Implementing modified swarm intelligence algorithm based on Slime moulds for path planning and obstacle avoidance problem in mobile robots
Planning a collision-free path in the least processing time and cost within constraints is a central issue in designing an autonomous mobile robot (AMR). Nature-inspired swarm intelligence (NISI) metaheuristic algorithms are gaining popularity in path planning and obstacle avoidance (PPOA) problem i...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Agarwal, Divya [verfasserIn] Bharti, Pushpendra S. [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2021 |
|---|
| Schlagwörter: |
Path planning and obstacle avoidance problem Autonomous mobile robots (AMRs) |
|---|
| Übergeordnetes Werk: |
Enthalten in: Applied soft computing - Amsterdam [u.a.] : Elsevier Science, 2001, 107 |
|---|---|
| Übergeordnetes Werk: |
volume:107 |
| DOI / URN: |
10.1016/j.asoc.2021.107372 |
|---|
| Katalog-ID: |
ELV006154638 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV006154638 | ||
| 003 | DE-627 | ||
| 005 | 20231205154448.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 230505s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.asoc.2021.107372 |2 doi | |
| 035 | |a (DE-627)ELV006154638 | ||
| 035 | |a (ELSEVIER)S1568-4946(21)00295-7 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 004 |q DE-600 |
| 084 | |a 54.00 |2 bkl | ||
| 100 | 1 | |a Agarwal, Divya |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Implementing modified swarm intelligence algorithm based on Slime moulds for path planning and obstacle avoidance problem in mobile robots |
| 264 | 1 | |c 2021 | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a Planning a collision-free path in the least processing time and cost within constraints is a central issue in designing an autonomous mobile robot (AMR). Nature-inspired swarm intelligence (NISI) metaheuristic algorithms are gaining popularity in path planning and obstacle avoidance (PPOA) problem in AMRs. An efficient PPOA algorithm’s objective encompasses the ability to read a workspace map and consequently create the shortest collision-free path for the robot to manoeuvre from start to goal in the least processing time and effort. The authors have implemented a modified NISI metaheuristic approach known as a Slime Mould Optimization Algorithm (SMOA) in this research. SMOA takes inspiration from the oscillatory nature of slime mould when it encounters prey. Its mathematical model utilizes adaptive weights to simulate an optimal path for capturing prey or food. The slime moulds produce positive and negative feedback while propagating towards food with excellent exploratory competency and exploitation propensity. For this, simulation has been carried out on MATLAB 2020a. Additionally, the performance of SMOA has been compared with other NISI metaheuristic approaches such as PSO, FA, SFLA and ABC. The results demonstrate that modified SMOA takes less time and effort to generate an optimal collision-free path as compared to other mentioned approaches. | ||
| 650 | 4 | |a Path planning and obstacle avoidance problem | |
| 650 | 4 | |a Autonomous mobile robots (AMRs) | |
| 650 | 4 | |a Nature-inspired swarm intelligence metaheuristic approaches | |
| 650 | 4 | |a Slime mould optimization algorithm | |
| 700 | 1 | |a Bharti, Pushpendra S. |e verfasserin |0 (orcid)0000-0002-2521-7922 |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Applied soft computing |d Amsterdam [u.a.] : Elsevier Science, 2001 |g 107 |h Online-Ressource |w (DE-627)334375754 |w (DE-600)2057709-6 |w (DE-576)256145733 |x 1568-4946 |7 nnns |
| 773 | 1 | 8 | |g volume:107 |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a GBV_ILN_20 | ||
| 912 | |a GBV_ILN_22 | ||
| 912 | |a GBV_ILN_23 | ||
| 912 | |a GBV_ILN_24 | ||
| 912 | |a GBV_ILN_31 | ||
| 912 | |a GBV_ILN_32 | ||
| 912 | |a GBV_ILN_40 | ||
| 912 | |a GBV_ILN_60 | ||
| 912 | |a GBV_ILN_62 | ||
| 912 | |a GBV_ILN_63 | ||
| 912 | |a GBV_ILN_65 | ||
| 912 | |a GBV_ILN_69 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_73 | ||
| 912 | |a GBV_ILN_74 | ||
| 912 | |a GBV_ILN_90 | ||
| 912 | |a GBV_ILN_95 | ||
| 912 | |a GBV_ILN_100 | ||
| 912 | |a GBV_ILN_101 | ||
| 912 | |a GBV_ILN_105 | ||
| 912 | |a GBV_ILN_110 | ||
| 912 | |a GBV_ILN_150 | ||
| 912 | |a GBV_ILN_151 | ||
| 912 | |a GBV_ILN_224 | ||
| 912 | |a GBV_ILN_370 | ||
| 912 | |a GBV_ILN_602 | ||
| 912 | |a GBV_ILN_702 | ||
| 912 | |a GBV_ILN_2003 | ||
| 912 | |a GBV_ILN_2004 | ||
| 912 | |a GBV_ILN_2005 | ||
| 912 | |a GBV_ILN_2006 | ||
| 912 | |a GBV_ILN_2008 | ||
| 912 | |a GBV_ILN_2011 | ||
| 912 | |a GBV_ILN_2014 | ||
| 912 | |a GBV_ILN_2015 | ||
| 912 | |a GBV_ILN_2020 | ||
| 912 | |a GBV_ILN_2021 | ||
| 912 | |a GBV_ILN_2025 | ||
| 912 | |a GBV_ILN_2027 | ||
| 912 | |a GBV_ILN_2034 | ||
| 912 | |a GBV_ILN_2038 | ||
| 912 | |a GBV_ILN_2044 | ||
| 912 | |a GBV_ILN_2048 | ||
| 912 | |a GBV_ILN_2049 | ||
| 912 | |a GBV_ILN_2050 | ||
| 912 | |a GBV_ILN_2056 | ||
| 912 | |a GBV_ILN_2059 | ||
| 912 | |a GBV_ILN_2061 | ||
| 912 | |a GBV_ILN_2064 | ||
| 912 | |a GBV_ILN_2065 | ||
| 912 | |a GBV_ILN_2068 | ||
| 912 | |a GBV_ILN_2088 | ||
| 912 | |a GBV_ILN_2111 | ||
| 912 | |a GBV_ILN_2112 | ||
| 912 | |a GBV_ILN_2113 | ||
| 912 | |a GBV_ILN_2118 | ||
| 912 | |a GBV_ILN_2122 | ||
| 912 | |a GBV_ILN_2129 | ||
| 912 | |a GBV_ILN_2143 | ||
| 912 | |a GBV_ILN_2147 | ||
| 912 | |a GBV_ILN_2148 | ||
| 912 | |a GBV_ILN_2152 | ||
| 912 | |a GBV_ILN_2153 | ||
| 912 | |a GBV_ILN_2190 | ||
| 912 | |a GBV_ILN_2336 | ||
| 912 | |a GBV_ILN_2470 | ||
| 912 | |a GBV_ILN_2507 | ||
| 912 | |a GBV_ILN_2522 | ||
| 912 | |a GBV_ILN_4035 | ||
| 912 | |a GBV_ILN_4037 | ||
| 912 | |a GBV_ILN_4046 | ||
| 912 | |a GBV_ILN_4112 | ||
| 912 | |a GBV_ILN_4125 | ||
| 912 | |a GBV_ILN_4126 | ||
| 912 | |a GBV_ILN_4242 | ||
| 912 | |a GBV_ILN_4251 | ||
| 912 | |a GBV_ILN_4305 | ||
| 912 | |a GBV_ILN_4313 | ||
| 912 | |a GBV_ILN_4322 | ||
| 912 | |a GBV_ILN_4323 | ||
| 912 | |a GBV_ILN_4324 | ||
| 912 | |a GBV_ILN_4325 | ||
| 912 | |a GBV_ILN_4326 | ||
| 912 | |a GBV_ILN_4333 | ||
| 912 | |a GBV_ILN_4334 | ||
| 912 | |a GBV_ILN_4335 | ||
| 912 | |a GBV_ILN_4338 | ||
| 912 | |a GBV_ILN_4393 | ||
| 936 | b | k | |a 54.00 |j Informatik: Allgemeines |
| 951 | |a AR | ||
| 952 | |d 107 | ||
| author_variant |
d a da p s b ps psb |
|---|---|
| matchkey_str |
article:15684946:2021----::mlmnigoiidwritliecagrtmaeosieolsoptpannadbtce |
| hierarchy_sort_str |
2021 |
| bklnumber |
54.00 |
| publishDate |
2021 |
| allfields |
10.1016/j.asoc.2021.107372 doi (DE-627)ELV006154638 (ELSEVIER)S1568-4946(21)00295-7 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Agarwal, Divya verfasserin aut Implementing modified swarm intelligence algorithm based on Slime moulds for path planning and obstacle avoidance problem in mobile robots 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Planning a collision-free path in the least processing time and cost within constraints is a central issue in designing an autonomous mobile robot (AMR). Nature-inspired swarm intelligence (NISI) metaheuristic algorithms are gaining popularity in path planning and obstacle avoidance (PPOA) problem in AMRs. An efficient PPOA algorithm’s objective encompasses the ability to read a workspace map and consequently create the shortest collision-free path for the robot to manoeuvre from start to goal in the least processing time and effort. The authors have implemented a modified NISI metaheuristic approach known as a Slime Mould Optimization Algorithm (SMOA) in this research. SMOA takes inspiration from the oscillatory nature of slime mould when it encounters prey. Its mathematical model utilizes adaptive weights to simulate an optimal path for capturing prey or food. The slime moulds produce positive and negative feedback while propagating towards food with excellent exploratory competency and exploitation propensity. For this, simulation has been carried out on MATLAB 2020a. Additionally, the performance of SMOA has been compared with other NISI metaheuristic approaches such as PSO, FA, SFLA and ABC. The results demonstrate that modified SMOA takes less time and effort to generate an optimal collision-free path as compared to other mentioned approaches. Path planning and obstacle avoidance problem Autonomous mobile robots (AMRs) Nature-inspired swarm intelligence metaheuristic approaches Slime mould optimization algorithm Bharti, Pushpendra S. verfasserin (orcid)0000-0002-2521-7922 aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 107 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:107 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 107 |
| spelling |
10.1016/j.asoc.2021.107372 doi (DE-627)ELV006154638 (ELSEVIER)S1568-4946(21)00295-7 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Agarwal, Divya verfasserin aut Implementing modified swarm intelligence algorithm based on Slime moulds for path planning and obstacle avoidance problem in mobile robots 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Planning a collision-free path in the least processing time and cost within constraints is a central issue in designing an autonomous mobile robot (AMR). Nature-inspired swarm intelligence (NISI) metaheuristic algorithms are gaining popularity in path planning and obstacle avoidance (PPOA) problem in AMRs. An efficient PPOA algorithm’s objective encompasses the ability to read a workspace map and consequently create the shortest collision-free path for the robot to manoeuvre from start to goal in the least processing time and effort. The authors have implemented a modified NISI metaheuristic approach known as a Slime Mould Optimization Algorithm (SMOA) in this research. SMOA takes inspiration from the oscillatory nature of slime mould when it encounters prey. Its mathematical model utilizes adaptive weights to simulate an optimal path for capturing prey or food. The slime moulds produce positive and negative feedback while propagating towards food with excellent exploratory competency and exploitation propensity. For this, simulation has been carried out on MATLAB 2020a. Additionally, the performance of SMOA has been compared with other NISI metaheuristic approaches such as PSO, FA, SFLA and ABC. The results demonstrate that modified SMOA takes less time and effort to generate an optimal collision-free path as compared to other mentioned approaches. Path planning and obstacle avoidance problem Autonomous mobile robots (AMRs) Nature-inspired swarm intelligence metaheuristic approaches Slime mould optimization algorithm Bharti, Pushpendra S. verfasserin (orcid)0000-0002-2521-7922 aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 107 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:107 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 107 |
| allfields_unstemmed |
10.1016/j.asoc.2021.107372 doi (DE-627)ELV006154638 (ELSEVIER)S1568-4946(21)00295-7 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Agarwal, Divya verfasserin aut Implementing modified swarm intelligence algorithm based on Slime moulds for path planning and obstacle avoidance problem in mobile robots 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Planning a collision-free path in the least processing time and cost within constraints is a central issue in designing an autonomous mobile robot (AMR). Nature-inspired swarm intelligence (NISI) metaheuristic algorithms are gaining popularity in path planning and obstacle avoidance (PPOA) problem in AMRs. An efficient PPOA algorithm’s objective encompasses the ability to read a workspace map and consequently create the shortest collision-free path for the robot to manoeuvre from start to goal in the least processing time and effort. The authors have implemented a modified NISI metaheuristic approach known as a Slime Mould Optimization Algorithm (SMOA) in this research. SMOA takes inspiration from the oscillatory nature of slime mould when it encounters prey. Its mathematical model utilizes adaptive weights to simulate an optimal path for capturing prey or food. The slime moulds produce positive and negative feedback while propagating towards food with excellent exploratory competency and exploitation propensity. For this, simulation has been carried out on MATLAB 2020a. Additionally, the performance of SMOA has been compared with other NISI metaheuristic approaches such as PSO, FA, SFLA and ABC. The results demonstrate that modified SMOA takes less time and effort to generate an optimal collision-free path as compared to other mentioned approaches. Path planning and obstacle avoidance problem Autonomous mobile robots (AMRs) Nature-inspired swarm intelligence metaheuristic approaches Slime mould optimization algorithm Bharti, Pushpendra S. verfasserin (orcid)0000-0002-2521-7922 aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 107 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:107 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 107 |
| allfieldsGer |
10.1016/j.asoc.2021.107372 doi (DE-627)ELV006154638 (ELSEVIER)S1568-4946(21)00295-7 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Agarwal, Divya verfasserin aut Implementing modified swarm intelligence algorithm based on Slime moulds for path planning and obstacle avoidance problem in mobile robots 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Planning a collision-free path in the least processing time and cost within constraints is a central issue in designing an autonomous mobile robot (AMR). Nature-inspired swarm intelligence (NISI) metaheuristic algorithms are gaining popularity in path planning and obstacle avoidance (PPOA) problem in AMRs. An efficient PPOA algorithm’s objective encompasses the ability to read a workspace map and consequently create the shortest collision-free path for the robot to manoeuvre from start to goal in the least processing time and effort. The authors have implemented a modified NISI metaheuristic approach known as a Slime Mould Optimization Algorithm (SMOA) in this research. SMOA takes inspiration from the oscillatory nature of slime mould when it encounters prey. Its mathematical model utilizes adaptive weights to simulate an optimal path for capturing prey or food. The slime moulds produce positive and negative feedback while propagating towards food with excellent exploratory competency and exploitation propensity. For this, simulation has been carried out on MATLAB 2020a. Additionally, the performance of SMOA has been compared with other NISI metaheuristic approaches such as PSO, FA, SFLA and ABC. The results demonstrate that modified SMOA takes less time and effort to generate an optimal collision-free path as compared to other mentioned approaches. Path planning and obstacle avoidance problem Autonomous mobile robots (AMRs) Nature-inspired swarm intelligence metaheuristic approaches Slime mould optimization algorithm Bharti, Pushpendra S. verfasserin (orcid)0000-0002-2521-7922 aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 107 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:107 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 107 |
| allfieldsSound |
10.1016/j.asoc.2021.107372 doi (DE-627)ELV006154638 (ELSEVIER)S1568-4946(21)00295-7 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Agarwal, Divya verfasserin aut Implementing modified swarm intelligence algorithm based on Slime moulds for path planning and obstacle avoidance problem in mobile robots 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Planning a collision-free path in the least processing time and cost within constraints is a central issue in designing an autonomous mobile robot (AMR). Nature-inspired swarm intelligence (NISI) metaheuristic algorithms are gaining popularity in path planning and obstacle avoidance (PPOA) problem in AMRs. An efficient PPOA algorithm’s objective encompasses the ability to read a workspace map and consequently create the shortest collision-free path for the robot to manoeuvre from start to goal in the least processing time and effort. The authors have implemented a modified NISI metaheuristic approach known as a Slime Mould Optimization Algorithm (SMOA) in this research. SMOA takes inspiration from the oscillatory nature of slime mould when it encounters prey. Its mathematical model utilizes adaptive weights to simulate an optimal path for capturing prey or food. The slime moulds produce positive and negative feedback while propagating towards food with excellent exploratory competency and exploitation propensity. For this, simulation has been carried out on MATLAB 2020a. Additionally, the performance of SMOA has been compared with other NISI metaheuristic approaches such as PSO, FA, SFLA and ABC. The results demonstrate that modified SMOA takes less time and effort to generate an optimal collision-free path as compared to other mentioned approaches. Path planning and obstacle avoidance problem Autonomous mobile robots (AMRs) Nature-inspired swarm intelligence metaheuristic approaches Slime mould optimization algorithm Bharti, Pushpendra S. verfasserin (orcid)0000-0002-2521-7922 aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 107 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:107 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 107 |
| language |
English |
| source |
Enthalten in Applied soft computing 107 volume:107 |
| sourceStr |
Enthalten in Applied soft computing 107 volume:107 |
| format_phy_str_mv |
Article |
| bklname |
Informatik: Allgemeines |
| institution |
findex.gbv.de |
| topic_facet |
Path planning and obstacle avoidance problem Autonomous mobile robots (AMRs) Nature-inspired swarm intelligence metaheuristic approaches Slime mould optimization algorithm |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
Applied soft computing |
| authorswithroles_txt_mv |
Agarwal, Divya @@aut@@ Bharti, Pushpendra S. @@aut@@ |
| publishDateDaySort_date |
2021-01-01T00:00:00Z |
| hierarchy_top_id |
334375754 |
| dewey-sort |
14 |
| id |
ELV006154638 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV006154638</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231205154448.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230505s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.asoc.2021.107372</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV006154638</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1568-4946(21)00295-7</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Agarwal, Divya</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Implementing modified swarm intelligence algorithm based on Slime moulds for path planning and obstacle avoidance problem in mobile robots</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Planning a collision-free path in the least processing time and cost within constraints is a central issue in designing an autonomous mobile robot (AMR). Nature-inspired swarm intelligence (NISI) metaheuristic algorithms are gaining popularity in path planning and obstacle avoidance (PPOA) problem in AMRs. An efficient PPOA algorithm’s objective encompasses the ability to read a workspace map and consequently create the shortest collision-free path for the robot to manoeuvre from start to goal in the least processing time and effort. The authors have implemented a modified NISI metaheuristic approach known as a Slime Mould Optimization Algorithm (SMOA) in this research. SMOA takes inspiration from the oscillatory nature of slime mould when it encounters prey. Its mathematical model utilizes adaptive weights to simulate an optimal path for capturing prey or food. The slime moulds produce positive and negative feedback while propagating towards food with excellent exploratory competency and exploitation propensity. For this, simulation has been carried out on MATLAB 2020a. Additionally, the performance of SMOA has been compared with other NISI metaheuristic approaches such as PSO, FA, SFLA and ABC. The results demonstrate that modified SMOA takes less time and effort to generate an optimal collision-free path as compared to other mentioned approaches.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Path planning and obstacle avoidance problem</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Autonomous mobile robots (AMRs)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Nature-inspired swarm intelligence metaheuristic approaches</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Slime mould optimization algorithm</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bharti, Pushpendra S.</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-2521-7922</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Applied soft computing</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 2001</subfield><subfield code="g">107</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)334375754</subfield><subfield code="w">(DE-600)2057709-6</subfield><subfield code="w">(DE-576)256145733</subfield><subfield code="x">1568-4946</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:107</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.00</subfield><subfield code="j">Informatik: Allgemeines</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">107</subfield></datafield></record></collection>
|
| author |
Agarwal, Divya |
| spellingShingle |
Agarwal, Divya ddc 004 bkl 54.00 misc Path planning and obstacle avoidance problem misc Autonomous mobile robots (AMRs) misc Nature-inspired swarm intelligence metaheuristic approaches misc Slime mould optimization algorithm Implementing modified swarm intelligence algorithm based on Slime moulds for path planning and obstacle avoidance problem in mobile robots |
| authorStr |
Agarwal, Divya |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)334375754 |
| format |
electronic Article |
| dewey-ones |
004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| issn |
1568-4946 |
| topic_title |
004 DE-600 54.00 bkl Implementing modified swarm intelligence algorithm based on Slime moulds for path planning and obstacle avoidance problem in mobile robots Path planning and obstacle avoidance problem Autonomous mobile robots (AMRs) Nature-inspired swarm intelligence metaheuristic approaches Slime mould optimization algorithm |
| topic |
ddc 004 bkl 54.00 misc Path planning and obstacle avoidance problem misc Autonomous mobile robots (AMRs) misc Nature-inspired swarm intelligence metaheuristic approaches misc Slime mould optimization algorithm |
| topic_unstemmed |
ddc 004 bkl 54.00 misc Path planning and obstacle avoidance problem misc Autonomous mobile robots (AMRs) misc Nature-inspired swarm intelligence metaheuristic approaches misc Slime mould optimization algorithm |
| topic_browse |
ddc 004 bkl 54.00 misc Path planning and obstacle avoidance problem misc Autonomous mobile robots (AMRs) misc Nature-inspired swarm intelligence metaheuristic approaches misc Slime mould optimization algorithm |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
Applied soft computing |
| hierarchy_parent_id |
334375754 |
| dewey-tens |
000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Applied soft computing |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 |
| title |
Implementing modified swarm intelligence algorithm based on Slime moulds for path planning and obstacle avoidance problem in mobile robots |
| ctrlnum |
(DE-627)ELV006154638 (ELSEVIER)S1568-4946(21)00295-7 |
| title_full |
Implementing modified swarm intelligence algorithm based on Slime moulds for path planning and obstacle avoidance problem in mobile robots |
| author_sort |
Agarwal, Divya |
| journal |
Applied soft computing |
| journalStr |
Applied soft computing |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2021 |
| contenttype_str_mv |
zzz |
| author_browse |
Agarwal, Divya Bharti, Pushpendra S. |
| container_volume |
107 |
| class |
004 DE-600 54.00 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Agarwal, Divya |
| doi_str_mv |
10.1016/j.asoc.2021.107372 |
| normlink |
(ORCID)0000-0002-2521-7922 |
| normlink_prefix_str_mv |
(orcid)0000-0002-2521-7922 |
| dewey-full |
004 |
| author2-role |
verfasserin |
| title_sort |
implementing modified swarm intelligence algorithm based on slime moulds for path planning and obstacle avoidance problem in mobile robots |
| title_auth |
Implementing modified swarm intelligence algorithm based on Slime moulds for path planning and obstacle avoidance problem in mobile robots |
| abstract |
Planning a collision-free path in the least processing time and cost within constraints is a central issue in designing an autonomous mobile robot (AMR). Nature-inspired swarm intelligence (NISI) metaheuristic algorithms are gaining popularity in path planning and obstacle avoidance (PPOA) problem in AMRs. An efficient PPOA algorithm’s objective encompasses the ability to read a workspace map and consequently create the shortest collision-free path for the robot to manoeuvre from start to goal in the least processing time and effort. The authors have implemented a modified NISI metaheuristic approach known as a Slime Mould Optimization Algorithm (SMOA) in this research. SMOA takes inspiration from the oscillatory nature of slime mould when it encounters prey. Its mathematical model utilizes adaptive weights to simulate an optimal path for capturing prey or food. The slime moulds produce positive and negative feedback while propagating towards food with excellent exploratory competency and exploitation propensity. For this, simulation has been carried out on MATLAB 2020a. Additionally, the performance of SMOA has been compared with other NISI metaheuristic approaches such as PSO, FA, SFLA and ABC. The results demonstrate that modified SMOA takes less time and effort to generate an optimal collision-free path as compared to other mentioned approaches. |
| abstractGer |
Planning a collision-free path in the least processing time and cost within constraints is a central issue in designing an autonomous mobile robot (AMR). Nature-inspired swarm intelligence (NISI) metaheuristic algorithms are gaining popularity in path planning and obstacle avoidance (PPOA) problem in AMRs. An efficient PPOA algorithm’s objective encompasses the ability to read a workspace map and consequently create the shortest collision-free path for the robot to manoeuvre from start to goal in the least processing time and effort. The authors have implemented a modified NISI metaheuristic approach known as a Slime Mould Optimization Algorithm (SMOA) in this research. SMOA takes inspiration from the oscillatory nature of slime mould when it encounters prey. Its mathematical model utilizes adaptive weights to simulate an optimal path for capturing prey or food. The slime moulds produce positive and negative feedback while propagating towards food with excellent exploratory competency and exploitation propensity. For this, simulation has been carried out on MATLAB 2020a. Additionally, the performance of SMOA has been compared with other NISI metaheuristic approaches such as PSO, FA, SFLA and ABC. The results demonstrate that modified SMOA takes less time and effort to generate an optimal collision-free path as compared to other mentioned approaches. |
| abstract_unstemmed |
Planning a collision-free path in the least processing time and cost within constraints is a central issue in designing an autonomous mobile robot (AMR). Nature-inspired swarm intelligence (NISI) metaheuristic algorithms are gaining popularity in path planning and obstacle avoidance (PPOA) problem in AMRs. An efficient PPOA algorithm’s objective encompasses the ability to read a workspace map and consequently create the shortest collision-free path for the robot to manoeuvre from start to goal in the least processing time and effort. The authors have implemented a modified NISI metaheuristic approach known as a Slime Mould Optimization Algorithm (SMOA) in this research. SMOA takes inspiration from the oscillatory nature of slime mould when it encounters prey. Its mathematical model utilizes adaptive weights to simulate an optimal path for capturing prey or food. The slime moulds produce positive and negative feedback while propagating towards food with excellent exploratory competency and exploitation propensity. For this, simulation has been carried out on MATLAB 2020a. Additionally, the performance of SMOA has been compared with other NISI metaheuristic approaches such as PSO, FA, SFLA and ABC. The results demonstrate that modified SMOA takes less time and effort to generate an optimal collision-free path as compared to other mentioned approaches. |
| collection_details |
GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 |
| title_short |
Implementing modified swarm intelligence algorithm based on Slime moulds for path planning and obstacle avoidance problem in mobile robots |
| remote_bool |
true |
| author2 |
Bharti, Pushpendra S. |
| author2Str |
Bharti, Pushpendra S. |
| ppnlink |
334375754 |
| mediatype_str_mv |
c |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1016/j.asoc.2021.107372 |
| up_date |
2024-07-06T20:24:37.967Z |
| _version_ |
1803862648258822144 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV006154638</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231205154448.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230505s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.asoc.2021.107372</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV006154638</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1568-4946(21)00295-7</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Agarwal, Divya</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Implementing modified swarm intelligence algorithm based on Slime moulds for path planning and obstacle avoidance problem in mobile robots</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Planning a collision-free path in the least processing time and cost within constraints is a central issue in designing an autonomous mobile robot (AMR). Nature-inspired swarm intelligence (NISI) metaheuristic algorithms are gaining popularity in path planning and obstacle avoidance (PPOA) problem in AMRs. An efficient PPOA algorithm’s objective encompasses the ability to read a workspace map and consequently create the shortest collision-free path for the robot to manoeuvre from start to goal in the least processing time and effort. The authors have implemented a modified NISI metaheuristic approach known as a Slime Mould Optimization Algorithm (SMOA) in this research. SMOA takes inspiration from the oscillatory nature of slime mould when it encounters prey. Its mathematical model utilizes adaptive weights to simulate an optimal path for capturing prey or food. The slime moulds produce positive and negative feedback while propagating towards food with excellent exploratory competency and exploitation propensity. For this, simulation has been carried out on MATLAB 2020a. Additionally, the performance of SMOA has been compared with other NISI metaheuristic approaches such as PSO, FA, SFLA and ABC. The results demonstrate that modified SMOA takes less time and effort to generate an optimal collision-free path as compared to other mentioned approaches.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Path planning and obstacle avoidance problem</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Autonomous mobile robots (AMRs)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Nature-inspired swarm intelligence metaheuristic approaches</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Slime mould optimization algorithm</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bharti, Pushpendra S.</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-2521-7922</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Applied soft computing</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 2001</subfield><subfield code="g">107</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)334375754</subfield><subfield code="w">(DE-600)2057709-6</subfield><subfield code="w">(DE-576)256145733</subfield><subfield code="x">1568-4946</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:107</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.00</subfield><subfield code="j">Informatik: Allgemeines</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">107</subfield></datafield></record></collection>
|
| score |
7.399457 |