An adaptive particle filter for soft fault compensation of mobile robots

作     者：DUAN ZhuoHua CAI ZiXing YU JinXia 

作者机构：School of Information Engineering Shaoguan University Shaoguan 512003 China School of Information Science and Engineering Central South University Changsha 410083 China College of Computer Science and Technology Henan Polytechnic University Jiaozuo 454003 China 

出 版 物：《Science in China(Series F)》 (中国科学（F辑英文版）)

年 卷 期：2008年第51卷第12期

页      面：2033-2046页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 080202[工学-机械电子工程] 0802[工学-机械工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the National Natural Science Foundation of China (Grant No. 60234030) National Basic Research Project (Grant No. A1420060159) 

主　　题：soft fault detection and compensation adaptive particle filter mobile robots 

摘      要：Soft fault compensation plays an important role in mobile robot locating, mapping, and navigating. It is difficult to achieve fast and accurate compensation for mobile robots because they are usually highly non-linear, non-Gaussian systems with limited computation and memory resources. An adaptive particle filter is presented to compensate two kinds of soft faults for mobile robots, i.e., noise or factor faults of dead reckoning sensors and slippage of wheels. Firstly, the kinematics models and the fault models are discussed, and five kinds of residual features are extracted to detect soft faults. Secondly, an adaptive particle filter is designed for fault compensation, and two kinds of adaptive scheme are discussed： 1） the noise variances of linear speed and yaw rate are adjusted according to residual features; 2） the particle number is adapted according to Kullback-Leibler divergence （KLD） of two approximate distribution denoted with two particle sets with different particles, i.e., increasing particle number if the KLD is large and decreasing particle number if the KLD is small. The theoretic proof is given and experimental results show the efficiency and accuracy of the presented approach.