Papers by Author: Guo Liang Zhang

Abstract: This paper proposes an adaptive neural network law for trajectory tracking of a class of free-floating space robot with actuator saturation. Using neural network with global approximation, the control strategy design an on-line real time adaptive learning law to approach the uncertain model and the actuator saturation nonlinearity. The neural network approach errors and outside disturbance can be eliminated by a robust controller.The control strategy need not depend on the model, and can be used under actuator saturation.The control strategy can guarantee the stability of system and the asymptotic convergence of tracking errors based on the Lyapunov’s theory. The simulation results indicate that the proposed strategy can effectively work with actuator saturation.

Abstract: Aim to the loop closure problems of mobile robots SLAM, a building mix cascading map approach base on topology-line segment feature is presented, and also an active loop closure Fast SLAM approach is proposed by using segments types of the section lines to form active loop closure tactics. According to topology nodes‘s relationship detect loop closure, and use a reverse movement model for optimization and modification to improve the accuracy and map consistency of robot’s localization. Experiment results show that the approach can well detect loop closure and reduce accumulated errors, and the robot’s localization accuracy and map consistency is remarkably improved.

Abstract: Because of the challenging data association in similar environments, a large number of particles are needed to improve the precision in particle filtering SLAM (simultaneous localization and mapping).An improved particle filter SLAM algorithm based on particle swarm optimization in similar environments is proposed. A multimode proposal distribution is acquired by combining the information of the odometry and the laser scanning. Particles are concentrated to the region of each posterior probability distribution maximum value by PSO. The performance of the conventional particle filter SLAM is improved. The simulation experiment results prove its effectiveness and feasibility.

Abstract: As for the SLAM/DR/MEMS integrated navigation system of mobile robot constructed on the self-made SaecROB-H mobile robot, there exist mutant faults which are likely to occur, such as environment feature recognition error and data association error during mobile robot SLAM procedure, or dead reckoning failure caused by wheel slipping and so on. According to these mutant faults, mutant fault detecting and fault-tolerant filtering method was presented in this paper based on finite memory on-line prediction. At last, simulation was carried out and the results show that the method can improve the stability of mobile robot integrated navigation efficiently.

Abstract: This article proposed a rapid mobile robot self-localization method based on laser data, encoder data and azimuth compass data. This method can avoid tremendous error from pure mileage integral, and tremendous compute task from pure laser data registration after extract laser character. It only based on the similarity of adjacent laser data after pretreatment, then syncretized the estimated rotation angel and parallel displacement from encoder and azimuth compass, find the best transform with quasi Newton method. At last the mobile robot self-localization was realized. Experiment result demonstrated the reliability of this method.