Xin Junfeng, Li Shixin, Sheng Jinlu, Zhang Yongbo, Cui Ying
College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China.
Transport College, Chongqing Jiaotong University, Chongqing 400074, China.
Sensors (Basel). 2019 Jul 13;19(14):3096. doi: 10.3390/s19143096.
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.
无人水面舰艇（USV）的多传感器融合是其自主导航的一个重要问题。本文提出一种改进的粒子群优化算法（PSO），用于无人水面舰艇在真实海洋环境中的实时自主导航。为克服传统粒子群优化算法存在的易早熟收敛以及参数依赖人为经验等固有缺点，提高求解精度和算法鲁棒性，提出线性递减惯性权重、自适应控制加速系数和随机分组变异三种优化策略。通过对五个TSPLIB实例的蒙特卡洛仿真以及基于多传感器数据的自主研发无人水面舰艇导航应用测试，研究它们各自或组合对路径规划有效性的影响。对比结果表明，自适应控制加速系数在缩短路径长度方面发挥着重要作用，线性递减惯性权重有助于提高算法鲁棒性。同时，随机分组变异通过将单一群体随机划分为若干子群体，优化了局部搜索能力并保持了种群多样性。此外，结合所有三种策略的粒子群优化算法表现出最佳性能，轨迹最短且鲁棒性优越，尽管保持求解精度并避免陷入局部最优需要更多时间消耗。无人水面舰艇的实验结果证明了所提基于多传感器融合的实时导航方法的有效性和高效性。
