The theoretical research on localization using improved particle filter for mine rescue robot in unknown underground space

The localization for mine rescue robot in unknown space of coal mine emer- gency was researched,basing on the requirement of mine rescue robot localization which enters the scene of the coal mine accident,unknown circumstance,the unstructured, non-Gaussian and nonlinear work-space for robot works.The localization using particle filter was proposed to which is applied in mine rescue robot in unknown under-ground space.Meanwhile,focusing on severe particle sample degeneracy in the primary particle filter,an improved particle filter was proposed to reinforce the stability of particle filter.Be- ing compared with localization using extended Kalman filter through simulation experiment the localization using particle filter is proved to have more locating accuracy in unknown underground space and better computational real-time ability,which solves the pre-local- ization problem of robot underground.

基于改进粒子滤波的煤矿机器人定位方法研究

针对基本粒子滤波方法在煤矿机器人井下未知空间定位应用中存在的粒子退化问题,提出了一种基于马尔可夫链蒙特卡尔重采样理论的改进粒子滤波定位方法,以增强粒子滤波的稳定性,并应用该方法与传统的扩展卡尔曼滤波定位方法进行了机器人在未知狭小空间中的定位仿真实验对比研究。实验结果表明,该方法比扩展卡尔曼滤波定位方法有更高的精度和实时性,有效解决了煤矿井下未知空间机器人预测定位问题。