A Map Segmentation Method Based on Image Processing for Robot Complete Coverage Operation

Abstract

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.