scholarly journals Task Assignment of UAV Swarm Based on Wolf Pack Algorithm

2020 ◽  
Vol 10 (23) ◽  
pp. 8335
Author(s):  
Yingtong Lu ◽  
Yaofei Ma ◽  
Jiangyun Wang ◽  
Liang Han
Keyword(s):  
Genetic Algorithm ◽  
Dimensional Space ◽  
Task Assignment ◽  
High Dimensional ◽  
Swarm Optimization ◽  
Uav Swarm ◽  
Aerial Vehicle ◽  
Key Issues ◽  
Comparative Results ◽  
Search Quality

To perform air missions with an unmanned aerial vehicle (UAV) swarm is a significant trend in warfare. The task assignment among the UAV swarm is one of the key issues in such missions. This paper proposes PSO-GA-DWPA (discrete wolf pack algorithm with the principles of particle swarm optimization and genetic algorithm) to solve the task assignment of a UAV swarm with fast convergence speed. The PSO-GA-DWPA is confirmed with three different ground-attack scenarios by experiments. The comparative results show that the improved algorithm not only converges faster than the original WPA and PSO, but it also exhibits excellent search quality in high-dimensional space.

scholarly journals Toward delay-tolerant multiple-unmanned aerial vehicle scheduling system using Multi-strategy Coevolution algorithm

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881523 ◽  
Author(s):  
Yohanes Khosiawan ◽  
Sebastian Scherer ◽  
Izabela Nielsen
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Unmanned Aerial Vehicle ◽  
Task Assignment ◽  
Computation Time ◽  
Task Sequence ◽  
Swarm Optimization ◽  
Assignment Algorithm ◽  
Aerial Vehicle ◽  
Delay Tolerant

Autonomous bridge inspection operations using unmanned aerial vehicles take multiple task assignments and constraints into account. To efficiently execute the operations, a schedule is required. Generating a cost optimum schedule of multiple-unmanned aerial vehicle operations is known to be Non-deterministic Polynomial-time (NP)-hard. This study approaches such a problem with heuristic-based algorithms to get a high-quality feasible solution in a short computation time. A constructive heuristic called Retractable Chain Task Assignment algorithm is presented to build an evaluable schedule from a task sequence. The task sequence representation is used during the search to perform seamless operations. Retractable Chain Task Assignment algorithm calculates and incorporates slack time to the schedule according to the properties of the task. The slack time acts as a cushion which makes the schedule delay-tolerant. This algorithm is incorporated with a metaheuristic algorithm called Multi-strategy Coevolution to search the solution space. The proposed algorithm is verified through numerical simulations, which take inputs from real flight test data. The obtained solutions are evaluated based on the makespan, battery consumption, computation time, and the robustness level of the schedules. The performance of Multi-strategy Coevolution is compared to Differential Evolution, Particle Swarm Optimization, and Differential Evolution–Fused Particle Swarm Optimization. The simulation results show that Multi-strategy Coevolution gives better objective values than the other algorithms.

QUANTUM PARTICLE SWARM OPTIMIZATION CLASSIFICATION ALGORITHM AND ITS APPLICATIONS

2014 ◽  
Vol 28 (02) ◽  
pp. 1459004 ◽  
Author(s):  
RUOCHEN LIU ◽  
PING ZHANG ◽  
LICHENG JIAO
Keyword(s):  
Particle Swarm Optimization ◽  
Quantum Particle ◽  
Dimensional Space ◽  
Particle Swarm ◽  
Detailed Comparison ◽  
Optimization Methods ◽  
Classification Algorithm ◽  
High Dimensional ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

Data processing in high-dimensional spaces is a challenging task. In order to effectively classify the data in a high-dimensional space, a quantum particle swarm optimization classification algorithm (QPSOCA) for high-dimensional datasets is proposed in this paper. In QPSOCA, an uncorrelated discriminant analysis algorithm is utilized to reduce the dimension of the data, which is implemented automatically and no extra parameters are needed. In addition, to avoid the randomness of the swarm and improve the convergence speed, quantum computation is introduced into particle swarm optimization (PSO). In the experimental section, a detailed comparison of three different combinatorial optimization methods is given to demonstrate the efficiency of the proposed algorithm. Comparative experiments show that the proposed algorithm can improve the classification accuracy.

scholarly journals A Two-Layer Task Assignment Algorithm for UAV Swarm Based on Feature Weight Clustering

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaowei Fu ◽  
Peng Feng ◽  
Bin Li ◽  
Xiaoguang Gao
Keyword(s):  
Large Scale ◽  
Task Assignment ◽  
Small Scale ◽  
Resource Assignment ◽  
Assignment Algorithm ◽  
Uav Swarm ◽  
Feature Weight ◽  
Bundle Algorithm ◽  
Aerial Vehicle ◽  
Simulation Results

For the large-scale operations of unmanned aerial vehicle (UAV) swarm and the large number of UAVs, this paper proposes a two-layer task and resource assignment algorithm based on feature weight clustering. According to the numbers and types of task resources of each UAV and the distances between different UAVs, the UAV swarm is divided into multiple UAV clusters, and the large-scale allocation problem is transformed into several related small-scale problems. A two-layer task assignment algorithm based on the consensus-based bundle algorithm (CBBA) is proposed, and this algorithm uses different consensus rules between clusters and within clusters, which ensures that the UAV swarm gets a conflict-free task assignment solution in real time. The simulation results show that the algorithm can assign tasks effectively and efficiently when the number of UAVs and targets is large.

scholarly journals A modified genetic algorithm for task assignment of heterogeneous unmanned aerial vehicle system

2021 ◽  
pp. 002029402110022
Author(s):  
Song Han ◽  
Chenchen Fan ◽  
Xinbin Li ◽  
Xi Luo ◽  
Zhixin Liu
Keyword(s):  
Genetic Algorithm ◽  
Unmanned Aerial Vehicle ◽  
Assignment Problem ◽  
Task Assignment ◽  
Good Genes ◽  
Workload Balance ◽  
Aerial Vehicle ◽  
Task Priority ◽  
Task Assignment Problem ◽  
And Task

This study deals with the task assignment problem of heterogeneous unmanned aerial vehicle (UAV) system with the limited resources and task priority constraints. The optimization model which comprehensively considers the resource consumption, task completion effect, and workload balance is formulated. Then, a concept of fuzzy elite degree is proposed to optimize and balance the transmission of good genes and the variation strength of population during the operations of algorithm. Based on the concept, we propose the fuzzy elite strategy genetic algorithm (FESGA) to efficiently solve the complex task assignment problem. In the proposed algorithm, two unlock methods are presented to solve the deadlock problem in the random optimization process; a sudden threat countermeasure (STC) mechanism is presented to help the algorithm quickly respond to the change of task environment caused by sudden threats. The simulation results demonstrate the superiority of the proposed algorithm. Meanwhile, the effectiveness and feasibility of the algorithm in workload balance and task priority constraints are verified.

scholarly journals Inferring Visual Biases in UAV Videos from Eye Movements

Drones ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 31
Author(s):  
Anne-Flore Perrin ◽  
Lu Zhang ◽  
Olivier Le Meur
Keyword(s):  
Dimensional Space ◽  
Visual Saliency ◽  
Low Complexity ◽  
Saliency Map ◽  
Point Of View ◽  
High Dimensional ◽  
Saliency Maps ◽  
Saliency Prediction ◽  
Aerial Vehicle ◽  
Definition Of

Unmanned Aerial Vehicle (UAV) imagery is gaining a lot of momentum lately. Indeed, gathered information from a bird-point-of-view is particularly relevant for numerous applications, from agriculture to surveillance services. We herewith study visual saliency to verify whether there are tangible differences between this imagery and more conventional contents. We first describe typical and UAV contents based on their human saliency maps in a high-dimensional space, encompassing saliency map statistics, distribution characteristics, and other specifically designed features. Thanks to a large amount of eye tracking data collected on UAV, we stress the differences between typical and UAV videos, but more importantly within UAV sequences. We then designed a process to extract new visual attention biases in the UAV imagery, leading to the definition of a new dictionary of visual biases. We then conduct a benchmark on two different datasets, whose results confirm that the 20 defined biases are relevant as a low-complexity saliency prediction system.

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