@stu.kust.edu.cn [5], and the clustered collaborative task allocation of underwater robots [6] have all been effectively solved.…”

Section: Introductionmentioning

confidence: 99%

Grid Operation and Inspection Resource Scheduling Based on an Adaptive Genetic Algorithm

Bingnan Tang,

Jing Bao,

Nan Pan

et al. 2024

Int. j. eng. technol. innov.

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Grid operation and inspection a key links to ensure the safe operation of the power system, which requires efficient task allocation and resource scheduling. To address this problem, this paper proposes a resource scheduling model for grid operation and inspection based on bi-level programming. Firstly, the O&I process is analyzed and defined as a combined optimization problem of the multiple traveling salesman problem (MTSP) and the job-shop scheduling problem (JSP). Secondly, a bi-level programming model of MTSP and JSP is established according to the characteristics of the problem. Finally, an adaptive genetic algorithm is used to solve the problem. The feasibility of the model and the advancement of the algorithm are verified through the simulation of real scenarios and a large number of tests, which provide strong support for the sustainable development of the power system.

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Multi-UAV Unbalanced Targets Coordinated Dynamic Task Allocation in Phases

Cited by 6 publications

References 35 publications

Comparative Analysis of Different UAV Swarm Control Methods on Unmanned Farms

Comparative Analysis of Different UAV Swarm Control Methods on Unmanned Farms

Cooperative task allocation method for multi‐unmanned aerial vehicles based on the modified genetic algorithm

Grid Operation and Inspection Resource Scheduling Based on an Adaptive Genetic Algorithm

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