Micro-Assembly of Micro Parts Using Uncalibrated Microscopes Visual Servoing Method

INTRODUCTION

Micromanipulation robotic has been applied widely in micro electromechanical system assembly. In order to meet the request of the high precise micro-manipulationb task, The robotic with the micro vision help becomes a very necessary part. Nowadays, the vision servoing control methods can be divided into three parts, which include vision servoing control based position vision servoing control based image and mix control based position and image. The methods mentioned in above need calibrate precisely intrinsic parameter of camera. However, the system calibration is the complicated and difficult problem, especially for micro-manipulation based on microscope vision. So, we employ the uncalibrated method to estimate image jacobian matrix online.

Kang et al. (2006), Hutchinson et al. (1996), Lu et al. (1996), Zhao et al. (2006), Malik and Choi (2007), Hosada and Asada (1994) and Shen et al. (2003) have been reported some researches about image jacobian matrix online estimation. Piepmeier et al. (1999) and Piepmeier et al. (2004) presents a moving target tracking task based on the quasi-Newton optimization method. This approach is adaptive, but cannot guarantee the stability of the visual servoing. Malis’s (2004) method can keep the parameter of vision servo controller constant, when intrinsic parameter of camera is changed. Liu et al. (2006) applies the depth independent matrix in uncalibrated vision, with Slotine and Li (1987) method to estimate online. Chen and Dawson (2003) presents a adaptive tracking controller based on homography. Su and Yugeng (2000) and Su et al. (2004) presents a motion 3D object tracking method based on the uncalibrated global vision feedback.

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.

To deal with those problems discussed above, We apply an broyden’s method to estimate the image jacobian matrix. the method employs Chebyshev polynomial to construct a cost function to approximate the optimization value, improving the converge of estimation. Present results show that, when calibration information is unavailable or highly uncertain, Chebyshev polynomial algorithm can achieve a satisfactory result, which can bring additional performance and flexibility for the control of complex robotic systems. To verify the effectiveness of the method, Using the estimated jacobian matrix, a PD visual controller is used to make features converge to desired values with satisfactory dynamic performance. The experiments of micro-assembly of micro parts in microscopes confirm that the proposed method is effective and feasible.

SYSTEM CONSTRUCTION OF MICROMANIPULATION

The micromanipulation system consists of micromanipulation stage, microscopes vision, micro-gripper. The system construction is showed in Fig 1.

Fig 1:	The system construction of three hands cooperation micromanipulation stage

Micromanipulation hands: The left and right hands consist of 3D high precise micro-motion stage driven by AC servoing motor and one DOF pose adjust joint driven by DC servoing motor. The motion range of 3D micro-motion stage is 50x50x50 mm and precise of position is 2.5 μm. The rotary range of pose adjust joint motor is ±180° and the resolution is 0.01. The third hand consists of three DOF motor driven by DC servoing motor and the operation range is 20x20x20 mm.

Microscope vision: Microscope vision is a main method that micromanipulation robotic obtains environment information. The vision system consists of vertical crossed two rays, which can monitor micro-assembly space by stereo method and obtain the space and pose information of object and end-effector, providing control and decision-making information for robotic.

End-effector: There are two driven types micro-gripper developed by us. One is micro-gripper driven by vacuum and another is driven by piezoelectricity ceramic, which can operate the micro parts with different size, shape and material.

ONLINE ESTIMATION IAMGE JACOBIAN MATRIX

Image jacobian: The image jacobian defines the relationship between the velocity of a robot end-effector and the change of an image feature. Considering q = [q₁, q₂…q_m]^R represents the coordinates of robot end-effector in the task space. A n-dimensional vector: f = [f₁, f₂…f_n]^T is corresponding position in image feature. Then, the image jacobian matrix J_q is defined as:

(1)

Where:

(2)

Image jacobian matrix estimation based on broyden’s method: The image jacobian matrix can be calculated by calibrating the inner and outer parameter of robotic system and sensor system. However, it is impossible to obtain precise system parameter under a dynamic or uncertainty environment. Considering those, we employ broyden’s method to estimate the image jacobian matrix.

The broyden method that solves the nonlinear equation can be shown in Eq. 3:

(3)

Now, we apply the broyden method to construct estimation model of image jacobian matrix. According to Eq. 1, If the feature error of two images represents as:

(4)

where, f^d is the feature of the expectation image and f^c is the feature of the current image. The taylor series expansion of e_f is shown as:

(5)

where, R_n(x) is Lagrange remaining. We define J*_q(q_n) as the Nth image jacobian to be estimated,

(6)

Ignoring the high order term and Lagrange remaining R_n(x), Eq. 7 can be obtained from (5) and (6), which is shown as:

(7)

So, if J’s counterpoint is AΔe’s counterpoint is y and Δq’s counterpoint is s, we can construct the image jacobian estimation model based broyden. The image jacobian estimation model based on broyden method is shown as Eq. 8:

(8)

The broyden algorithm estimates the optimization value by employing iterative computation. Therefore, it need the end condition of iterative computation, we employ Chebyshev polynomial to construct the cost function to approximate the optimization value.

The cost function based on Chebyshev polynomial: Provided that

(9)

If N_k(q) ε c[-1,1], For Chebyshev polynomial serial {T_n, n = 0, 1,...} with weight ρ(x) = (1-x²)^-1/2, it’s optimization square approximation polynomial can be shown as:

(10)

Where:

(11)

Then

(12)

Usually, N_k(q) ε c[a, b] we must convert c[a,b] into c[-1,1]. The Eq. 13 can finish the convert.

(13)

if we use part sum s*_n as N(q)’s approximation, under some conditions, there is a fast speed for a_n→ 0. Theoretically, compared with RLS algorithm, Chebyshev polynomial approximation algorithm is independent of the prior knowledge of system and it has more fast approximate speed than that of other methods. Experiments will prove its correctness.

Surely, The unsatisfied thing of Chebyshev polynomial approximation algorithm, we encountered, lies in that it require N(q)’s good smoothness. It is a difficulty for us to meet this need for most conditions.

Comparison Chebyshev polynomial approximation with RLS: Piepmeier et al. (1999, 2004) provides RLS algorithm to approximate best value for minimum cost function. The cost function using RLS is shown as 14.

(14)

where, λ is a rate of dependency for prior data. As shown in Eq. 14, In order to obtain some performance, the cost function using RLS algorithm depends on the data of the several past steps, it mean that the prior knowledge must be obtained for finishing the task.

Similarly, The cost function using Chebyshev polynomial is shown as 15.

(15)

Fig 2:	A broyden with Chebyshev polynomial approximation estimator of image jacobian

Clearly, The cost function using Chebyshev polynomial is independent of the prior data.

Jacobian estimator with improved Broyden’s method: As discussed earlier, an broyden with Chebyshev polynomial approximate algorithm estimator of image jacobian is developed. A graphical representation of the estimate process is shown in Fig 2. Firstly, The broyden estimator starts with initial endeffector position q⁰ and precision ε. Then, Camera captures an image of endeffector for extracting corresponding image coordinate feature f^k, Which provides the possibility for calculating J*(q^k) by formula J*(q^k) = [f′(q^k)]^–1. Secondly, Camera captures an image of target to obtain expectative image coordinate feature f^k+1. With the obtained J*(q^k), the servoing control law can be deduced detail in section of the vision controller design. Finally, Program judges whether precision ε satisfies system requirement or not. If precision ε arrives the requirement, system will be ended, otherwise system will be executed repeat processing.

THE VISION CONTROLLER DESIGN

In order to finish three-dimensional small object positioning task, in the actual operation, micro-manipulation tasks will be divided into horizontal direction (XY plane) movement and the vertical direction (Z axis) movement. The manipulator in the XY plane moves first, positioning small parts in the above, then does so in the Z-axis vertical movement, positioning small parts at the centre. Therefore, we apply two image jacobian matrixs, including horizontial view field of image jacobian matrix and vertical view field of image jacobian matrix, which can complete the positioning and tracking three-dimensional objects.

Fig.3:	Micromanipulator servo control structure

The change of robot movement [dx,dy]^T and the change of image characteristics [du,dv]^T can be wirte as (16):

(16)

According to the online estimtion image Jacobian matrix J based on broyden method, sets the position of the error e = f^d-f^c, which f^d is the expectations of position of objects (small cylindrical parts, 600 um diameter) and f^c is the centre of endeffector. Then, the control law of PD controller u (k) is:

(17)

where, T_s is the time interval, K_p is proportional gain and K_d is differential gain. It’s control structure is shown in Fig. 3: