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

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。

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.