AKH-NFIS: Adaptive Krill Herd Network Fuzzy Inference System for Mobile Robot Navigation
Abstract Mobile robot navigation has been a current issue in the most recent two decades. Mobile robots are necessary to explore in obscure and dynamic situations. To solve the aforementioned issues an extended Kalman filter (EKF) and adaptive Krill Herd network fuzzy inference system (AKH-NFIS) tec...
Ausführliche Beschreibung
Autor*in: |
Das, Madhu Sudan [verfasserIn] Samanta, Anu [verfasserIn] Sanyal, Sourish [verfasserIn] Mandal, Sanjoy [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2021 |
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Schlagwörter: |
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Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Übergeordnetes Werk: |
Enthalten in: Wireless personal communications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994, 120(2021), 4 vom: 16. Juni, Seite 3389-3413 |
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Übergeordnetes Werk: |
volume:120 ; year:2021 ; number:4 ; day:16 ; month:06 ; pages:3389-3413 |
Links: |
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DOI / URN: |
10.1007/s11277-021-08619-5 |
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Katalog-ID: |
SPR045331553 |
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520 | |a Abstract Mobile robot navigation has been a current issue in the most recent two decades. Mobile robots are necessary to explore in obscure and dynamic situations. To solve the aforementioned issues an extended Kalman filter (EKF) and adaptive Krill Herd network fuzzy inference system (AKH-NFIS) techniques are proposed for the self-sufficient portable robot route. This is in charge of avoidance of obstacles in an obscure static and dynamic environment. Initially, the start and goal position will be set and the obstacles identified in front of the robot will be checked using the sensor. This sensor captures the environmental information around the mobile robot. Subsequently, to deal with the filtering problem of sensor data, EKF will be used. By EKF more accurate position estimation will be obtained by using dynamic information of data. Subsequently, the obstacle distances from the robot and the obstacle avoidance angle are calculated and fed as input to the training dataset. This training data set trains AKH-NFIS controller obtained by designing a Krill herd optimization algorithm adaptive network fuzzy logic-based navigation controller. The left wheel velocity and right wheel velocity are the output from the proposed system. The robustness of the proposed navigation controller will be assessed by exploring the mobile robot in various conditions. The experimental result demonstrates that our proposed strategy outperforms by correlation with existing strategies. | ||
650 | 4 | |a Obstacles position estimation |7 (dpeaa)DE-He213 | |
650 | 4 | |a Distance estimation |7 (dpeaa)DE-He213 | |
650 | 4 | |a Angle estimation |7 (dpeaa)DE-He213 | |
650 | 4 | |a Optimization |7 (dpeaa)DE-He213 | |
650 | 4 | |a Wheel velocity estimation |7 (dpeaa)DE-He213 | |
700 | 1 | |a Samanta, Anu |e verfasserin |4 aut | |
700 | 1 | |a Sanyal, Sourish |e verfasserin |4 aut | |
700 | 1 | |a Mandal, Sanjoy |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Wireless personal communications |d Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 |g 120(2021), 4 vom: 16. Juni, Seite 3389-3413 |w (DE-627)271179120 |w (DE-600)1479327-1 |x 1572-834X |7 nnns |
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10.1007/s11277-021-08619-5 doi (DE-627)SPR045331553 (SPR)s11277-021-08619-5-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Das, Madhu Sudan verfasserin aut AKH-NFIS: Adaptive Krill Herd Network Fuzzy Inference System for Mobile Robot Navigation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Mobile robot navigation has been a current issue in the most recent two decades. Mobile robots are necessary to explore in obscure and dynamic situations. To solve the aforementioned issues an extended Kalman filter (EKF) and adaptive Krill Herd network fuzzy inference system (AKH-NFIS) techniques are proposed for the self-sufficient portable robot route. This is in charge of avoidance of obstacles in an obscure static and dynamic environment. Initially, the start and goal position will be set and the obstacles identified in front of the robot will be checked using the sensor. This sensor captures the environmental information around the mobile robot. Subsequently, to deal with the filtering problem of sensor data, EKF will be used. By EKF more accurate position estimation will be obtained by using dynamic information of data. Subsequently, the obstacle distances from the robot and the obstacle avoidance angle are calculated and fed as input to the training dataset. This training data set trains AKH-NFIS controller obtained by designing a Krill herd optimization algorithm adaptive network fuzzy logic-based navigation controller. The left wheel velocity and right wheel velocity are the output from the proposed system. The robustness of the proposed navigation controller will be assessed by exploring the mobile robot in various conditions. The experimental result demonstrates that our proposed strategy outperforms by correlation with existing strategies. Obstacles position estimation (dpeaa)DE-He213 Distance estimation (dpeaa)DE-He213 Angle estimation (dpeaa)DE-He213 Optimization (dpeaa)DE-He213 Wheel velocity estimation (dpeaa)DE-He213 Samanta, Anu verfasserin aut Sanyal, Sourish verfasserin aut Mandal, Sanjoy verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 120(2021), 4 vom: 16. Juni, Seite 3389-3413 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:120 year:2021 number:4 day:16 month:06 pages:3389-3413 https://dx.doi.org/10.1007/s11277-021-08619-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 120 2021 4 16 06 3389-3413 |
spelling |
10.1007/s11277-021-08619-5 doi (DE-627)SPR045331553 (SPR)s11277-021-08619-5-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Das, Madhu Sudan verfasserin aut AKH-NFIS: Adaptive Krill Herd Network Fuzzy Inference System for Mobile Robot Navigation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Mobile robot navigation has been a current issue in the most recent two decades. Mobile robots are necessary to explore in obscure and dynamic situations. To solve the aforementioned issues an extended Kalman filter (EKF) and adaptive Krill Herd network fuzzy inference system (AKH-NFIS) techniques are proposed for the self-sufficient portable robot route. This is in charge of avoidance of obstacles in an obscure static and dynamic environment. Initially, the start and goal position will be set and the obstacles identified in front of the robot will be checked using the sensor. This sensor captures the environmental information around the mobile robot. Subsequently, to deal with the filtering problem of sensor data, EKF will be used. By EKF more accurate position estimation will be obtained by using dynamic information of data. Subsequently, the obstacle distances from the robot and the obstacle avoidance angle are calculated and fed as input to the training dataset. This training data set trains AKH-NFIS controller obtained by designing a Krill herd optimization algorithm adaptive network fuzzy logic-based navigation controller. The left wheel velocity and right wheel velocity are the output from the proposed system. The robustness of the proposed navigation controller will be assessed by exploring the mobile robot in various conditions. The experimental result demonstrates that our proposed strategy outperforms by correlation with existing strategies. Obstacles position estimation (dpeaa)DE-He213 Distance estimation (dpeaa)DE-He213 Angle estimation (dpeaa)DE-He213 Optimization (dpeaa)DE-He213 Wheel velocity estimation (dpeaa)DE-He213 Samanta, Anu verfasserin aut Sanyal, Sourish verfasserin aut Mandal, Sanjoy verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 120(2021), 4 vom: 16. Juni, Seite 3389-3413 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:120 year:2021 number:4 day:16 month:06 pages:3389-3413 https://dx.doi.org/10.1007/s11277-021-08619-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 120 2021 4 16 06 3389-3413 |
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10.1007/s11277-021-08619-5 doi (DE-627)SPR045331553 (SPR)s11277-021-08619-5-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Das, Madhu Sudan verfasserin aut AKH-NFIS: Adaptive Krill Herd Network Fuzzy Inference System for Mobile Robot Navigation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Mobile robot navigation has been a current issue in the most recent two decades. Mobile robots are necessary to explore in obscure and dynamic situations. To solve the aforementioned issues an extended Kalman filter (EKF) and adaptive Krill Herd network fuzzy inference system (AKH-NFIS) techniques are proposed for the self-sufficient portable robot route. This is in charge of avoidance of obstacles in an obscure static and dynamic environment. Initially, the start and goal position will be set and the obstacles identified in front of the robot will be checked using the sensor. This sensor captures the environmental information around the mobile robot. Subsequently, to deal with the filtering problem of sensor data, EKF will be used. By EKF more accurate position estimation will be obtained by using dynamic information of data. Subsequently, the obstacle distances from the robot and the obstacle avoidance angle are calculated and fed as input to the training dataset. This training data set trains AKH-NFIS controller obtained by designing a Krill herd optimization algorithm adaptive network fuzzy logic-based navigation controller. The left wheel velocity and right wheel velocity are the output from the proposed system. The robustness of the proposed navigation controller will be assessed by exploring the mobile robot in various conditions. The experimental result demonstrates that our proposed strategy outperforms by correlation with existing strategies. Obstacles position estimation (dpeaa)DE-He213 Distance estimation (dpeaa)DE-He213 Angle estimation (dpeaa)DE-He213 Optimization (dpeaa)DE-He213 Wheel velocity estimation (dpeaa)DE-He213 Samanta, Anu verfasserin aut Sanyal, Sourish verfasserin aut Mandal, Sanjoy verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 120(2021), 4 vom: 16. Juni, Seite 3389-3413 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:120 year:2021 number:4 day:16 month:06 pages:3389-3413 https://dx.doi.org/10.1007/s11277-021-08619-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 120 2021 4 16 06 3389-3413 |
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10.1007/s11277-021-08619-5 doi (DE-627)SPR045331553 (SPR)s11277-021-08619-5-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Das, Madhu Sudan verfasserin aut AKH-NFIS: Adaptive Krill Herd Network Fuzzy Inference System for Mobile Robot Navigation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Mobile robot navigation has been a current issue in the most recent two decades. Mobile robots are necessary to explore in obscure and dynamic situations. To solve the aforementioned issues an extended Kalman filter (EKF) and adaptive Krill Herd network fuzzy inference system (AKH-NFIS) techniques are proposed for the self-sufficient portable robot route. This is in charge of avoidance of obstacles in an obscure static and dynamic environment. Initially, the start and goal position will be set and the obstacles identified in front of the robot will be checked using the sensor. This sensor captures the environmental information around the mobile robot. Subsequently, to deal with the filtering problem of sensor data, EKF will be used. By EKF more accurate position estimation will be obtained by using dynamic information of data. Subsequently, the obstacle distances from the robot and the obstacle avoidance angle are calculated and fed as input to the training dataset. This training data set trains AKH-NFIS controller obtained by designing a Krill herd optimization algorithm adaptive network fuzzy logic-based navigation controller. The left wheel velocity and right wheel velocity are the output from the proposed system. The robustness of the proposed navigation controller will be assessed by exploring the mobile robot in various conditions. The experimental result demonstrates that our proposed strategy outperforms by correlation with existing strategies. Obstacles position estimation (dpeaa)DE-He213 Distance estimation (dpeaa)DE-He213 Angle estimation (dpeaa)DE-He213 Optimization (dpeaa)DE-He213 Wheel velocity estimation (dpeaa)DE-He213 Samanta, Anu verfasserin aut Sanyal, Sourish verfasserin aut Mandal, Sanjoy verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 120(2021), 4 vom: 16. Juni, Seite 3389-3413 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:120 year:2021 number:4 day:16 month:06 pages:3389-3413 https://dx.doi.org/10.1007/s11277-021-08619-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 120 2021 4 16 06 3389-3413 |
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10.1007/s11277-021-08619-5 doi (DE-627)SPR045331553 (SPR)s11277-021-08619-5-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Das, Madhu Sudan verfasserin aut AKH-NFIS: Adaptive Krill Herd Network Fuzzy Inference System for Mobile Robot Navigation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Mobile robot navigation has been a current issue in the most recent two decades. Mobile robots are necessary to explore in obscure and dynamic situations. To solve the aforementioned issues an extended Kalman filter (EKF) and adaptive Krill Herd network fuzzy inference system (AKH-NFIS) techniques are proposed for the self-sufficient portable robot route. This is in charge of avoidance of obstacles in an obscure static and dynamic environment. Initially, the start and goal position will be set and the obstacles identified in front of the robot will be checked using the sensor. This sensor captures the environmental information around the mobile robot. Subsequently, to deal with the filtering problem of sensor data, EKF will be used. By EKF more accurate position estimation will be obtained by using dynamic information of data. Subsequently, the obstacle distances from the robot and the obstacle avoidance angle are calculated and fed as input to the training dataset. This training data set trains AKH-NFIS controller obtained by designing a Krill herd optimization algorithm adaptive network fuzzy logic-based navigation controller. The left wheel velocity and right wheel velocity are the output from the proposed system. The robustness of the proposed navigation controller will be assessed by exploring the mobile robot in various conditions. The experimental result demonstrates that our proposed strategy outperforms by correlation with existing strategies. Obstacles position estimation (dpeaa)DE-He213 Distance estimation (dpeaa)DE-He213 Angle estimation (dpeaa)DE-He213 Optimization (dpeaa)DE-He213 Wheel velocity estimation (dpeaa)DE-He213 Samanta, Anu verfasserin aut Sanyal, Sourish verfasserin aut Mandal, Sanjoy verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 120(2021), 4 vom: 16. Juni, Seite 3389-3413 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:120 year:2021 number:4 day:16 month:06 pages:3389-3413 https://dx.doi.org/10.1007/s11277-021-08619-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 120 2021 4 16 06 3389-3413 |
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Mobile robots are necessary to explore in obscure and dynamic situations. To solve the aforementioned issues an extended Kalman filter (EKF) and adaptive Krill Herd network fuzzy inference system (AKH-NFIS) techniques are proposed for the self-sufficient portable robot route. This is in charge of avoidance of obstacles in an obscure static and dynamic environment. Initially, the start and goal position will be set and the obstacles identified in front of the robot will be checked using the sensor. This sensor captures the environmental information around the mobile robot. Subsequently, to deal with the filtering problem of sensor data, EKF will be used. By EKF more accurate position estimation will be obtained by using dynamic information of data. Subsequently, the obstacle distances from the robot and the obstacle avoidance angle are calculated and fed as input to the training dataset. 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Das, Madhu Sudan |
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Das, Madhu Sudan ddc 620 bkl 53.00 misc Obstacles position estimation misc Distance estimation misc Angle estimation misc Optimization misc Wheel velocity estimation AKH-NFIS: Adaptive Krill Herd Network Fuzzy Inference System for Mobile Robot Navigation |
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620 ASE 53.00 bkl AKH-NFIS: Adaptive Krill Herd Network Fuzzy Inference System for Mobile Robot Navigation Obstacles position estimation (dpeaa)DE-He213 Distance estimation (dpeaa)DE-He213 Angle estimation (dpeaa)DE-He213 Optimization (dpeaa)DE-He213 Wheel velocity estimation (dpeaa)DE-He213 |
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ddc 620 bkl 53.00 misc Obstacles position estimation misc Distance estimation misc Angle estimation misc Optimization misc Wheel velocity estimation |
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akh-nfis: adaptive krill herd network fuzzy inference system for mobile robot navigation |
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AKH-NFIS: Adaptive Krill Herd Network Fuzzy Inference System for Mobile Robot Navigation |
abstract |
Abstract Mobile robot navigation has been a current issue in the most recent two decades. Mobile robots are necessary to explore in obscure and dynamic situations. To solve the aforementioned issues an extended Kalman filter (EKF) and adaptive Krill Herd network fuzzy inference system (AKH-NFIS) techniques are proposed for the self-sufficient portable robot route. This is in charge of avoidance of obstacles in an obscure static and dynamic environment. Initially, the start and goal position will be set and the obstacles identified in front of the robot will be checked using the sensor. This sensor captures the environmental information around the mobile robot. Subsequently, to deal with the filtering problem of sensor data, EKF will be used. By EKF more accurate position estimation will be obtained by using dynamic information of data. Subsequently, the obstacle distances from the robot and the obstacle avoidance angle are calculated and fed as input to the training dataset. This training data set trains AKH-NFIS controller obtained by designing a Krill herd optimization algorithm adaptive network fuzzy logic-based navigation controller. The left wheel velocity and right wheel velocity are the output from the proposed system. The robustness of the proposed navigation controller will be assessed by exploring the mobile robot in various conditions. The experimental result demonstrates that our proposed strategy outperforms by correlation with existing strategies. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
abstractGer |
Abstract Mobile robot navigation has been a current issue in the most recent two decades. Mobile robots are necessary to explore in obscure and dynamic situations. To solve the aforementioned issues an extended Kalman filter (EKF) and adaptive Krill Herd network fuzzy inference system (AKH-NFIS) techniques are proposed for the self-sufficient portable robot route. This is in charge of avoidance of obstacles in an obscure static and dynamic environment. Initially, the start and goal position will be set and the obstacles identified in front of the robot will be checked using the sensor. This sensor captures the environmental information around the mobile robot. Subsequently, to deal with the filtering problem of sensor data, EKF will be used. By EKF more accurate position estimation will be obtained by using dynamic information of data. Subsequently, the obstacle distances from the robot and the obstacle avoidance angle are calculated and fed as input to the training dataset. This training data set trains AKH-NFIS controller obtained by designing a Krill herd optimization algorithm adaptive network fuzzy logic-based navigation controller. The left wheel velocity and right wheel velocity are the output from the proposed system. The robustness of the proposed navigation controller will be assessed by exploring the mobile robot in various conditions. The experimental result demonstrates that our proposed strategy outperforms by correlation with existing strategies. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
abstract_unstemmed |
Abstract Mobile robot navigation has been a current issue in the most recent two decades. Mobile robots are necessary to explore in obscure and dynamic situations. To solve the aforementioned issues an extended Kalman filter (EKF) and adaptive Krill Herd network fuzzy inference system (AKH-NFIS) techniques are proposed for the self-sufficient portable robot route. This is in charge of avoidance of obstacles in an obscure static and dynamic environment. Initially, the start and goal position will be set and the obstacles identified in front of the robot will be checked using the sensor. This sensor captures the environmental information around the mobile robot. Subsequently, to deal with the filtering problem of sensor data, EKF will be used. By EKF more accurate position estimation will be obtained by using dynamic information of data. Subsequently, the obstacle distances from the robot and the obstacle avoidance angle are calculated and fed as input to the training dataset. This training data set trains AKH-NFIS controller obtained by designing a Krill herd optimization algorithm adaptive network fuzzy logic-based navigation controller. The left wheel velocity and right wheel velocity are the output from the proposed system. The robustness of the proposed navigation controller will be assessed by exploring the mobile robot in various conditions. The experimental result demonstrates that our proposed strategy outperforms by correlation with existing strategies. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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title_short |
AKH-NFIS: Adaptive Krill Herd Network Fuzzy Inference System for Mobile Robot Navigation |
url |
https://dx.doi.org/10.1007/s11277-021-08619-5 |
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true |
author2 |
Samanta, Anu Sanyal, Sourish Mandal, Sanjoy |
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Samanta, Anu Sanyal, Sourish Mandal, Sanjoy |
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doi_str |
10.1007/s11277-021-08619-5 |
up_date |
2024-07-03T15:18:18.652Z |
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|
score |
7.40189 |