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Wang Xianpeng,Ma Xinglu,Li Xiaoxu,Ma Xiaoyu,Li Chunxu 한국CDE학회 2022 Journal of computational design and engineering Vol.9 No.2
Aiming at the problem that the progressively optimized Rapidly-exploring Random Trees Star (RRT*) algorithm generates a large number of redundant nodes, which causes slow convergence and low search efficiency in high-dimensional and complex environments. In this paper we present Target-biased Informed Trees (TBIT*), an improved RRT* path planning algorithm based on target-biased sampling strategy and heuristic optimization strategy. The algorithm adopts a combined target bias strategy in the search phase of finding the initial path to guide the random tree to grow rapidly toward the target direction, thereby reducing the generation of redundant nodes and improving the search efficiency of the algorithm; after the initial path is searched, heuristic sampling is used to optimize the initial path instead of optimizing the random tree, which can benefit from reducing useless calculations, and improve the convergence capability of the algorithm. The experimental results show that the algorithm proposed in this article changes the randomness of the algorithm to a certain extent, and the search efficiency and convergence capability in complex environments have been significantly improved, indicating that the improved algorithm is feasible and efficient.
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A Novel TDOA-Based Localization Algorithm Using Sequential Quadratic Programming
Xianpeng Liu,Yi Huang,Jin Wang 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.11
Linear algebraic equations based methods in the time difference of arrival (TDOA) localization are akind of excellent algorithms. In consideration of the constraints on parameters to be estimated, the first-order error in matrix and the second-order noise in vector, a sequential-quadratic-programming-based localization algorithm isproposed in this paper. An optimal solution of the linear equation model was obtained in the algorithm. Meanwhile, with the rank 1 constraint ignored, the problem was rewritten to a series of convex functions and then a semi-definite relaxation (SDR) solution was yielded additionally. However, the solution can only be used as an initial value for other TDOA localization algorithms due to the insufficient accuracy of the SDR solution. Final localization experiments demonstrate that the proposed algorithm has a higher positioning accuracy compared with other TDOA localization algorithms based on linear model. At the same time the result can be closer to the Cramer-Rao Lower Bound, especially at the low signal to noise ratio regime.
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A Research about Adaptive Subdivision Algorithm Based On Doo-Sabin Mode
Xumin Liu,Yongxiu Xu,Xianpeng Yang,Xiaojun Wang 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.2
Subdivision surface method is a series of iterative operation adopts a certain subdivision formula for an initial grid, obtains the smooth limits surface finally, and can dispose any arbitrary complex topology grid. At present most of the subdivision algorithm are 1-4 subdivisions and as the number of subdivision increase, the grid grow so too-rapid in the number of patch that it is difficult for the model after subdivision to deal with other things. We proposed an adaptive Doo-Sabin Mode subdivision algorithm to solve this problem, which take the average vector of the vertex and the angle between the intersecting surfaces of the vertex as a measurement criterion. This criterion is used to divide the surface, and then make local subdivision. In this way, when the times of subdivision are fewer (the demand of smoothness is not too high), the effect of subdivision has little difference, but efficiency of the algorithm can be greatly improved. Compared with the normal Doo-Sabin subdivision model, experimental results showed that adaptive Doo-Sabin subdivision algorithm can largely slow the growth speed of the amount of model data on the premise that guarantee the quality of surface.
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