Autonomous Cross-Floor Navigation System for a ROS-Based Modular Service Robot

In this paper, we present an autonomous cross-floor navigation system including mapping, localization, path planning, and scene recognition based on robot operating system (ROS) architecture. The Gmapping algorithm is utilized to build a 2D map with a laser range-finder, and AMCL algorithm is utilized in the robot localization. Moreover, an improved A* algorithm is proposed to prevent robot from getting too close to the wall. Because our robot needs to navigate in the multi-floor environment, a decision system using deep convolutional neural network (DCNN) is also designed to recognize the current floor and the associated map can be download to the robot system. By training with the scene images of the featured location in each floor, the robot can recognize the current floor and then complete the navigation task. Finally, real test of our robot is conducted to demonstrate the feasibility of the proposed method.