粒子滤波器在移动机器人故障诊断中的应用

针对移动机器人存在的8种不同模式,引入粒子滤波器算法,用于解决移动机器人系统故障诊断问题。基于粒子滤波器的故障诊断算法,通过一组带权值的粒子估计系统状态,计算故障状态的分布情况和故障发生的概率,从而判断是否发生故障以及所发生的故障类型。对粒子滤波器在移动机器人故障诊断中的应用进行仿真实验,并与CMAC神经网络故障诊断方法比较。实验结果表明,采用该方法能有效诊断移动机器人的故障模式,与CMAC神经网络故障诊断方法相比具有优越性。

Application of Particle Filter in Mobile Robot Fault Diagnosis

Fault diagnosis and fault prediction is a new area in mobile robots. Eight fault modes are discussed in this paper. An improved particle filter based approach is proposed to diagnosis and prediction the fault modes of mobile robots. Particle filter is an algorithm that uses swarms of weighted particles in state space to approximate the probability density function of the state. According to the probability distribution of the state, the probability of fault diagnosis can be obtained. The proposed approach is implemented on a mobile robot, and compared with CMAC neural network based fault diagnosis approach. The simulation results show the effectiveness and superiority of the method.