FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W235791050> ?p ?o ?g. }

• W235791050 abstract "This article proposes a method to solve route plan problem of unmanned underwater vehicle (UUV) using particle swarm optimization (PSO). Firstly, traditional electronic nautical map is preprocessed to decrease calculated amount in computation of route cost and improve plan instantaneity using technique of threaten circles covering. Secondly, choose appropriate route expression and cost computation. Lastly, route with minimum cost is received using particle swarm optimization. Result indicates that particle swarm optimization based on swarm intelligence can insure optimality most out of limited computation time, relative to traditional method using graph theory and dynamic programming. Introduction Path plan is the important content of task assignment of UUV, and purpose of path plan is to find the route from start point to goal point which satisfies UUV performance request. Generally, in the process of path plan, first step is to create a data structure represent path in the three dimension space, second step is to create appropriate cost function involve fuel, threat and etc, third step is to find optimum path using optimization algorithm which cost function achieves minimum. Swarm Intelligence is popular method to solve path plan problem in the recent years, in which particle swarm algorithm is heuristic optimization method using heuristic random search. The algorithm is started from the swarm, every particle in the swarm represents solution to UUV path plan problem. In the search process, global optimum particle sharing global optimum information with every particle and local historical optimum particle sharing local optimum information with self particle. New position of particle is addition of global optimum position and local optimum position in some extent and after some iteration particle swarm finish search to global search space. Differences of PSO and other swarm intelligence algorithm are: every particle has memory of local historical optimum solution and whole swarm has memory of global optimum solution. The principle of PSO is simple and parameters of PSO are less than other Swarm Intelligence algorithm to programming. So PSO is adapt to solve path plan problem has short time demand. This article solves path plan problem of UUV, path plan method is constituted with four steps: (1) route representation; (2) cost function choose; (3) optimum path solution using PSO. Route representation Route representation is more important problem in the path plan, which affects cost value computation and effect of path plan. Better route representation not only represents motion character, but eliminates route not satisfies plan request from standby route, and increase efficiency of PSO algorithm. Traditional method of route representation is to plot grid map from the start to the goal, which is shown in the Picture (1). Using this method, Path plan problem can be seen as shortest route on the direct map with some weight. Limitation of this method is no matter search algorithm is powerful, method can not search path point between grid points and affect optimality of UUV plan route. PSO algorithm is pointed to continue search space, and fit search map without grids, so take" @default.
• W235791050 created "2016-06-24" @default.
• W235791050 creator A5039412958 @default.
• W235791050 creator A5059936764 @default.
• W235791050 date "2015-01-01" @default.
• W235791050 modified "2023-09-27" @default.
• W235791050 title "Path Plan of Unmanned Underwater Vehicle Using Particle Swarm Optimization" @default.
• W235791050 doi "https://doi.org/10.2991/isrme-15.2015.358" @default.
• W235791050 hasPublicationYear "2015" @default.
• W235791050 type Work @default.
• W235791050 sameAs 235791050 @default.
• W235791050 citedByCount "3" @default.
• W235791050 countsByYear W2357910502016 @default.
• W235791050 countsByYear W2357910502019 @default.
• W235791050 countsByYear W2357910502022 @default.
• W235791050 crossrefType "proceedings-article" @default.
• W235791050 hasAuthorship W235791050A5039412958 @default.
• W235791050 hasAuthorship W235791050A5059936764 @default.
• W235791050 hasBestOaLocation W2357910501 @default.
• W235791050 hasConcept C111368507 @default.
• W235791050 hasConcept C11413529 @default.
• W235791050 hasConcept C127313418 @default.
• W235791050 hasConcept C127413603 @default.
• W235791050 hasConcept C145424490 @default.
• W235791050 hasConcept C146978453 @default.
• W235791050 hasConcept C151730666 @default.
• W235791050 hasConcept C154945302 @default.
• W235791050 hasConcept C199104240 @default.
• W235791050 hasConcept C19966478 @default.
• W235791050 hasConcept C2776505523 @default.
• W235791050 hasConcept C2777735758 @default.
• W235791050 hasConcept C31258907 @default.
• W235791050 hasConcept C41008148 @default.
• W235791050 hasConcept C81074085 @default.
• W235791050 hasConcept C85617194 @default.
• W235791050 hasConcept C90509273 @default.
• W235791050 hasConcept C98083399 @default.
• W235791050 hasConceptScore W235791050C111368507 @default.
• W235791050 hasConceptScore W235791050C11413529 @default.
• W235791050 hasConceptScore W235791050C127313418 @default.
• W235791050 hasConceptScore W235791050C127413603 @default.
• W235791050 hasConceptScore W235791050C145424490 @default.
• W235791050 hasConceptScore W235791050C146978453 @default.
• W235791050 hasConceptScore W235791050C151730666 @default.
• W235791050 hasConceptScore W235791050C154945302 @default.
• W235791050 hasConceptScore W235791050C199104240 @default.
• W235791050 hasConceptScore W235791050C19966478 @default.
• W235791050 hasConceptScore W235791050C2776505523 @default.
• W235791050 hasConceptScore W235791050C2777735758 @default.
• W235791050 hasConceptScore W235791050C31258907 @default.
• W235791050 hasConceptScore W235791050C41008148 @default.
• W235791050 hasConceptScore W235791050C81074085 @default.
• W235791050 hasConceptScore W235791050C85617194 @default.
• W235791050 hasConceptScore W235791050C90509273 @default.
• W235791050 hasConceptScore W235791050C98083399 @default.
• W235791050 hasLocation W2357910501 @default.
• W235791050 hasLocation W2357910502 @default.
• W235791050 hasOpenAccess W235791050 @default.
• W235791050 hasPrimaryLocation W2357910501 @default.
• W235791050 hasRelatedWork W1485496002 @default.
• W235791050 hasRelatedWork W1986845419 @default.
• W235791050 hasRelatedWork W2254436626 @default.
• W235791050 hasRelatedWork W2265719637 @default.
• W235791050 hasRelatedWork W2571982302 @default.
• W235791050 hasRelatedWork W2989639874 @default.
• W235791050 hasRelatedWork W4308734120 @default.
• W235791050 hasRelatedWork W4310520215 @default.
• W235791050 hasRelatedWork W4352980952 @default.
• W235791050 hasRelatedWork W4380079759 @default.
• W235791050 isParatext "false" @default.
• W235791050 isRetracted "false" @default.
• W235791050 magId "235791050" @default.
• W235791050 workType "article" @default.