Array
(
[page] => 97
[item_count] => 2086
[items_per_page] => 15
[data] => Array
(
[0] => Array
(
[vysledek_id] => 55116
[vysledek_druh_id] => CHAPT
[ex_vysledek_id] => 66925
[vysledek_rok] => 2006
[nazev] => Nonholonomic Mobile Robot Path Planning by Means of Case-Based Reasoning.
[nazev_orig] => Nonholonomic Mobile Robot Path Planning by Means of Case-Based Reasoning.
[duvernost_udaju_id] => S
[popis] => The purpose of the robot path planning is to find a path from a robot start configuration to a goal configuration without collisions with known obstacles minimizing such criteria as length, difficulty and risk of the path. We consider a nonholonomic robot moving in a dynamic partially known two-dimensional space with polygonal obstacles We propose path planning methods combining case-based reasoning with graph searching methods in a rectangular grid and rapidly exploring random trees in a continuous space. Proposed methods are based on a case graph, which is a structure composed of segments of already used paths. Graph searching algorithms for the case graph and the grid are modified with respect to nonholonomic constraints.
[popis_orig] => The purpose of the robot path planning is to find a path from a robot start configuration to a goal configuration without collisions with known obstacles minimizing such criteria as length, difficulty and risk of the path. We consider a nonholonomic robot moving in a dynamic partially known two-dimensional space with polygonal obstacles We propose path planning methods combining case-based reasoning with graph searching methods in a rectangular grid and rapidly exploring random trees in a continuous space. Proposed methods are based on a case graph, which is a structure composed of segments of already used paths. Graph searching algorithms for the case graph and the grid are modified with respect to nonholonomic constraints.
[klicova_slova] => Nonholonmomic mobile robot; path planning; case-based reasoning; graph searching algorithms; rapidly exploring random trees.
[klicova_slova_orig] => Nonholonmomic mobile robot; path planning; case-based reasoning; graph searching algorithms; rapidly exploring random trees.
[url] =>
[oecd_obor_id] => 20204
[odpovedny_utvar_id] => 207
[odpovedny_utvar_nazev] => Institute of Automation and Computer Science
[odpovedny_utvar_zkratka] => IACS
[nadrazena_soucast_id] => 4
[nadrazena_soucast_zkratka] => FME
[nadrazena_soucast_nazev] => Faculty of Mechanical Engineering
[originalni_jazyk] => en
[schvalil_id] => 999999
[schvaleno] => 2010-12-03
[vykazovat_riv] => 0
[vykazovat_riv_zmeny] => 1
[slozka_id] =>
[posledni_diagnostika] =>
[vycet_osob] => DVOŘÁK, J.; KRČEK, P.
[pocet_tvurcu] => 2
[tvurci_ids] =>
[poznamka] =>
[typ_nazev] =>
[kod_doi] =>
[kod_dspace] =>
[rok_vytvoreni] =>
[pocet_zaznamu] =>
[zverejneno] => 1
[prvni_autor] =>
[korespondencni_autor] =>
[posledni_autor] =>
[znamka] =>
[kategorie_nazev] => Publication results
[druh_nazev] => Chapter in a book
[druh_popis] => Chapter in a book
[stav] => Approved
[vysledek_kategorie_id] => PV
[vysledek_system_kategorie_id] => PU
[vysledek_stav_id] => 3
[vlozil] => Informační systém Automat
[upravil] => Informační systém Automat
[ins_uid] => 999999
[upd_uid] => 999999
[ins_ts] => 2025-09-22
[upd_ts] => 2025-09-22
[status] => 9
[identifikator] => ISBN 80-214-3341-8
[identifikator_popis] => ISBN - Simulation Modelling of Mechatronic Systems II
[riv_dodavka_id] => 98
[riv_dodavka_oznaceni] => RIV10-MSM-26210___
[riv_dodavka_rok] => 2010
[diagnostika_pocet] => 0
[diagnostika_pocet_chyba] => 0
[diagnostika_pocet_upozorneni] => 0
[diagnostika_pocet_informace] => 0
[citace_text] => DVOŘÁK, J.; KRČEK, P. Nonholonomic Mobile Robot Path Planning by Means of Case-Based Reasoning. In Simulation Modelling of Mechatronic Systems II. Mechatronics. Brno: Brno University of Technology, 2006. p. 131-137. ISBN: 80-214-3341-8.
[citace_html] => DVOŘÁK, J.; KRČEK, P. Nonholonomic Mobile Robot Path Planning by Means of Case-Based Reasoning. In Simulation Modelling of Mechatronic Systems II. Mechatronics. Brno: Brno University of Technology, 2006. p. 131-137. ISBN: 80-214-3341-8.
[citace_rtf] =>
[citace_bibtex] => @inbook{BUT55116,
author="Jiří {Dvořák} and Petr {Krček}",
title="Nonholonomic Mobile Robot Path Planning by Means of Case-Based Reasoning.",
booktitle="Simulation Modelling of Mechatronic Systems II",
year="2006",
publisher="Brno University of Technology",
address="Brno",
series="Mechatronics",
edition="1",
pages="131--137",
isbn="80-214-3341-8"
}
[vykazano] =>
[vykazano_aspon_jednou] =>
[identifikacni_kod] =>
[neautorsky_vysledek] => 0
[if] =>
[if_q] =>
[if_m17_q] =>
[if_m25_q] =>
[if_d] =>
[if_m17_d] =>
[if_m25_d] =>
[if_percentil] =>
[if_m17_percentil] =>
[if_m25_percentil] =>
[ais] =>
[ais_m17_q] =>
[ais_m25_q] =>
[ais_m17_d] =>
[ais_m25_d] =>
[ais_m17_percentil] =>
[ais_m25_percentil] =>
[jci] =>
[jci_q] =>
[jci_percentil] =>
[ef] =>
[scopus_sjr] =>
[scopus_sjr_q] =>
[scopus_sjr_d] =>
[nature_index_group] =>
[incites_times_cited] =>
[incites_open_access] =>
[incites_jnci] =>
[incites_is_int_collab] =>
[incites_is_industry_collab] =>
[incites_esi_hot_paper] =>
[incites_esi_highly_cited_paper] =>
[incites_avg_cnci] =>
[incites_avg_percentile] =>
[scival_citations_count] =>
[scival_fwci] =>
[core_rank] => NA
[oecd_tree_oblast_id] => 20000
[oecd_tree_oblast_nazev] => 2. Engineering and Technology
[oecd_tree_obor_id] => 20200
[oecd_tree_obor_nazev] => 2.2 Electrical engineering, Electronic engineering, Information engineering
[oecd_tree_podobor_id] => 20204
[oecd_tree_podobor_nazev] => Robotics and automatic control
[poznamka_metriky] =>
[nazev_en] => Nonholonomic Mobile Robot Path Planning by Means of Case-Based Reasoning.
[popis_en] => The purpose of the robot path planning is to find a path from a robot start configuration to a goal configuration without collisions with known obstacles minimizing such criteria as length, difficulty and risk of the path. We consider a nonholonomic robot moving in a dynamic partially known two-dimensional space with polygonal obstacles We propose path planning methods combining case-based reasoning with graph searching methods in a rectangular grid and rapidly exploring random trees in a continuous space. Proposed methods are based on a case graph, which is a structure composed of segments of already used paths. Graph searching algorithms for the case graph and the grid are modified with respect to nonholonomic constraints.
[klicova_slova_en] => Nonholonmomic mobile robot; path planning; case-based reasoning; graph searching algorithms; rapidly exploring random trees.
[vysledek_datum] => 2006-12-15T00:00:00+01:00
)
[1] => Array
(
[vysledek_id] => 55121
[vysledek_druh_id] => CHAPT
[ex_vysledek_id] => 67054
[vysledek_rok] => 2006
[nazev] => Mobile robots localization and path planning
[nazev_orig] => Mobile robots localization and path planning
[duvernost_udaju_id] => S
[popis] => We developed simple but fast
method for real-time localization in static environment (only static obstacles are considered)
based on a popular Markov localization which can not be used in real world application due to
computational demands. PCSM (Pre-computed Scan Matching) localization method was
developed for small robots with low memory and low speed processors. PCSM belongs to the
group of global localization algorithms which solve the problem of unknown initial position.
[popis_orig] => We developed simple but fast
method for real-time localization in static environment (only static obstacles are considered)
based on a popular Markov localization which can not be used in real world application due to
computational demands. PCSM (Pre-computed Scan Matching) localization method was
developed for small robots with low memory and low speed processors. PCSM belongs to the
group of global localization algorithms which solve the problem of unknown initial position.
[klicova_slova] => Mobile robots, localization, path planning
[klicova_slova_orig] => Mobile robots, localization, path planning
[url] =>
[oecd_obor_id] => 10201
[odpovedny_utvar_id] => 207
[odpovedny_utvar_nazev] => Institute of Automation and Computer Science
[odpovedny_utvar_zkratka] => IACS
[nadrazena_soucast_id] => 4
[nadrazena_soucast_zkratka] => FME
[nadrazena_soucast_nazev] => Faculty of Mechanical Engineering
[originalni_jazyk] => en
[schvalil_id] => 999999
[schvaleno] =>
[vykazovat_riv] => 0
[vykazovat_riv_zmeny] => 1
[slozka_id] =>
[posledni_diagnostika] =>
[vycet_osob] => VĚCHET, S.; KREJSA, J.
[pocet_tvurcu] => 2
[tvurci_ids] =>
[poznamka] =>
[typ_nazev] =>
[kod_doi] =>
[kod_dspace] =>
[rok_vytvoreni] =>
[pocet_zaznamu] =>
[zverejneno] => 1
[prvni_autor] =>
[korespondencni_autor] =>
[posledni_autor] =>
[znamka] =>
[kategorie_nazev] => Publication results
[druh_nazev] => Chapter in a book
[druh_popis] => Chapter in a book
[stav] => Approved
[vysledek_kategorie_id] => PV
[vysledek_system_kategorie_id] => PU
[vysledek_stav_id] => 3
[vlozil] => Informační systém Automat
[upravil] => Informační systém Automat
[ins_uid] => 999999
[upd_uid] => 999999
[ins_ts] => 2025-09-22
[upd_ts] => 2025-09-22
[status] => 9
[identifikator] => ISBN 80-214-3341-8
[identifikator_popis] => ISBN - Simulation Modelling of Mechatronic Systems II
[riv_dodavka_id] =>
[riv_dodavka_oznaceni] =>
[riv_dodavka_rok] =>
[diagnostika_pocet] => 0
[diagnostika_pocet_chyba] => 0
[diagnostika_pocet_upozorneni] => 0
[diagnostika_pocet_informace] => 0
[citace_text] => VĚCHET, S.; KREJSA, J. Mobile robots localization and path planning. In Simulation Modelling of Mechatronic Systems II. mechatronika. Brno: Brno University of Technology, 2006. p. 149-156. ISBN: 80-214-3341-8.
[citace_html] => VĚCHET, S.; KREJSA, J. Mobile robots localization and path planning. In Simulation Modelling of Mechatronic Systems II. mechatronika. Brno: Brno University of Technology, 2006. p. 149-156. ISBN: 80-214-3341-8.
[citace_rtf] =>
[citace_bibtex] => @inbook{BUT55121,
author="Stanislav {Věchet} and Jiří {Krejsa}",
title="Mobile robots localization and path planning",
booktitle="Simulation Modelling of Mechatronic Systems II",
year="2006",
publisher="Brno University of Technology",
address="Brno",
series="mechatronika",
edition="1",
pages="149--156",
isbn="80-214-3341-8"
}
[vykazano] =>
[vykazano_aspon_jednou] =>
[identifikacni_kod] =>
[neautorsky_vysledek] => 0
[if] =>
[if_q] =>
[if_m17_q] =>
[if_m25_q] =>
[if_d] =>
[if_m17_d] =>
[if_m25_d] =>
[if_percentil] =>
[if_m17_percentil] =>
[if_m25_percentil] =>
[ais] =>
[ais_m17_q] =>
[ais_m25_q] =>
[ais_m17_d] =>
[ais_m25_d] =>
[ais_m17_percentil] =>
[ais_m25_percentil] =>
[jci] =>
[jci_q] =>
[jci_percentil] =>
[ef] =>
[scopus_sjr] =>
[scopus_sjr_q] =>
[scopus_sjr_d] =>
[nature_index_group] =>
[incites_times_cited] =>
[incites_open_access] =>
[incites_jnci] =>
[incites_is_int_collab] =>
[incites_is_industry_collab] =>
[incites_esi_hot_paper] =>
[incites_esi_highly_cited_paper] =>
[incites_avg_cnci] =>
[incites_avg_percentile] =>
[scival_citations_count] =>
[scival_fwci] =>
[core_rank] => NA
[oecd_tree_oblast_id] => 10000
[oecd_tree_oblast_nazev] => 1. Natural Sciences
[oecd_tree_obor_id] => 10200
[oecd_tree_obor_nazev] => 1.2 Computer and information sciences
[oecd_tree_podobor_id] => 10201
[oecd_tree_podobor_nazev] => Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
[poznamka_metriky] =>
[nazev_en] => Mobile robots localization and path planning
[popis_en] => We developed simple but fast
method for real-time localization in static environment (only static obstacles are considered)
based on a popular Markov localization which can not be used in real world application due to
computational demands. PCSM (Pre-computed Scan Matching) localization method was
developed for small robots with low memory and low speed processors. PCSM belongs to the
group of global localization algorithms which solve the problem of unknown initial position.
[klicova_slova_en] => Mobile robots, localization, path planning
[vysledek_datum] => 2006-12-15T00:00:00+01:00
)
[2] => Array
(
[vysledek_id] => 55142
[vysledek_druh_id] => CHAPT
[ex_vysledek_id] => 67202
[vysledek_rok] => 2006
[nazev] => On Shortest Paths in Partially Known Environment
[nazev_orig] => On Shortest Paths in Partially Known Environment
[duvernost_udaju_id] => S
[popis] => In robot motion planning a robot should pass around obstacles, from a given starting position to a given target position, touching none of them, i.e., the goal is to find a collision-free path from the starting to the target position. This task has many specific formulations depending on the shape of obstacles, eligible directions of movements, knowledge of the scene, etc. The basic step in all methods used for solving this problem, e.g., roadmap methods, cell decomposition and case-based reasoning is to find the shortest path between starting and target positions or to find the shortest paths among all pairs of positions when we use a database of stored solutions and try to adapt these old solutions to a new problem. However, in a partially known scene, we must approximate the lengths of edges in a graph representation of the scene. In this contribution, we deal with the All-Pairs Shortest Paths Problem (APSPP) on a graph in which a fuzzy number, rather than a real number, is assigned to each edge. Since the fuzzy min operator based on the extension principle leads to non-dominated solutions, we propose another approach to solving the APSPP using a suitable fuzzy ranking method. We also show that the efficiency of computations may be improved by the proposed APSPP modification of the Dijkstra algorithm based on a binary heap data structure.
[popis_orig] => In robot motion planning a robot should pass around obstacles, from a given starting position to a given target position, touching none of them, i.e., the goal is to find a collision-free path from the starting to the target position. This task has many specific formulations depending on the shape of obstacles, eligible directions of movements, knowledge of the scene, etc. The basic step in all methods used for solving this problem, e.g., roadmap methods, cell decomposition and case-based reasoning is to find the shortest path between starting and target positions or to find the shortest paths among all pairs of positions when we use a database of stored solutions and try to adapt these old solutions to a new problem. However, in a partially known scene, we must approximate the lengths of edges in a graph representation of the scene. In this contribution, we deal with the All-Pairs Shortest Paths Problem (APSPP) on a graph in which a fuzzy number, rather than a real number, is assigned to each edge. Since the fuzzy min operator based on the extension principle leads to non-dominated solutions, we propose another approach to solving the APSPP using a suitable fuzzy ranking method. We also show that the efficiency of computations may be improved by the proposed APSPP modification of the Dijkstra algorithm based on a binary heap data structure.
[klicova_slova] => Shortest path problem, fuzzy ranking, binary heap, priority queue
[klicova_slova_orig] => Shortest path problem, fuzzy ranking, binary heap, priority queue
[url] =>
[oecd_obor_id] => 20204
[odpovedny_utvar_id] => 207
[odpovedny_utvar_nazev] => Institute of Automation and Computer Science
[odpovedny_utvar_zkratka] => IACS
[nadrazena_soucast_id] => 4
[nadrazena_soucast_zkratka] => FME
[nadrazena_soucast_nazev] => Faculty of Mechanical Engineering
[originalni_jazyk] => en
[schvalil_id] => 999999
[schvaleno] => 2010-12-03
[vykazovat_riv] => 0
[vykazovat_riv_zmeny] => 1
[slozka_id] =>
[posledni_diagnostika] =>
[vycet_osob] => ŠEDA, M.
[pocet_tvurcu] => 1
[tvurci_ids] =>
[poznamka] =>
[typ_nazev] =>
[kod_doi] =>
[kod_dspace] =>
[rok_vytvoreni] =>
[pocet_zaznamu] =>
[zverejneno] => 1
[prvni_autor] =>
[korespondencni_autor] =>
[posledni_autor] =>
[znamka] =>
[kategorie_nazev] => Publication results
[druh_nazev] => Chapter in a book
[druh_popis] => Chapter in a book
[stav] => Approved
[vysledek_kategorie_id] => PV
[vysledek_system_kategorie_id] => PU
[vysledek_stav_id] => 3
[vlozil] => Informační systém Automat
[upravil] => Informační systém Automat
[ins_uid] => 999999
[upd_uid] => 999999
[ins_ts] => 2025-09-22
[upd_ts] => 2025-09-22
[status] => 9
[identifikator] => ISBN 80-214-3341-8
[identifikator_popis] => ISBN - Březina, T. (ed.), Simulation Modelling of Mechatronic Systems II
[riv_dodavka_id] => 98
[riv_dodavka_oznaceni] => RIV10-MSM-26210___
[riv_dodavka_rok] => 2010
[diagnostika_pocet] => 0
[diagnostika_pocet_chyba] => 0
[diagnostika_pocet_upozorneni] => 0
[diagnostika_pocet_informace] => 0
[citace_text] => ŠEDA, M. On Shortest Paths in Partially Known Environment. In Březina, T. (ed.), Simulation Modelling of Mechatronic Systems II. Mechatronika. Brno: Brno University of Technology, Faculty of Mechanical Engineering, 2006. p. 139-147. ISBN: 80-214-3341-8.
[citace_html] => ŠEDA, M. On Shortest Paths in Partially Known Environment. In Březina, T. (ed.), Simulation Modelling of Mechatronic Systems II. Mechatronika. Brno: Brno University of Technology, Faculty of Mechanical Engineering, 2006. p. 139-147. ISBN: 80-214-3341-8.
[citace_rtf] =>
[citace_bibtex] => @inbook{BUT55142,
author="Miloš {Šeda}",
title="On Shortest Paths in Partially Known Environment",
booktitle="Březina, T. (ed.), Simulation Modelling of Mechatronic Systems II",
year="2006",
publisher="Brno University of Technology, Faculty of Mechanical Engineering",
address="Brno",
series="Mechatronika",
edition="2",
pages="139--147",
isbn="80-214-3341-8"
}
[vykazano] =>
[vykazano_aspon_jednou] =>
[identifikacni_kod] =>
[neautorsky_vysledek] => 0
[if] =>
[if_q] =>
[if_m17_q] =>
[if_m25_q] =>
[if_d] =>
[if_m17_d] =>
[if_m25_d] =>
[if_percentil] =>
[if_m17_percentil] =>
[if_m25_percentil] =>
[ais] =>
[ais_m17_q] =>
[ais_m25_q] =>
[ais_m17_d] =>
[ais_m25_d] =>
[ais_m17_percentil] =>
[ais_m25_percentil] =>
[jci] =>
[jci_q] =>
[jci_percentil] =>
[ef] =>
[scopus_sjr] =>
[scopus_sjr_q] =>
[scopus_sjr_d] =>
[nature_index_group] =>
[incites_times_cited] =>
[incites_open_access] =>
[incites_jnci] =>
[incites_is_int_collab] =>
[incites_is_industry_collab] =>
[incites_esi_hot_paper] =>
[incites_esi_highly_cited_paper] =>
[incites_avg_cnci] =>
[incites_avg_percentile] =>
[scival_citations_count] =>
[scival_fwci] =>
[core_rank] => NA
[oecd_tree_oblast_id] => 20000
[oecd_tree_oblast_nazev] => 2. Engineering and Technology
[oecd_tree_obor_id] => 20200
[oecd_tree_obor_nazev] => 2.2 Electrical engineering, Electronic engineering, Information engineering
[oecd_tree_podobor_id] => 20204
[oecd_tree_podobor_nazev] => Robotics and automatic control
[poznamka_metriky] =>
[nazev_en] => On Shortest Paths in Partially Known Environment
[popis_en] => In robot motion planning a robot should pass around obstacles, from a given starting position to a given target position, touching none of them, i.e., the goal is to find a collision-free path from the starting to the target position. This task has many specific formulations depending on the shape of obstacles, eligible directions of movements, knowledge of the scene, etc. The basic step in all methods used for solving this problem, e.g., roadmap methods, cell decomposition and case-based reasoning is to find the shortest path between starting and target positions or to find the shortest paths among all pairs of positions when we use a database of stored solutions and try to adapt these old solutions to a new problem. However, in a partially known scene, we must approximate the lengths of edges in a graph representation of the scene. In this contribution, we deal with the All-Pairs Shortest Paths Problem (APSPP) on a graph in which a fuzzy number, rather than a real number, is assigned to each edge. Since the fuzzy min operator based on the extension principle leads to non-dominated solutions, we propose another approach to solving the APSPP using a suitable fuzzy ranking method. We also show that the efficiency of computations may be improved by the proposed APSPP modification of the Dijkstra algorithm based on a binary heap data structure.
[klicova_slova_en] => Shortest path problem, fuzzy ranking, binary heap, priority queue
[vysledek_datum] => 2006-12-15T00:00:00+01:00
)
[3] => Array
(
[vysledek_id] => 55154
[vysledek_druh_id] => CHAPT
[ex_vysledek_id] => 67488
[vysledek_rok] => 2006
[nazev] => Evolution of complexity
[nazev_orig] => Evolution of complexity
[duvernost_udaju_id] => S
[popis] => Evolution of complex nonlinear systems
[popis_orig] => Evolution of complex nonlinear systems
[klicova_slova] => Evolution, complexity
[klicova_slova_orig] => Evolution, complexity
[url] =>
[oecd_obor_id] => 20206
[odpovedny_utvar_id] => 207
[odpovedny_utvar_nazev] => Institute of Automation and Computer Science
[odpovedny_utvar_zkratka] => IACS
[nadrazena_soucast_id] => 4
[nadrazena_soucast_zkratka] => FME
[nadrazena_soucast_nazev] => Faculty of Mechanical Engineering
[originalni_jazyk] => en
[schvalil_id] => 999999
[schvaleno] =>
[vykazovat_riv] => 0
[vykazovat_riv_zmeny] => 1
[slozka_id] =>
[posledni_diagnostika] =>
[vycet_osob] => OŠMERA, P.
[pocet_tvurcu] => 1
[tvurci_ids] =>
[poznamka] =>
[typ_nazev] =>
[kod_doi] =>
[kod_dspace] =>
[rok_vytvoreni] =>
[pocet_zaznamu] =>
[zverejneno] => 1
[prvni_autor] =>
[korespondencni_autor] =>
[posledni_autor] =>
[znamka] =>
[kategorie_nazev] => Publication results
[druh_nazev] => Chapter in a book
[druh_popis] => Chapter in a book
[stav] => Approved
[vysledek_kategorie_id] => PV
[vysledek_system_kategorie_id] => PU
[vysledek_stav_id] => 3
[vlozil] => Informační systém Automat
[upravil] => Informační systém Automat
[ins_uid] => 999999
[upd_uid] => 999999
[ins_ts] => 2025-09-22
[upd_ts] => 2025-09-22
[status] => 9
[identifikator] => ISBN 3-540-26899-5
[identifikator_popis] => ISBN - Integration of Fuzzy Logic and Chaos Theory
[riv_dodavka_id] =>
[riv_dodavka_oznaceni] =>
[riv_dodavka_rok] =>
[diagnostika_pocet] => 0
[diagnostika_pocet_chyba] => 0
[diagnostika_pocet_upozorneni] => 0
[diagnostika_pocet_informace] => 0
[citace_text] => OŠMERA, P. Evolution of complexity. In Integration of Fuzzy Logic and Chaos Theory. Studies in Fuzziness and Softcomputing. 2006. 51 p. ISBN: 3-540-26899-5.
[citace_html] => OŠMERA, P. Evolution of complexity. In Integration of Fuzzy Logic and Chaos Theory. Studies in Fuzziness and Softcomputing. 2006. 51 p. ISBN: 3-540-26899-5.
[citace_rtf] =>
[citace_bibtex] => @inbook{BUT55154,
author="Pavel {Ošmera}",
title="Evolution of complexity",
booktitle="Integration of Fuzzy Logic and Chaos Theory",
year="2006",
series="Studies in Fuzziness and Softcomputing",
edition="1",
pages="51",
isbn="3-540-26899-5"
}
[vykazano] =>
[vykazano_aspon_jednou] =>
[identifikacni_kod] =>
[neautorsky_vysledek] => 0
[if] =>
[if_q] =>
[if_m17_q] =>
[if_m25_q] =>
[if_d] =>
[if_m17_d] =>
[if_m25_d] =>
[if_percentil] =>
[if_m17_percentil] =>
[if_m25_percentil] =>
[ais] =>
[ais_m17_q] =>
[ais_m25_q] =>
[ais_m17_d] =>
[ais_m25_d] =>
[ais_m17_percentil] =>
[ais_m25_percentil] =>
[jci] =>
[jci_q] =>
[jci_percentil] =>
[ef] =>
[scopus_sjr] =>
[scopus_sjr_q] =>
[scopus_sjr_d] =>
[nature_index_group] =>
[incites_times_cited] =>
[incites_open_access] =>
[incites_jnci] =>
[incites_is_int_collab] =>
[incites_is_industry_collab] =>
[incites_esi_hot_paper] =>
[incites_esi_highly_cited_paper] =>
[incites_avg_cnci] =>
[incites_avg_percentile] =>
[scival_citations_count] =>
[scival_fwci] =>
[core_rank] => NA
[oecd_tree_oblast_id] => 20000
[oecd_tree_oblast_nazev] => 2. Engineering and Technology
[oecd_tree_obor_id] => 20200
[oecd_tree_obor_nazev] => 2.2 Electrical engineering, Electronic engineering, Information engineering
[oecd_tree_podobor_id] => 20206
[oecd_tree_podobor_nazev] => Computer hardware and architecture
[poznamka_metriky] =>
[nazev_en] => Evolution of complexity
[popis_en] => Evolution of complex nonlinear systems
[klicova_slova_en] => Evolution, complexity
[vysledek_datum] => 2006-01-26T00:00:00+01:00
)
[4] => Array
(
[vysledek_id] => 55260
[vysledek_druh_id] => CHAPT
[ex_vysledek_id] => 77910
[vysledek_rok] => 2008
[nazev] => Genetic Algorithms for Scenario Generation in Stochastic Programming: Motivation and General Framework
[nazev_orig] => Genetic Algorithms for Scenario Generation in Stochastic Programming: Motivation and General Framework
[duvernost_udaju_id] => S
[popis] => Stochastic programs have been developed as useful tools for modeling of various application problems. The developed algorithms usually require a solution of large-scale linear and nonlinear programs because the deterministic reformulations of the original stochastic programs are based on empirical or sampling discrete probability distributions, i.e. on so-called scenario sets. The scenario sets are often large, so the reformulated programs must be solved. Therefore, the suitable scenario set generation techniques are required. Hence, randomly selected reduced scenario sets are often employed. Related confidence intervals for the optimal objective function values have been derived and are often presented as tight enough. However, there is also demand for goal-oriented scenario generation to learn more about the extreme cases. Traditional deterministic max-min and min-min techniques are significantly limited by the size of scenario set. Therefore, this text introduces a general framework how to generate and modify suitable scenario sets by using genetic algorithms. As an example, the search of absolute lower and upper bounds by using GA is presented and further enhancements are discussed. The proposed technique is implemented in C++ and GAMS and then tested on real-data examples.
[popis_orig] => Stochastic programs have been developed as useful tools for modeling of various application problems. The developed algorithms usually require a solution of large-scale linear and nonlinear programs because the deterministic reformulations of the original stochastic programs are based on empirical or sampling discrete probability distributions, i.e. on so-called scenario sets. The scenario sets are often large, so the reformulated programs must be solved. Therefore, the suitable scenario set generation techniques are required. Hence, randomly selected reduced scenario sets are often employed. Related confidence intervals for the optimal objective function values have been derived and are often presented as tight enough. However, there is also demand for goal-oriented scenario generation to learn more about the extreme cases. Traditional deterministic max-min and min-min techniques are significantly limited by the size of scenario set. Therefore, this text introduces a general framework how to generate and modify suitable scenario sets by using genetic algorithms. As an example, the search of absolute lower and upper bounds by using GA is presented and further enhancements are discussed. The proposed technique is implemented in C++ and GAMS and then tested on real-data examples.
[klicova_slova] => Stochastic programming, scenarios, worst case analysis, heuristic and genetic algorithms
[klicova_slova_orig] => Stochastic programming, scenarios, worst case analysis, heuristic and genetic algorithms
[url] =>
[oecd_obor_id] => 10103
[odpovedny_utvar_id] => 207
[odpovedny_utvar_nazev] => Institute of Automation and Computer Science
[odpovedny_utvar_zkratka] => IACS
[nadrazena_soucast_id] => 4
[nadrazena_soucast_zkratka] => FME
[nadrazena_soucast_nazev] => Faculty of Mechanical Engineering
[originalni_jazyk] => en
[schvalil_id] => 15592
[schvaleno] => 2016-04-26
[vykazovat_riv] => 0
[vykazovat_riv_zmeny] => 1
[slozka_id] =>
[posledni_diagnostika] =>
[vycet_osob] => ROUPEC, J.; POPELA, P.
[pocet_tvurcu] => 2
[tvurci_ids] =>
[poznamka] =>
[typ_nazev] =>
[kod_doi] =>
[kod_dspace] =>
[rok_vytvoreni] =>
[pocet_zaznamu] =>
[zverejneno] => 1
[prvni_autor] =>
[korespondencni_autor] =>
[posledni_autor] =>
[znamka] =>
[kategorie_nazev] => Publication results
[druh_nazev] => Chapter in a book
[druh_popis] => Chapter in a book
[stav] => Approved
[vysledek_kategorie_id] => PV
[vysledek_system_kategorie_id] => PU
[vysledek_stav_id] => 3
[vlozil] => Informační systém Automat
[upravil] => Informační systém Automat
[ins_uid] => 999999
[upd_uid] => 999999
[ins_ts] => 2025-09-22
[upd_ts] => 2025-09-22
[status] => 9
[identifikator] => ISBN 978-1-4020-8918-3
[identifikator_popis] => ISBN - Lecture Notes in Electrical Engineering, book series: Advances in Computational Algorithms and Data Analysis, Vol. 14 Ao, S.L., Rieger, B., Chen, S.S. (Eds.).
[riv_dodavka_id] => 24
[riv_dodavka_oznaceni] => RIV12-MSM-26210___
[riv_dodavka_rok] => 2012
[diagnostika_pocet] => 0
[diagnostika_pocet_chyba] => 0
[diagnostika_pocet_upozorneni] => 0
[diagnostika_pocet_informace] => 0
[citace_text] => ROUPEC, J.; POPELA, P. Genetic Algorithms for Scenario Generation in Stochastic Programming: Motivation and General Framework. In Lecture Notes in Electrical Engineering, book series: Advances in Computational Algorithms and Data Analysis, Vol. 14 Ao, S.L., Rieger, B., Chen, S.S. (Eds.). 1. Netherlands: Springer, 2008. p. 527-536. ISBN: 978-1-4020-8918-3.
[citace_html] => ROUPEC, J.; POPELA, P. Genetic Algorithms for Scenario Generation in Stochastic Programming: Motivation and General Framework. In Lecture Notes in Electrical Engineering, book series: Advances in Computational Algorithms and Data Analysis, Vol. 14 Ao, S.L., Rieger, B., Chen, S.S. (Eds.). 1. Netherlands: Springer, 2008. p. 527-536. ISBN: 978-1-4020-8918-3.
[citace_rtf] =>
[citace_bibtex] => @inbook{BUT55260,
author="Jan {Roupec} and Pavel {Popela}",
title="Genetic Algorithms for Scenario Generation in Stochastic Programming: Motivation and General Framework",
booktitle="Lecture Notes in Electrical Engineering, book series: Advances in Computational Algorithms and Data Analysis, Vol. 14 Ao, S.L., Rieger, B., Chen, S.S. (Eds.).",
year="2008",
publisher="Springer",
address="Netherlands",
series="1",
edition="1",
pages="527--536",
isbn="978-1-4020-8918-3"
}
[vykazano] =>
[vykazano_aspon_jednou] =>
[identifikacni_kod] =>
[neautorsky_vysledek] => 0
[if] =>
[if_q] =>
[if_m17_q] =>
[if_m25_q] =>
[if_d] =>
[if_m17_d] =>
[if_m25_d] =>
[if_percentil] =>
[if_m17_percentil] =>
[if_m25_percentil] =>
[ais] =>
[ais_m17_q] =>
[ais_m25_q] =>
[ais_m17_d] =>
[ais_m25_d] =>
[ais_m17_percentil] =>
[ais_m25_percentil] =>
[jci] =>
[jci_q] =>
[jci_percentil] =>
[ef] =>
[scopus_sjr] =>
[scopus_sjr_q] =>
[scopus_sjr_d] =>
[nature_index_group] =>
[incites_times_cited] =>
[incites_open_access] =>
[incites_jnci] =>
[incites_is_int_collab] =>
[incites_is_industry_collab] =>
[incites_esi_hot_paper] =>
[incites_esi_highly_cited_paper] =>
[incites_avg_cnci] =>
[incites_avg_percentile] =>
[scival_citations_count] =>
[scival_fwci] =>
[core_rank] => NA
[oecd_tree_oblast_id] => 10000
[oecd_tree_oblast_nazev] => 1. Natural Sciences
[oecd_tree_obor_id] => 10100
[oecd_tree_obor_nazev] => 1.1 Mathematics
[oecd_tree_podobor_id] => 10103
[oecd_tree_podobor_nazev] => Statistics and probability
[poznamka_metriky] =>
[nazev_en] => Genetic Algorithms for Scenario Generation in Stochastic Programming: Motivation and General Framework
[popis_en] => Stochastic programs have been developed as useful tools for modeling of various application problems. The developed algorithms usually require a solution of large-scale linear and nonlinear programs because the deterministic reformulations of the original stochastic programs are based on empirical or sampling discrete probability distributions, i.e. on so-called scenario sets. The scenario sets are often large, so the reformulated programs must be solved. Therefore, the suitable scenario set generation techniques are required. Hence, randomly selected reduced scenario sets are often employed. Related confidence intervals for the optimal objective function values have been derived and are often presented as tight enough. However, there is also demand for goal-oriented scenario generation to learn more about the extreme cases. Traditional deterministic max-min and min-min techniques are significantly limited by the size of scenario set. Therefore, this text introduces a general framework how to generate and modify suitable scenario sets by using genetic algorithms. As an example, the search of absolute lower and upper bounds by using GA is presented and further enhancements are discussed. The proposed technique is implemented in C++ and GAMS and then tested on real-data examples.
[klicova_slova_en] => Stochastic programming, scenarios, worst case analysis, heuristic and genetic algorithms
[vysledek_datum] => 2008-09-01T00:00:00+02:00
)
[5] => Array
(
[vysledek_id] => 55270
[vysledek_druh_id] => CHAPT
[ex_vysledek_id] => 86290
[vysledek_rok] => 2009
[nazev] => Plánování cesty mobilního robotu pomocí genetických algoritmů
[nazev_orig] => Plánování cesty mobilního robotu pomocí genetických algoritmů
[duvernost_udaju_id] => S
[popis] => Tento článek se zabývá plánováním cesty holonomního mobilního robotu ve dvourozměrném spojitém prostředí se známými polygonálními překážkami. Cílem plánování cesty robotu je nalezení cesty z počáteční do koncové pozice bez kolize se známými statickými překážkami, přičemž se minimalizuje ohodnocení cesty. Pro řešení tohoto problému zkoumáme použití genetického algoritmu a navrhujeme různé problémově specifické operátory. Dále zkoumáme schopnost rychlé adaptace populace navrženého algoritmu na změny zadání.
[popis_orig] => Tento článek se zabývá plánováním cesty holonomního mobilního robotu ve dvourozměrném spojitém prostředí se známými polygonálními překážkami. Cílem plánování cesty robotu je nalezení cesty z počáteční do koncové pozice bez kolize se známými statickými překážkami, přičemž se minimalizuje ohodnocení cesty. Pro řešení tohoto problému zkoumáme použití genetického algoritmu a navrhujeme různé problémově specifické operátory. Dále zkoumáme schopnost rychlé adaptace populace navrženého algoritmu na změny zadání.
[klicova_slova] => plánování cesty, genetický algoritmus
[klicova_slova_orig] => plánování cesty, genetický algoritmus
[url] =>
[oecd_obor_id] => 20204
[odpovedny_utvar_id] => 207
[odpovedny_utvar_nazev] => Institute of Automation and Computer Science
[odpovedny_utvar_zkratka] => IACS
[nadrazena_soucast_id] => 4
[nadrazena_soucast_zkratka] => FME
[nadrazena_soucast_nazev] => Faculty of Mechanical Engineering
[originalni_jazyk] => cs
[schvalil_id] => 1116
[schvaleno] => 2017-07-03
[vykazovat_riv] => 1
[vykazovat_riv_zmeny] => 1
[slozka_id] =>
[posledni_diagnostika] =>
[vycet_osob] => KRČEK, P.; DVOŘÁK, J.
[pocet_tvurcu] => 2
[tvurci_ids] =>
[poznamka] =>
[typ_nazev] =>
[kod_doi] =>
[kod_dspace] =>
[rok_vytvoreni] =>
[pocet_zaznamu] =>
[zverejneno] => 1
[prvni_autor] =>
[korespondencni_autor] =>
[posledni_autor] =>
[znamka] =>
[kategorie_nazev] => Publication results
[druh_nazev] => Chapter in a book
[druh_popis] => Chapter in a book
[stav] => Approved
[vysledek_kategorie_id] => PV
[vysledek_system_kategorie_id] => PU
[vysledek_stav_id] => 3
[vlozil] => Informační systém Automat
[upravil] => Informační systém Automat
[ins_uid] => 999999
[upd_uid] => 999999
[ins_ts] => 2025-09-22
[upd_ts] => 2025-09-22
[status] => 9
[identifikator] => ISBN 978-80-7204-662-1
[identifikator_popis] => ISBN - Šedesát Let Kybernetiky
[riv_dodavka_id] => 98
[riv_dodavka_oznaceni] => RIV10-MSM-26210___
[riv_dodavka_rok] => 2010
[diagnostika_pocet] => 0
[diagnostika_pocet_chyba] => 0
[diagnostika_pocet_upozorneni] => 0
[diagnostika_pocet_informace] => 0
[citace_text] => KRČEK, P.; DVOŘÁK, J. Plánování cesty mobilního robotu pomocí genetických algoritmů. In Šedesát Let Kybernetiky. 1. Brno: Akademické nakladatelství CERM, 2009. s. 90-95. ISBN: 978-80-7204-662-1.
[citace_html] => KRČEK, P.; DVOŘÁK, J. Plánování cesty mobilního robotu pomocí genetických algoritmů. In Šedesát Let Kybernetiky. 1. Brno: Akademické nakladatelství CERM, 2009. s. 90-95. ISBN: 978-80-7204-662-1.
[citace_rtf] =>
[citace_bibtex] => @inbook{BUT55270,
author="Petr {Krček} and Jiří {Dvořák}",
title="Plánování cesty mobilního robotu pomocí genetických algoritmů",
booktitle="Šedesát Let Kybernetiky",
year="2009",
publisher="Akademické nakladatelství CERM",
address="Brno",
series="1",
edition="1",
pages="90--95",
isbn="978-80-7204-662-1"
}
[vykazano] =>
[vykazano_aspon_jednou] =>
[identifikacni_kod] =>
[neautorsky_vysledek] => 0
[if] =>
[if_q] =>
[if_m17_q] =>
[if_m25_q] =>
[if_d] =>
[if_m17_d] =>
[if_m25_d] =>
[if_percentil] =>
[if_m17_percentil] =>
[if_m25_percentil] =>
[ais] =>
[ais_m17_q] =>
[ais_m25_q] =>
[ais_m17_d] =>
[ais_m25_d] =>
[ais_m17_percentil] =>
[ais_m25_percentil] =>
[jci] =>
[jci_q] =>
[jci_percentil] =>
[ef] =>
[scopus_sjr] =>
[scopus_sjr_q] =>
[scopus_sjr_d] =>
[nature_index_group] =>
[incites_times_cited] =>
[incites_open_access] =>
[incites_jnci] =>
[incites_is_int_collab] =>
[incites_is_industry_collab] =>
[incites_esi_hot_paper] =>
[incites_esi_highly_cited_paper] =>
[incites_avg_cnci] =>
[incites_avg_percentile] =>
[scival_citations_count] =>
[scival_fwci] =>
[core_rank] => NA
[oecd_tree_oblast_id] => 20000
[oecd_tree_oblast_nazev] => 2. Engineering and Technology
[oecd_tree_obor_id] => 20200
[oecd_tree_obor_nazev] => 2.2 Electrical engineering, Electronic engineering, Information engineering
[oecd_tree_podobor_id] => 20204
[oecd_tree_podobor_nazev] => Robotics and automatic control
[poznamka_metriky] =>
[nazev_en] => Mobile robot path planning by means of genetic algorithms
[popis_en] => In this paper, we deal with mobile robot path planning in a two-dimensional continuous space in which known static polygonal obstacles are defined. The aim of the path planning is searching for a path from a start to a goal position without collisions with known obstacles minimizing an evaluation function. We investigate possibilities of using genetic algorithms for solving this problem and describe various problem specific genetic operators. We study also an ability of proposed algorithm to adapt a previous solution to changes of start or goal position and changes in the environment.
[klicova_slova_en] => path planning, genetic algorithms
[vysledek_datum] => 2009-12-01T00:00:00+01:00
)
[6] => Array
(
[vysledek_id] => 55305
[vysledek_druh_id] => CHAPT
[ex_vysledek_id] => 40441
[vysledek_rok] => 2003
[nazev] => Planning Trajectories in the Plane with Obstacles Using Voronoi Diagrams
[nazev_orig] => Planning Trajectories in the Plane with Obstacles Using Voronoi Diagrams
[duvernost_udaju_id] => S
[popis] => The task of planning trajectories plays an important role in transportation, robotics, even in information systems (sending messages). In robot motion planning the robot should pass around the obstacles, from a given starting position to a given target position, touching none of them, i.e. the goal is to find a collision-free path from the starting to the target position. This task has many specific formulations depending on the shape of obstacles, allowable directions of movements, knowledge of the scene, etc. Research on path planning has yielded many fundamentally different approaches to its solution, e.g. visibility graph method or the shortest path map method. Assuming movements only in a restricted number of directions (eight directional, horizontal/vertical) the task, with respect to its combinatorial nature, can be solved by heuristic techniques (genetic algorithms, simulated annealing, tabu-search). We present a framework of such approach and show its drawbacks (combinatorial explosion, limited granularity, generating infeasible solutions). Application of the Voronoi diagrams to the studied tasks can be seen as the main result of this paper. This approach needs only polynomial time and choosing Euclidean or rectilinear metric it can be adapted to tasks with general or directional-constrained movements.
[popis_orig] => The task of planning trajectories plays an important role in transportation, robotics, even in information systems (sending messages). In robot motion planning the robot should pass around the obstacles, from a given starting position to a given target position, touching none of them, i.e. the goal is to find a collision-free path from the starting to the target position. This task has many specific formulations depending on the shape of obstacles, allowable directions of movements, knowledge of the scene, etc. Research on path planning has yielded many fundamentally different approaches to its solution, e.g. visibility graph method or the shortest path map method. Assuming movements only in a restricted number of directions (eight directional, horizontal/vertical) the task, with respect to its combinatorial nature, can be solved by heuristic techniques (genetic algorithms, simulated annealing, tabu-search). We present a framework of such approach and show its drawbacks (combinatorial explosion, limited granularity, generating infeasible solutions). Application of the Voronoi diagrams to the studied tasks can be seen as the main result of this paper. This approach needs only polynomial time and choosing Euclidean or rectilinear metric it can be adapted to tasks with general or directional-constrained movements.
[klicova_slova] =>
[klicova_slova_orig] =>
[url] =>
[oecd_obor_id] => 20204
[odpovedny_utvar_id] => 207
[odpovedny_utvar_nazev] => Institute of Automation and Computer Science
[odpovedny_utvar_zkratka] => IACS
[nadrazena_soucast_id] => 4
[nadrazena_soucast_zkratka] => FME
[nadrazena_soucast_nazev] => Faculty of Mechanical Engineering
[originalni_jazyk] => en
[schvalil_id] => 999999
[schvaleno] => 2016-04-15
[vykazovat_riv] => 0
[vykazovat_riv_zmeny] => 1
[slozka_id] =>
[posledni_diagnostika] =>
[vycet_osob] => ŠEDA, M.
[pocet_tvurcu] => 1
[tvurci_ids] =>
[poznamka] =>
[typ_nazev] =>
[kod_doi] =>
[kod_dspace] =>
[rok_vytvoreni] =>
[pocet_zaznamu] =>
[zverejneno] => 1
[prvni_autor] =>
[korespondencni_autor] =>
[posledni_autor] =>
[znamka] =>
[kategorie_nazev] => Publication results
[druh_nazev] => Chapter in a book
[druh_popis] => Chapter in a book
[stav] => Approved
[vysledek_kategorie_id] => PV
[vysledek_system_kategorie_id] => PU
[vysledek_stav_id] => 3
[vlozil] => Informační systém Automat
[upravil] => Informační systém Automat
[ins_uid] => 999999
[upd_uid] => 999999
[ins_ts] => 2025-09-22
[upd_ts] => 2025-09-22
[status] => 9
[identifikator] => ISBN 3-901509-36-4 ISSN 1726-9687
[identifikator_popis] => ISBN - Katalinic, B. (ed.): DAAAM International Scientific Book 2003 ISSN - DAAAM International Scientific Book (AT)
[riv_dodavka_id] =>
[riv_dodavka_oznaceni] =>
[riv_dodavka_rok] =>
[diagnostika_pocet] => 0
[diagnostika_pocet_chyba] => 0
[diagnostika_pocet_upozorneni] => 0
[diagnostika_pocet_informace] => 0
[citace_text] => ŠEDA, M. Planning Trajectories in the Plane with Obstacles Using Voronoi Diagrams. In Katalinic, B. (ed.): DAAAM International Scientific Book 2003. DAAAM International Scientific Book. 2003. Wien (Austria): DAAAM International Wien, 2003. 14 p. ISBN: 3-901509-36-4. ISSN: 1726-9687.
[citace_html] => ŠEDA, M. Planning Trajectories in the Plane with Obstacles Using Voronoi Diagrams. In Katalinic, B. (ed.): DAAAM International Scientific Book 2003. DAAAM International Scientific Book. 2003. Wien (Austria): DAAAM International Wien, 2003. 14 p. ISBN: 3-901509-36-4. ISSN: 1726-9687.
[citace_rtf] =>
[citace_bibtex] => @inbook{BUT55305,
author="Miloš {Šeda}",
title="Planning Trajectories in the Plane with Obstacles Using Voronoi Diagrams",
booktitle="Katalinic, B. (ed.): DAAAM International Scientific Book 2003",
year="2003",
publisher="DAAAM International Wien",
address="Wien (Austria)",
series="2003",
pages="14",
isbn="3-901509-36-4"
}
[vykazano] =>
[vykazano_aspon_jednou] =>
[identifikacni_kod] =>
[neautorsky_vysledek] => 0
[if] =>
[if_q] =>
[if_m17_q] =>
[if_m25_q] =>
[if_d] =>
[if_m17_d] =>
[if_m25_d] =>
[if_percentil] =>
[if_m17_percentil] =>
[if_m25_percentil] =>
[ais] =>
[ais_m17_q] =>
[ais_m25_q] =>
[ais_m17_d] =>
[ais_m25_d] =>
[ais_m17_percentil] =>
[ais_m25_percentil] =>
[jci] =>
[jci_q] =>
[jci_percentil] =>
[ef] =>
[scopus_sjr] =>
[scopus_sjr_q] =>
[scopus_sjr_d] =>
[nature_index_group] =>
[incites_times_cited] =>
[incites_open_access] =>
[incites_jnci] =>
[incites_is_int_collab] =>
[incites_is_industry_collab] =>
[incites_esi_hot_paper] =>
[incites_esi_highly_cited_paper] =>
[incites_avg_cnci] =>
[incites_avg_percentile] =>
[scival_citations_count] =>
[scival_fwci] =>
[core_rank] => NA
[oecd_tree_oblast_id] => 20000
[oecd_tree_oblast_nazev] => 2. Engineering and Technology
[oecd_tree_obor_id] => 20200
[oecd_tree_obor_nazev] => 2.2 Electrical engineering, Electronic engineering, Information engineering
[oecd_tree_podobor_id] => 20204
[oecd_tree_podobor_nazev] => Robotics and automatic control
[poznamka_metriky] =>
[nazev_en] => Planning Trajectories in the Plane with Obstacles Using Voronoi Diagrams
[popis_en] => The task of planning trajectories plays an important role in transportation, robotics, even in information systems (sending messages). In robot motion planning the robot should pass around the obstacles, from a given starting position to a given target position, touching none of them, i.e. the goal is to find a collision-free path from the starting to the target position. This task has many specific formulations depending on the shape of obstacles, allowable directions of movements, knowledge of the scene, etc. Research on path planning has yielded many fundamentally different approaches to its solution, e.g. visibility graph method or the shortest path map method. Assuming movements only in a restricted number of directions (eight directional, horizontal/vertical) the task, with respect to its combinatorial nature, can be solved by heuristic techniques (genetic algorithms, simulated annealing, tabu-search). We present a framework of such approach and show its drawbacks (combinatorial explosion, limited granularity, generating infeasible solutions). Application of the Voronoi diagrams to the studied tasks can be seen as the main result of this paper. This approach needs only polynomial time and choosing Euclidean or rectilinear metric it can be adapted to tasks with general or directional-constrained movements.
[klicova_slova_en] => Motion planning, genetic algorithm, case-based reasoning, Voronoi diagram, rectilinear metric
[vysledek_datum] => 2003-09-01T00:00:00+02:00
)
[7] => Array
(
[vysledek_id] => 55413
[vysledek_druh_id] => CHAPT
[ex_vysledek_id] => 53769
[vysledek_rok] => 2005
[nazev] => Mixed Integer Programming vs. Genetic Algorithm Approach to Scheduling Permutation Flow Shop
[nazev_orig] => Mixed Integer Programming vs. Genetic Algorithm Approach to Scheduling Permutation Flow Shop
[duvernost_udaju_id] => S
[popis] => Flow shop scheduling problems represent scheduling a set of jobs (composed of tasks) in shops with a product machine layout. Thus, the jobs have the same manufacturing order. A permutation flow shop scheduling problem (PFSSP) is a special version of the problem where each machine processes the jobs in the same order. In this paper, two different approaches to PFSSP with makespan objective are investigated. First a mixed integer programming model is formulated and it is used for solving the problem by an optimisation package GAMS. Since the problem belongs to NP-complete problems, this approach is limited to smaller instances. Its reasonable bounds are indicated using benchmarks from OR-Library. For large instances, an approach using genetic algorithm is proposed including its appropriate parameter settings. Computational results show a good performance of genetic algorithm. For suitable parameter settings presented in the paper, this approach is able to find the optimal solution almost in all cases or at least a solution very close to optimum when the test is executed several times.
[popis_orig] => Flow shop scheduling problems represent scheduling a set of jobs (composed of tasks) in shops with a product machine layout. Thus, the jobs have the same manufacturing order. A permutation flow shop scheduling problem (PFSSP) is a special version of the problem where each machine processes the jobs in the same order. In this paper, two different approaches to PFSSP with makespan objective are investigated. First a mixed integer programming model is formulated and it is used for solving the problem by an optimisation package GAMS. Since the problem belongs to NP-complete problems, this approach is limited to smaller instances. Its reasonable bounds are indicated using benchmarks from OR-Library. For large instances, an approach using genetic algorithm is proposed including its appropriate parameter settings. Computational results show a good performance of genetic algorithm. For suitable parameter settings presented in the paper, this approach is able to find the optimal solution almost in all cases or at least a solution very close to optimum when the test is executed several times.
[klicova_slova] =>
[klicova_slova_orig] =>
[url] =>
[oecd_obor_id] => 10103
[odpovedny_utvar_id] => 207
[odpovedny_utvar_nazev] => Institute of Automation and Computer Science
[odpovedny_utvar_zkratka] => IACS
[nadrazena_soucast_id] => 4
[nadrazena_soucast_zkratka] => FME
[nadrazena_soucast_nazev] => Faculty of Mechanical Engineering
[originalni_jazyk] => en
[schvalil_id] => 999999
[schvaleno] => 2016-04-15
[vykazovat_riv] => 0
[vykazovat_riv_zmeny] => 1
[slozka_id] =>
[posledni_diagnostika] =>
[vycet_osob] => ŠEDA, M.
[pocet_tvurcu] => 1
[tvurci_ids] =>
[poznamka] =>
[typ_nazev] =>
[kod_doi] =>
[kod_dspace] =>
[rok_vytvoreni] =>
[pocet_zaznamu] =>
[zverejneno] => 1
[prvni_autor] =>
[korespondencni_autor] =>
[posledni_autor] =>
[znamka] =>
[kategorie_nazev] => Publication results
[druh_nazev] => Chapter in a book
[druh_popis] => Chapter in a book
[stav] => Approved
[vysledek_kategorie_id] => PV
[vysledek_system_kategorie_id] => PU
[vysledek_stav_id] => 3
[vlozil] => Informační systém Automat
[upravil] => Informační systém Automat
[ins_uid] => 999999
[upd_uid] => 999999
[ins_ts] => 2025-09-22
[upd_ts] => 2025-09-22
[status] => 9
[identifikator] => ISBN 3-901509-43-7 ISSN 1726-9687
[identifikator_popis] => ISBN - Katalinic, B. (ed.): DAAAM International Scientific Book 2005 ISSN - DAAAM International Scientific Book (AT)
[riv_dodavka_id] =>
[riv_dodavka_oznaceni] =>
[riv_dodavka_rok] =>
[diagnostika_pocet] => 0
[diagnostika_pocet_chyba] => 0
[diagnostika_pocet_upozorneni] => 0
[diagnostika_pocet_informace] => 0
[citace_text] => ŠEDA, M. Mixed Integer Programming vs. Genetic Algorithm Approach to Scheduling Permutation Flow Shop. In Katalinic, B. (ed.): DAAAM International Scientific Book 2005. DAAAM International Scientific Book. DAAAM International Scientific Book. Wien (Austria): DAAAM International, 2005. 12 p. ISBN: 3-901509-43-7. ISSN: 1726-9687.
[citace_html] => ŠEDA, M. Mixed Integer Programming vs. Genetic Algorithm Approach to Scheduling Permutation Flow Shop. In Katalinic, B. (ed.): DAAAM International Scientific Book 2005. DAAAM International Scientific Book. DAAAM International Scientific Book. Wien (Austria): DAAAM International, 2005. 12 p. ISBN: 3-901509-43-7. ISSN: 1726-9687.
[citace_rtf] =>
[citace_bibtex] => @inbook{BUT55413,
author="Miloš {Šeda}",
title="Mixed Integer Programming vs. Genetic Algorithm Approach to Scheduling Permutation Flow Shop",
booktitle="Katalinic, B. (ed.): DAAAM International Scientific Book 2005",
year="2005",
publisher="DAAAM International",
address="Wien (Austria)",
series="DAAAM International Scientific Book",
pages="12",
isbn="3-901509-43-7"
}
[vykazano] =>
[vykazano_aspon_jednou] =>
[identifikacni_kod] =>
[neautorsky_vysledek] => 0
[if] =>
[if_q] =>
[if_m17_q] =>
[if_m25_q] =>
[if_d] =>
[if_m17_d] =>
[if_m25_d] =>
[if_percentil] =>
[if_m17_percentil] =>
[if_m25_percentil] =>
[ais] =>
[ais_m17_q] =>
[ais_m25_q] =>
[ais_m17_d] =>
[ais_m25_d] =>
[ais_m17_percentil] =>
[ais_m25_percentil] =>
[jci] =>
[jci_q] =>
[jci_percentil] =>
[ef] =>
[scopus_sjr] =>
[scopus_sjr_q] =>
[scopus_sjr_d] =>
[nature_index_group] =>
[incites_times_cited] =>
[incites_open_access] =>
[incites_jnci] =>
[incites_is_int_collab] =>
[incites_is_industry_collab] =>
[incites_esi_hot_paper] =>
[incites_esi_highly_cited_paper] =>
[incites_avg_cnci] =>
[incites_avg_percentile] =>
[scival_citations_count] =>
[scival_fwci] =>
[core_rank] => NA
[oecd_tree_oblast_id] => 10000
[oecd_tree_oblast_nazev] => 1. Natural Sciences
[oecd_tree_obor_id] => 10100
[oecd_tree_obor_nazev] => 1.1 Mathematics
[oecd_tree_podobor_id] => 10103
[oecd_tree_podobor_nazev] => Statistics and probability
[poznamka_metriky] =>
[nazev_en] => Mixed Integer Programming vs. Genetic Algorithm Approach to Scheduling Permutation Flow Shop
[popis_en] => Flow shop scheduling problems represent scheduling a set of jobs (composed of tasks) in shops with a product machine layout. Thus, the jobs have the same manufacturing order. A permutation flow shop scheduling problem (PFSSP) is a special version of the problem where each machine processes the jobs in the same order. In this paper, two different approaches to PFSSP with makespan objective are investigated. First a mixed integer programming model is formulated and it is used for solving the problem by an optimisation package GAMS. Since the problem belongs to NP-complete problems, this approach is limited to smaller instances. Its reasonable bounds are indicated using benchmarks from OR-Library. For large instances, an approach using genetic algorithm is proposed including its appropriate parameter settings. Computational results show a good performance of genetic algorithm. For suitable parameter settings presented in the paper, this approach is able to find the optimal solution almost in all cases or at least a solution very close to optimum when the test is executed several times.
[klicova_slova_en] => permutation flow shop, integer programming, NP-complete problems, stochastic heuristics, genetic algorithm
[vysledek_datum] => 2005-10-01T00:00:00+02:00
)
[8] => Array
(
[vysledek_id] => 55427
[vysledek_druh_id] => CHAPT
[ex_vysledek_id] => 71297
[vysledek_rok] => 2007
[nazev] => Four legged robot walking gait generation
[nazev_orig] => Four legged robot walking gait generation
[duvernost_udaju_id] => S
[popis] => This contribution is focused on the walking gait generation for a four legged robot using state space search algorithms and extends previous work, mainly (Ondroušek, 2006, 2007). A-star algorithm and beam search algorithm were implemented and verified by means of software simulation in 2006. Following goals were set for 2007: improvement of walking gait generation by using branch-and-bound algorithm and perfoming real tests on the four-legged walking robot with two degrees of freedom for each leg.
[popis_orig] => This contribution is focused on the walking gait generation for a four legged robot using state space search algorithms and extends previous work, mainly (Ondroušek, 2006, 2007). A-star algorithm and beam search algorithm were implemented and verified by means of software simulation in 2006. Following goals were set for 2007: improvement of walking gait generation by using branch-and-bound algorithm and perfoming real tests on the four-legged walking robot with two degrees of freedom for each leg.
[klicova_slova] => robot, walking gait, A-star, beam search
[klicova_slova_orig] => robot, walking gait, A-star, beam search
[url] =>
[oecd_obor_id] => 20204
[odpovedny_utvar_id] => 207
[odpovedny_utvar_nazev] => Institute of Automation and Computer Science
[odpovedny_utvar_zkratka] => IACS
[nadrazena_soucast_id] => 4
[nadrazena_soucast_zkratka] => FME
[nadrazena_soucast_nazev] => Faculty of Mechanical Engineering
[originalni_jazyk] => en
[schvalil_id] => 999999
[schvaleno] => 2010-12-03
[vykazovat_riv] => 0
[vykazovat_riv_zmeny] => 1
[slozka_id] =>
[posledni_diagnostika] =>
[vycet_osob] => ONDROUŠEK, V.; VĚCHET, S.; KREJSA, J.; HOUŠKA, P.
[pocet_tvurcu] => 4
[tvurci_ids] =>
[poznamka] =>
[typ_nazev] =>
[kod_doi] =>
[kod_dspace] =>
[rok_vytvoreni] =>
[pocet_zaznamu] =>
[zverejneno] => 1
[prvni_autor] =>
[korespondencni_autor] =>
[posledni_autor] =>
[znamka] =>
[kategorie_nazev] => Publication results
[druh_nazev] => Chapter in a book
[druh_popis] => Chapter in a book
[stav] => Approved
[vysledek_kategorie_id] => PV
[vysledek_system_kategorie_id] => PU
[vysledek_stav_id] => 3
[vlozil] => Informační systém Automat
[upravil] => Informační systém Automat
[ins_uid] => 999999
[upd_uid] => 999999
[ins_ts] => 2025-09-22
[upd_ts] => 2025-09-22
[status] => 9
[identifikator] => ISBN 978-80-214-3559-9
[identifikator_popis] => ISBN - Simulation Modelling of Mechatronic Systems III
[riv_dodavka_id] => 98
[riv_dodavka_oznaceni] => RIV10-MSM-26210___
[riv_dodavka_rok] => 2010
[diagnostika_pocet] => 0
[diagnostika_pocet_chyba] => 0
[diagnostika_pocet_upozorneni] => 0
[diagnostika_pocet_informace] => 0
[citace_text] => ONDROUŠEK, V.; VĚCHET, S.; KREJSA, J.; HOUŠKA, P. Four legged robot walking gait generation. In Simulation Modelling of Mechatronic Systems III. mechatronics. Brno: VUT v Brně, 2007. p. 123-129. ISBN: 978-80-214-3559-9.
[citace_html] => ONDROUŠEK, V.; VĚCHET, S.; KREJSA, J.; HOUŠKA, P. Four legged robot walking gait generation. In Simulation Modelling of Mechatronic Systems III. mechatronics. Brno: VUT v Brně, 2007. p. 123-129. ISBN: 978-80-214-3559-9.
[citace_rtf] =>
[citace_bibtex] => @inbook{BUT55427,
author="Vít {Ondroušek} and Stanislav {Věchet} and Jiří {Krejsa} and Pavel {Houška}",
title="Four legged robot walking gait generation",
booktitle="Simulation Modelling of Mechatronic Systems III",
year="2007",
publisher="VUT v Brně",
address="Brno",
series="mechatronics",
edition="1",
pages="123--129",
isbn="978-80-214-3559-9"
}
[vykazano] =>
[vykazano_aspon_jednou] =>
[identifikacni_kod] =>
[neautorsky_vysledek] => 0
[if] =>
[if_q] =>
[if_m17_q] =>
[if_m25_q] =>
[if_d] =>
[if_m17_d] =>
[if_m25_d] =>
[if_percentil] =>
[if_m17_percentil] =>
[if_m25_percentil] =>
[ais] =>
[ais_m17_q] =>
[ais_m25_q] =>
[ais_m17_d] =>
[ais_m25_d] =>
[ais_m17_percentil] =>
[ais_m25_percentil] =>
[jci] =>
[jci_q] =>
[jci_percentil] =>
[ef] =>
[scopus_sjr] =>
[scopus_sjr_q] =>
[scopus_sjr_d] =>
[nature_index_group] =>
[incites_times_cited] =>
[incites_open_access] =>
[incites_jnci] =>
[incites_is_int_collab] =>
[incites_is_industry_collab] =>
[incites_esi_hot_paper] =>
[incites_esi_highly_cited_paper] =>
[incites_avg_cnci] =>
[incites_avg_percentile] =>
[scival_citations_count] =>
[scival_fwci] =>
[core_rank] => NA
[oecd_tree_oblast_id] => 20000
[oecd_tree_oblast_nazev] => 2. Engineering and Technology
[oecd_tree_obor_id] => 20200
[oecd_tree_obor_nazev] => 2.2 Electrical engineering, Electronic engineering, Information engineering
[oecd_tree_podobor_id] => 20204
[oecd_tree_podobor_nazev] => Robotics and automatic control
[poznamka_metriky] =>
[nazev_en] => Four legged robot walking gait generation
[popis_en] => This contribution is focused on the walking gait generation for a four legged robot using state space search algorithms and extends previous work, mainly (Ondroušek, 2006, 2007). A-star algorithm and beam search algorithm were implemented and verified by means of software simulation in 2006. Following goals were set for 2007: improvement of walking gait generation by using branch-and-bound algorithm and perfoming real tests on the four-legged walking robot with two degrees of freedom for each leg.
[klicova_slova_en] => robot, walking gait, A-star, beam search
[vysledek_datum] => 2007-12-15T00:00:00+01:00
)
)
)
Array
(
[total] => 2086
[page] => 97
[count] => 9
[n_pages] => 140
[pagelen] => 15
[odkaz] => typVysledku=&rok=&ftext=&btnSubmit=1
[base_detail] => /en/veda/publikace/detail/
[base_page] => /en/veda/publikace
[vysledek] => Array
(
[0] => Array
(
[quotations] => DVOŘÁK, J.; KRČEK, P.
[title] => Nonholonomic Mobile Robot Path Planning by Means of Case-Based Reasoning.
[typ] => PV
[year] => 2006
[id_vav] => 55116
)
[1] => Array
(
[quotations] => VĚCHET, S.; KREJSA, J.
[title] => Mobile robots localization and path planning
[typ] => PV
[year] => 2006
[id_vav] => 55121
)
[2] => Array
(
[quotations] => ŠEDA, M.
[title] => On Shortest Paths in Partially Known Environment
[typ] => PV
[year] => 2006
[id_vav] => 55142
)
[3] => Array
(
[quotations] => OŠMERA, P.
[title] => Evolution of complexity
[typ] => PV
[year] => 2006
[id_vav] => 55154
)
[4] => Array
(
[quotations] => ROUPEC, J.; POPELA, P.
[title] => Genetic Algorithms for Scenario Generation in Stochastic Programming: Motivation and General Framework
[typ] => PV
[year] => 2008
[id_vav] => 55260
)
[5] => Array
(
[quotations] => KRČEK, P.; DVOŘÁK, J.
[title] => Plánování cesty mobilního robotu pomocí genetických algoritmů
[typ] => PV
[year] => 2009
[id_vav] => 55270
)
[6] => Array
(
[quotations] => ŠEDA, M.
[title] => Planning Trajectories in the Plane with Obstacles Using Voronoi Diagrams
[typ] => PV
[year] => 2003
[id_vav] => 55305
)
[7] => Array
(
[quotations] => ŠEDA, M.
[title] => Mixed Integer Programming vs. Genetic Algorithm Approach to Scheduling Permutation Flow Shop
[typ] => PV
[year] => 2005
[id_vav] => 55413
)
[8] => Array
(
[quotations] => ONDROUŠEK, V.; VĚCHET, S.; KREJSA, J.; HOUŠKA, P.
[title] => Four legged robot walking gait generation
[typ] => PV
[year] => 2007
[id_vav] => 55427
)
)
)