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.
Highlights
It is well known that multi-sensor fusion is an important issue for autonomous navigation of unmanned vehicles, especially when operating in real environments with unanticipated changes
Three algorithms with progressively improved strategies are proposed based on the conventional particle swarm optimization (CPSO): the algorithm with adaptively controlled acceleration coefficients (APSO), the algorithm with both adaptively controlled acceleration coefficients and linearly descending inertia weight (AWPSO), and the algorithm combining the advantages of adaptively controlled acceleration coefficients, linearly descending inertia weight, and random grouping inversion (AWIPSO)
Multi-sensor fusion is an important issue for autonomous navigation of unmanned surface vehicles (USVs)
Summary
It is well known that multi-sensor fusion is an important issue for autonomous navigation of unmanned vehicles, especially when operating in real environments with unanticipated changes. In order to avoid premature convergence, route self-crossing, and to enhance the robustness, this work proposes three improved algorithms on the basis of the PSO method by combining one or two optimization strategies as follows: linearly descending inertia weight, adaptively controlled acceleration coefficients, and random grouping inversion. The main contributions of this work are as follows: (1) The important parameters, including the acceleration coefficients and inertia weight, are adjusted iteratively, with the aim of effectively reducing the path length and enhancing the robustness; (2) The strategy of random grouping inversion maintains the swarm diversity and accelerates the global convergence, which can avoid premature convergence and retain solution precision; (3) Path planning for a USV is conducted by combining the conventional PSO with the three optimization strategies, which generates feasible routes with satisfactory length and no self-crossing.
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