Journal of Intelligent and Robotic Systems: Theory and Applications
Cleaning multi-storey buildings need to be considered while developing autonomous service robots. In this paper, we introduce a novel reconfigurable platform called sTetro with the abilitiesto navigate on the floor as well as to detect then climb the staircase autonomously. To this end, an operational framework for this cleaning robot that leverages on customized deep convolution neural network (DCNN) and the RGBD camera to locate staircases in the 3D prebuilt map and then to plan trajectories by maximizing area coverage for both floor and staircase in the multi-storey environments is proposed. While building a 3D map, the staircase location is identified at the 3D point close to the center of the staircase first step using a contour detection algorithm from the boundary of the detected staircase by DCNN. The robot follows the planned trajectory to clear the floor then approaching the staircase location accurately to execute the climbing mode while cleaning the staircase to reach the next floor. The proposed methods archive the high accuracy in identifying the presence of the different staircase types, and the first step locations. Moreover, the multi-storey building evaluations have demonstrated the efficiency of the sTetro in terms of the area coverage both staircase and floor free space.