Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles
Multi-sensor fusion for unmanned surface vehicles (USVs) is an important issue for autonomous navigation of USVs. In this paper, an improved particle swarm optimization (PSO) is proposed for real-time autonomous navigation of a USV in real maritime environment. To overcome the conventional PSO&#...
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doaj-188b07c6815c4798903c88d306f641d32020-11-25T01:13:25ZengMDPI AGSensors1424-82202019-07-011914309610.3390/s19143096s19143096Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface VehiclesJunfeng Xin0Shixin Li1Jinlu Sheng2Yongbo Zhang3Ying Cui4College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, ChinaCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, ChinaTransport College, Chongqing Jiaotong University, Chongqing 400074, ChinaQingdao National Marine Science Research Center, Qingdao 266071, ChinaCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, ChinaMulti-sensor fusion for unmanned surface vehicles (USVs) is an important issue for autonomous navigation of USVs. In this paper, an improved particle swarm optimization (PSO) is proposed for real-time autonomous navigation of a USV in real maritime environment. To overcome the conventional PSO’s inherent shortcomings, such as easy occurrence of premature convergence and human experience-determined parameters, and to enhance the precision and algorithm robustness of the solution, this work proposes three optimization strategies: linearly descending inertia weight, adaptively controlled acceleration coefficients, and random grouping inversion. Their respective or combinational effects on the effectiveness of path planning are investigated by Monte Carlo simulations for five TSPLIB instances and application tests for the navigation of a self-developed unmanned surface vehicle on the basis of multi-sensor data. Comparative results show that the adaptively controlled acceleration coefficients play a substantial role in reducing the path length and the linearly descending inertia weight help improve the algorithm robustness. Meanwhile, the random grouping inversion optimizes the capacity of local search and maintains the population diversity by stochastically dividing the single swarm into several subgroups. Moreover, the PSO combined with all three strategies shows the best performance with the shortest trajectory and the superior robustness, although retaining solution precision and avoiding being trapped in local optima require more time consumption. The experimental results of our USV demonstrate the effectiveness and efficiency of the proposed method for real-time navigation based on multi-sensor fusion.https://www.mdpi.com/1424-8220/19/14/3096travelling salesman problemparticle swarm optimizationparameter settingrandom grouping inversionunmanned surface vehiclemulti-sensor data |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Junfeng Xin Shixin Li Jinlu Sheng Yongbo Zhang Ying Cui |
spellingShingle |
Junfeng Xin Shixin Li Jinlu Sheng Yongbo Zhang Ying Cui Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles Sensors travelling salesman problem particle swarm optimization parameter setting random grouping inversion unmanned surface vehicle multi-sensor data |
author_facet |
Junfeng Xin Shixin Li Jinlu Sheng Yongbo Zhang Ying Cui |
author_sort |
Junfeng Xin |
title |
Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles |
title_short |
Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles |
title_full |
Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles |
title_fullStr |
Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles |
title_full_unstemmed |
Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles |
title_sort |
application of improved particle swarm optimization for navigation of unmanned surface vehicles |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2019-07-01 |
description |
Multi-sensor fusion for unmanned surface vehicles (USVs) is an important issue for autonomous navigation of USVs. In this paper, an improved particle swarm optimization (PSO) is proposed for real-time autonomous navigation of a USV in real maritime environment. To overcome the conventional PSO’s inherent shortcomings, such as easy occurrence of premature convergence and human experience-determined parameters, and to enhance the precision and algorithm robustness of the solution, this work proposes three optimization strategies: linearly descending inertia weight, adaptively controlled acceleration coefficients, and random grouping inversion. Their respective or combinational effects on the effectiveness of path planning are investigated by Monte Carlo simulations for five TSPLIB instances and application tests for the navigation of a self-developed unmanned surface vehicle on the basis of multi-sensor data. Comparative results show that the adaptively controlled acceleration coefficients play a substantial role in reducing the path length and the linearly descending inertia weight help improve the algorithm robustness. Meanwhile, the random grouping inversion optimizes the capacity of local search and maintains the population diversity by stochastically dividing the single swarm into several subgroups. Moreover, the PSO combined with all three strategies shows the best performance with the shortest trajectory and the superior robustness, although retaining solution precision and avoiding being trapped in local optima require more time consumption. The experimental results of our USV demonstrate the effectiveness and efficiency of the proposed method for real-time navigation based on multi-sensor fusion. |
topic |
travelling salesman problem particle swarm optimization parameter setting random grouping inversion unmanned surface vehicle multi-sensor data |
url |
https://www.mdpi.com/1424-8220/19/14/3096 |
work_keys_str_mv |
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