A high-precision posture relationship calibration method for a split minimally invasive surgical robot

Abstract

A high-precision posture relationship calibration method for a split minimally invasive surgical robot, which relates to a posture relationship calibration method. In order to solve the problem that the equipment installation process is cumbersome, the cost is high, the use process is complicated, the precision is low, and it is difficult to apply to the actual scene of the operating room when using auxiliary equipment such as optical measuring devices for measurement. The present invention determines the posture of the surgical robot base through ARUCO marking points, uses Kalman filtering to correct the obtained posture data, and then uses two monocular cameras to respectively obtain the posture of the surgical robot base, and then uses Kalman filtering to fuse the obtained postures. This method can significantly improve the robustness of the posture of the split surgical robot obtained based on vision, and improve the calibration accuracy. Solve the problem of determining the posture relationship between split surgical robots. The present invention is used for the calculation of the posture relationship of any split surgical robot.

Description

Technical Field

[0001] The present invention relates to a posture relationship calibration method, and in particular to a vision-based high-precision posture relationship calibration method for a split-type minimally invasive surgical robot, and belongs to the field of medical devices. Background Art

[0002] At present, most surgical robots adopt an integrated design, where the scope holding arm and the instrument holding arm are fixed on the same base, so they all use the base coordinate system on the same base. The design of a split surgical robot can greatly improve the flexibility of minimally invasive surgical robots and can be applied to more complex surgical scenarios. Since the scope holding arm and the instrument holding arm of a split minimally invasive surgical robot are independent, in order to determine the coordinate system relationship between the scope holding arm and the instrument holding arm, it is necessary to obtain the posture transformation relationship of the base coordinate systems on the two bases, which is the premise and key to realizing the surgical operation of the split minimally invasive surgical robot.

[0003] At present, the common solutions at home and abroad mainly use auxiliary equipment such as optical measuring devices for measurement. However, these devices have cumbersome installation processes, high costs, complex use processes, low accuracy, and are difficult to apply to actual scenarios in operating rooms. Summary of the invention

[0004] The purpose of the present invention is to solve the problems of the existing use of auxiliary equipment such as optical measuring devices for measurement, which has the problems of cumbersome equipment installation process, high cost, complicated use process, low precision, and difficulty in application in actual operating room scenarios. Furthermore, a high-precision posture relationship calibration method for a split minimally invasive surgical robot is provided.

[0005] The technical solution of the present invention is: a high-precision posture relationship calibration method for a split minimally invasive surgical robot, wherein the split minimally invasive surgical robot comprises a robot body and a visual system, the robot body comprises a mirror holding arm and a tool holding arm; the visual system comprises two monocular cameras, and the two monocular cameras are installed at any known position; the high-precision posture relationship calibration method for a split minimally invasive surgical robot comprises the following steps:

[0006] Step 1: Place ARUCO on the top of the base of the surgical robot body, so that the mirror holding arm ARUCO_L and the robot holding arm ARUCO_I are both within the camera's field of view. ARUCO refers to the QR code array positioning system.

[0007] Step 2: Construct the base coordinate system T of the split minimally invasive surgical robot holding arm OL , ARUCO coordinate system T at the top of the mirror arm AL , and determine the ARUCO coordinate system T AL Relationship with respect to the base coordinate system of the mirror holding arm

[0008] Step 3: Construct the base coordinate system T of the split minimally invasive surgical robot arm OI , ARUCO coordinate system T at the top of the robotic arm AI , and determine the ARUCO coordinate system T AI Relationship with respect to the base coordinate system of the robot arm

[0009] Step 4: Construct two monocular camera coordinate systems T respectively C1 and T C2 ;

[0010] Step 5: Calculate the coordinate systems T of the mirror arm ARUCO_L and the robot arm ARUCO_I respectively AL and T AI To the monocular camera coordinate system T C1 The conversion relationship and Then get T AL and T AI Coordinate transformation matrix And use Kalman filtering to correct the obtained values;

[0011] Step 6: Calculate the coordinate systems T of the mirror arm ARUCO_L and the robot arm ARUCO_I respectively AL and T AI To the monocular camera coordinate system T C2 The conversion relationship and Then get T AL and T AI Coordinate transformation matrix And use Kalman filtering to correct the obtained values;

[0012] Step 7: After obtaining the two coordinate transformation matrices in steps 5 and 6, the data obtained by the two monocular cameras are fused using Kalman filtering, and finally the transformation relationship between the coordinate systems of the mirror arm ARUCO_L and the robot arm ARUCO_I is obtained.

[0013] Step 8: Obtain the conversion relationship between the base coordinate system of the robot arm and the base coordinate system of the robot arm in The calculation method is

[0014] Furthermore, in step 4, before constructing the coordinate systems of the two monocular cameras, the geometric imaging models of the two monocular cameras are used and the Zhang Zhengyou calibration method is used to calibrate their internal parameter matrices. After the calibration, the focal lengths and aperture positions of the two monocular cameras remain unchanged.

[0015] Furthermore, the marking points of the mirror holding arm ARUCO_L and the robot holding arm ARUCO_I in step 2 and step 3 are respectively fixed on the robot holding arm base and the mirror holding arm base by clamps.

[0016] Furthermore, the coordinate transformation matrix obtained in step five and step six is ​​corrected by using Kalman filtering.

[0017] Furthermore, in step seven, the coordinate transformation matrix finally obtained by the two monocular cameras is subjected to two-dimensional data fusion processing using Kalman filtering.

[0018] Compared with the prior art, the present invention has the following effects:

[0019] The present invention realizes the calculation of the posture conversion relationship between the base coordinate system of the scope holding arm base and the instrument holding arm of the surgical robot. With the help of two monocular cameras, the installation is simple and no other auxiliary optical measurement equipment is required, which overcomes the shortcomings of the traditional method such as high cost, low accuracy, and complicated operation method. The two monocular cameras can respectively obtain the posture conversion relationship between the base coordinate system of the scope holding arm base and the instrument holding arm base, and through two-dimensional data fusion, a split surgical robot base posture relationship with good robustness and high accuracy is obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of the position and posture relationship calibration of the split-type surgical robot of the present invention.

[0021] Figure 2 It is a schematic diagram of the coordinate system relationship of the present invention. DETAILED DESCRIPTION

[0022] Specific implementation method 1: Combination Figure 1 to Figure 2 The present embodiment is described. The present embodiment is a method for calibrating a high-precision posture relationship of a split-type minimally invasive surgical robot. The split-type minimally invasive surgical robot includes a robot body and a visual system. The robot body includes a mirror holding arm and a tool holding arm. The visual system includes two monocular cameras. The two monocular cameras are installed at any known position, such as the ceiling of the operating room, next to the operating bed, etc. The method for calibrating a high-precision posture relationship of a split-type minimally invasive surgical robot includes the following steps:

[0023] Step 1: Place ARUCO on the top of the base of the surgical robot body, so that the mirror holding arm ARUCO_L and the robot holding arm ARUCO_I are both within the camera's field of view. ARUCO refers to the QR code array positioning system.

[0024] Step 2: Construct the base coordinate system T of the split minimally invasive surgical robot holding arm OL , ARUCO coordinate system T at the top of the mirror arm AL, and determine the ARUCO coordinate system T AL Relationship with respect to the base coordinate system of the mirror holding arm

[0025] Step 3: Construct the base coordinate system T of the split minimally invasive surgical robot arm OI , ARUCO coordinate system T at the top of the robotic arm AI , and determine the ARUCO coordinate system T AI Relationship with respect to the base coordinate system of the robot arm

[0026] Step 4: Construct two monocular camera coordinate systems T respectively C1 and T C2 ;

[0027] Step 5: Calculate the coordinate systems T of the mirror arm ARUCO_L and the robot arm ARUCO_I respectively AL and T AI To the monocular camera coordinate system T C1 The conversion relationship and Then get T AL and T AI Coordinate transformation matrix And use Kalman filtering to correct the obtained values;

[0028] Step 6: Calculate the coordinate systems T of the mirror arm ARUCO_L and the robot arm ARUCO_I respectively AL and T AI To the monocular camera coordinate system T C2 The conversion relationship and Then get T AL and T AI Coordinate transformation matrix And use Kalman filtering to correct the obtained values;

[0029] Step 7: After obtaining the two coordinate transformation matrices in steps 5 and 6, the data obtained by the two monocular cameras are fused using Kalman filtering, and finally the transformation relationship between the coordinate systems of the mirror arm ARUCO_L and the robot arm ARUCO_I is obtained.

[0030] Step 8: Obtain the conversion relationship between the base coordinate system of the robot arm and the base coordinate system of the robot arm in The calculation method is

[0031] The high-precision posture relationship calibration method of the split-type minimally invasive surgical robot of this embodiment is used in robot design and debugging environments in non-surgical processes. Without acting on the human body, the posture of the machine holding mirror arm and the robot holding arm during movement is calibrated and optimized.

[0032] In the process of calculating the posture transformation relationship between the two cameras, Kalman filtering is used to correct the obtained coordinate transformation matrix to reduce the measurement error caused by factors such as lighting.

[0033] Then, for the measurement results obtained by the two monocular cameras, Kalman filtering is used to fuse the two-dimensional data to make the measurement results more stable and with smaller errors.

[0034] Specific implementation method 2: Combination Figure 1 and Figure 2 To explain this embodiment, in step 4 of this embodiment, before the two monocular camera coordinate systems are assembled, the geometric imaging models of the two monocular cameras are used and the Zhang Zhengyou calibration method is used to calibrate their internal parameter matrices. After calibration, the focal lengths and aperture positions of the two monocular cameras remain unchanged. This arrangement is convenient for ensuring the consistency and stability of the two monocular camera coordinate systems, and preventing the accurate calibration of the posture from being affected by changes in the two monocular camera coordinate systems. The other components and connection relationships are the same as those in the first embodiment.

[0035] Specific implementation method three: Combination Figure 2 To illustrate this embodiment, the marking points of the mirror holding arm ARUCO_L and the mechanical holding arm ARUCO_I in step 2 and step 3 of this embodiment are respectively fixed on the mechanical holding arm base and the mirror holding arm base by a clamp. This arrangement is convenient for ensuring the consistency and stability of the marking points. The other components and connection relationships are the same as those of the specific embodiment 2.

[0036] Specific implementation method four: Combination Figure 1 To illustrate this embodiment, the coordinate transformation matrix obtained in step 5 and step 6 of this embodiment is corrected by Kalman filtering. This arrangement is convenient for reducing the measurement error caused by factors such as illumination. Other components and connection relationships are the same as those of the third embodiment.

[0037] Specific implementation method five: Combination Figure 1 To illustrate this embodiment, in step 7 of this embodiment, the coordinate transformation matrix finally obtained by the two monocular cameras is processed by two-dimensional data fusion using Kalman filtering. This arrangement makes the measurement result more stable and the error is small. The other components and connection relationships are the same as those of the specific embodiments one, two, three or four.

[0038] The present invention discloses a method for calibrating the posture relationship of a split surgical robot based on vision, which is used to solve the posture conversion matrix between the base coordinate systems of the split surgical robot. The posture of the base of the surgical robot is determined by ARUCO markers, and the posture data obtained is corrected by Kalman filtering. Then, two monocular cameras are used to obtain the posture of the base of the surgical robot respectively, and then the postures obtained are fused by Kalman filtering. This method can significantly improve the robustness of the posture of the split surgical robot obtained based on vision, and improve the calibration accuracy. It solves the problem of determining the posture relationship between split surgical robots. The method of the present invention is simple to calculate, low in cost, and high in accuracy, and is suitable for the calculation of the posture relationship of any split surgical robot.

[0039] The above only describes the preferred embodiments of the present invention, but the present invention is not limited to the above-mentioned specific embodiments. Under the guidance of the present invention and without departing from the purpose of the invention, various modifications or equivalent substitutions made by technicians in this field to the features and embodiments of the present invention to adapt to specific circumstances will not depart from the spirit of the present invention and the scope of protection of the claims.

Claims

1. A high-precision posture relationship calibration method for a split minimally invasive surgical robot, wherein: The split minimally invasive surgical robot includes a robot body and a visual system, wherein the robot body includes a mirror holding arm and a tool holding arm; the visual system includes two monocular cameras, which are installed at any known position; It is characterized in that: the high-precision posture relationship calibration method of a split minimally invasive surgical robot comprises the following steps: Step 1: Place ARUCO on the top of the base of the surgical robot body, so that the mirror holding arm ARUCO_L and the robot holding arm ARUCO_I are both within the camera's field of view. ARUCO refers to the QR code array positioning system. Step 2: Construct the base coordinate system T of the split minimally invasive surgical robot holding arm OL , ARUCO coordinate system T at the top of the mirror arm AL , and determine the ARUCO coordinate system T AL Relationship with respect to the base coordinate system of the mirror holding arm Step 3: Construct the base coordinate system T of the split minimally invasive surgical robot arm OI , ARUCO coordinate system T at the top of the robotic arm AI , and determine the ARUCO coordinate system T AI Relationship with respect to the base coordinate system of the robot arm Step 4: Construct two monocular camera coordinate systems T respectively C1 and T C2 ; Step 5: Calculate the coordinate systems T of the mirror arm ARUCO_L and the robot arm ARUCO_I respectively AL and T AI To the monocular camera coordinate system T C1 The conversion relationship and Then get T AL and T AI Coordinate transformation matrix And use Kalman filtering to correct the obtained values; Step 6: Calculate the coordinate systems T of the mirror arm ARUCO_L and the robot arm ARUCO_I respectively AL and T AI To the monocular camera coordinate system T C2 The conversion relationship and Then get T AL and T AI Coordinate transformation matrix And use Kalman filtering to correct the obtained values; Step 7: After obtaining the two coordinate transformation matrices in steps 5 and 6, the data obtained by the two monocular cameras are fused using Kalman filtering, and finally the transformation relationship between the coordinate systems of the mirror arm ARUCO_L and the robot arm ARUCO_I is obtained. Step 8: Obtain the conversion relationship between the base coordinate system of the robot arm and the base coordinate system of the robot arm in The calculation method is 2. According to claim 1, a high-precision posture relationship calibration method for a split-type minimally invasive surgical robot is characterized by: Step 4: Before constructing the coordinate systems of the two monocular cameras, the geometric imaging models of the two monocular cameras are used and the Zhang Zhengyou calibration method is used to calibrate their internal parameter matrices. After calibration, the focal lengths and aperture positions of the two monocular cameras remain unchanged.

3. A high-precision posture relationship calibration method for a split-type minimally invasive surgical robot according to claim 1 or 2, characterized in that: The marking points of the mirror holding arm ARUCO_L and the robot holding arm ARUCO_I in step 2 and step 3 are respectively fixed on the robot holding arm base and the mirror holding arm base through clamps.

4. A high-precision posture relationship calibration method for a split-type minimally invasive surgical robot according to claim 3, characterized in that: The coordinate transformation matrix obtained in step 5 and step 6 is corrected using Kalman filtering.

5. A high-precision posture relationship calibration method for a split-type minimally invasive surgical robot according to claim 4, characterized in that: In step 7, the coordinate transformation matrix finally obtained by the two monocular cameras is processed by two-dimensional data fusion using Kalman filtering.

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