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Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles

1
College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China
2
Transport College, Chongqing Jiaotong University, Chongqing 400074, China
3
Qingdao National Marine Science Research Center, Qingdao 266071, China
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(14), 3096; https://doi.org/10.3390/s19143096
Received: 14 June 2019 / Revised: 8 July 2019 / Accepted: 11 July 2019 / Published: 13 July 2019
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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Abstract

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. View Full-Text
Keywords: travelling salesman problem; particle swarm optimization; parameter setting; random grouping inversion; unmanned surface vehicle; multi-sensor data travelling salesman problem; particle swarm optimization; parameter setting; random grouping inversion; unmanned surface vehicle; multi-sensor data
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Xin, J.; Li, S.; Sheng, J.; Zhang, Y.; Cui, Y. Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles. Sensors 2019, 19, 3096.

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