Abstract
SLAM (Simultaneous Localization and Mapping) and autonomous navigation systems, as fundamental components of mobile robots, largely determine their ability to accomplish tasks. While research on laser-based SLAM and autonomous navigation algorithms in simulated environments has achieved significant success, there is limited research on deploying these algorithms on physical mobile robots, conducting real-world experiments to provide practical data for validating the accuracy and applicability of theoretical models.
This paper begins by presenting the system architecture of the mobile robot used in this study. Subsequently, it delves into the research on SLAM algorithms, analyzing and selecting the Gmapping algorithm as the solution to the SLAM problem. Furthermore, the paper introduces the research on autonomous navigation, providing a detailed explanation of the principles behind the A* global path planning algorithm combined with the Dynamic Window Approach (DWA) for local path planning. Finally, by controlling the mobile robot through a distributed communication network, the paper tests the performance of the mobile robot, constructs maps in a real indoor environment, achieving a relative map accuracy error of less than 3%, thus validating the effectiveness of the Gmapping algorithm for practical mapping. Autonomous navigation tasks are also performed, testing the mobile robot's local obstacle avoidance capabilities using temporary obstacles.
Overall, this research demonstrates that the designed mobile robot can effectively accomplish SLAM and autonomous navigation tasks. The results of this study can also assist beginners in rapidly and clearly building physical mobile robots and deploying SLAM systems.
Export citation and abstract BibTeX RIS
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.