Greedy Mechanism Based Particle Swarm Optimization for Path Planning Problem of an Unmanned Surface Vehicle.

Recently, issues of climate change, environment abnormality, individual requirements, and national defense have caused extensive attention to the commercial, scientific, and military development of unmanned surface vehicles (USVs). In order to design high-quality routes for a multi-sensor integrated USV, this work improves the conventional particle swarm optimization algorithm by introducing the greedy mechanism and the 2-opt operation, based on a combination strategy. First, a greedy black box is established for particle initialization, overcoming the randomness of the conventional method and excluding a great number of infeasible solutions. Then the greedy selection strategy and 2-opt operation are adopted together for local searches, to maintain population diversity and eliminate path crossovers. In addition, Monte-Carlo simulations of eight instances are conducted to compare the improved algorithm with other existing algorithms. The computation results indicate that the improved algorithm has the superior performance, with the shortest route and satisfactory robustness, although a fraction of computing efficiency becomes sacrificed. Moreover, the effectiveness and reliability of the improved method is also verified by its multi-sensor-based application to a USV model in real marine environments.

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'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.

An Improved Genetic Algorithm for Path-Planning of Unmanned Surface Vehicle.

The genetic algorithm (GA) is an effective method to solve the path-planning problem and help realize the autonomous navigation for and control of unmanned surface vehicles. In order to overcome the inherent shortcomings of conventional GA such as population premature and slow convergence speed, this paper proposes the strategy of increasing the number of offsprings by using the multi-domain inversion. Meanwhile, a second fitness evaluation was conducted to eliminate undesirable offsprings and reserve the most advantageous individuals. The improvement could help enhance the capability of local search effectively and increase the probability of generating excellent individuals. Monte-Carlo simulations for five examples from the library for the travelling salesman problem were first conducted to assess the effectiveness of algorithms. Furthermore, the improved algorithms were applied to the navigation, guidance, and control system of an unmanned surface vehicle in a real maritime environment. Comparative study reveals that the algorithm with multi-domain inversion is superior with a desirable balance between the path length and time-cost, and has a shorter optimal path, a faster convergence speed, and better robustness than the others.