LSDA-APF: A Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on 5G Communication Environment
In view of the complex marine environment of navigation, especially in the case of multiple static and dynamic obstacles, the traditional obstacle avoidance algorithms applied to unmanned surface vehicles (USV) are prone to fall into the trap of local optimization. Therefore, this paper proposes an...
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| Published in: | Computer modeling in engineering & sciences Vol. 138; no. 1; pp. 595 - 617 |
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| Abstract | In view of the complex marine environment of navigation, especially in the case of multiple static and dynamic obstacles, the traditional obstacle avoidance algorithms applied to unmanned surface vehicles (USV) are prone to fall into the trap of local optimization. Therefore, this paper proposes an improved artificial potential field (APF) algorithm, which uses 5G communication technology to communicate between the USV and the control center. The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios. Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks, the algorithm introduces the concept of dynamic artificial potential field. For the multiple obstacles encountered in the process of USV sailing, based on the International Regulations for Preventing Collisions at Sea (COLREGS), the USV determines whether the next step will fall into local optimization through the discrimination mechanism. The local potential field of the USV will dynamically adjust, and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions. The objective function and cost function are designed at the same time, so that the USV can smoothly switch between the global path and the local obstacle avoidance. The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment, and take navigation time and economic cost into account. |
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| AbstractList | In view of the complex marine environment of navigation, especially in the case of multiple static and dynamic obstacles, the traditional obstacle avoidance algorithms applied to unmanned surface vehicles (USV) are prone to fall into the trap of local optimization. Therefore, this paper proposes an improved artificial potential field (APF) algorithm, which uses 5G communication technology to communicate between the USV and the control center. The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios. Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks, the algorithm introduces the concept of dynamic artificial potential field. For the multiple obstacles encountered in the process of USV sailing, based on the International Regulations for Preventing Collisions at Sea (COLREGS), the USV determines whether the next step will fall into local optimization through the discrimination mechanism. The local potential field of the USV will dynamically adjust, and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions. The objective function and cost function are designed at the same time, so that the USV can smoothly switch between the global path and the local obstacle avoidance. The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment, and take navigation time and economic cost into account. |
| Author | Ma, Jinfeng Duan, Dongxing Jiao, Tongtong Li, Xiaoli Liang, Shengbin |
| Author_xml | – sequence: 1 givenname: Xiaoli surname: Li fullname: Li, Xiaoli – sequence: 2 givenname: Tongtong surname: Jiao fullname: Jiao, Tongtong – sequence: 3 givenname: Jinfeng surname: Ma fullname: Ma, Jinfeng – sequence: 4 givenname: Dongxing surname: Duan fullname: Duan, Dongxing – sequence: 5 givenname: Shengbin surname: Liang fullname: Liang, Shengbin |
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| Cites_doi | 10.1109/ACCESS.2019.2907783 10.1016/j.apor.2020.102230 10.1109/TII.2020.2995598 10.1016/j.oceaneng.2022.111972 10.1016/j.oceaneng.2019.05.017 10.1007/978-1-4757-1895-9_26 10.3390/electronics11172720 10.1080/00207721.2014.929191 10.1016/j.oceaneng.2022.112525 10.1155/2021/1609612 10.3390/ijgi10090618 10.3390/machines10080707 10.1080/00207160.2010.527002 10.3390/app11146532 17914238 10.1177/027836499801700706 10.1109/100.580977 10.1109/70.88137 10.1109/IROS.2006.282649 10.1002/rob.21935 10.1109/JOE.2013.2254214 10.1155/2020/3095426 10.1016/j.oceaneng.2018.10.008 |
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| SubjectTerms | Algorithms Collisions Cost function Economic impact Local optimization Marine environment Navigation Obstacle avoidance Optimization Potential fields Surface vehicles Unmanned vehicles |
| Title | LSDA-APF: A Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on 5G Communication Environment |
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