Path planning method for sugarcane harvesters based on remote sensing images
To solve the issues of high manual dependence and low autonomous navigation rates in sugarcane harvester operation in China,a path planning method based on remote sensing images was proposed.Firstly,an environmental model for remote sensing images of sugarcane fields was established through image processing techniques.Next,a search and length calculation for inter-field transition paths were implemented based on the Floyd algorithm.The length of transition paths was then used as an affinity evaluation index to improve the immune algorithm,which was used to optimise the traversal order of multiple fields,and solve the problen of global path planning for irregularly shaped sugarcane fields.The improved immune algorithm based on the Floyd algorithm has the fastest iterative evolution,the shortest algorithm convergence time,and it can best adapt to remote sensing images of sugarcane fields with varying complexities compared to other path planning algorithms.
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