An energy-efficient hierarchical algorithm of dynamic obstacle avoidance for unmanned surface vehicle
Most of the existing studies developed and improved local path planning algorithms independently of global planning, i.e., ignoring the global optimal constrains. To meet the requirements of practical applications, this paper presented an energy-efficient hierarchical collision avoidance algorithm f...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-01-01
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| Series: | International Journal of Naval Architecture and Ocean Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2092678223000171 |
| _version_ | 1827400231635910656 |
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| author | Jiaqi Wang Shixin Li Boyang Li Chenyu Zhao Ying Cui |
| author_facet | Jiaqi Wang Shixin Li Boyang Li Chenyu Zhao Ying Cui |
| author_sort | Jiaqi Wang |
| collection | DOAJ |
| description | Most of the existing studies developed and improved local path planning algorithms independently of global planning, i.e., ignoring the global optimal constrains. To meet the requirements of practical applications, this paper presented an energy-efficient hierarchical collision avoidance algorithm for unmanned surface vehicle operating in clustered dynamic environments. For the global level, genetic algorithm was modified by strategies of greedy-inspired population initialization, penalty-based multi-objective fitness function, and joint crossover. For the local level, velocity obstacle was combined with dynamic window approach to provide the kinematic constraints of the vehicle to its admissible velocities and simplified collision avoidance rules to guide the evasive maneuvers. Simulations showed that the proposed global algorithm was superior to three other algorithms in terms of path length, path smoothness, and convergence speed regardless of the environment size. The performance of the local algorithm was also verified for various encounter scenarios and speed ratios. In addition, the combination of the global and local planning can effectively solve the path optimization and dynamic obstacle avoidance in a designed offshore environment of fish cage culture. |
| first_indexed | 2024-03-08T19:59:02Z |
| format | Article |
| id | doaj.art-4916e4e365d446c8a5d32ea5496adb2b |
| institution | Directory Open Access Journal |
| issn | 2092-6782 |
| language | English |
| last_indexed | 2024-03-08T19:59:02Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Naval Architecture and Ocean Engineering |
| spelling | doaj.art-4916e4e365d446c8a5d32ea5496adb2b2023-12-24T04:45:12ZengElsevierInternational Journal of Naval Architecture and Ocean Engineering2092-67822023-01-0115100528An energy-efficient hierarchical algorithm of dynamic obstacle avoidance for unmanned surface vehicleJiaqi Wang0Shixin Li1Boyang Li2Chenyu Zhao3Ying Cui4College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao, ChinaCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao, China; Corresponding author.College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao, ChinaCollege of Engineering, Mathematics and Physical Sciences, University of Exeter, Devon, UKCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao, China; Corresponding author.Most of the existing studies developed and improved local path planning algorithms independently of global planning, i.e., ignoring the global optimal constrains. To meet the requirements of practical applications, this paper presented an energy-efficient hierarchical collision avoidance algorithm for unmanned surface vehicle operating in clustered dynamic environments. For the global level, genetic algorithm was modified by strategies of greedy-inspired population initialization, penalty-based multi-objective fitness function, and joint crossover. For the local level, velocity obstacle was combined with dynamic window approach to provide the kinematic constraints of the vehicle to its admissible velocities and simplified collision avoidance rules to guide the evasive maneuvers. Simulations showed that the proposed global algorithm was superior to three other algorithms in terms of path length, path smoothness, and convergence speed regardless of the environment size. The performance of the local algorithm was also verified for various encounter scenarios and speed ratios. In addition, the combination of the global and local planning can effectively solve the path optimization and dynamic obstacle avoidance in a designed offshore environment of fish cage culture.http://www.sciencedirect.com/science/article/pii/S2092678223000171Unmanned surface vehicleGenetic algorithmVelocity obstacleGlobal path planningLocal path planning |
| spellingShingle | Jiaqi Wang Shixin Li Boyang Li Chenyu Zhao Ying Cui An energy-efficient hierarchical algorithm of dynamic obstacle avoidance for unmanned surface vehicle International Journal of Naval Architecture and Ocean Engineering Unmanned surface vehicle Genetic algorithm Velocity obstacle Global path planning Local path planning |
| title | An energy-efficient hierarchical algorithm of dynamic obstacle avoidance for unmanned surface vehicle |
| title_full | An energy-efficient hierarchical algorithm of dynamic obstacle avoidance for unmanned surface vehicle |
| title_fullStr | An energy-efficient hierarchical algorithm of dynamic obstacle avoidance for unmanned surface vehicle |
| title_full_unstemmed | An energy-efficient hierarchical algorithm of dynamic obstacle avoidance for unmanned surface vehicle |
| title_short | An energy-efficient hierarchical algorithm of dynamic obstacle avoidance for unmanned surface vehicle |
| title_sort | energy efficient hierarchical algorithm of dynamic obstacle avoidance for unmanned surface vehicle |
| topic | Unmanned surface vehicle Genetic algorithm Velocity obstacle Global path planning Local path planning |
| url | http://www.sciencedirect.com/science/article/pii/S2092678223000171 |
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