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A Map Segmentation Method Based on Image Processing for Robot Complete Coverage Operation
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AI
DOI：10.1002/rob.22504.png)
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Abstract
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Path planning is crucial for autonomous robot navigation and operation. Tasks like cleaning, inspection, and mining, all require complete coverage operation. For maps of convex regions, a reciprocating coverage method can be used. However, for maps of concave shapes, it is unsuitable. For this purpose, this paper proposes an image-based map segmentation method for complete coverage path planning. Taking the grip map as an image, it is used to divide a concave map into convex subregions. For each convex region, it will generate a batch of waypoints for the robot controller. The subregions are then connected to achieve a complete coverage of the entire region. On the basis of a global path planning, a local path following, and real-time obstacle avoidance methods, the complete coverage operation is achieved. Moreover, a coverage ratio calculation method is proposed and shown real-timely in a visual interface. Extensive experiments in simulations and real-world environments demonstrate the effectiveness of this method, achieving an average coverage ratio of 97.89% and a maximum of 92.19% in the presence of obstacles. Most importantly, this method has been successfully tested on an autonomous mining vehicle, achieving an average coverage ratio of 96% in given maps.
Keywords:
complete coverage path planning
image processing
map segmentation
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