Optimal path planning in field based on traversability prediction for mobile robot

This paper presents a novel method on building relationship between the optimal path and the terrain traversability. some color and texture features are used for the input set to train a self learning function. The trained function is used for the traversability prediction. Considering the traveling smoothness of the field robot, the sub-regions with minimal original traversability is not the optimal path. The distance coefficient is suggested which is depending on the optimal subregion in the last searching row and the original traversability prediction is transformed to computed traversability prediction based on the distance coefficient. The pathes with different initial sub-regions is formed and the optimal path is picked up following the minimal sum of computed traversability prediction of all sub-regions in this path. And two experiments are shown and discussed to demonstrate the effectiveness and efficiency of the method mentioned in this paper.