Mowing robot path planning method based on grid map

Abstract

The invention provides a mowing robot path planning method based on a grid map. The method comprises the following steps: building an initial grid map with the lateral contour size of a robot as the side length, and solving a grid refinement dimension; judging a distance between adjacent obstacles in the rasterized map, and when a preset condition is met, taking grids occupied by the obstacles asa basis, conducting expanding outwards by a grid size, taking an expanded area as a buffer area, wherein the expanded area is a grid refining area, and conducting grid refining according to the grid refinement dimension; calculating the multiple N of the lateral overall dimension to the grid refinement dimension, enabling M to be equal to int(N / 2), and outwardly expanding the refined grids occupied by the obstacles by M grids; and judging whether the mowing robot is in the buffer area or not after a starting point and a terminal point are appointed, and planning a next path based on the initial grid or a refined raster map until the terminal point is reached. According to the method, path calculation resources are saved, mowing coverage after path planning is improved, and mowing efficiency and mowing quality are improved.

Description

technical field [0001] The invention belongs to the field of intelligent mowing robot control, and in particular relates to a path planning method for a mowing robot based on a grid map. Background technique [0002] When an intelligent mowing robot works in an outdoor environment, it usually uses a gridded map of the working lawn to guide the robot's travel. At present, image pixels are widely used to create the 01 matrix of the grid map, but the 01 matrix of the grid map created directly with the image pixels is too large, which will cause a significant decrease in the calculation speed. Therefore, the theory of block matrix will be adopted Perform block operation on the 01 matrix to obtain a grid map that can be used by the mowing robot. [0003] In the prior art, due to the different sizes and shapes of lawns and various obstacles, it is difficult to create a "high-quality" grid map in such a complex environment. Based on the block theory to rasterize the map, there is...

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Problems solved by technology

Similarly, when the lawn mowing robot is planning a path with full coverage in the area, since the grid map identifies the area between obstacle A and obstacle B as an impassable area, the path planned for full coverage of the area will not pass through this area, resulting in Mowing robots cannot achieve true full coverage of this area

[0005] Therefore, in the mowing path planning of the intelligent robot in the prior art, it is impossible to achieve the optimal balance between low computing resource consumption and maximum efficiency traversal planning strategy

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