Reconfigurable Pavement Sweeping Robot and Pedestrian Cohabitant Framework by Vision Techniques

Abstract

The periodic cleaning pavement is a requirement for a clean environment in urban areas. The robot named Panthera introduced in this paper has the ability to change the width of the frame to help the cleaning tasks become suitable for different pavement types and friendly with the activities of the pedestrian. The Panthera cleaning operations which allow pedestrian walks freely on the pavement is modeled as a pedestrian robot cohabitant framework. To this end, the mask based deep convolutional neural network (DCNN) is used to archive segmented label maps of pedestrians in color image, then the distance from detected objects to the robot is estimated and tracked by averaging filtered depth values in the corresponding region in the refined depth image. The width and the distance from the robot to the approaching pedestrians are used to adjust the robot width. The enlarging and squeezing operations of the Panthera width are conducted by rotating one motor to change the length of the lead screw rod and the angle linkage hinges from the information of encoders sensors. The experiments carried out on real environments demonstrated the autonomous avoiding pedestrians ability by the kinematic model of Panthera.