Adaptive task allocation for multi-UAV systems based on bacteria foraging behaviour

Kurdi, Heba; Aldaood, Munirah F.; Al-Megren, Shiroq; Aloboud, Ebtesam; Aldawood, Abdulrahman S.

doi:10.1016/j.asoc.2019.105643

Cited by 47 publications

(22 citation statements)

References 63 publications

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“…where C, A, V separately represent the classify, attack, and verify tasks. The task coupling constraints reflect two aspects: (1) The SEAD mission of a certain target is accomplished only if C, A, V tasks are all performed. According to Equations (1) and (2), the number of executed tasks on each target is |M T | = 3, and the total number of tasks is N t = N T · |M T | = 3N T .…”

Section: Task Coupling Constraintsmentioning

confidence: 99%

“…The increasing complexity of the electromagnetic environment and the integration of weapons systems in modern battlefields have brought unprecedented challenges to the mission execution of aerial vehicles (AV) [1,2]. Due to the low cost, zero casualty, and good flexibility of unmanned aerial vehicles (UAVs), UAVs have gradually replaced the role of AV in carrying out dangerous and harsh tasks [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…State transition of three elements.InFigure 7, the original state[1,2,3] representing elements [X, Y, Z] is transferred to [2,3,1] representing elements [Y, Z, X]. There are (n!…”

mentioning

confidence: 99%

“…− 1) potential states for the transferred elements. For example, the original state is[1,2,3]. The possible transferred states of three elements are:…”

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confidence: 99%

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Cooperative Task Assignment of a Heterogeneous Multi-UAV System Using an Adaptive Genetic Algorithm

Chen

Tian

et al. 2020

Electronics

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The cooperative multiple task assignment problem (CMTAP) is an NP-hard combinatorial optimization problem. In this paper, CMTAP is to allocate multiple heterogeneous fixed-wing UAVs to perform a suppression of enemy air defense (SEAD) mission on multiple stationary ground targets. To solve this problem, we study the adaptive genetic algorithm (AGA) under the assumptions of the heterogeneity of UAVs and task coupling constraints. Firstly, the multi-type gene chromosome encoding scheme is designed to generate feasible chromosomes that satisfy the heterogeneity of UAVs and task coupling constraints. Then, AGA introduces the Dubins car model to simulate the UAV path formation and derives the fitness value of each chromosome. In order to comply with the chromosome coding strategy of multi-type genes, we designed the corresponding crossover and mutation operators to generate feasible offspring populations. Especially, the proposed mutation operators with the state-transition scheme enhance the stochastic searching ability of the proposed algorithm. Last but not least, the proposed AGA dynamically adjusts the number of crossover and mutation populations to avoid the subjective selection of simulation parameters. The numerical simulations verify that the proposed AGA has a better optimization ability and convergence effect compared with the random search method, genetic algorithm, ant colony optimization method, and particle search optimization method. Therefore, the effectiveness of the proposed algorithm is proven.

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Section: Task Coupling Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…State transition of three elements.InFigure 7, the original state[1,2,3] representing elements [X, Y, Z] is transferred to [2,3,1] representing elements [Y, Z, X]. There are (n!…”

mentioning

confidence: 99%

“…− 1) potential states for the transferred elements. For example, the original state is[1,2,3]. The possible transferred states of three elements are:…”

mentioning

confidence: 99%

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Cooperative Task Assignment of a Heterogeneous Multi-UAV System Using an Adaptive Genetic Algorithm

Chen

Tian

et al. 2020

Electronics

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“…Alotaibi [25]consider the use of a team of multiple UAVs to accomplish a search and rescue (SAR) mission. In the context of search and rescue, the locust-inspired approaches for multi-UAV task allocation [26,27]and layered search and rescue (LSAR) methods are studied separately. Among them, LIAM is a distributed architecture, and LSAR is a centralized architecture.…”

Section: Introductionmentioning

confidence: 99%

UAV Cooperative Search in Dynamic Environment Based on Hybrid-Layered APF

Shao

Tao

Liu

et al. 2021

Preprint

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Unmanned Aerial Vehicle (UAV) detection has the advantages of flexible deployment, no casualties. It has become a force that cannot be ignored in the battlefield. Scientific and efficient mission planning can help improving the survival rate and mission completion rate of the UAV search in dynamic environments. Towards the mission planning problem of UAV collaborative search for multi-types of time-sensitive moving targets, a search algorithm based on hybrid layered artificial potential fields algorithm (HL-APF) was proposed. This paper utilized a new target attraction field function which was segmented by the search distance to quickly search for quickly targets searching. Moreover, in order to solve the problem of repeated search by the UAV in a short time interval, a search repulsion field generated by the UAV search path was proposed. Besides, in order to solve the unknown target search and improve the area coverage, a centralized layered scheduling algorithm controlled by the cloud server (CS) was added. CS divides the mission area into several sub-areas, and allocates UAV according to the priority function based on the search map. The CS activation mechanism can make full use of prior information, and the UAV assignment cool-down mechanism can avoid the repeated assignment of the same UAV. The simulation results show that compared with the hybrid artificial potential field and ant colony optimization (HAPF-ACO) and improved artificial potential field(IAPF), this method can significantly improve the found number of targets and mission area coverage. At the same time, comparative experiment results of CS mechanism proved the necessity of setting CS activation and cool-down for improving the search performance.

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