Xinhan Huang’s research while affiliated with Huazhong University of Science and Technology and other places

Visual servoing has been around for decades, but time delay is still one of the most troublesome problems to achieve target tracking. To circumvent the problem, in this paper, the Kalman filter is employed to estimate the future position of the object. In order to introduce the Kalman filter, accurate time delays, which include the processing lag and the motion lag, need to be obtained. Thus, the delays of the visual control servoing systems are discussed and a generic timing model for the system is provided. Then, we present a current statistical model for a moving target. A fuzzy adaptive Kalman filter, which is evolved from the Kalman filter, is introduced based on the current statistical model. Finally, a two DOF visual controller based variable structure control law for micro-manipulation is presented. The results show that the proposed adaptive Kalman filter can improve the ability to track moving targets.

Research into robotics visual servoing is an important area in the field of robotics. It has proven difficult to achieve successful results for machine vision and robotics in unstructured environments without using any a priori camera or kinematic models. In uncalibrated visual servoing, image Jacobian matrix estimation methods can be divided into two groups: the online method and the offline method. The offline method is not appropriate for most natural environments. The online method is robust but rough. Moreover, if the images feature configuration changes, it needs to restart the approximating procedure. A novel approach based on an online support vector regression (OL-SVR) algorithm is proposed which overcomes the drawbacks and combines the virtues just mentioned.

In this paper, considering the information fusion of multi-sensors on mobile robot, a new tool (the fusion machine of DSmT (Dezert-Smarandache Theory) coupling with PCR5 (Proportional Conflict Redistribution Rule)) is applied to autonomous mobile robot’s map reconstruction from 16 sonar rangefinders.

In multi-sources information fusion, because of the diversity of evidences, especially the imperfect evidences, it’s difficult to get a satisfied result. To avoid and reduce the influence of the mistaken or conflicting information on the final precision of fusion, a pre-processor of evidence sources is proposed and proved.

In order to make an exact prediction for the density of the lead-acid battery electrolyte, this paper proposes a method by using a genetic algorithm to optimise the support vector regression. In this AGA-SVR model, a kind of adaptive genetic algorithm is exploited to choose the model parameters of support vector regression for obtaining better prediction performance. The proposed predicting model is applied to the density predicting for lead-acid battery. The experimental results indicate that both GA and AGA have good efficiency on parameter optimisation. Furthermore, the AGA-SVR model provides a superior prediction performance than the other three models including SPSO-SVR model, IPSO-SVR model and GA-SVR model. Therefore, the AGA method could be considered as an effective alternative method for predicting electrolyte density.

This paper proposes a new solution for reducing the number of sources of evidence to be combined in order to diminish the complexity of the fusion process required in some applications where the real-time constraint and strong computing resource limitation are of prime importance.

In this paper, a new multi-robot system framework is introduced to solve the problem of multi-robot map building. Compared with the classical architectures introduced in the literature, this kind of framework enhances overall system performance, especially in reducing calculations and communications, improving real-time performance. Meanwhile a general basic belief assignment function (gbbaf) is constructed to deal with the uncertain and imprecise information obtained by sonar sensors with the application of new information fusion method DSmT (Dezert and Smarandache Theory) in the system and consideration of characteristics of sonar sensors. At last, Pioneer II mobile robots are used to carry out experiments of map building for both single-robot system and multi-robot system.

SLAM (Simultaneous Localization and Mapping) and path planning are two important research directions in the field of robotics. How to explore an entirely unknown dynamic environment efficiently is a difficult problem for intelligent mobile robots. In this study, a new method of information fusion i.e. DSmT (Dezert-Smarandache Theory) which is an extension of DST (Dempster-Shafer Theory) is introduced to deal with high conflicting and uncertain information and then multi-agent robot system with GREM (Generalized Evidence Reasoning Machine) based on DSmT is presented for mobile robot's SLAM and efficiently planning smooth paths in unknown dynamic environment. The single robot is treated as a multi-agent system and the corresponding architecture combined with cooperative control is constructed. Considering the characteristics of sonar sensor, the grid map method is adopted and a sonar sensor mathematical model is constructed based on DSmT. Meanwhile a few of gbbaf (general basic belief assignment functions) are constructed for fusion. In order to make the A* algorithm which is the classical method for the global path planning suitable for local path planning, safety guard district search method and an optimizing approach for searched paths are proposed. Finally, SLAM and path planning experiments are carried out with Pioneer 2-DXe mobile robot. The experimental results testify the validity of hybrid DSm (Dezert-Smarandache) model under DSmT framework for fusing imprecise information during map building and also reveal the validity and superiority of the multi-agent system for path planning in unknown dynamic environment.

The focus of this paper is the design and development of an automatic system for microassembly. The automatic processing is made possible by (i) the development of a machine vision algorithm to identify the targets and end-effectors, (ii) an uncalibrated visual servoing algorithm to lead the end-effector to grasp the micro-pieces and then assemble the target. The experimental results demonstrate that this prototype microassembly system is effective and practicable for automatic microassembly applications.