A novel route planning method based on the RRT algorithm is proposed in this study. To enhance the structure of the state tree, a general-purpose pseudorandom number generator is inserted into RRT. In addition, the proposed method includes a distance restriction that helps reduce the number of possible candidate nodes. MATLAB has been utilized to examine the efficacy of the proposed technique using three different types of maps. The simulation results demonstrate that the revised method is advantageous for route planning due to its superior convergence and efficacy over the original.

This paper aims to develop an automatic 3D object segmentation method for the large-scale point clouds. Given a range image, the preprocessing is first applied to get the optimal 3D point cloud. A [Formula: see text]-nearest neighbor is built, and a segmentation algorithm based on the conditional angular clustering technique is used to segment the objects from the point cloud. The algorithm is tested on the real point cloud datasets. The experiment results demonstrated that the developed segmentation method can be used to localize the object with the relative uncertainty of 0.27%.

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