Multi-model cooperative task assignment and path planning of multiple UCAV formation

Huang, Hanqiao; Zhuo, Tao

doi:10.1007/s11042-017-4956-7

Cited by 35 publications

(26 citation statements)

References 34 publications

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“…Many scholars use intelligent bionic algorithms such as particle swarm optimization (PSO) and ant colony optimization (ACO) to solve path-planning problems. 3 Among them, ACO is a positive feedback moreover, heuristic random search method to simulate biological population foraging. It has the advantages of good robustness, strong global search ability and convenient expression of environmental constraints.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Path planning for unmanned wheeled robot based on improved ant colony optimization

Wang

2020

Measurement and Control

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This research presents a simple and novel improved ant colony optimization for path planning of unmanned wheeled robot. Our main concern is to avoid the random deadlock situation and to reach at the destination using the shortest path, to decrease lost ants and improve the efficiency of solutions. The aforementioned reasons, we design an adaptive heuristic function by adopting the Euclidean distance between the ant and the target destination, in order to avoid the initial blindness and later singleness of ant path searching. The historical best path when appropriate to retain the previous effort would supersede the current worst path. Simulation results under random maps show that the improved ant colony optimization considerably increases the number of effective ants. During the searching process, the probability to find the optimal path increases, as well as the search speed. Moreover, we also compare the improved ant colony optimization performance with the simple ant colony optimization.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Path planning for unmanned wheeled robot based on improved ant colony optimization

Wang

2020

Measurement and Control

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“…The novel method had improved the overall task allocation efficiency and obtained a reasonable allocation scheme. Huang and Zhuo [51] firstly established a multi-UCAV cooperative combat model with collaborative decision-making and control. Then, the task allocation model was established based on the flight characteristics of UCAV and the constraints of the battlefield environment.…”

Section: Ucavs Task Allocation Using Gds-woamentioning

confidence: 99%

An Adaptive Whale Optimization Algorithm Using Gaussian Distribution Strategies and Its Application in Heterogeneous UCAVs Task Allocation

Han

Zhao

et al. 2019

IEEE Access

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To overcome the defect of whale optimization algorithm (WOA) being easily fallen into local optimum caused by the ill-distribution of solutions, this paper explores an adaptive WOA variant using Gaussian distribution strategies (GDSs), named GDS-WOA. In GDS-WOA, by means of one GDS, named the Gaussian estimation of distribution method, the superior population information is used to evolve the distribution scope and modify the evolution direction. Moreover, an adaptive framework is adopted to integrate the Gaussian estimation of distribution method and WOA together, in which each individual can update its position using Gaussian estimation of distribution method or WOA according to an adaptive probability parameter. When the algorithm falls into stagnation, another GDS, named Gaussian random walk, is activated to enrich the population diversity and help the algorithm get rid of the local optimum. Additionally, the greedy strategy is carried out to select the offspring from the parents and the generated candidates to fully retain the promising solutions. The GDS-WOA is benchmarked on CEC 2014 test suite, and the performance of GDS-WOA is evaluated by comparing with WOA and its promising variant IWOA, as well as other five state-of-the-art evolutionary algorithms, i.e., COA, VCS, CoBiDE, HFPSO and GWO. The statistical results demonstrate that GDS-WOA outperforms other competitors in terms of convergence efficiency and accuracy. Finally, GDS-WOA is applied to solve the optimal task allocation problem of heterogeneous unmanned combat aerial vehicles (UCAVs). To address this constrained real-world optimizing problem efficiently, the mathematical model of heterogeneous UCAVs task allocation is described with the operational effectiveness value as the objective. The validity and practicauility of the model as well as the performance of GDS-WOA for solving constrained optimization problem are demonstrated by the experimental results.

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“…It is a hybrid differential evolution algorithm that taking advantages of the modified CIPDE (MCIPDE) and modified JADE (MJADE) with great searchability. Moreover, Huang et al [12] applied the SI algorithms to multi-model cooperative task assignment and path-planning of multiple UCAV formation, where the task assignment model of the UCAV formation is developed based on the flight characteristics of the UCAV formation and constraints in the battlefield. Dewangan et al [13] applied the grey wolf optimization to the multi-UCAV path-planning problem in the three-dimensional environment.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Swarm Intelligence Algorithms for UCAV Path-Planning Problems

Zhu

Wang

et al. 2021

Mathematics

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Path-planning for uninhabited combat air vehicles (UCAV) is a typically complicated global optimization problem. It seeks a superior flight path in a complex battlefield environment, taking into various constraints. Many swarm intelligence (SI) algorithms have recently gained remarkable attention due to their capability to address complex optimization problems. However, different SI algorithms present various performances for UCAV path-planning since each algorithm has its own strengths and weaknesses. Therefore, this study provides an overview of different SI algorithms for UCAV path-planning research. In the experiment, twelve algorithms that published in major journals and conference proceedings are surveyed and then applied to UCAV path-planning. Moreover, to demonstrate the performance of different algorithms in further, we design different scales of problem cases for those comparative algorithms. The experimental results show that UCAV can find the safe path to avoid the threats efficiently based on most SI algorithms. In particular, the Spider Monkey Optimization is more effective and robust than other algorithms in handling the UCAV path-planning problem. The analysis from different perspectives contributes to highlight trends and open issues in the field of UCAVs.

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Multi-model cooperative task assignment and path planning of multiple UCAV formation

Cited by 35 publications

References 34 publications

RETRACTED: Path planning for unmanned wheeled robot based on improved ant colony optimization

RETRACTED: Path planning for unmanned wheeled robot based on improved ant colony optimization

An Adaptive Whale Optimization Algorithm Using Gaussian Distribution Strategies and Its Application in Heterogeneous UCAVs Task Allocation

A Comparative Study of Swarm Intelligence Algorithms for UCAV Path-Planning Problems

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