Hybrid Path Planning Using a Bionic-Inspired Optimization Algorithm for Autonomous Underwater Vehicles
This research presents a hybrid approach for path planning of autonomous underwater vehicles (AUVs). During path planning, static obstacles affect the desired path and path distance which result in collision penalties. In this study, the merits of grey wolf optimization (GWO) and genetic algorithm (...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/11/4/761 |
| _version_ | 1827744812229459968 |
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| author | Sarada Prasanna Sahoo Bikramaditya Das Bibhuti Bhusan Pati Fausto Pedro Garcia Marquez Isaac Segovia Ramirez |
| author_facet | Sarada Prasanna Sahoo Bikramaditya Das Bibhuti Bhusan Pati Fausto Pedro Garcia Marquez Isaac Segovia Ramirez |
| author_sort | Sarada Prasanna Sahoo |
| collection | DOAJ |
| description | This research presents a hybrid approach for path planning of autonomous underwater vehicles (AUVs). During path planning, static obstacles affect the desired path and path distance which result in collision penalties. In this study, the merits of grey wolf optimization (GWO) and genetic algorithm (GA) of bionic-inspired algorithms are integrated to implement a hybrid grey wolf optimization (HGWO) algorithm which allows AUVs to reach their destination safely in an obstacle rich environment. The proposed hybrid path planner is employed for path planning of a single AUV based on collision avoidance. It uses the GA as an initialization generator to overcome the random initialization problem of GWO. In this research, the total cost is considered to be a function of path distance and collision penalties. Further, the application of the proposed hybrid path planner is extended for cooperative path planning of AUVs while avoiding collision using communication consensus. Simulation results are obtained for both a single AUV and multiple AUV path planning in a 3D obstacle rich environment using a proportional-derivative controller. The Kruskal–Wallis test is employed for a non-parametric statistical analysis, where the independence of the results given by the algorithms is demonstrated. |
| first_indexed | 2024-03-11T04:52:23Z |
| format | Article |
| id | doaj.art-ca41168ee66947ad8e1fc34adbf04e8d |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-11T04:52:23Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-ca41168ee66947ad8e1fc34adbf04e8d2023-11-17T19:55:40ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-03-0111476110.3390/jmse11040761Hybrid Path Planning Using a Bionic-Inspired Optimization Algorithm for Autonomous Underwater VehiclesSarada Prasanna Sahoo0Bikramaditya Das1Bibhuti Bhusan Pati2Fausto Pedro Garcia Marquez3Isaac Segovia Ramirez4Department of Electrical Engineering, VSSUT, Burla 768018, IndiaDepartment of Electronics and Telecommunication Engineering, VSSUT, Burla 768018, IndiaDepartment of Electrical Engineering, VSSUT, Burla 768018, IndiaIngenium Research Group, Universidad Castilla-La Mancha, Campus Universitario s/n, 13071 Ciudad Real, SpainIngenium Research Group, Universidad Castilla-La Mancha, Campus Universitario s/n, 13071 Ciudad Real, SpainThis research presents a hybrid approach for path planning of autonomous underwater vehicles (AUVs). During path planning, static obstacles affect the desired path and path distance which result in collision penalties. In this study, the merits of grey wolf optimization (GWO) and genetic algorithm (GA) of bionic-inspired algorithms are integrated to implement a hybrid grey wolf optimization (HGWO) algorithm which allows AUVs to reach their destination safely in an obstacle rich environment. The proposed hybrid path planner is employed for path planning of a single AUV based on collision avoidance. It uses the GA as an initialization generator to overcome the random initialization problem of GWO. In this research, the total cost is considered to be a function of path distance and collision penalties. Further, the application of the proposed hybrid path planner is extended for cooperative path planning of AUVs while avoiding collision using communication consensus. Simulation results are obtained for both a single AUV and multiple AUV path planning in a 3D obstacle rich environment using a proportional-derivative controller. The Kruskal–Wallis test is employed for a non-parametric statistical analysis, where the independence of the results given by the algorithms is demonstrated.https://www.mdpi.com/2077-1312/11/4/761autonomous underwater vehicle (AUV)cooperative path planninggenetic algorithm (GA)grey wolf optimization (GWO)proportional-derivative controller |
| spellingShingle | Sarada Prasanna Sahoo Bikramaditya Das Bibhuti Bhusan Pati Fausto Pedro Garcia Marquez Isaac Segovia Ramirez Hybrid Path Planning Using a Bionic-Inspired Optimization Algorithm for Autonomous Underwater Vehicles Journal of Marine Science and Engineering autonomous underwater vehicle (AUV) cooperative path planning genetic algorithm (GA) grey wolf optimization (GWO) proportional-derivative controller |
| title | Hybrid Path Planning Using a Bionic-Inspired Optimization Algorithm for Autonomous Underwater Vehicles |
| title_full | Hybrid Path Planning Using a Bionic-Inspired Optimization Algorithm for Autonomous Underwater Vehicles |
| title_fullStr | Hybrid Path Planning Using a Bionic-Inspired Optimization Algorithm for Autonomous Underwater Vehicles |
| title_full_unstemmed | Hybrid Path Planning Using a Bionic-Inspired Optimization Algorithm for Autonomous Underwater Vehicles |
| title_short | Hybrid Path Planning Using a Bionic-Inspired Optimization Algorithm for Autonomous Underwater Vehicles |
| title_sort | hybrid path planning using a bionic inspired optimization algorithm for autonomous underwater vehicles |
| topic | autonomous underwater vehicle (AUV) cooperative path planning genetic algorithm (GA) grey wolf optimization (GWO) proportional-derivative controller |
| url | https://www.mdpi.com/2077-1312/11/4/761 |
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