Xiangjin Zeng

Adaptive Task-Space Tracking Control of Robots Without Task-Space- and Joint-Space-Velocity Measurements

The task-space tracking control of robots without the exact knowledge of kinematics and dynamics has been studied before with the assumption that the joint velocities are available for controller designs. However, the velocity measurements can be contaminated by noises, thereby resulting in poor system performance, or even leading to instability problems. Therefore, in this paper, we propose a new tracking controller for robots in the task space without the use of both task-space and joint-space-velocity measurements, under the condition that both the robot kinematics and dynamics are unknown. To overcome these incapacities without the velocity measurements, we introduce the well-known sliding-observer-design techniques to estimate the joint velocities for the purpose of our controller design. Our main concern, i.e., the stability analysis of our controller design incorporated with the siding observer, is presented with the help of Lyapunov-analysis methodology and the sliding-patch concept. Simulation results are presented to show the performance of our controller-observer designs.

Improved Stability Results for Visual Tracking of Robotic Manipulators Based on the Depth-Independent Interaction Matrix

As we know, the dynamic visual-tracking control, which is based on the depth-independent interaction matrix, has been proposed to cope with the general 3-D motion of robot manipulators. To deal with the unknown camera parameters, an adaptive law has been designed. It is noted, however, that the designed adaptive law uses a vector signal defined by the true depths of feature points, which are unavailable when the camera parameters are unknown. Nevertheless, such an adaptive law can still be implemented by replacing the exact depths with the estimated depths, since the estimated depths can be readily calculated by using the estimated values of the camera parameters. In this case, however, we cannot definitely say whether or not the robot system can be guaranteed to achieve asymptotical stability or even stable behavior. To overcome this problem, in this paper, we redefine the mentioned vector signal using the estimated depths and show that the design based on the new vector signal can make the robot system asymptotically stable. Additionally, the existing tracking control design based on the concept of depth-independent interaction matrix is initial-state dependent. Thus, in this paper, we also modify the existing design to obtain an initial-state-independent result. To show the performance of the proposed designs, simulation results based on a two-link planar manipulator are presented. In addition, preliminary experimental results using an industrial manipulator are also given.

The Improved SVM Multi Objects's Identification For the Uncalibrated Visual Servoing

For the assembly of multi micro objects in micromanipulation, the first task is to identify multi micro parts. We present an improved support vector machine algorithm, which employs invariant moments based edge extraction to obtain feature attribute and then presents a heuristic attribute reduction algorithm based on rough sets discernibility matrix to obtain attribute reduction, with using support vector machine to identify and classify the targets. The visual servoing is the second task. For avoiding the complicated calibration of intrinsic parameter of camera, We apply an improved broydens method to estimate the image jacobian matrix online, which employs chebyshev polynomial to construct a cost function to approximate the optimization value, obtaining a fast convergence for online estimation. Last, a two DOF visual controller based fuzzy adaptive PD control law for micro-manipulation is presented. The experiments of micro-assembly of micro parts in microscopes confirm that the proposed methods are effective and feasible.

A novel sub-pixel edge detection for micro-parts manipulation

This paper presents a novel sub-pixel edge detection and center localization algorithm to improve the resolution and accuracy for micro-assembly systems. There are three steps to realize the algorithm. First the invariant moment is used to describe the feature of micro-parts and the edge of the micro-parts is extracted at pixel level by using Canny operator. Secondly, the image information of gradient direction from the edge neighborhood is employed to locate the edge of the micro-parts at sub-pixel level. Last, apply least squares ellipse to get the position of the parts centers. Experiment results show that the presented algorithm could achieve higher location accuracy and consume less time.

The Uncalibrated Microscope Visual Servoing for Micromanipulation Robotic System

MEMS technology exploits the existing microelectronics infrastructure to create complex machines with micron feature sizes. These machines can perform complex functions including communication, actuation and sensing. However, micron sized devices with incompatible processes, different materials, or complex geometries, have to be ‘assembled’. Manual assembly tasks need highly skilled operator to pick and place micro-parts manually by means of microscopes and micro-tweezers. This is a difficult, tedious and time consuming work. Visual feedback is an important approach to improve the control performance of micro manipulators since it mimics the human sense of vision and allows for operating on the noncontact measurement environment. The image jacobian matrix model has been proved to be an effective tool to approach the robotic visual servoing problem theoretically and practically. It directly bridges the visual sensing and the robot motion with linear relations, without knowing the calibration model of the visual sensor such as cameras. However, image jacobian matrix is a dynamic timevarying matrix, which cannot be calibrated by fix robotic or CCD camera parameters, especially for micro-manipulation based on micro vision. So, it is an exigent request for us to estimate parameters of image jacobian matrix on-line. Many papers about image jacobian matrix online estimation have been reported. Clearly, Performance of the online estimation of the image jacobian matrix is the key issue for the quality of the uncalibrated micro-vision manipulation robotic. Unfortunately, the current estimation methods have problems such as estimation-lag, singularity, convergence and its speed. Especially in dynamic circumstances, these problems become more serious. There are other efforts to deal with the online estimation of the image jacobian matrix and the uncalibrated coordination control. Piepmeier et al. present a moving target tracking task based on the quasi-Newton optimization method. In order to compute the control signal, the jacobian of the objective function is estimated on-line with a broyden’s update formula (equivalent to a RLS algorithm). This approach is adaptive, but cannot guarantee the stability of the visual servoing. Furthermore, the cost function using RLS is restricted by prior knowledge for obtaining some performance. To deal with those problems discussed above, we apply an improved broydens method to estimate the image jacobian matrix. Without prior knowledge, the method employs chebyshev polynomial as a cost function to approximate the best value. Our results show that, when calibration information is unavailable or highly uncertain, chebyshev polynomial

Multiple Mobile Robots Map Building Based on DSmT

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. The comparison of created ichnography with the real map testified the validity of DSmT and efficiency of multi-robot system for fusing imprecise information and map building proposed by this research.

Visual servoing based on fuzzy adaptive PID with Modified Smith Predictor for micromanipulation

A control scheme based on fuzzy adaptive PID with a Modified Smith Predicator (MSP) for the control of micromanipulation is proposed. For the vision delay, a timing modeling of visual servoing system is built. According to analysis for the position based dynamic look and move control scheme, the control scheme employs fuzzy PID with a similar structure to the Smith predictor called modified Smith predictor to eliminate the vision delay. The simulations and experiments show that the vision control system with the proposed control scheme has better dynamic performance than the vision control system with a single PID controller. The proposed control scheme resolves the problems of vision servoingpsilas inherent time delay, which meets the requirements of micromanipulation.

The Depth Information Estimation of Microscope Defocus Image Based-on Markov Random Field

For depth information estimation of microscope defocus image, a blur parameter model of defocus image based on Markov random field has been present. It converts problem of depth estimation into optimization problem. An improved iterated conditional modes algorithm has been applied to complete optimization problem, which the select of initial point employed least squares estimate algorithm prevents that the result gets into local optimization. The experiments and simulations prove that the model and algorithm is efficiency.

On-line estimation of image jacobian matrix by improved Broyden's method in uncalibrated visual servoing

A novel improved broydens method has been presented to estimate image jacobian matrix for uncalibrated visual servoing. In this paper, we apply chebyshev polynomial as a cost function to approximate best value. Compared with recursive least square (RLS) algorithm which is restricted by the prior knowledge for obtaining some performances, chebyshev polynomial algorithm has a great adaptability to estimate jacobian parameter, even without the prior knowledge. A microscopic image jacobian model has been developed for the four degree-of-freedom micromanipulator in our microassembly system. The performance has been confirmed by simulations and experiments.