Abstract
In this study, image processing was combined with path-planning object-avoidance technology to determine the shortest path to the destination. The content of this article comprises two parts: in the first part, image processing was used to establish a model of obstacle distribution in the environment, and boundary sequence permutation method was used to conduct orderly arrangement of edge point coordinates of all objects, to determine linking relationship between each edge point, and to individually classify objects in the image. Then, turning point detection method was used to compare the angle size between vectors before and after each edge point and to determine vertex coordinates of polygonal obstacles. In the second part, a modified Dijkstra’s algorithm was used to turn vertices of convex-shaped obstacles into network nodes, to determine the shortest path by a cost function, and to find an obstacle avoidance path connecting the start and end points. In order to verify the feasibility of the proposed architecture, an obstacle avoidance path simulation was made by the graphical user interface of the programming language MATLAB. The results show that the proposed method in path planning not only is feasible but can also obtain good results.
Highlights
Mobile robot navigation involves finding a reasonable path in a limited working environment, to connect the initial configuration to the final configuration, and successful avoidance of the obstacle
Some recent literature works1–6 have discussed about this issue, and how to use image processing techniques to create an obstacle distribution model plays a key role in such matters
The turning point detection methods discussed in the literature can be broadly divided into two types: edge-based shape detection methods, which include Medioni– Yasumoto’s method,7 Beus–Tiu’s method,8 Rosenfeld– Johnston’s method,9 Rosenfeld–Weszka’s method,10 and weight type k-curvature method;11 these methods calculate the curvature value at each point of the edge coordinates after edge detection processing
Summary
Mobile robot navigation involves finding a reasonable path in a limited working environment, to connect the initial configuration (including location point and azimuth angle) to the final configuration, and successful avoidance of the obstacle. If the edge point pixel nj of the jth object is below threshold value, that is, nj\Tb, it is regarded as image noise. The eight-neighbor searching algorithm sequentially detects whether ‘‘1, 2, ., 8’’ are edge point coordinates (or foreground pixels) of that object. Rosenfeld–Johnston’s method was adopted in this study to detect the turning point in the image by comparing the angles between edge point vectors of each object to determine the position of the turning point. Given a two-dimensional smoothing curve S(t) = 1⁄2x(t), y(t) expressed with time parameter, curvature radius of each point in the curve can be expressed as In this way, the minimum turning radius specifications can be used to design the width of the obstacle safety boundary
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