| 焦传佳,江 明,徐劲松,张 刚,孙龙龙,童胜杰,徐印赟.基于激光信息的移动机器人定位研究[J].电子测量与仪器学报,2021,35(9):1-9 |
| 基于激光信息的移动机器人定位研究 |
| Research on positioning of mobile robot based on laser information |
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| DOI： |
| 中文关键词: 激光信息 重定位 粒子滤波 划分 移动机器人 |
| 英文关键词:laser information relocation particle filter divide mobile robot |
| 基金项目:国家自然科学基金（61271377）、徐州市重点研发计划（KC18079）项目资助 |
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| Author | Institution |
| Jiao Chuanjia | 1. Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, Anhui Polytechnic University，2. School of Electrical Engineering, Anhui Polytechnic University |
| Jiang Ming | 1. Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, Anhui Polytechnic University，2. School of Electrical Engineering, Anhui Polytechnic University |
| Xu Jinsong | 3. JSNU SPBPU Institute of Engineering,Jiangsu Normal University |
| Zhang Gang | 4. College of Electrical and Opto Electronic Engineering, West Anhui University |
| Sun Longlong | 2. School of Electrical Engineering, Anhui Polytechnic University |
| Tong Shengjie | 2. School of Electrical Engineering, Anhui Polytechnic University |
| Xu Yinyun | 2. School of Electrical Engineering, Anhui Polytechnic University |
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| 中文摘要: |
| 针对移动机器人在导航定位过程中,使用传统蒙特卡罗定位算法会产生粒子收敛较慢和定位精度不高,以及发生人为
绑架情况后重定位效率较低的问题,给出了一种改进的粒子滤波定位方法来提高移动机器人的导航定位效率。 首先,在蒙特卡
罗定位算法的基础上进行改进,融入自适应区域划分的方法,保证所划区域包含更多有效信息,减少粒子的收敛时间,完成机器
人初步粗定位。 然后,在粒子采样和重采样阶段,使用正态分布概率模型进行粒子权重更新,实现更加快速高效地全局精定位。
通过实验对比分析,所给方法与基于蒙特卡罗定位算法相比较,耗时缩短了 4 s,且本文的自适应蒙特卡罗定位方法,能够将定
位误差保持在 6 cm 左右,从而验证了所给方法的有效性和稳定性。 |
| 英文摘要: |
| Aiming at the problems of slower particle convergence and poor positioning accuracy when using traditional Monte Carlo
positioning algorithms in the navigation and positioning process of mobile robots, as well as low relocation efficiency after artificial
kidnapping, this article gives an improved Particle filter positioning method to improve the navigation and positioning efficiency of mobile
robots. First of all, it is improved on the basis of the Monte Carlo positioning algorithm and integrated into the method of adaptive region
division to ensure that the region contains more effective information, reduce the convergence time of particles, and complete the
preliminary coarse positioning of the robot. Then, in the particle sampling and resampling stage, the normal distribution probability
model is used to update the particle weights to achieve faster and more efficient global positioning. Through experimental comparison and
analysis, compared with the Monte Carlo positioning algorithm, the given method has shortened the time consumption by 4 s, and the
adaptive Monte Carlo positioning method in this paper can keep the positioning error at about 6 cm, thus verifying the given method
Effectiveness and stability. |
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