基于改进粒子滤波的煤矿机器人定位方法研究

摘要

To solve the particle degradation problems of basic particle filter in the unknown space localization method, an improved particle filter algorithm based on MCMC is proposed to enhance the robustness. The localization simulation test using improved particle filter is developed and applied in coal mining robot in unknown underground space, and the method is compared with extended Kalman filter localization method, the results show that the proposed method has better locating accuracy in unknown underground space and better computational real-time ability than extended Kalman filter, which can solve the problems pre-localization of underground coal mining robot.%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。