摘要
An adaptive particle filter for fault diagnosis of dead-reckoning system was presented,which applied a general framework to integrate rule-based domain knowledge into particle filter.Domain knowledge was exploited to constrain the state space to certain subset.The state space was adjusted by setting the transition matrix.Firstly,the monitored mobile robot and its kinematics models,measurement models and fault models were given.Then,5 kinds of planar movement states of the robot were estimated with driving speeds of left and right side.After that,the possible(or detectable)fault modes were obtained to modify the transitional probability.There are two typical advantages of this method,i.e.particles will never be drawn from hopeless area of the state space,and the particle number is reduced.
作者
DUAN Zhuo-hua
FU Ming
CAI Zi-xing
YU Jin-xia
段琢华;傅明;蔡自兴;于金霞(Computer Science and Technology Postdoctoral Research Station,School of Information Science and Engineering,Central South University,Changsha 410083,China;Department of Computer Science,Shaoguan University,Shaoguan 512003,China;Department of Computer Science and Technology,Henan Polytechnic University,Jiaozuo 454000,China)
基金
Project(60234030)supported by the National Natural Science Foundation of China
