Highly Autonomous Visualization Map-Generation Mobile Robot System Design Through the Robot Operating System Platform

摘要

This article proposes highly autonomous map generation and path navigation based on the Robot Operating System (ROS) platform. The mobile robot concurrently completes visualized map generation and path navigation even in an unknown environment. Autonomous visualization robot systems combine the Simultaneous Localization and Mapping (SLAM) and dynamic search techniques to self-drive to any desired target. The Hector SLAM is applied with only one LiDAR to continuously extract high-accuracy information from grid maps of neighboring environments. Due to the related robot radius, the grid maps are flexibly approximated by weighted scalar formulas. Then, the novel hybrid neighboring and global path planning is determined to achieve the appropriate position for fitting mobile robot navigation applications. In neighborhood search, the A* algorithm first explores the shortest path selection between robot and target with the perceptual information of the LiDAR. Global path selection with the dynamic window approach (DWA) is applied to improve the previous neighborhood search of the A* algorithm. The DWA accurately predicts all possible moving paths and chooses the best path planning. The mobile robot follows the shortest path and avoids obstacles to achieve the appropriate target. Based on repeated executions, the mobile robot explores its neighboring block and updates into global maps. The global path-planning scheme is restarted if the robot finds obstacles. This strategy allows robots to fit the appropriate maps, and to quickly react and effectively avoid the danger when they encounter some unexpected conditions. Several mobile robot navigation experiments illustrate that the autonomous path-planning and self-localization abilities can achieve the desired goals through the support of the flexible ROS platform. It is expedient to rebuild the visualized maps for the appropriate mobile robot applications even in unknown, unusual and complicated environments. (C) 2018 Society for Imaging Science and Technology.