KR20050104902A - Robot system for spacecraft docking using vision sensor - Google Patents

Abstract

The present invention relates to a spacecraft docking robot system using a vision sensor. More specifically, the relationship between a tracking object and a target object is obtained in two dimensions, and the positional information and attitude information are obtained through an extended Kalman filter, an inertial navigation system, and a gyro. A system for accurately estimating and allowing a tracking object to dock to a target object using the estimated information as described above.

The vision sensor includes a CCD camera and a plurality of beacons, and the CCD camera and the beacons are projected onto a plane to estimate the positional information and the attitude information using their geometric relationship and the extended Kalman filter.

It is a system for docking to a target object by controlling the position and attitude of the tracking object based on the estimated information as described above.

Description

Robot System for Spacecraft Docking Using Vision Sensor}

The present invention relates to a spacecraft docking robot system using a vision sensor, and more specifically, to obtain a two-dimensional relationship between a tracking object and a target object, and to estimate position information and attitude information through an extended Kalman filter. A system for enabling a target object to dock to a tracking object using estimated information.

The rendezvous is where two spacecraft enter the same orbit and fly together in close proximity, followed by a docking process in which the two spacecraft combine. In 1966 Gemini 10 and its target line were the first to manned docking, and in 1967 Cosmos 186 and 188 succeeded in unmanned docking. Rendezvous and docking technologies have been considered important because they are essential for repair and recovery of satellites, manned spaceship construction, and construction of large structures such as space colonies and space plants. However, automation of such a process greatly reduces the cost and complexity of the system, and thus automation problems in mass view or docking have been regarded as one of the most important and necessary technologies. In addition, the Automated Rendezvous and Docking (ARD) technology developed in this way can be used for the formation of aerospace flight, or fueling the aircraft in flight. Since the start of the space development competition, Russia, the United States, Europe and Japan have been focusing on developing ARD individually.

A typical docking process usually indicates when the distance between a chaser and a target spacecraft is less than 100 m. There are generally two ways to use the precise navigation required in this close operation: have. One technique is to use GPS using Kalman filter, and the other technique is to use vision sensor. The first technique has a margin of error of approximately 1 cm, but it is likely that there is a problem between GPS when close, and it is difficult to operate on a planet other than Earth. The second technique has no problem with the above GPS related technology, but has a problem that additional vision related technology is required. The latter technique is used here.

In order to develop such a technique, an algorithm for properly filtering the surrounding information during the docking process is necessary. The vision system to be used in the present invention can obtain accurate information in close operation, but has a disadvantage that the sampling frequency is only a few Hz due to hardware limitations. On the other hand, the Inertial Measurement Unit (IMU), including the gyro, is stable in the short term and has a sufficient sampling frequency, but emits in the long term. Therefore, using these two sensors in combination is essential for spacecraft docking technology.

On the other hand, the Kalman filter is very useful in posture measurement. The Kalman filter minimizes the error between the response of the system model equations and the actual measurements, allowing for better state estimation.

1960 R.E. Discrete Kalman filtering, proposed by Kalman, is a filtering method that is widely used in many fields due to the rapid development of digital computers. Especially in the field of automation systems, guidance and navigation, Kalman filtering is an indispensable technique.

Discrete Kalman filters are used to estimate states in discrete control processes governed by linear probability differential equations such as The actually measured value is represented by Equation 2.

[Equation 1]

[Equation 2]

here Is State value in the first step Is The output value in the first step is shown. silver Random variable representing the th input value Wow Denote modeling noise and measurement noise, respectively. These two variables are considered independent of each other and mean zero white noise. Equation 3 shows the characteristics of the two noises.

[Equation 3]

here , Are random variables Wow Represents the probability function.

Practically Covariance of Modeling Noise And covariance of measured noise The matrix changes with each time and measurement but is generally assumed to be a constant.

The stochastic characteristic of the discrete Kalman filter is sufficient to set the first and second moments of the state distribution as shown in Equation 4.

[Equation 4]

here Is a variable Represents the expected value of Is a variable Means the average. Also Is a variable The dispersion of Discrete Kalman filters with these characteristics are configured to continuously update each time and measurement. Equation 5 shows an update structure with respect to time, and Equation 6 shows an update structure with respect to the measurement.

[Equation 5]

[Equation 6]

here Is the Kalman filter gain and the remaining variables have the same meanings as the variables used in Equations 1 to 4

Discrete Kalman filters as described above are applicable to linear systems, and for nonlinear systems that are difficult to represent linearly, the form of extended Kalman filters should be used. The system to which the Extended Kalman Filter is applied may be represented by a nonlinear probability differential equation such as Equation (7).

[Equation 7]

The time and measurement update structure described by Equations 5 and 6 in the linear system is modified to Equations 8 and 9 in the nonlinear system.

[Equation 8]

[Equation 9]

The basic use of the Extended Kalman Filter is the same as the linear system.

In order to obtain position and angle information between two objects using vision, at least one CCD camera and several beacons that provide coordinates to recognize the object must be provided simultaneously. Beacons must be made of a material that can be easily detected or a light-emitting material such as an LED and attached to a known location on the target. A tracking object with a CCD camera recognizes the beacons of the target object and calculates the position and attitude of each other.

1 shows the relationship between a beacon and a CCD camera in three dimensions. Indicates the position of the tracking object relative to the target object. Represents the Euler angle. At this time, the direction cosine matrix describing the relationship between the coordinates of the target object and the coordinates of the tracking object is expressed by Equation 10 using 3-2-1 rotation.

[Equation 10]

From the geometry shown in FIG. 1, the i-th beacon and its measurements on the CCD are represented as in equation (11). Used here Is the matrix of the equation Element

[Equation 11]

In addition, the air bearing test apparatus has been useful for the past 40 years in the position / posture control experiments of space vehicles due to economic and safety problems. That is, with the beginning of the space development competition, it has developed almost together. In the case of the two-dimensional type, a free flying robot having a total of three degrees of freedom (one degree of freedom for rotational motion and two degrees of freedom for translational motion) is used. The main purpose of these experiments is to accurately describe the space vehicle dynamics. Free flying robots and granite tables can bring the friction between the robots and the tablets to almost zero, making them an excellent ground tester for rendezvous or docking systems. At present, several universities in the United States and Japan use this two-dimensional type of experimental apparatus.

Consider a linearized governing equation that describes the relative motion between space vehicles. 3 shows coordinates representing a target object and a tracking object. If the target object rotates in a circular orbit around the earth at a constant angular velocity, and the coordinate system shown in Fig. 3 is attached to the target object, the equation of motion can be linearized in close proximity. The well known Clohessy-Wiltshire (CW) equation is described as:

[Equation 12]

here , , Are each Acceleration component acting on the tracking object in the coordinate axis. The above two equations in (12) represent in-plane motion and are related to each other. The final equation is out-plane motion and is not related to in-plane components. In general relative orbital motion, in-plane motion is more important than out-of-plane motion. Since the experimental apparatus developed in this invention is a two-dimensional type, it follows the in-plane equation of motion.

The vision system was used as a way to smoothly approach and dock the free flying robot to another free flying robot. In in-plane motion, it is possible to obtain relative position and posture information with the vision system alone. However, since the vision system does not provide enough sampling frequency, it must be used with information from the Inertial Measurement Unit (IMU), including the gyro.

The present invention is to solve the problems of the conventional free flying robot described above, one CCD camera is installed on the tracking object and a plurality of beacons are arranged on the target object. The aim is to build a free flying robot system that accurately acquires relative position and attitude information by the vision system and compensates for the low sampling frequency characteristics of the vision system by the IMU system including the gyro. In other words, Kalman filtering is performed using all of the vision system, IMU, and gyro information if information can be obtained from the vision system, and Kalman filtering is performed using only IMU and gyro information if information cannot be obtained from the vision system. Perform.

The present invention provides a spacecraft docking robot system, characterized in that the CCD camera is installed on the tracking object, the vision sensor including a plurality of beacons are installed on the target object.

The present invention also provides a space vehicle docking robot system, characterized in that the spacecraft docking robot is composed of a thrust system, power system, CCD camera, gyro, IMU, control and command system.

In addition, the present invention is provided with at least one or more CCD cameras on the tracking object and at least two beacons on the target object, two-dimensional relationship expressed by projecting their relationship on a suitable plane, expressed as described above Distance from the tracking object to the target And angle , Angle It provides a space vehicle docking robot system comprising a vision sensor for calculating the.

In addition, the present invention according to claim 3, wherein the distance between the tracking object and the target object And angle , Angle To provide a space vehicle docking robot system, characterized in that the angle (psi) is calculated by the following equations (a) and (b), respectively.

(a)

(b)

The present invention also provides a space vehicle docking robot system, characterized in that the use of an extended Kalman filter to update the information of the gyro and inertial navigation system and the vision sensor.

In addition, the present invention provides a space vehicle docking robot system characterized in that the position control and attitude control separately in controlling the tracking object.

The present invention also provides a space vehicle docking robot system, characterized in that the control uses a PID controller.

In addition, the present invention uses the information of the vision sensor when the information of the vision sensor is available, the spacecraft docking, characterized in that to estimate the state using the inertial navigation system and gyro information when the information of the vision sensor is not available Provide a robotic system.

In addition, the present invention is characterized by using the measurement equation as shown in the following equation (c) when the information of the vision sensor is available, and using the measurement equation as shown in (d) when the information of the vision sensor is not available. To provide a spacecraft docking robot system.

(c)

(d)

In addition, the present invention is provided with at least one or more CCD cameras on the tracking object and at least two beacons on the target object, two-dimensional relationship expressed by projecting their relationship on a suitable plane, expressed as described above Distance from the tracking object to the target And angle , Angle It includes a vision sensor that calculates the, and adds a gas support pad for spraying the gas on the tracking object and the lower part of the target object to support the space robot, the suspended space vehicle robot moves on the stone platform test It provides a space vehicle docking ground test apparatus, characterized in that it can be.

In addition, the present invention is the space vehicle docking ground test apparatus, the distance between the tracking object and the target object And angle , Angle Angle of difference It provides a space vehicle docking ground test apparatus, characterized in that the calculation by the formula (a) and (b), respectively.

In addition, the present invention is provided with at least one or more CCD cameras on the tracking object and at least two beacons on the target object, two-dimensional relationship expressed by projecting their relationship on a suitable plane, expressed as described above Distance from the tracking object to the target And angle , Angle It includes a vision sensor that calculates the, and adds a gas support pad for spraying the gas on the tracking object and the lower part of the target object to support the space robot, the suspended space vehicle robot moves on the stone platform test It provides a space vehicle alignment flight ground test apparatus characterized in that it is possible to.

In addition, the present invention, in the space vehicle alignment flight ground test apparatus, the distance between the tracking object and the target object And angle , Angle Angle of difference It provides a space vehicle alignment flight ground test apparatus, characterized in that the calculation by the formula (a) and (b), respectively.

In addition, the present invention is provided with at least one or more CCD cameras on the tracking object and at least two beacons on the target object, two-dimensional relationship expressed by projecting their relationship on a suitable plane, expressed as described above Distance from the tracking object to the target And angle , Angle It includes a vision sensor that calculates the, and adds a gas support pad for spraying the gas on the tracking object and the lower part of the target object to support the space robot, the suspended space vehicle robot moves on the stone platform test It provides a space vehicle deployment ground test apparatus, characterized in that it is possible to.

In addition, the present invention, the space vehicle deployment ground test apparatus, the distance between the tracking object and the target object And angle , Angle Angle of difference It provides a space vehicle deployment ground test apparatus, characterized in that to be calculated by the formula (a) and (b), respectively.

Hereinafter, the present invention will be described in more detail.

4 shows the overall system of the free flying robot developed in the present invention. The robotic system consists of three layers, depending on the functional characteristics. The first floor, located at the bottom, consists of a thrust system with a gas tank and eight thrusters, and the second, located in the middle, consists of a power system with a CCD camera, gyro, IMU, and batteries. Finally, the third floor at the top consists of a control and command system with a computer running flight control software. 5 is an overall signal flow diagram as an embodiment of a free flying robot. The free-flying robotic system, loaded with all of these equipments, runs from the initial state (initial position and initial posture) to the target state (target position and target position) with the appropriate thruster fully automatic without any command from the outside.

The lower thruster system is configured to fill the gas tank with nitrogen gas and supply gas to the eight thrusters from the gas tank. Each thruster is connected to the switching circuit and recognizes the signal coming from the control software as an on-off signal. When on, the thruster inlet is opened to allow gas to leak out and thrust is obtained. When off, the thruster inlet is closed to close the gas. To prevent release; The reaction force obtained from the release of the gas thus becomes the thrust source of the aircraft.

Flight control software that commands thrusters includes eight thruster injection algorithms for two-dimensional planar motion and a vision-based state estimator loop. The thruster injection algorithm defines thruster states that must be turned on and off in order to generate a constant torque or force and uses thrust control logic such as Equation 13.

[Equation 13]

here Represents the force or torque required for the aircraft. Indicates the on (1) or off (0) state of the eight thrusters. And Is a matrix describing the relationship between the two. The aircraft used in the present invention used eight thrusters as shown in FIG. 6 to move in a two-dimensional plane. Such a vehicle is represented by Equation 14 The matrix is appropriate. The first and second lines from the thruster state Axis The force generated on the shaft and the third line from the thruster The torque generated on the shaft is related.

[Equation 14]

Equation 13 calculates the torque or force applied to the vehicle from the current state of the thruster, and vice versa. In other words, it is important to know which thrusters are turned on and which thrusters must be turned off so that the torque or force calculated from the software is applied to the aircraft. By defining a pseudo inverse as shown in Equation 15, another equation can be obtained from Equation 13.

[Equation 15]

Using Equations 13 and 15, an equation for obtaining an appropriate F from U can be defined as in Equation 16.

[Equation 16]

Where function Is a function to put a 1 (on) or 0 (off) signal into the thruster, 0.3 is used as a variable that should be appropriately selected in the experiment.

The vision based state estimation used in the present invention is given analytically. Vision-based state estimation in three-dimensional space cannot be obtained analytically because of its nonlinearity, but relative position and attitude information can be analytically obtained from vision information in two-dimensional plane motion.

2 shows the relationship between beacons and CCD cameras in two-dimensional plane motion. Shown in Figure 2 Since the sign of affects the rotation angle calculation, it is necessary to decide in advance. In FIG. 2 Are all negative values. Direction angle Is defined as in equation (17).

[Equation 17]

The pixel position of the beacons on the CCD camera And distance between real beacons Focal length of lens Are five known variables, And the distance between the tracking object and the target object Are the three variables to figure out. Can be found simply as in Equation 18.

Equation 18

P and q, the distances between the beacons on the CCD camera, are defined by Equation 19.

[Equation 19]

From the geometry shown in Figure 2 , Is represented by Equation 20.

[Equation 20]

Solving the equations (18) and (20) together, we can finally calculate the direction angle psi that we want to obtain as shown in equation (21).

[Equation 21]

Since we know all the variables in the right term in Equation 21, we use only one CCD camera and beacons Can be obtained analytically. In a similar manner, the distance between the center beacon and the lens can be obtained as shown in Equation 22.

[Equation 22]

Direction angle Is shown in Equation 21 Wow Represented by the size difference of As shown in Equation 22, it can be seen that mainly depends on the focal length of the lens.

The system to which the algorithm developed in the present invention is applied is a free flying robot system for estimating position and attitude. It has two translational degrees of freedom and one rotational degree of freedom and has a total of three degrees of freedom, and the state vector can be expressed by Equation 23.

[Equation 23]

here And y represent positional information, and psi represents attitude information as a robot's orientation angle variable. At this time, a dynamic PD equation of a free flying robot and a simple PD controller for controlling a position and attitude are described as in Equation (24).

[Equation 24]

here Is a 3 × 3 diagonal inertia matrix Is the force and torque applied to the free flying robot. Also Indicates the state (position and attitude) that the aircraft is aiming for Indicates the state (position and attitude) of the current vehicle. Wow Is the control gain that must be determined through tuning.

The state variables used in (24) are only three as defined in (23). However, in practice, the variables obtained in the sensor are not the variables defined in Equation 23, but their derivative form. Therefore, in order to use the information using the Kalman filter form, the system state vector must be extended as shown in Equation 25.

[Equation 25]

Rewriting Equation 24 into the form of a well-known optimal linear estimator gives the form of Equation 26.

[Equation 26]

here Is assumed to be white noise with constant covariance to make it easier to solve the optimal problem. Is the sampling time, Is the current system state vector.

As shown above, since the vision-based system can obtain position and attitude information directly, and acceleration and yaw velocity from the IMU and gyro, the measurement equation can be expressed as It is described as 27.

[Equation 27]

Is The measured noise and the covariance matrix are set to meaningful values according to the sensor accuracy.

Since Equation 25 and Equation 27 describe the state equation and the measurement equation, respectively, the Kalman filtering described above can be used to estimate the state. However, because the sampling frequency of the vision sensor and the IMU and gyro are different, it is not always possible to get both of them. So when you get two pieces of information at the same time, Information from IMU and Gyro Since information can be obtained, a measurement equation such as Equation 27 is used. However, when only the IMU and gyro information with high sampling frequency is obtained because the vision sensor information is not available. Since only Kalman can be obtained, Kalman filtering is performed using a measurement equation such as Equation 28.

[Equation 28]

7 is a flowchart of a vision based system algorithm in accordance with the present invention.

As shown in (24), the system is clearly linear and the position and attitude control systems are not related to each other. However, in a real system, discrepancies in thruster placement or center of gravity can cause unexpected torque during translation. Therefore, posture correction must be performed continuously. If a simultaneous position and attitude control is to be performed, the appropriate force or torque required for the control can be reduced by the thruster control logic (TCL). In order to eliminate this problem and simplify the docking process, the position and posture control are performed separately.

The vision based system used in the present invention consists of a CCD camera and three beacons. If the CCD camera recognizes the position of the beacon and goes through the appropriate calculation algorithm, the position and attitude information between the tracking and target objects can be obtained very accurately. However, as the tracking robot approaches the beacon, it becomes difficult to accurately recognize the beacon due to the focal length and field of view (FOV) limitations of the CCD camera. Therefore, the tracking object and the target object must be separated by a certain distance to ensure beacon recognition performance. 8 shows a flowchart of an automatic docking procedure.

The present invention adds a gas flotation pad that allows the space robot to support the jet by spraying the gas at the bottom thereof, and is also applied to the spacecraft docking ground test apparatus that enables the floating robot to move and test on the stone platform. This is possible.

The present invention adds a gas flotation pad that allows the space robot to support the jet by spraying the gas to the bottom, and also in the space vehicle alignment flight ground test apparatus that allows the floating space robot to move and test on the stone platform Application is possible.

The present invention adds a gas flotation pad for spraying a gas to a lower portion of the space robot so that the spacecraft robot can support it, and a spacecraft deployment ground test for allowing the floating spacecraft robot to move and test on a stone platform. It can also be applied to devices.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the following examples are not intended to limit the present invention, but various limitations, modifications, and changes are possible without departing from the spirit of the present invention. It will be obvious to him.

Example 1 and Comparative Example 1

A space vehicle docking robot system as shown in FIG. 4 was constructed using a device having the following specifications.

part Specification computer Pentium III 850 MHz CPU, 128 MB RAM, PCI Expansion, IDD (5400 RPM) Data acquisition board 12-bit resolution, 32 digital inputs and outputs, 2 analog outputs Frame grabber 30 MHz sampling, 47 dB signal to noise ratio, 4 channels CCD camera 646 (horizontal) 485 (vertical) pixels, progressive scan IMU 3-axis tachometer and accelerometer, 15 degree / second meander stability, low cost Thruster DC 24 Volt Solenoid Valve Air pad 120 psi operation, recommended weight 50 lbs Stone tablet 2000 (H) 2000 (W) 300 (T) Size Particle size less than 20 microns

A 10mm wide field of view lens was used, and the dispersion of the error of the vision sensor was 0.1 pixel. The controller used PD controller and gain values were K _D = 1.5 and K _P = 1.2.

The following results were obtained for the case of using only the vision sensor and using the expansion Kalman filter.

The use of vision sensors alone converged eventually, but they involved a lot of vibrations until convergence, resulting in poor quality control. However, in the case of using the extended Kalman filter, it was not vibrationless at all, but converged very quickly, and high quality control was obtained.

Example 2 and Comparative Examples 2 and 3

The experiment was performed under the same conditions as in Example 1 and Comparative Example 1.

In the case of using only the gyro, using only the vision sensor, and using both the gyro, the vision sensor, and the extended Kalman filter, the posture control experiments were performed.

When only the vision sensor was used, it was difficult to achieve stable convergence and severe vibration. However, the vision sensor, gyro, and extended Kalman filter converged very quickly and showed satisfactory results with little vibration. The use of gyro alone did not even converge.

Example 3 and Comparative Example 4

The experiment was performed under the same conditions as in Example 1 and Comparative Example 1.

In the case of using only the vision sensor, the position control was experimented by dividing into the case of using both the vision sensor and the extended Kalman filter.

When only the vision sensor was used, it was difficult to obtain stable convergence as in the case of Comparative Example 2. However, when the extended Kalman filter was also used, relatively stable convergence was obtained.

According to the present invention, a ground-based system capable of conducting a space vehicle rendezvous and docking technology on the ground was established. We also developed a free flying robot system that can use the vision system. After the new control software is installed, it runs completely automatically and can easily compare and analyze the performance of rendezvous and docking algorithms.

1 is a conceptual diagram showing a three-dimensional relationship between beacons and a CCD camera in the spacecraft docking robot system according to the present invention.

FIG. 2 is a conceptual diagram illustrating a relationship between a beacon and a CCD camera converted into a two-dimensional plane by projecting a beacon and a CCD camera in an appropriate two-dimensional plane in the spacecraft docking robot system according to the present invention.

3 is a conceptual diagram illustrating coordinates representing a target object and a tracking object in the spacecraft docking robot system according to the present invention.

4 is a conceptual diagram showing the overall appearance of the free flying robot in the spacecraft docking robot system according to the present invention.

5 is a conceptual diagram illustrating an embodiment of the present invention.

6 is a conceptual diagram showing a possible embodiment of the thruster in the spacecraft docking robot system according to the present invention.

7 is a flowchart of a position and attitude state estimation algorithm included in the spacecraft docking robot system according to the present invention.

8 is a flowchart of an automatic docking algorithm included in the spacecraft docking robot system according to the present invention.

<Description of Major Codes in Drawings>

A, B, C: Actual location of beacons A, B, C :

: The angle between the line of sight and the vertical line of the beacon

: The angle between the line of sight and the vertical line of the image plane

Focal length : Distance between tracking object and target object

: Coordinates of beacons A, B, and C on the image plane

: Distance between beacons projected on the plane

: Measurement matrix when the vision sensor is available (measurement sensitivity matrix)

: Measurement matrix when the vision sensor is not available (measurement sensitivity matrix)

Claims (15)

A spacecraft docking robot system, characterized in that the CCD camera is installed on the tracking object, and a vision sensor is installed that includes a plurality of beacons installed on the target object. The method of claim 1, The spacecraft docking robot is a spacecraft docking robot system, characterized in that consisting of a thrust system, power system, CCD camera, gyro, IMU, control and command system. At least one CCD camera is installed in the tracking object and at least two beacons are installed in the target object to express the relationship in two dimensions by projecting the relation on an appropriate plane. Distance of object And angle , Angle Spacecraft docking robot system comprising a vision sensor for calculating the. The method of claim 3, wherein Distance between the tracking object and the target object And angle , Angle Angle of difference The spacecraft docking robot system, characterized in that the calculation by the following formula (A) and (B), respectively. (A) (B) The method of claim 1, A spacecraft docking robot system that uses an extended Kalman filter to update information of gyro and inertial navigation systems and vision sensors. Spacecraft docking robot system, characterized in that the position control and attitude control to control the tracking object separately. The method of claim 6, The control is a space vehicle docking robot system, characterized in that using a PID controller. The spacecraft docking robot system, characterized by using the information of the vision sensor when the information of the vision sensor is available, and using the inertial navigation system and gyro information when the information of the vision sensor is not available. The method of claim 8, When the information of the vision sensor is available, a measurement equation such as the following (C) is used, and when the information of the vision sensor is not available, the spacecraft docking robot is characterized by using the measurement equation of the following (D). system. (C) (D) At least one CCD camera is installed in the tracking object and at least two beacons are installed in the target object to express the relationship in two dimensions by projecting the relation on an appropriate plane. Distance of object And angle , Angle Including a vision sensor to calculate, A gas support pad is added to spray the gas to the tracking object and the lower part of the target object so that the space vehicle robot can support it, and the supported space vehicle robot can move and test on the stone platform. Aircraft docking ground tester. The method of claim 10, Distance between the tracking object and the target object And angle , Angle Angle of difference The spacecraft docking ground test apparatus, characterized in that it is calculated by the formulas (A) and (B), respectively. At least one CCD camera is installed in the tracking object and at least two beacons are installed in the target object to express the relationship in two dimensions by projecting the relation on an appropriate plane. Distance of object And angle , Angle Including a vision sensor to calculate, A gas support pad is added to spray the gas to the tracking object and the lower part of the target object so that the space vehicle robot can support it, and the supported space vehicle robot can move and test on the stone platform. Air vehicle alignment flight tester. The method of claim 12, Distance between the tracking object and the target object And angle , Angle Angle of difference The spacecraft alignment flight ground test apparatus, characterized in that the calculation by the formulas (A) and (B), respectively. At least one CCD camera is installed in the tracking object and at least two beacons are installed in the target object to express the relationship in two dimensions by projecting the relation on an appropriate plane. Distance of object And angle , Angle Including a vision sensor to calculate, A gas support pad is added to spray the gas to the tracking object and the lower part of the target object so that the space vehicle robot can support it, and the supported space vehicle robot can move and test on the stone platform. Air vehicle deployment ground tester. The method of claim 14, Distance between the tracking object and the target object And angle , Angle Angle of difference The spacecraft deployment ground test apparatus, characterized in that it is calculated by the formulas (A) and (B), respectively.