Multi-UAV Cooperative Task Assignment Based on Orchard Picking Algorithm

Liu, Weiheng; Zheng, Xin; Garg, Harish

doi:10.2991/ijcis.d.210423.003

Cited by 10 publications

(3 citation statements)

References 27 publications

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“…The results indicate that the algorithm possesses enhanced global search capabilities and offers improved efficiency and stability. Chen et al [76] introduced a chaotic wolf pack algorithm based on an enhanced stochastic fractal search (MSFS-CWPA) to address the task assignment problem, considering the performance characteristics of specific payloads. This approach exhibits superior convergence accuracy and robustness.…”

Section: Other Algorithmsmentioning

confidence: 99%

Survey on Collaborative Task Assignment for Heterogeneous UAVs Based on Artificial Intelligence Methods

Li,

Shao

et al. 2024

CAAI Artificial Intelligence Research

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Heterogeneous unmanned aerial vehicle (UAV) swarms have garnered significant attention from researchers worldwide due to their remarkable flexibility, diverse mission capabilities, and wide-ranging potential applications. Mission planning stands at the core of UAV swarm operations, requiring consideration of various factors including mission environment, requirements, and inherent characteristics. In this paper, we investigate the model of the cooperative tasking problem in heterogeneous UAV swarms. We provide a comprehensive review of artificial intelligence algorithms applied in UAV swarm mission planning, analyzing their strengths and weaknesses in multi-UAV cooperative environments. By discussing these key techniques and their practical applications, the article highlights future research trends and challenges. This review serves as a valuable reference for understanding the current state of AI algorithm applications in heterogeneous UAV swarm task assignments.

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Section: Other Algorithmsmentioning

confidence: 99%

Survey on Collaborative Task Assignment for Heterogeneous UAVs Based on Artificial Intelligence Methods

Li,

Shao

et al. 2024

CAAI Artificial Intelligence Research

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show abstract

“…These components share real-time location information with each other via a communication mode. Subsequently, they an- In [112], the authors propose a UAV swarm-based cooperative task assignment model specifically for orchard picking. This heuristic cooperative method, with the shortest travelling distance as a key indicator, swiftly addresses multiple tasks.…”

Section: Uav Swarms In Harvestingmentioning

confidence: 99%

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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“…Including the genetic algorithm (GA) [18], simulated annealing (SA) algorithm [19], gravitational search algorithm (GSA) [20], ant colony optimization (ACO) algorithm [21], particle swarm optimization algorithm(PSO) [22], shuffled frog leaping algorithm, bacterial foraging optimization, artificial bee colony(ABC) algorithm [23]. New algorithm structures are used or the advantages of different algorithms are combined to improve the quality and search speed of candidate solutions [24], [25]. A comprehensive review paper on swarm intelligence algorithms has been published, covering various optimization techniques [26].…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Reconnaissance Task Allocation in 3D Urban Environments

Tian,

Xu,

Luo

et al. 2024

IEEE Access

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This paper considers a reconnaissance task allocation problem for multiple unmanned aerial vehicles (UAVs) in 3D urban environments. In this paper, we present an extended heterogeneous targets reconnaissance task allocation model which introduced cuboid targets for 3D urban environment to improve the fidelity of the model. A reconnaissance method is designed for each type of target, and the mission is described as a heterogeneous target multi-traveling salesman problem model for solving complex optimization problems with multiple constraints.To address these complex optimization problems, multi-group symbiotic organisms search algorithms (MGSOS) are proposed, which maintain the diversity of species in the population through multi-group strategies and enhance information exchange between individuals in three stages. Real-number encoding is used to satisfy partial constraints and simplify the search space, improving the optimization efficiency of the solution. The simulation results show that the MGSOS algorithm can consider the characteristics of UAV sensor performance and heterogeneous targets. It outperforms the common symbiotic organisms search (SOS) algorithm in terms of the optimality of assignment results, and is suitable for larger scale urban reconnaissance task allocation problems.INDEX TERMS 3D urban environment, generalized multi-traveling salesman problem, np-hard problem, symbiotic organisms search, task allocation, unmanned aerial vehicle.

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Multi-UAV Cooperative Task Assignment Based on Orchard Picking Algorithm

Cited by 10 publications

References 27 publications

Survey on Collaborative Task Assignment for Heterogeneous UAVs Based on Artificial Intelligence Methods

Survey on Collaborative Task Assignment for Heterogeneous UAVs Based on Artificial Intelligence Methods

Comparative Analysis of Different UAV Swarm Control Methods on Unmanned Farms

Multi-UAV Reconnaissance Task Allocation in 3D Urban Environments

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