Using Genetic Algorithms for Tasking Teams of Raven UAVs

Darrah, Marjorie; Fuller, Edgar; Munasinghe, Thilanka; Duling, Kristin; Gautam, Mridul; Wathen, Mitchell

doi:10.1007/s10846-012-9696-3

Cited by 16 publications

(9 citation statements)

References 14 publications

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“…GA are well known optimizer that have been shown to successfully plan balanced missions for simultaneous mutli-UAV sets [18] surveying multiple POIs [2] and are particularly well-suited in UAV tasking and path planning [18][19][20][21][22]. Several GA approaches for coordinated tasking in surveillance missions under number of targets, task load requirements and reconnaissance time constraints exist in literature [23][24][25][26].…”

Section: Multiple Aircraft Path Planning Literaturementioning

confidence: 99%

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Heterogeneous Aerial Platform Adaptive Mission Planning Using Genetic Algorithms

et al. 2017

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The focus of this study was to examine an automated mission planner that utilized a heterogeneous set of small aerial assets simultaneously surveying several Points of Interest (POI). The concept mission was to aerially search for an unknown Target of Interest (TOI) located amongst a set of POIs. In order to develop a planning system that is capable of meeting mission requirements, an adaptive mission planner, based on a Genetic Algorithm (GA), was investigated that seeks to task a heterogeneous set of unmanned aerial vehicles. Initially, a set of fixed wing UAVs were tasked to survey a set of POIs in search of a TOI, a POI that requires additional or long term surveillance. Once a TOI was located, a multi-rotor UAV was deployed to visit the TOI and additional POIs. Remaining POIs that were not tasked to the multirotor UAV were then re-tasked amongst the fixed wing aircraft set. The mission planner was implemented using a GA that planned initial and post TOI identification UAV paths. Mission simulations were conducted and the mission time increase was analyzed against different TOI locations and number of UAVs searching. Simulation results indicated that the deployment of a multi-rotor UAV not only provided additional surveillance of the TOI, but reduced overall mission times as well.

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Section: Multiple Aircraft Path Planning Literaturementioning

confidence: 99%

“…Real Representation (RR) was used to encode the chromosome parameters which means that parameters were represented using real number vectors [23]. Chromosomes were expressed by a 2 Â L vector, where L is the length of the chromosome.…”

Section: Genetic Representationmentioning

confidence: 99%

Heterogeneous Aerial Platform Adaptive Mission Planning Using Genetic Algorithms

et al. 2017

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“…It consists of four motive forces and these are crossover where two members of a population mate to produce an offspring, mutation where a member of the population changes aspect of its genes and reproduces according to the principles of the survival of the fitness. Genetic algorithm was applied successfully to optimize resource levels in surgical services (Lin et al, 2013), optimize stay cables in bridges (Hassan, 2013), optimize steel panels (Poirier et al, 2013), schedule shop floor (Wang and Liu, 2013) and task teams of unmanned aerial vehicles (Darrah et al, 2013). Genetic algorithm is used in this book to build a correlation function.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Introduction to Artificial Intelligence based Decision Making

Marwala¹

2015

Causality, Correlation and Artificial Intelligence for Rational Decision Making

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In this chapter, decision making which is based on causality, correlation and artificial intelligence is introduced. The concept of correlation is introduced and is applied to build a correlation function. Furthermore, it relates causality to correlation and uses it to build a causal function. Artificial intelligence methods are introduced and these include neural networks, particle swarm optimization, genetic algorithm and simulated annealing. It describes decision making and outlines the rest of the book. 1 Causality, Correlation and Artificial Intelligence for Rational Decision Making Downloaded from www.worldscientific.com by KAINAN UNIVERSITY on 02/04/15. For personal use only.

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“…They include the mixed integer linear programming (MILP) formulation [4][5][6], the branch and bound tree search algorithm [7,8], and the dynamic programming algorithm [9] among others. Most research to date on the application of heuristic (metaheuristic) algorithms have either extracted some specific search rules based on the properties of the problem to obtain optimal or suboptimal solutions rapidly or introduced some local search mechanism to the basic algorithm framework to improve the solution quality, such as tabu search algorithms [10], auction algorithms [11], genetic algorithms [12][13][14][15], ant colony algorithms [16], and particle swarm optimization [17,18]. The algorithms mentioned above-whether belonging to exact algorithms or heuristic (metaheuristic) algorithms-have demonstrated the ability to provide optimal or suboptimal solutions for task assignment problems of UAVs or UCAVs in various mission scenarios but may become difficult to apply when there are uncertain parameters related to mission scenarios.…”

Section: Introductionmentioning

confidence: 99%

Combining an Extended SMAA-2 Method with Integer Linear Programming for Task Assignment of Multi-UCAV under Multiple Uncertainties

Wang

Luo²,

et al. 2018

Symmetry

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Uncertainty should be taken into account when establishing multiobjective task assignment models for multiple unmanned combat aerial vehicles (UCAVs) due to errors in the target information acquired by sensors, implicit preferences of the commander for operational objectives, and partially known weights of sensors. In this paper, we extend the stochastic multicriteria acceptability analysis-2 (SMAA-2) method and combine it with integer linear programming to achieve multiobjective task assignment for multi-UCAV under multiple uncertainties. We first represent the uncertain target information as normal distribution interval numbers so that the values of criteria (operational objectives) concerned can be computed based on the weighted arithmetic averaging operator. Thus, we obtain multiple criteria value matrices for each UCAV. Then, we propose a novel aggregation method to generate the final criteria value matrix based on which the holistic acceptability indices are computed by the extended SMAA-2 method. On this basis, we convert the task assignment model with uncertain parameters into an integer linear programming model without uncertainty so as to implement task assignment using the integer linear programming method. Finally, we conduct a case study and demonstrate the feasibility of the proposed method in solving the multiobjective task assignment problem multi-UCAV under multiple uncertainties.

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Using Genetic Algorithms for Tasking Teams of Raven UAVs

Cited by 16 publications

References 14 publications

Heterogeneous Aerial Platform Adaptive Mission Planning Using Genetic Algorithms

Heterogeneous Aerial Platform Adaptive Mission Planning Using Genetic Algorithms

Introduction to Artificial Intelligence based Decision Making

Combining an Extended SMAA-2 Method with Integer Linear Programming for Task Assignment of Multi-UCAV under Multiple Uncertainties

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