An Improved Localization of Mobile Robotic System Based on AMCL Algorithm

• Published in: IEEE sensors journal Vol. 22; no. 1; pp. 900 - 908
• Main Authors: Chung, Ming-An, Lin, Chia-Wei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Algorithms; COVID-19; indoor localization; Localization; Location awareness; Medical services; mobile robot; Mobile robots; Monte Carlo localization; Monte Carlo methods; particle filter; Range finders; Robotics; Robots; ROS; Sensors
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2021.3126605

In the Mobile Robotics domain, the ability of robots to locate themselves is one of the most important events. By locating, mobile robots can obtain information about the environment and continuously track their position and direction. Among localization algorithms, the Adaptive Monte Carlo Localization (AMCL) algorithm is applied most often in robot localization, a two-dimensional environment probabilistic localization system to improve the problems such as high computational complexity and hijacking of mobile robots that exist in the traditional MCL method. The proposed method is based on 2D laser information, range finder information, and AMCL to accomplish the localization task. Furthermore, an optimized AMCL algorithm is proposed to increase the accuracy of localization in terrain that is easy to fail to locate, have a chance to locate successfully when a localization error occurs, and apply the optimized AMCL to the mobile robot system. From the experimental results, we know that the improved AMCL algorithm can enhance the positioning accuracy of the robot effectively, which has better practicality than the original AMCL.