Yu Jinxia

Fault Diagnosis and Fault Tolerant Control for Wheeled Mobile Robots under Unknown Environments: A Survey

Fault detection and diagnosis (FDD) and fault tolerant control (FTC) are increasingly important for wheeled mobile robots (WMRs), especially those in unknown environments such as planetary exploration. Due to the importance of reliability and safe operation of WMRs, this paper presents a survey of state-of-the-art in FDD & FTC of WMRs under unknown environments. Firstly, we briefly introduce main components, typical kinematics models and fault models of WMRs and error models of inertial navigation sensors. Secondly, we discuss main approaches for FDD/FTC of WMRs, including multiple model based approach, particle filter based approach, sensor fusion based approach, layered fault tolerant architecture and so on. At last, the main challenges, difficulties and some future trends for the field are offered.

Research on Particle Filter Based on an Improved Hybrid Proposal Distribution with Adaptive Parameter Optimization

Although it has attracted widespread attentions in the nonlinear filtering field, particle filter algorithm exists the sample degradation problem. In order to improve the algorithm performance, an improved hybrid proposal distribution with adaptive parameter optimization for particle filter is studied. Firstly, based on the performance analysis of different proposal distribution, a hybrid proposal distribution with fixed annealing parameter (called improved hybrid proposal distribution) is utilized to consider current information of the latest observed measurement. Then, aimed at the deficiency about annealing parameter using fixed value, improved hybrid proposal distribution with adaptive optimization of annealing parameter is proposed by comparison with the relationship among true state, observational data and forecast data based on proposal distribution. With the simulation program, the performance of the proposed strategy is evaluated and its validity is verified.

Research on the adaptive mechanisms in particle filter

Particle filter based on the adaptive mechanisms has become a key issue for the recursive Bayesian estimation problem with non-linear, non-Gaussian and multi-modal distribution. Aimed at the inherent deficiency in particle filter and combined with the up-to-date research and application in the mobile robot field, some key technologies in current study are respectively summarized from the adaptive mechanism of sample size, the resampling strategy, proposal distribution, motion / likelihood model and the integration with other methods. At the same time, the main challenges that need to be solved in this field are concluded and some future trends about the technology of these difficulties are also presented.

Improved method for the feature extraction of laser scanner using genetic clustering

Feature extraction of range images provided by ranging sensor is a key issue of pattern recognition. To automatically extract the environmental feature sensed by a 2D ranging sensor laser scanner, an improved method based on genetic clustering VGA-clustering is presented. By integrating the spatial neighbouring information of range data into fuzzy clustering algorithm, a weighted fuzzy clustering algorithm (WFCA) instead of standard clustering algorithm is introduced to realize feature extraction of laser scanner. Aimed at the unknown clustering number in advance, several validation index functions are used to estimate the validity of different clustering algorithms and one validation index is selected as the fitness function of genetic algorithm so as to determine the accurate clustering number automatically. At the same time, an improved genetic algorithm IVGA on the basis of VGA is proposed to solve the local optimum of clustering algorithm, which is implemented by increasing the population diversity and improving the genetic operators of elitist rule to enhance the local search capacity and to quicken the convergence speed. By the comparison with other algorithms, the effectiveness of the algorithm introduced is demonstrated.

Study on kinematics modeling of mobile robot in rough terrain

Kinematics modeling is an indispensable component for dead reckoning of mobile robot. Aimed at this problem in rough terrain, the kinematics model is analyzed by the rigid-body kinematical constraints of mobile robot that is on the basis of locomotion architecture with the wheeled and rocker-bogie suspension system. At the same time, a method of kinematics model integrated with wheel-ground contact angle is suggested, which is able to fuse the information from multiple sensors and to estimate the three-dimensional motion trajectory of mobile robot. With the simulation and real experiment in different terrain, the exactness and effectiveness of this method are demonstrated.

Research on the improvement of Rao-Blackwellized particle filter for the incremental environment mapping and self-localization of a mobile robot

Aimed at the problem of the incremental environment mapping and self-localization of a mobile robot, the Rao-Blackwellized particle filter (RBPF) algorithm is improved to get the unite estimation of the pose of mobile robot and the position of the environmental landmarks. There are two parts in the RBPF algorithm to be studied. One is that the pose estimation of mobile robot is mended by adapting the resampling process grounded on the effective sample size (ESS) and by adopting mixture Gaussian distribution to approximate proposal distribution so as to improve the sample weight computation in obtaining ESS. The other is that the unscented Kalman filter with the adaptation estimation for the process noise is introduced into the position evaluation of the environmental landmarks. With mobile robot MORCS-1 as experimental platform, the validity of the proposed algorithm in this paper is proved.