Teleoperation robot auxiliary control method based on safety margin theory

Abstract

The invention relates to the technical field of teleoperation, in particular to a teleoperation robot auxiliary control method based on a safety margin theory. The method comprises the following steps: establishing a three-dimensional digital twinborn model according to an image acquisition device, and obtaining a target object distance, a communication time delay size and a motion tracking error; based on the three factors, a fuzzy control theory is adopted to calculate a teleoperation safety margin value, and the safety of the current teleoperation is quantitatively evaluated. And calculating a master-slave motion mapping ratio and an end view display ratio according to the safety margin value. And constructing virtual force guidance according to the safety margin value, feeding back the virtual force guidance to an operator through force feedback equipment, and guiding the operator to complete a teleoperation task in combination with visual interaction equipment. Compared with a traditional teleoperation method, the method can effectively improve teleoperation efficiency and safety, and can better adapt to a complex long-distance working environment.

Description

Technical Field

[0001] The invention relates to the technical field of teleoperation, and in particular to a teleoperation robot auxiliary control method based on safety margin theory. Background Art

[0002] In the face of complex space payload on-orbit services such as space assembly, component replacement, and equipment maintenance, the existing fully autonomous intelligent robots are unable to perform the tasks due to the uncertainty and complexity of the space environment, and teleoperation technology is needed to replace the operator to complete the tasks. Compared with traditional ground teleoperation, teleoperation for on-orbit services also faces the difficulties of large communication delays, uncertain delays, and high requirements for the safety and stability of the task.

[0003] In order to improve the accuracy and stability of teleoperation and overcome operational errors and even collisions caused by hand shaking and misoperation, researchers have proposed auxiliary methods such as virtual force guidance based on the artificial potential field method. However, in the establishment of the artificial potential field, the end of the robotic arm and external objects are generally simplified to a point, ignoring the three-dimensional properties of the entire component. At the same time, the current two-dimensional artificial potential field model is generally not suitable for space environments. In addition, the traditional operation auxiliary guidance does not take into account the large delay factor of space communication, but the delay has become the core factor restricting the stability of teleoperation. Therefore, it is necessary to establish a new teleoperation auxiliary guidance method to assist operators to complete precision teleoperation tasks more successfully and stably. Summary of the invention

[0004] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a remote operation safety evaluation method and a remote operation auxiliary control method that comprehensively considers three factors: the distance of the robot arm relative to obstacles and targets, communication delay, and motion tracking error. The remote operation safety margin is established based on fuzzy theory, and then the virtual guidance force and motion mapping scale factor are calculated based on the safety margin value, and the target area at the end of the robot arm is dynamically enlarged and displayed based on the motion mapping scale factor. The problem that the traditional remote operation control method cannot adapt to the overall obstacle avoidance of the robot arm and it is difficult to control the robot arm to complete complex and precise operations under large delays is solved, thereby assisting the operator to more successfully and stably complete the variable delay precision remote operation task.

[0005] To achieve the above object, according to the present invention, a remote-operated robot auxiliary control method based on safety margin theory for remote control of a space manipulator is provided, the method comprising the following steps:

[0006] Step 1: Obtain the position and posture of obstacles and targets, as well as the position and posture of the end of the slave robot arm according to the image acquisition device, and establish a digital twin 3D model of the work scene based on the 3D data of the assembly scene.

[0007] Step 2: Use the expansion method to calculate the relative distance of the robot arm to the obstacle and the target, obtain the current teleoperation communication delay value, and obtain the size of the current teleoperation posture tracking deviation.

[0008] Step 3: Considering the distance of the robot arm relative to obstacles and targets, communication delay and posture tracking deviation, the safety margin of the current teleoperation is calculated by fuzzy method.

[0009] Step 4: Calculate the motion mapping scale factor based on the current safety margin and dynamically adjust the master-slave motion mapping relationship.

[0010] Step 5: Create a point cloud image of the work scene based on the point cloud data of the depth camera. Calculate the view focal length of the digital twin model and point cloud image based on the current safety margin, and dynamically zoom in on the target area.

[0011] Step 6: The virtual feedback force generated according to the current safety margin is fed back to the operator through the force feedback device to achieve physical feedback of remote operation safety. Combined with the visual interaction device, the grasping and assembly tasks are performed on the target.

[0012] The image acquisition device is an RGB-D camera, and the acquired images also include RGB images;

[0013] As a preferred implementation, in step S1, the image acquisition device acquires the position and posture of obstacles and targets in the following process:

[0014] According to the pixel coordinate Z of the feature point of the positioning marker in the RGB image i (u i , v i ) and the imaging model of the RGB-D camera, and using the positional relationship between the depth camera and the color camera in the RGB-D camera, the three-dimensional coordinates P of the feature points of the positioning marker in the camera coordinate system are obtained. i '(x ci ,y ci , z ci ).

[0015] Get the coordinates Pi(x) of the positioning marker in the robot coordinate system wi ,y wi , z wi ) and the three-dimensional coordinates P of the feature points of the positioning marker in the camera coordinate system i '(x ci ,y ci , z ci ), calculate the transformation matrix between the robot coordinate system and the camera coordinate system.

[0016] The end position of the slave robot arm is read by the robot arm controller.

[0017] Furthermore, a digital twin model of the teleoperation environment is established based on the three-dimensional model of the current assembly, the three-dimensional model of the robot, and the relative position relationship between the current assembly and the robot.

[0018] Further preferably, in step S2, the relative distance of the robot arm to the obstacle and the target object is calculated in the following manner:

[0019] In the digital twin model established in real time, each joint link of the robotic arm is enlarged at a certain ratio, and the proportional coefficient when the enlarged robotic arm collides with an external object is used as the relative distance value.

[0020] The proportional amplification relationship calculation formula is as follows:

[0021] Among them, R i represents the diameter of joint link i, H i represents the length of joint link i, R i0 represents the initial diameter of joint link i, H i0 It represents the initial length of joint link i, i represents the joint link number, k is the magnification ratio, and also the relative distance value.

[0022] The communication delay is calculated as follows:

[0023] The master sends a mark signal, the slave receives the mark signal and feeds back a master response signal, and the time difference from the master sending the mark signal to receiving the slave response signal is used as the delay value of the current instruction cycle.

[0024] The delay value calculation formula of the current instruction cycle is as follows: t i =t i-recevie --t i-send

[0025] Among them, t i Indicates the delay value of instruction cycle i, t i-recevie Indicates the time when the master sends the mark signal in cycle i, t i-send It indicates the time when the master receives the mark signal in cycle i, where i indicates the instruction cycle.

[0026] The current tracking position deviation is calculated as follows:

[0027] Among them, D i Indicates the position tracking deviation value of instruction cycle i, P i-tar represents the expected position of the end of the robot arm in period i (P x-tar , P y-tar , Pz-tar ), P i Indicates the actual position of the end of the robot in period i (P x , P y , P z ), R i represents the desired posture of the end of the robot arm in period i (R x-tar ,R y-tar , R z-tar ), R i Indicates the actual posture of the end of the robot arm in period i (R x , R y , R z ).

[0028] As a preferred implementation, in step S3, the method for calculating the safety margin by fuzzy theory is:

[0029] Fuzzification of input parameters: The three input quantities of the collected robot arm's distance to obstacles and targets, communication delay and motion tracking error are scaled to be transformed into their respective domains, and the input quantities transformed into the domain are fuzzily segmented to achieve fuzzification of input parameters.

[0030] Further preferably, the relative obstacle distance is fuzzified into: {collision, very close, very close, relatively close, relatively far}; the communication delay is fuzzified into: {super long, very long, very long, relatively long, short}; the motion tracking error is fuzzified into: {super large, very large, very large, relatively large, almost none};

[0031] Establishment of fuzzy rule base: 1) Based on the experience and control engineering knowledge of experts; 2) Based on the actual control process of operators; Establish a fuzzy rule base between input parameters and safety margins.

[0032] Fuzzy reasoning: fuzzy rule reasoning based on the established fuzzy rule base;

[0033] Preferably, the fuzzy reasoning method is fuzzy reasoning based on the Mamdani model.

[0034] Fuzzy subset clarification: transform the safety fuzzy control subset obtained by fuzzy reasoning into a clarified safety margin with a clear value.

[0035] Preferably, in step S4, the relationship for calculating the motion mapping scale factor based on the safety margin value is: K p =q*S v

[0036] Where K p represents the motion mapping ratio, S v represents the safety margin, and q is the conversion ratio.

[0037] Preferably, the conversion relationship between the master end teaching posture and the slave end robot arm tracking posture is: P s =K p *P m

[0038] Where P s represents the motion pose increment of the slave end, K p represents the motion mapping ratio, P m Indicates the motion pose increment of the master end.

[0039] Further preferably, in step S5, the method for calculating the view focal length of the digital twin model and the point cloud image according to the current safety margin is: V k =V 0 *S v

[0040] Where V k Represents the focal length of the view, S v Represents the safety margin, V 0 is the initial focal length.

[0041] The point cloud image data is acquired by an RGB-G camera and presented in a rendering engine.

[0042] Further preferably, in step S6, the machine virtual force guidance is calculated in the following manner: F g =f 0 *S v

[0043] Among them, F g represents the feedback guiding force, S v represents the safety margin, f 0 is the initial force.

[0044] The force feedback device is a desktop force feedback device or a fingertip tactile force feedback device:

[0045] The present invention has the following beneficial effects and advantages:

[0046] 1. The present invention uses a safety margin method, which uses fuzzy theory to construct a remote operation safety evaluation method. The three factors of the relative distance between the target and the obstacle, the size of the time delay and the position deviation are superimposed, and the fuzzy theory is used to obtain the relative safety margin value of the end of the manipulator moving in an unknown environment, and the safety of the current remote operation is quantitatively evaluated;

[0047] 2. The present invention adopts an expansion method to calculate the distance between the robot arm and the obstacle and the target object, avoiding the complexity and oversimplification of the traditional point distance calculation. The calculation of the three-dimensional distance between the robot arm and the target object is realized, with small calculation amount, high real-time performance and strong practicality.

[0048] 3. The present invention creates a virtual feedback force based on the safety margin. The virtual feedback force is linearly mapped to the safety margin, realizing intuitive physical feedback of the current operation safety. This force information can be used as a supplement to the visual information, greatly reducing the operator's operating burden while improving the operating efficiency, and ensuring the efficient and accurate completion of the obstacle avoidance task.

[0049] 4. Aiming at the robot's obstacle avoidance and target position approach operations, the present invention introduces a safety margin method, a variable-scale motion mapping method, and a prominent target area display method, which are applied to the robot's control system and interactive interface. In the case of low safety margins such as approaching obstacles or targets and when the delay is large, the motion mapping ratio of the master and slave ends is reduced and the display ratio of the terminal view area is increased to ensure efficient and accurate completion of obstacle avoidance and assembly tasks.

[0050] 5. The present invention establishes a remote operation behavior safety evaluation method and an auxiliary control method based on multi-factor and fuzzy control theory, which effectively improves the obstacle avoidance rate of the robotic arm, enhances the safety of operation, reduces the operator's operating load, etc., and effectively helps the operator control the robotic arm to stably and safely complete precision operation tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 4 is a framework diagram of a remote operation system according to an embodiment of the present invention.

[0052] Figure 2 4 is a control flow chart of the remote process according to an embodiment of the present invention.

[0053] Figure 3 Schematic diagram of obstacle distance calculation using the remote expansion method according to an embodiment of the present invention.

[0054] Figure 4 This is a schematic diagram of the safety margin calculation principle based on fuzzy theory according to an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation method of the present invention is described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the invention, so the present invention is not limited by the specific implementation disclosed below.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0057] The present invention proposes a safety evaluation method that comprehensively considers the superposition of three factors: the distance of the robot arm relative to obstacles and targets, the size of the communication delay, and the position deviation. The safety margin is calculated by a fuzzy method. The virtual guidance force and the motion mapping scale factor are calculated according to the safety margin, and the dynamic enlarged display target area is adjusted according to the motion mapping scale factor. The traditional artificial potential field method is unable to adapt to the overall obstacle avoidance of the robot arm and the problem of completing complex and precise operations under difficult-to-control robot arms with large delays. The method assists operators to complete precise teleoperation tasks more successfully and stably.

[0058] The present invention provides a remote robot auxiliary control method based on safety margin theory, as shown in the attached Figure 1 The remote operation system mainly includes a physical interaction unit, a digital twin unit, and a slave control unit.

[0059] The physical interaction unit includes visual devices, motion trackers, force tactile interactors and other devices.

[0060] The visual device of this embodiment is preferably an HTC visual display and display.

[0061] The motion tracker is preferably an HTC vive tracker locator, and the force tactile sensor is preferably a self-developed feedback exoskeleton glove and airbag tactile glove to achieve interaction between force and touch.

[0062] The slave control unit includes visual sensors, force sensors, slave robots and other equipment.

[0063] The visual sensor in this embodiment is preferably a Kinect V2 depth camera, and the collected images also include RGB images; the slave robot is preferably a UR robot.

[0064] As attached Figure 2 As shown in the figure, a teleoperated robot assisted control method based on safety margin theory, the overall process can be expressed as:

[0065] Step 1: Obtain the position and posture of obstacles and targets, as well as the position and posture of the end of the slave robot arm according to the image acquisition device, and establish a digital twin 3D model of the work scene based on the 3D data of the assembly scene.

[0066] The process of obtaining the position and posture of obstacles and targets by the image acquisition device is as follows:

[0067] According to the pixel coordinate Z of the feature point of the positioning marker in the RGB image i (u i ,v i ) and the imaging model of the RGB-D camera, and using the positional relationship between the depth camera and the color camera in the RGB-D camera, the three-dimensional coordinates P of the feature points of the positioning marker in the camera coordinate system are obtained. i '(x ci ,y ci ,z ci ).

[0068] Get the coordinates P of the positioning marker in the robot coordinate system i (x wi ,y wi ,z wi ) and the three-dimensional coordinates P of the feature points of the positioning marker in the camera coordinate system i '(x ci ,y ci ,z ci ), calculate the transformation matrix between the robot coordinate system and the camera coordinate system.

[0069] The end position of the slave robot arm is directly read by the robot arm controller through instructions.

[0070] Based on the 3D model of the current assembly, the 3D model of the robot, and the relative position relationship between the current assembly and the robot, a digital twin model of the teleoperation environment is established.

[0071] Step 2: Calculate the relative distance of the robot arm to the obstacle and the target. Obtain the current teleoperation communication delay. Obtain the current teleoperation motion tracking deviation.

[0072] The relative distances of the robot arm to obstacles and targets are calculated as follows:

[0073] In the digital twin model established in real time, each joint link of the robotic arm is enlarged at a certain ratio, and the enlarged proportional coefficient when the enlarged robotic arm collides with an external object is used as the relative distance value.

[0074] The formula for calculating the proportional amplification relationship is as follows:

[0075] Among them, R i represents the diameter of joint link i, H i represents the length of joint link i, R i0 represents the initial diameter of joint link i, H i0 It represents the initial length of joint link i, i represents the joint link number, k is the magnification ratio, and also the relative distance value.

[0076] The communication delay is calculated as follows:

[0077] The master sends a mark signal, the slave receives the mark signal and feeds back a master response signal, and the time difference from the master sending the mark signal to receiving the slave response signal is used as the delay value of the current instruction cycle.

[0078] The delay value calculation formula of the current instruction cycle is as follows: t i =t i-recevie --t i-send

[0079] Among them, t i Indicates the delay value of instruction cycle i, t i-recevie Indicates the time when the master sends the mark signal in cycle i, t i-send It indicates the time when the master receives the mark signal in cycle i, where i indicates the instruction cycle.

[0080] The current tracking position deviation is calculated as follows:

[0081] Among them, D i Indicates the position tracking deviation value of instruction cycle i, P i-tar represents the expected position of the end of the robot arm in period i (P x-tar , P y-tar , P z-tar ), P i Indicates the actual position of the end of the robot in period i (P x , P y , P z ), R i represents the desired posture of the end of the robot arm in period i (R x-tar , R y-tar , R z-tar ), R i Indicates the actual posture of the end of the robot arm in period i (R x , R y , R z ).

[0082] Step 3: Comprehensively consider the distance of the robot arm relative to obstacles and targets, the communication delay and the current movement speed, and calculate the safety margin through fuzzy method.

[0083] The method for calculating the safety margin by fuzzy theory is as follows:

[0084] Input quantity transformation: The three input quantities of the collected robot arm's distance to obstacles and targets, communication delay and motion tracking error are scaled and transformed into their respective domains.

[0085] The input scale transformation method is:

[0086] where [x min , x max ] is the domain, and k is the input scale factor.

[0087] Fuzzy segmentation: Fuzzy segmentation is performed on the input quantity transformed into the domain range to realize the fuzzification of input parameters. The relative obstacle distance is fuzzy segmented as: {collision, very close, very close, relatively close, relatively far}; the communication delay is fuzzy as: {very long, very long, very long, relatively long, short}; the motion tracking error is fuzzy as: {very large, very large, very large, relatively large, almost non-existent};

[0088] Establishment of fuzzy rule base: 1) Based on the experience and control engineering knowledge of experts; 2) Based on the actual control process of operators; Establish a fuzzy rule base between input parameters and safety margins.

[0089] The fuzzy relationship between the safety margin and the three factors can be described by the following fuzzy rules: R1: If the relative distance is very close, AND the delay is very long, AND the tracking error is very large, then the safety margin is very dangerous R2: If the relative distance is far, AND the delay is short, AND the tracking error is large, then the safety margin is safer ...... R25: If the relative distance is very close, AND the delay is long, AND the tracking error is large, then the safety margin is more dangerous

[0090] Fuzzy reasoning: fuzzy rule reasoning is performed based on the established fuzzy rule base; the fuzzy reasoning method is fuzzy reasoning based on the Mamdani model.

[0091] Fuzzy subset clarification: transform the safety fuzzy control subset obtained by fuzzy reasoning into a clarified safety margin with a clear value.

[0092] Step 4: Calculate the motion mapping scale factor based on the current safety margin and dynamically adjust the motion mapping relationship between the master and slave ends.

[0093] The relationship for calculating the motion mapping scale factor based on the safety margin is: K p =q*S v

[0094] Where K p represents the motion mapping ratio, S v represents the safety margin, and q is the conversion ratio.

[0095] The conversion relationship between the master end teaching posture and the slave end robot arm tracking posture is: P s =K p *P m

[0096] Where P s represents the motion pose increment of the slave end, K p represents the motion mapping ratio, P m Indicates the motion pose increment of the master end.

[0097] Step 5: Display the focal length according to the current safety margin meter view image, and dynamically zoom in on the target area. Create a point cloud image of the working scene based on the point cloud data of the depth camera.

[0098] The conversion relationship between the safety margin and the view display focal length is: V k =V 0 *S v

[0099] Among them, V k Represents the focal length of the view, S v Represents the safety margin, V 0 is the initial focal length.

[0100] Step 6: The virtual force feedback generated based on the constructed safety field is fed back to the operator through the force feedback device. Combined with the visual interaction device, the teleoperator is guided to control the robotic arm to reach the target position and perform grasping and assembly tasks on the target.

[0101] The virtual force guidance of the machine is calculated in the following manner: F g =f 0 *S v

[0102] Among them, F g represents the feedback guiding force, S v represents the safety margin, f 0 is the initial force.

[0103] The force feedback device is a fingertip tactile force feedback device:

[0104] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A remote robot assisted control method based on safety margin theory, characterized in that: The method comprises the following steps: Step 1: Obtain the positions and postures of obstacles and targets, as well as the end position and posture of the slave robot arm according to the image acquisition device, and establish a digital twin 3D model of the work scene according to the 3D data of the assembly scene; Step 2: Calculate the relative distance of the manipulator to the obstacle and the target object by using the expansion method, obtain the current teleoperation communication delay, and obtain the magnitude of the current teleoperation posture tracking deviation; Step 3: Comprehensively consider the distance between the robot arm and the obstacle and the target, the communication delay and the posture tracking deviation, and calculate the safety margin through fuzzy theory; Step 4: Calculate the motion mapping scale factor according to the current safety margin and dynamically adjust the master-slave motion mapping relationship; Step 5: Create a point cloud image of the working scene based on the point cloud data of the depth camera; calculate the view focal length of the digital twin model and the point cloud image based on the current safety margin, and dynamically zoom in to display the target area; Step 6: Generate virtual feedback force based on the current safety margin, and feed it back to the operator through the force feedback device. Combined with the visual interaction device, guide the remote operator to control the robotic arm to reach the target position and perform grasping and assembly tasks on the target.

2. A teleoperated robot assisted control method based on safety margin theory as claimed in claim 1, characterized in that: In step S1, the method by which the image acquisition device obtains the position and posture of obstacles and targets is: According to the pixel coordinate Z of the feature point of the positioning marker in the RGB image i (u i ,v i ) and the imaging model of the RGB-D camera, and using the positional relationship between the depth camera and the color camera in the RGB-D camera, the three-dimensional coordinates P of the feature points of the positioning marker in the camera coordinate system are obtained. i '(x ci ,y ci ,z ci ); Get the coordinates P of the positioning marker in the robot coordinate system i (x wi ,y wi ,z wi ) and the three-dimensional coordinates P of the feature points of the positioning marker in the camera coordinate system i '(x ci ,y ci ,z ci ), calculate the transformation matrix between the robot coordinate system and the camera coordinate system; A digital twin model of the teleoperation environment is established based on the 3D model of the current assembly, the 3D model of the robot, and the relative position relationship between the current assembly and the robot; The end position of the slave robot arm is read by the robot arm controller.

3. A remote robot assisted control method based on safety margin theory as claimed in claim 1, characterized in that: In step S2, the relative distance of the robot arm to the obstacle and the target is calculated in the following manner: In the digital twin model established in real time, each joint link of the robotic arm is enlarged at a certain ratio, and the proportional coefficient when the enlarged robotic arm collides with the external object is used as the relative distance value; The formula for calculating the proportional amplification relationship is as follows: Among them, R i The table shows the diameter of the joint link i, H i represents the length of joint link i, R i0 represents the initial diameter of joint link i, H i0 represents the initial length of joint link i, i represents the joint link number, k is the magnification ratio, and is also the relative distance value; The communication delay is calculated as follows: The master sends a mark signal, the slave receives the mark signal and feeds back the master response signal, and the time difference from the master sending the mark signal to receiving the slave response signal is used as the delay value of the current instruction cycle; The delay value calculation formula of the current instruction cycle is as follows: t i =t i-recevie -t i-send Among them, t i Indicates the delay value of instruction cycle i, t i-recevie Indicates the time when the master sends the mark signal in cycle i, t i-send It indicates the time when the master receives the mark signal in cycle i, where i indicates the instruction cycle; The current tracking position deviation is calculated as follows: Among them, D i Indicates the position tracking deviation value of instruction cycle i, P i-tar represents the expected position of the end of the robot in period i (P x-tar ,P y-tar ,P z-tar ),P i Indicates the actual position of the end of the robot in period i (P x ,P y ,P z ), R i represents the desired posture of the end of the robot arm in period i (R x-tar ,R y-tar ,R z-tar ), R i Indicates the actual posture of the end of the robot arm in period i (R x ,R y ,R z ).

4. A teleoperated robot assisted control method based on safety margin theory as claimed in claim 1, characterized in that: In step S3, the method for calculating the safety margin by fuzzy theory is: Fuzzification of input parameters: The three input quantities of the collected robot arm relative to obstacles and targets, communication delay and motion tracking error are scaled to transform them into their respective domains, and the input quantities transformed into the domains are fuzzily segmented to achieve fuzzification of input parameters; Establishment of fuzzy rule base: 1) Based on the experience and control engineering knowledge of experts; 2) Based on the actual control process of operators; Establishment of fuzzy rule base between input parameters and safety margins; Fuzzy reasoning: fuzzy rule reasoning is performed based on the established fuzzy rule base; further, the fuzzy reasoning method is fuzzy reasoning based on the Mamdani model; Fuzzy subset clarification: transform the safety fuzzy control subset obtained by fuzzy reasoning into a clarified safety margin with a clear value.

5. A three-dimensional virtual force field visual enhancement method for robot arm manipulation as claimed in claim 1, characterized in that: The relationship for calculating the motion mapping scale factor based on the safety margin is: K p =q*S v Where K p represents the motion mapping ratio, S v represents the safety margin, and q is the conversion ratio.

6. A teleoperated robot assisted control method based on safety margin theory as claimed in claim 1, characterized in that: The conversion relationship between the master end teaching posture and the slave end robot arm tracking posture is: P s =K p *P m Where P s represents the motion pose increment of the slave end, K p represents the motion mapping ratio, P m Indicates the motion pose increment of the master end.

7. A teleoperated robot assisted control method based on safety margin theory as claimed in claim 1, characterized in that: The virtual feedback force of the machine is calculated in the following manner: F g =f0*S v Among them, F g represents the virtual feedback force, S v represents the safety margin, and f0 is the initial force.

8. A teleoperated robot assisted control method based on safety margin theory as claimed in claim 1, characterized in that: The image acquisition device is an RGB-D camera, and the acquired images also include RGB images; the force feedback device is a desktop force feedback device or a fingertip tactile force feedback device.

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