| Author Name | Affiliation | Postcode | | Jiang Lin | Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Wuhan 430081, China | 430081 | | Han Wang | Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Wuhan 430081, China | 430081 | | Bin Lei* | Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Wuhan 430081, China | 430081 | | Jianyang Zhu | Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Wuhan 430081, China | 430081 | | Huaiguang Liu | Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Wuhan 430081, China | 430081 | | Hui Zhao | Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology, Wuhan 430081, China | 430081 |
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| Abstract: |
| In order to solve the problem of localization loss that an autonomous mobile robot may encounter in indoor environment, an improved Monte Carlo localization algorithm is proposed in this paper. The algorithm can identify the state of the robot by real-time monitoring of the mean weight changes of the particles and introduce more high-weight particles through the divergent sampling function when the robot is in the state of localization loss. The observation model will make the particle set slowly approach to the real position of the robot and the new particles are then sampled to reach the position. The loss self-recovery experiments of different algorithms under different experimental scenarios are presented in this paper. |
| Key words: indoor mobile robot self-recovery localization loss improved Monte Carlo localization algorithm |
| DOI：10.11916/j.issn.1005-9113.18116 |
| Clc Number:TP24 |
| Fund: |
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| Descriptions in Chinese: |
| 针对自主移动机器人在室内环境中可能遇到的定位失效情况，提出了一种改进的蒙特卡罗定位算法。该算法可以通过实时监测粒子的均权重的变化来确定机器人的状态。当机器人处于定位失效时，可以通过发散重采样函数产生更多高权重的粒子。同时，观测模型将使粒子集慢慢接近机器人的真实位置，然后对新粒子进行采样收敛到机器人的实际位置。最后，验证了不同算法在不同实验场景下的定位失效自恢复实验，验证了本文提出的改进蒙特卡罗定位算法的有效性。 |
