基于多模态进化Rao-Blackwellized粒子滤波器的移动机器人航迹推算系统的故障诊断  

Fault diagnosis for mobile-robot dead reckoning system based on multimodality evolutionary Rao-Blackwellized particle filter

作　　者：余伶俐[1] 蔡自兴[1] 谭平[1] 段琢华[2] 

机构地区：[1]中南大学信息科学与工程学院,长沙410083 [2]韶关学院计算机科学系,广东韶关512003

出　　处：《控制与决策》2010年第12期1787-1792,共6页Control and Decision

基　　金：国家自然科学基金重大专项重点项目(90820302)

摘　　要：针对一类移动机器人航迹推算系统的故障诊断问题,提出一种多模态进化Rao-Blackwellized粒子滤波器(MERBPF)算法.为解决由粒子贫乏引起的不一致性问题,采用交叉与变异种群策略优化,根据粒子多样性加入扰动因子.利用专家规则判定机器人运动状态所对应的MERBPF,构造复杂逻辑表述方法.仿真实验结果表明:在强过程噪声下,MERBPF仍具有较高的鲁棒性,提高了诊断机器人航迹推算系统的准确率.A multi-modality evolutionary Rao-Blackwellized particle filter（MERBPF） algorithm is proposed for mobilerobot fault diagnosis of dead reckoning system.The inconsistency from particle degeneration problem is solved by integrating swarms＇ intercross and mutation strategy and adding disturbance factors accoding to diversity.Robot moving states are determined by expert rules reasoning mechanism and monitored by each different ERBPF.Finally,the multi-modality ERBPF is formed,which expresses complex logic clearly.The experimental results show that MERBPF maintains a strong robustness even under the strong process noise,which improves the accuracy for the fault diagnosis of robot＇s dead reckoning investigation system.

关 键 词：故障诊断 移动机器人 RAO-BLACKWELLIZED粒子滤波器 航迹推算系统 

分 类 号：TP24[自动化与计算机技术—检测技术与自动化装置]