Multi-objective UAV mission planning using evolutionary computation

Pohl,; Lamont,

doi:10.1109/wsc.2008.4736199

Cited by 38 publications

(19 citation statements)

References 23 publications

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“…The UAV is expected to use the route that satisfies multiple objectives, such as the shortest distance traveled, lowest fuel consumption, minimum total flight time, least detection threat, maximum safety and etc.. Usually, these objectives are conflicting with each other. How to properly manage and fully realize the capabilities of UAV force, ensure UAV's effective deployment to outstanding targets, and find the "best" route that the UAV should follow through a defended area is critically important, which leads to the study of multiobjective UAV ISR mission planning problem [1][2][3][4]. These studies have largely treated the UAV ISR mission planning problem as a deterministic multiobjective programming (MOP) problem with perfectly known parameters.…”

Section: Introductionmentioning

confidence: 99%

Uncertain UAV ISR mission planning problem with multiple correlated objectives

Wang

Ming

Guo

et al. 2017

IFS

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Unmanned Aerial Vehicle (UAV) is very useful for information gathering in Intelligence, Surveillance, and Reconnaissance (ISR) mission. On the basis of uncertainty theory, the main purpose of this paper is to study a new kind of UAV ISR mission planning problem involving multiple objectives under uncertain environment. In particular, the mission planning objectives are influenced by the same uncertain factors simultaneously, that is to say, they are correlated or dependent with each other. In this case, the traditional multiobjective approach cannot guarantee the uncertain nature and take into account the dependence among uncertain objectives in the UAV ISR mission planning problem. In order to overcome the disadvantage in traditional approach, a new solution approach is introduced for obtaining the P E Pareto efficient routes for it, which involves transforming the original ISR mission planning problem into a single objective uncertain programming problem. Two specific compromise models are proposed using this new solution approach respectively, whose validity is proved theoretically. Finally, an application case study with 13 ISR targets is provided and solved. The results show that the proposed model and solution approach have excellent consistency and efficiency in solving the uncertain UAV ISR mission planning problem presented in this paper.

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Section: Introductionmentioning

confidence: 99%

Uncertain UAV ISR mission planning problem with multiple correlated objectives

Wang

Ming

Guo

et al. 2017

IFS

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“…Also, pseudospectral methods have been implemented to optimally solve these problems [7]. Other kind of methods proposed are: evolutionary techniques [8] [9], particle swarm optimization [10] [11] and multi-objective evolutionary algorithms [12].…”

Section: Introductionmentioning

confidence: 99%

Collision-free trajectory planning based on Maneuver Selection-Particle Swarm Optimization

Alejo

Cobano

Heredia

et al. 2015

2015 International Conference on Unmanned Aircraft Systems (ICUAS)

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This paper presents a system for collisionfree trajectory planning with multiple Unmanned Aerial Vehicles (UAVs) which automatically identifies conflicts among them. After detecting conflicts between UAVs, the system resolves them cooperatively using a collisionfree trajectory planning algorithm based on a stochastic optimization technique named Particle Swarm Optimization (PSO). The new implementation of the PSO algorithm, named Maneuver Selection Particle Swarm Optimization (MS-PSO), presents improvements with respect to previous implementations. The execution time is reduced because the dimension of the problem is reduced, and different kinds of maneuvers can be selected to solve the detected conflicts: course/heading, speed or altitude changes. The MS-PSO has been validated with simulations in scenarios with multiple UAVs in a common airspace. Also, a comparison to a genetic algorithm and a PSO algorithm has been performed to highlight the advantages of the MS-PSO. The main advantage is that MS-PSO always ensures solution from the first iteration. This requirement is essential in safe cooperative missions.

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“…The performance of mSPEA-II is tested on randomly generated problems of different sizes. Results are compared with the solutions of NSGA-II and SPEA-II, since they are the representatives of the state-of-the-art MOGAs (Coello Coello et al 2007, p. 274;Zhou et al 2011;Durillo et al 2009;Pohl and Lamonth 2008). Although there are many variations of these state-of-the-art algorithms, original versions are used in comparative studies of the newly developed MOGAs (Coello Coello et al 2007, p. 98;Konak et al 2006).…”

Section: Introductionmentioning

confidence: 99%

A multi-objective genetic algorithm for a bi-objective facility location problem with partial coverage

Karasakal

Silav

2015

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In this study, we present a bi-objective facility location model that considers both partial coverage and service to uncovered demands. Due to limited number of facilities to be opened, some of the demand nodes may not be within full or partial coverage distance of a facility. However, a demand node that is not within the coverage distance of a facility should get service from the nearest facility within the shortest possible time. In this model, it is assumed that demand nodes within the predefined distance of opened facilities are fully covered, and after that distance the coverage level decreases linearly. The objectives are defined as the maximization of full and partial coverage, and the minimization of the maximum distance between uncovered demand nodes and their nearest facilities. We develop a new multi-objective genetic algorithm (MOGA) called modified SPEA-II (mSPEA-II). In this method, the fitness function of SPEA-II is modified and the crowding distance of NSGA-II is used. The performance of mSPEA-II is tested on randomly generated problems of different sizes. The results are compared with the solutions of the most well-known MOGAs, NSGA-II and SPEA-II. Computational experiments show that mSPEA-II outperforms both NSGA-II and SPEA-II.

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Multi-objective UAV mission planning using evolutionary computation

Abstract: This investigation purports to develop a new model for multiple autonomous aircraft mission routing. Previous research both related and unrelated to this endeavor have used classic combinatoric problems as models for Unmanned Aerial

Cited by 38 publications

References 23 publications

Uncertain UAV ISR mission planning problem with multiple correlated objectives

Uncertain UAV ISR mission planning problem with multiple correlated objectives

Collision-free trajectory planning based on Maneuver Selection-Particle Swarm Optimization

A multi-objective genetic algorithm for a bi-objective facility location problem with partial coverage

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