Hucheng Chao
ABSTRACT
Indoor rock climbing, as a challenging and fun sport, has become more and more popular among sports enthusiasts in recent years, and in order to successfully complete the climb, it is necessary to select suitable routes and evaluate the difficulty of the routes. This study focuses on rock climbing route planning, evaluation and optimization. Canny edge detection is used to preprocess the image to obtain the coordinates of the rock point locations, K-means clustering is used to classify the rock points based on the rock point location-based model, DFS algorithm is used for data analysis, and multiple linear regression modelling is used to optimize the climbing routes using the A* algorithm by taking into account a number of key variables, including the weather, the temperature, and the density of the rock points, and to determine the best climbing route profile..
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