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.