Due to the complex constraints, more uncertain factors and critical real-time demand of path planning for multiple unmanned surface vehicle (multi-USV), an improved artificial bee colony (I-ABC) algorithm were proposed to solve the model of cooperative path planning for multi-USV. First the Voronoi diagram of battle field space is conceived to generate the optimal area of USVs paths. Then the chaotic searching algorithm is used to initialize the collection of paths, which is regard as foods of the ABC algorithm. With the limited data, the initial collection can search the optimal area of paths perfectly. Finally simulations of the multi-USV path planning under various threats have been carried out. Simulation results verify that the I-ABC algorithm can improve the diversity of nectar source and the convergence rate of algorithm. It can increase the adaptability of dynamic battlefield and unexpected threats for USV.
With the wide application of machine vision technology in agricultural fields, the image-based pests diagnosis of rice planthoppers becomes a fast and effective approach. Although the effective automatic segmentation is a very important pretreatment technology for the analysis of rice planthopper images, the traditional graph cuts based active contour method has the shrinking bias problem during segmentation. This paper proposes an innovative approach to overcome that problem. By changing bidirection dilation of the contours to inside direction dilation to improve the overlap of adjacent contour neighborhoods and reduce the computation scale, the shrinking bias problem is improved effectively. The result shows that the approach adopted in this paper can clearly segment the contour of rice planthoppers.
Due to the complex constraints, more uncertain factors and critical real-time demand of path planning for USV(Unmanned Surface Vehicle), an approach of fast path planning based on voronoi diagram and improved Genetic Algorithm is proposed, which makes use of the principle of hierarchical path planning. First the voronoi diagram is utilized to generate the initial paths and then the optimal path is searched by using the improved Genetic Algorithm, which use multiprocessors parallel computing techniques to improve the traditional genetic algorithm. Simulation results verify that the optimal time is greatly reduced and path planning based on voronoi diagram and the improved Genetic Algorithm is more favorable in the real-time operation.