Cooperative Task Assignment/Path Planning of Multiple Unmanned Aerial Vehicles Using Genetic Algorithm

Eun, Yeonju; Bang, Hyochoong

doi:10.2514/1.38510

Cited by 93 publications

(29 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For large-scale problems, the most efficient and scalable approaches have used some form of randomized search algorithm, such as a genetic algorithm (GA) [11][12][13][14][15]. Other heuristic approaches to the problem include simulated annealing [16] and particle swarm optimization [17].…”

Section: Introductionmentioning

confidence: 99%

A Flexible Genetic Algorithm System for Multi-UAV Surveillance: Algorithm and Flight Testing

et al. 2015

View full text Add to dashboard Cite

This paper discusses the development and testing of a flexible genetic algorithm (GA)-based system used for tasking a team of unmanned aerial vehicles (UAVs) to complete a coordinated surveillance mission. The GA development, laboratory testing of the GA to ensure convergence to a \good" solution, integration testing with two ground stations, and the field testing of the algorithms are explained. The algorithm was found to be robust and flexible enough to work in various settings with different UAV types and ground stations.

show abstract

Section: Introductionmentioning

confidence: 99%

A Flexible Genetic Algorithm System for Multi-UAV Surveillance: Algorithm and Flight Testing

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Multiple aircraft are required to complete the mission cooperatively under the dynamic environment because it increases the probability of a successful mission [11,12]. To make the complicated mission simple, the mission is usually decomposed into a series of basic tasks so that the tasks can be executed directly by every aircraft [13][14][15]. Hierarchical planning method is effective in dealing with the complicated task allocation problem [16,17].…”

Section: Introductionmentioning

confidence: 99%

A Task Allocation Model for a Team of Aircraft Launching on the Carrier

Cui

Su³

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

High level efficiency and safety are of paramount importance for the improvement of fighting capability of an aircraft carrier. The task allocation problem for a team of aircraft launching on the carrier is studied in this paper. Although the study of this problem is of great significance, no relevant literature has been found on this issue. Firstly, the conceptual model of problem is formulated with the planning objectives and the constraints defined. Then the multi-aircraft and multi-catapult launching task allocation problem is decomposed into two consecutive sub-tasks, that is, catapult allocation and the launching sequence determination. The taxi time of aircraft is considered during the catapult allocation process, and the launching position of each aircraft is determined using a decision-making method. In the launching sequence determination step, the starting collision risk of aircraft is introduced to optimize the launching sequence which results in the minimum collision risk on each catapult. Thirdly, the proposed method is validated using the setups of the Nimitz-class aircraft carrier. The proposed method is used to solve the task allocation problem and is compared to the artificial heuristics approach and the brute force approach. Experiment results demonstrate that the proposed method has better performance than the artificial heuristics approach and has better performance than the brute force approach in balancing efficiency and safety.

show abstract

“…The literature [5] adopted PSO route search in a two-dimensional environment, to convert a variety of threats into the ground radar detection model, and circumvent the treats, which can better handle the situation in real-time multi-threats airlines planning. The literature [6] proposed genetic algorithms to route search, it can achieve adaptive route planning in an uncertain battlefield environment, but when in a large-scale environments, it is easy to fall into local optimum, not to get the best route. The complex battlefield situation needs to be adjusted quickly.…”

Section: Introductionmentioning

confidence: 99%

Airlines Automatic Generation used in hostile environments

Cheng¹,

Chen²

2016

Proceedings of the 2016 3rd International Conference on Mechatronics and Information Technology

View full text Add to dashboard Cite

Abstract. In a hostile environment, it is important for avoiding the threat of ground radar and ground air defense missile. So, in the airlines planning process these are some factors to be studied carefully, such as the terrain, ground radar and anti-aircraft missiles and other factors. Firstly, it is important to construct the battle environment spatial modeling. The battle space is divided by grid-like space nodes. Then set the values of modes attributes in the space node, such as the terrain model, radar model and anti-aircraft missiles model. The probability of detection was calculated based on the positional relationship between the radar and radar aircraft, and anti-aircraft missile shot probability, etc. Then the fitness function of a space node was gotten. Finally, using the ant colony algorithm to search the optimal airline, generate best performance of the airline.

show abstract

Cooperative Task Assignment/Path Planning of Multiple Unmanned Aerial Vehicles Using Genetic Algorithm

Cited by 93 publications

References 12 publications

A Flexible Genetic Algorithm System for Multi-UAV Surveillance: Algorithm and Flight Testing

A Flexible Genetic Algorithm System for Multi-UAV Surveillance: Algorithm and Flight Testing

A Task Allocation Model for a Team of Aircraft Launching on the Carrier

Airlines Automatic Generation used in hostile environments

Contact Info

Product

Resources

About