Simulation for Path Planning of SLOCUM Glider in Near-bottom Ocean Currents Using Heuristic Algorithms and Q-Learning
Addressing the need for exploration of benthic zones utilising autonomous underwater vehicles, this paper presents a simulation for an optimised path planning from the source node to the destination node of the autonomous underwater vehicle SLOCUM Glider in near-bottom ocean environment. Near-bottom...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Utkarsh Gautam [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
ant colony optimisation algorithm Autonomous underwater vehicles |
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| Übergeordnetes Werk: |
Enthalten in: Defence science journal - New Delhi : Centre, 1951, 65(2015), 3, Seite 220-225 |
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| Übergeordnetes Werk: |
volume:65 ; year:2015 ; number:3 ; pages:220-225 |
| Links: |
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| DOI / URN: |
10.14429/dsj.65.7855 |
|---|
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| 520 | |a Addressing the need for exploration of benthic zones utilising autonomous underwater vehicles, this paper presents a simulation for an optimised path planning from the source node to the destination node of the autonomous underwater vehicle SLOCUM Glider in near-bottom ocean environment. Near-bottom ocean current data from the Bedford Institute of Oceanography, Canada, have been used for this simulation. A cost function is formulated to describe the dynamics of the autonomous underwater vehicle in near-bottom ocean currents. This cost function is then optimised using various biologically-inspired algorithms such as genetic algorithm, Ant Colony optimisation algorithm and particle swarm optimisation algorithm. The simulation of path planning is also performed using Q-learning technique and the results are compared with the biologically-inspired algorithms. The results clearly show that the Q-learning algorithm is better in computational complexity than the biologically-inspired algorithms. The ease of simulating the environment is also more in the case of Q-learning techniques. Hence this paper presents an effective path planning technique, which has been tested for the SLOCUM glider and it may be extended for use in any standard autonomous underwater vehicle. Defence Science Journal, Vol. 65, No. 3, May 2015, pp.220-225, DOI: http://dx.doi.org/10.14429/dsj.65.7855 | ||
| 650 | 4 | |a Ocean currents | |
| 650 | 4 | |a Heuristic | |
| 650 | 4 | |a Q-learning | |
| 650 | 4 | |a near-bottom ocean currents | |
| 650 | 4 | |a genetic algorithm | |
| 650 | 4 | |a ant colony optimisation algorithm | |
| 650 | 4 | |a Algorithms | |
| 650 | 4 | |a Simulation | |
| 650 | 4 | |a Autonomous underwater vehicles | |
| 650 | 4 | |a path planning | |
| 650 | 4 | |a AUV SLOCUM Glider | |
| 650 | 4 | |a particle swarm optimisation algorithm | |
| 650 | 4 | |a Simulation, path planning, AUV SLOCUM Glider, near-bottom ocean currents, Q-learning, genetic algorithm, ant colony optimisation algorithm, particle swarm optimisation algorithm | |
| 650 | 4 | |a Military Science | |
| 700 | 0 | |a Malmathanraj Ramanathan |4 oth | |
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10.14429/dsj.65.7855 doi PQ20160617 (DE-627)OLC1966606400 (DE-599)GBVOLC1966606400 (PRQ)d2357-dcd681b141df4ed3f6e18426b73a710d669420b23b4bd2c9e56ed5934e0b33c23 (KEY)0060171320150000065000300220simulationforpathplanningofslocumgliderinnearbotto DE-627 ger DE-627 rakwb eng 320 ZDB 89.00 bkl Utkarsh Gautam verfasserin aut Simulation for Path Planning of SLOCUM Glider in Near-bottom Ocean Currents Using Heuristic Algorithms and Q-Learning 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Addressing the need for exploration of benthic zones utilising autonomous underwater vehicles, this paper presents a simulation for an optimised path planning from the source node to the destination node of the autonomous underwater vehicle SLOCUM Glider in near-bottom ocean environment. Near-bottom ocean current data from the Bedford Institute of Oceanography, Canada, have been used for this simulation. A cost function is formulated to describe the dynamics of the autonomous underwater vehicle in near-bottom ocean currents. This cost function is then optimised using various biologically-inspired algorithms such as genetic algorithm, Ant Colony optimisation algorithm and particle swarm optimisation algorithm. The simulation of path planning is also performed using Q-learning technique and the results are compared with the biologically-inspired algorithms. The results clearly show that the Q-learning algorithm is better in computational complexity than the biologically-inspired algorithms. The ease of simulating the environment is also more in the case of Q-learning techniques. Hence this paper presents an effective path planning technique, which has been tested for the SLOCUM glider and it may be extended for use in any standard autonomous underwater vehicle. Defence Science Journal, Vol. 65, No. 3, May 2015, pp.220-225, DOI: http://dx.doi.org/10.14429/dsj.65.7855 Ocean currents Heuristic Q-learning near-bottom ocean currents genetic algorithm ant colony optimisation algorithm Algorithms Simulation Autonomous underwater vehicles path planning AUV SLOCUM Glider particle swarm optimisation algorithm Simulation, path planning, AUV SLOCUM Glider, near-bottom ocean currents, Q-learning, genetic algorithm, ant colony optimisation algorithm, particle swarm optimisation algorithm Military Science Malmathanraj Ramanathan oth Enthalten in Defence science journal New Delhi : Centre, 1951 65(2015), 3, Seite 220-225 (DE-627)130313327 (DE-600)588844-X (DE-576)9130313325 0011-748X nnns volume:65 year:2015 number:3 pages:220-225 http://dx.doi.org/10.14429/dsj.65.7855 Volltext http://search.proquest.com/docview/1725480503 https://doaj.org/article/1be5aa29e2e74a74acfcda41c5c48345 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-POL SSG-OLC-TEC SSG-OLC-IBL GBV_ILN_11 GBV_ILN_70 89.00 AVZ AR 65 2015 3 220-225 |
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10.14429/dsj.65.7855 doi PQ20160617 (DE-627)OLC1966606400 (DE-599)GBVOLC1966606400 (PRQ)d2357-dcd681b141df4ed3f6e18426b73a710d669420b23b4bd2c9e56ed5934e0b33c23 (KEY)0060171320150000065000300220simulationforpathplanningofslocumgliderinnearbotto DE-627 ger DE-627 rakwb eng 320 ZDB 89.00 bkl Utkarsh Gautam verfasserin aut Simulation for Path Planning of SLOCUM Glider in Near-bottom Ocean Currents Using Heuristic Algorithms and Q-Learning 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Addressing the need for exploration of benthic zones utilising autonomous underwater vehicles, this paper presents a simulation for an optimised path planning from the source node to the destination node of the autonomous underwater vehicle SLOCUM Glider in near-bottom ocean environment. Near-bottom ocean current data from the Bedford Institute of Oceanography, Canada, have been used for this simulation. A cost function is formulated to describe the dynamics of the autonomous underwater vehicle in near-bottom ocean currents. This cost function is then optimised using various biologically-inspired algorithms such as genetic algorithm, Ant Colony optimisation algorithm and particle swarm optimisation algorithm. The simulation of path planning is also performed using Q-learning technique and the results are compared with the biologically-inspired algorithms. The results clearly show that the Q-learning algorithm is better in computational complexity than the biologically-inspired algorithms. The ease of simulating the environment is also more in the case of Q-learning techniques. Hence this paper presents an effective path planning technique, which has been tested for the SLOCUM glider and it may be extended for use in any standard autonomous underwater vehicle. Defence Science Journal, Vol. 65, No. 3, May 2015, pp.220-225, DOI: http://dx.doi.org/10.14429/dsj.65.7855 Ocean currents Heuristic Q-learning near-bottom ocean currents genetic algorithm ant colony optimisation algorithm Algorithms Simulation Autonomous underwater vehicles path planning AUV SLOCUM Glider particle swarm optimisation algorithm Simulation, path planning, AUV SLOCUM Glider, near-bottom ocean currents, Q-learning, genetic algorithm, ant colony optimisation algorithm, particle swarm optimisation algorithm Military Science Malmathanraj Ramanathan oth Enthalten in Defence science journal New Delhi : Centre, 1951 65(2015), 3, Seite 220-225 (DE-627)130313327 (DE-600)588844-X (DE-576)9130313325 0011-748X nnns volume:65 year:2015 number:3 pages:220-225 http://dx.doi.org/10.14429/dsj.65.7855 Volltext http://search.proquest.com/docview/1725480503 https://doaj.org/article/1be5aa29e2e74a74acfcda41c5c48345 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-POL SSG-OLC-TEC SSG-OLC-IBL GBV_ILN_11 GBV_ILN_70 89.00 AVZ AR 65 2015 3 220-225 |
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10.14429/dsj.65.7855 doi PQ20160617 (DE-627)OLC1966606400 (DE-599)GBVOLC1966606400 (PRQ)d2357-dcd681b141df4ed3f6e18426b73a710d669420b23b4bd2c9e56ed5934e0b33c23 (KEY)0060171320150000065000300220simulationforpathplanningofslocumgliderinnearbotto DE-627 ger DE-627 rakwb eng 320 ZDB 89.00 bkl Utkarsh Gautam verfasserin aut Simulation for Path Planning of SLOCUM Glider in Near-bottom Ocean Currents Using Heuristic Algorithms and Q-Learning 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Addressing the need for exploration of benthic zones utilising autonomous underwater vehicles, this paper presents a simulation for an optimised path planning from the source node to the destination node of the autonomous underwater vehicle SLOCUM Glider in near-bottom ocean environment. Near-bottom ocean current data from the Bedford Institute of Oceanography, Canada, have been used for this simulation. A cost function is formulated to describe the dynamics of the autonomous underwater vehicle in near-bottom ocean currents. This cost function is then optimised using various biologically-inspired algorithms such as genetic algorithm, Ant Colony optimisation algorithm and particle swarm optimisation algorithm. The simulation of path planning is also performed using Q-learning technique and the results are compared with the biologically-inspired algorithms. The results clearly show that the Q-learning algorithm is better in computational complexity than the biologically-inspired algorithms. The ease of simulating the environment is also more in the case of Q-learning techniques. Hence this paper presents an effective path planning technique, which has been tested for the SLOCUM glider and it may be extended for use in any standard autonomous underwater vehicle. Defence Science Journal, Vol. 65, No. 3, May 2015, pp.220-225, DOI: http://dx.doi.org/10.14429/dsj.65.7855 Ocean currents Heuristic Q-learning near-bottom ocean currents genetic algorithm ant colony optimisation algorithm Algorithms Simulation Autonomous underwater vehicles path planning AUV SLOCUM Glider particle swarm optimisation algorithm Simulation, path planning, AUV SLOCUM Glider, near-bottom ocean currents, Q-learning, genetic algorithm, ant colony optimisation algorithm, particle swarm optimisation algorithm Military Science Malmathanraj Ramanathan oth Enthalten in Defence science journal New Delhi : Centre, 1951 65(2015), 3, Seite 220-225 (DE-627)130313327 (DE-600)588844-X (DE-576)9130313325 0011-748X nnns volume:65 year:2015 number:3 pages:220-225 http://dx.doi.org/10.14429/dsj.65.7855 Volltext http://search.proquest.com/docview/1725480503 https://doaj.org/article/1be5aa29e2e74a74acfcda41c5c48345 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-POL SSG-OLC-TEC SSG-OLC-IBL GBV_ILN_11 GBV_ILN_70 89.00 AVZ AR 65 2015 3 220-225 |
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320 ZDB 89.00 bkl Simulation for Path Planning of SLOCUM Glider in Near-bottom Ocean Currents Using Heuristic Algorithms and Q-Learning Ocean currents Heuristic Q-learning near-bottom ocean currents genetic algorithm ant colony optimisation algorithm Algorithms Simulation Autonomous underwater vehicles path planning AUV SLOCUM Glider particle swarm optimisation algorithm Simulation, path planning, AUV SLOCUM Glider, near-bottom ocean currents, Q-learning, genetic algorithm, ant colony optimisation algorithm, particle swarm optimisation algorithm Military Science |
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Simulation for Path Planning of SLOCUM Glider in Near-bottom Ocean Currents Using Heuristic Algorithms and Q-Learning |
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Addressing the need for exploration of benthic zones utilising autonomous underwater vehicles, this paper presents a simulation for an optimised path planning from the source node to the destination node of the autonomous underwater vehicle SLOCUM Glider in near-bottom ocean environment. Near-bottom ocean current data from the Bedford Institute of Oceanography, Canada, have been used for this simulation. A cost function is formulated to describe the dynamics of the autonomous underwater vehicle in near-bottom ocean currents. This cost function is then optimised using various biologically-inspired algorithms such as genetic algorithm, Ant Colony optimisation algorithm and particle swarm optimisation algorithm. The simulation of path planning is also performed using Q-learning technique and the results are compared with the biologically-inspired algorithms. The results clearly show that the Q-learning algorithm is better in computational complexity than the biologically-inspired algorithms. The ease of simulating the environment is also more in the case of Q-learning techniques. Hence this paper presents an effective path planning technique, which has been tested for the SLOCUM glider and it may be extended for use in any standard autonomous underwater vehicle. Defence Science Journal, Vol. 65, No. 3, May 2015, pp.220-225, DOI: http://dx.doi.org/10.14429/dsj.65.7855 |
| abstractGer |
Addressing the need for exploration of benthic zones utilising autonomous underwater vehicles, this paper presents a simulation for an optimised path planning from the source node to the destination node of the autonomous underwater vehicle SLOCUM Glider in near-bottom ocean environment. Near-bottom ocean current data from the Bedford Institute of Oceanography, Canada, have been used for this simulation. A cost function is formulated to describe the dynamics of the autonomous underwater vehicle in near-bottom ocean currents. This cost function is then optimised using various biologically-inspired algorithms such as genetic algorithm, Ant Colony optimisation algorithm and particle swarm optimisation algorithm. The simulation of path planning is also performed using Q-learning technique and the results are compared with the biologically-inspired algorithms. The results clearly show that the Q-learning algorithm is better in computational complexity than the biologically-inspired algorithms. The ease of simulating the environment is also more in the case of Q-learning techniques. Hence this paper presents an effective path planning technique, which has been tested for the SLOCUM glider and it may be extended for use in any standard autonomous underwater vehicle. Defence Science Journal, Vol. 65, No. 3, May 2015, pp.220-225, DOI: http://dx.doi.org/10.14429/dsj.65.7855 |
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Addressing the need for exploration of benthic zones utilising autonomous underwater vehicles, this paper presents a simulation for an optimised path planning from the source node to the destination node of the autonomous underwater vehicle SLOCUM Glider in near-bottom ocean environment. Near-bottom ocean current data from the Bedford Institute of Oceanography, Canada, have been used for this simulation. A cost function is formulated to describe the dynamics of the autonomous underwater vehicle in near-bottom ocean currents. This cost function is then optimised using various biologically-inspired algorithms such as genetic algorithm, Ant Colony optimisation algorithm and particle swarm optimisation algorithm. The simulation of path planning is also performed using Q-learning technique and the results are compared with the biologically-inspired algorithms. The results clearly show that the Q-learning algorithm is better in computational complexity than the biologically-inspired algorithms. The ease of simulating the environment is also more in the case of Q-learning techniques. Hence this paper presents an effective path planning technique, which has been tested for the SLOCUM glider and it may be extended for use in any standard autonomous underwater vehicle. Defence Science Journal, Vol. 65, No. 3, May 2015, pp.220-225, DOI: http://dx.doi.org/10.14429/dsj.65.7855 |
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Simulation for Path Planning of SLOCUM Glider in Near-bottom Ocean Currents Using Heuristic Algorithms and Q-Learning |
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