Hip and knee joint bone cutting robot motion control method based on virtual constraining force field

Abstract

The invention belongs to the field of robot control, and particularly relates to a hip and knee joint osteotomy robot motion control method based on a virtual constraint force field, which comprises the following steps: generating an initial constraint boundary according to bone surface point cloud data, generating an inner boundary and an outer boundary based on a boundary center point and a scaling factor, and constructing the virtual constraint force field in the outer boundary range; the input force applied to the tail end of the robot is compensated, and the compensation rate of the field intensity to the stress is obtained; the compensation rates corresponding to the current and next moment positions are compared through the index, the motion trend of the robot tail end relative to the constraint boundary is judged, a complete control strategy is provided, and the situation that the initial position of the robot tail end is outside the boundary is solved; an attenuation function is introduced to adjust the control strategy, the response of the robot to misjudgment is inhibited, an admittance formula is modified in combination with the control strategy, and the position actually issued to the robot is calculated; a control strategy is verified through a Matlab simulation experiment, and boundary changes are dynamically adapted.

Description

Technical Field

[0001] The present invention belongs to the field of robot control, and specifically relates to a motion control method for a hip-knee osteotomy robot based on a virtual constraint force field. Background Art

[0002] With the development of science and technology, the in-depth integration of key technologies such as visual positioning, force control, trajectory planning, and constraint with robots enables robots to have certain perception and decision-making abilities and are widely used in the medical field. With high precision, stability, and flexibility, robots have gradually become important auxiliary tools and can complete a variety of complex tasks. Along with the increasing complexity of the experimental environment of hip-knee models and the high requirements for mechanical operation accuracy, robot control decisions face huge challenges. In view of the above situation, how robots make correct decisions in a complex and changeable environment has become a key research issue.

[0003] In a hip-knee osteotomy robot system, a six-dimensional force sensor is installed at the end of the robotic arm, and the other end of the sensor is connected to a saw. The six-dimensional force sensor provides accurate force feedback for admittance control, thereby guiding the movement of the robot end to follow the arm. In high-precision simulation experiment operations, it is crucial to ensure the safety and stability of the robot, and it is necessary to effectively constrain the movement range of the robot. As a mechanical boundary, virtual constraints provide spatial constraints for the robot in real time, restrict the movement of the robot, prevent improper operations, and avoid the robot exceeding the safe operation range. In order to achieve precise force control and boundary constraints for the robot, a large amount of calculations usually need to be performed in real time to obtain the distance information between the robot end and the constraint boundary. At the same time, during the experimental osteotomy process, in the face of changes in the bone model morphology and adjustments of the robot posture, the virtual boundary will change, resulting in the initial position of the robot end being outside the virtual boundary. In addition, due to the geometric complexity of the osteotomy plane polygon and the discreteness of the robot's issued trajectory, during the process of the robot end moving away from or approaching the boundary, the distance information at some intermediate moments may violate the overall trend, which will affect the overall decision-making process. In addition, the requirement for the motion compliance of the robot also needs to be met.

[0004] These problems pose strict requirements for the virtual constraint motion control strategy of the hip-knee osteotomy robot end, and an efficient method is needed to cope with the dynamic changes of the environment while taking into account compliance, safety, and precision. Summary of the Invention

[0005] The purpose of the present invention is to provide a motion control method for a hip-knee osteotomy robot based on a virtual constraint force field, which can effectively constrain the movement range of the robot end during the experimental simulation process and make correct strategy adjustments according to the changing environment.

[0006] The technical solution adopted by the present invention to achieve the above object is: a motion control method for a hip and knee osteotomy robot based on a virtual binding force field, including the following steps:

[0007] 1) Generate an initial constraint boundary based on the bone point cloud data, generate inner and outer boundaries based on the boundary center point and the scaling factor, and construct a virtual binding force field within the outer boundary range;

[0008] 2) Calculate the field strength according to the distance information of the boundary obtained in step 1), the field strength corresponds to a compensation rate, and then dynamically compensate the input force through the compensation rate;

[0009] 3) Obtain the three-dimensional force / torque information of the robot end in real time through a six-dimensional force sensor, calculate the target position at the next moment in combination with the admittance control algorithm, and judge the motion trend of the robot end based on the comparison between the compensation rate at the current moment and the compensation rate at the next moment, and dynamically adjust the force compensation intensity;

[0010] 4) Introduce an attenuation function to correct the motion control strategy in step 3) to suppress the control instability caused by sudden changes in local distance information;

[0011] 5) Verify the control strategy through Matlab simulation experiments to ensure that when the initial position of the robot end is outside the boundary, it is pulled back inside the boundary and its motion trajectory is constrained within the virtual boundary, and dynamically adapt to boundary changes.

[0012] The virtual binding force field is divided into an internal area, a transition area, and an external strong field area, where the internal area is not affected by the force field, the field strength in the transition area gradually increases as it approaches the boundary, and the field strength in the external strong field area gradually increases as it moves away from the boundary.

[0013] In step 1), the generation of the inner and outer boundaries based on the boundary center point and the scaling factor is specifically as follows:

[0014] According to the boundary center point (x center , y center ) and the scaling factor scale_factor, generate the inner and outer boundaries in equal proportion, that is:

[0015] x new = x center + scale_factor·(x - x center )

[0016] y new = y center + scale_factor·(y - y center )

[0017] Among them, x is the boundary point, x newFor the generated inner and outer boundary points, the scaling factor takes values of 0.5 and 1.5, which are used to generate the contracted and expanded boundaries respectively.

[0018] In step 1), constructing a virtual constraint force field within the outer boundary specifically includes:

[0019] It is stipulated that the internal area within the inner boundary is not affected by the constraint force field, and the system responds to forces normally in this area;

[0020] The transition area outside the inner boundary and within the outer boundary is a weak constraint force field, and this force field gradually increases as it approaches the boundary. The system will compensate for part of the force in this area;

[0021] The external area outside the boundary and within the outer boundary is a strong constraint force field, and the field strength gradually increases as it moves away from the boundary. This strong field will completely compensate for the given force.

[0022] The specific content of step 2) is as follows:

[0023] Discrete points are generated equidistantly along the x and y axes within the outer boundary, and the shortest distance distance from each discrete point to the boundary is calculated min , and the field strength of the constraint force field is calculated based on this distance, and the field strength corresponds to the compensation rate to complete the mapping from discrete points to the compensation rate;

[0024] Let the compensation rate be c, and define distance maxmin as the maximum value of the shortest distances from all points within the transition area to the boundary, then the compensation rate is:

[0025]

[0026] When the point is located in the external strong field area, its compensation rate is greater than 1. If the compensation condition is met, the robot system will compensate 100% of the input force.

[0027] In step 3), adjusting the force compensation based on the change trend of the compensation rate between the current moment and the next moment specifically includes:

[0028] Index four discrete points near the current position of the robot end, and calculate their average compensation rate to determine the current compensation rate c t ;

[0029] According to the compensation rate c t+1 corresponding to the position at the next moment and c t to judge the movement trend of the robot end:

[0030] If c t+1 > c t and the robot end is located within the boundary, it is determined to be approaching the boundary and the force compensation needs to be enhanced;

[0031] If c t+1>c t And the end of the robot is located outside the boundary, it is determined to be far from the boundary, and the input force needs to be fully compensated;

[0032] If c t+1 <c t And the end of the robot is located inside the boundary, it is determined to be far from the boundary, and the force compensation is stopped;

[0033] If c t+1 <c t And the end of the robot is located outside the boundary, it is determined to be approaching the boundary, and the force compensation is stopped.

[0034] In step 4), the attenuation function is specifically:

[0035] When the compensation rate continues to decline, the amplitude mutation is suppressed by introducing the attenuation function f(x). The expression of the attenuation function f(x) is:

[0036]

[0037] Among them, x is the continuous number of times of the compensation rate decline, and only takes integer values;

[0038] The attenuation values for x = 1 to 30 are pre-calculated and stored in an array, and directly indexed during real-time call; when x > 30, it is assigned 0 to improve the calculation efficiency.

[0039] The introduction of the attenuation function to correct the motion control is specifically:

[0040] The specific formula of the admittance control introducing the control strategy and the attenuation function is:

[0041]

[0042] Among them, x t+1 is the position of the next moment actually sent to the robot. M, B, and K are the inertia parameter, damping parameter, and stiffness parameter of the admittance control respectively. c t is the compensation rate corresponding to the current position, and c t+1 is the compensation rate corresponding to the position of the next moment calculated by the normal admittance formula.

[0043] The step 5) is specifically:

[0044] A constant force of 30 N is applied by hand, the arrow points to the direction of the force, the initial position of the end of the robot is given, and the actual position of the end at the next moment is calculated by the above-mentioned admittance formula introducing the control strategy and the attenuation function;

[0045] Given Δt = 0.07, admittance control parameters: M = 1, B = 10, K = 0; according to the initial position and the direction of the force, the relevant force information of the four cases of the control strategy is respectively plotted to obtain the compensation force curve without introducing the attenuation function and the compensation force and resultant force curves with the introduction of the attenuation function during the movement process for comparison and verification.

[0046] The present invention has the following beneficial effects and advantages:

[0047] 1. The present invention constructs a virtual constraint force field based on the distance information, and can smoothly and flexibly compensate the force applied to the end of the bone-cutting robot based on the distance information of the end of the bone-cutting robot obtained in real time, thereby adjusting the force response of the end of the robot and restricting its movement range. This process does not introduce repulsive force, ensuring the compliance, accuracy and safety of the model bone-cutting experiment process.

[0048] 2. The present invention takes into account the dynamic change of the virtual constraint boundary during the model bone-cutting experiment process. Especially when the constraint boundary shrinks, the initial position of the end of the robot will exceed the set boundary range. The present invention processes the above situation by adjusting the compensation method, dynamically adapting to the change of the virtual constraint boundary during the model bone-cutting experiment process, and ensuring that the robot can return and remain within the updated constraint range.

[0049] 3. The present invention introduces an attenuation function to deal with the complexity of the polygon structure of the bone-cutting plane and the discreteness of the robot path points and the virtual constraint force field, which may lead to improper judgment of the overall movement trend of the end of the robot relative to the boundary, and suppress the local control instability caused by the above problems. When facing the sudden change of the distance trend information, it still maintains a robust response to the overall trend, avoiding the abrupt response caused by local errors, and ensuring the stability and safety of the robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic diagram of the boundary of the bone-cutting plane in the present invention;

[0051] Figure 2 It is a schematic diagram of the boundary of the bone-cutting plane and the scaling boundary in the present invention;

[0052] Figure 3 It is a schematic diagram of the virtual constraint force field constructed in the present invention;

[0053] Figure 4 It is a schematic diagram of the principle of the virtual constraint motion control method in the present invention;

[0054] Figure 5 It is a graph of the attenuation function designed in the present invention;

[0055] Figure 6 It is a graph of the running trajectory of the end of the robot in four cases of the simulation experiment in the present invention;

[0056] Figure 7 This is the force information curve graph for four cases of the simulation experiment of the present invention;

[0057] Figure 8 This is the overall method flowchart of the robot motion control method of the present invention. Detailed implementation manners

[0058] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0059] As Figure 8 shown, this is the overall method flowchart of the robot motion control method of the present invention. The present invention establishes internal and external boundaries based on the boundary and constructs a virtual constraint force field based on distance information to compensate for the input force, and the field strength corresponds to the compensation rate. The motion trend of the robot end is judged according to the compensation rates corresponding to the current and next moment positions, and a complete motion control strategy is formulated. An attenuation function is introduced to further adjust the above control strategy, modify the admittance formula, and calculate the actual position of the robot at the next moment according to it.

[0060] Specifically, the hip and knee osteotomy robot motion control method based on the virtual constraint force field of the present invention includes the following steps:

[0061] 1) Generate an initial constraint boundary according to the bone point cloud data, generate internal and external boundaries based on the boundary center point and the scaling factor, and construct a virtual constraint force field within the external boundary range;

[0062] 2) Calculate the field strength according to the distance information of the boundary obtained in step 1), the field strength corresponds to the compensation rate, and then dynamically compensate the input force through the compensation rate;

[0063] 3) Real-time obtain the three-dimensional force / torque information of the robot end through a six-dimensional force sensor, calculate the target position at the next moment in combination with the admittance control algorithm, and judge the motion trend of the robot end based on the comparison between the compensation rate at the current moment and the compensation rate at the next moment, and dynamically adjust the force compensation intensity;

[0064] 4) Introduce an attenuation function to correct the motion control strategy in step 3) to suppress the control instability caused by the sudden change of local distance information;

[0065] 5) Verify the control strategy through Matlab simulation experiments to ensure that when the initial position of the robot end is outside the boundary, it is pulled back into the boundary and its motion trajectory is constrained within the virtual boundary, and dynamically adapts to the boundary change.

[0066] Furthermore, for step 1): The present invention processes the actually collected model bone point cloud data to obtain an initial constraint boundary, which represents the motion range of the robot end, and the drawn boundary is as Figure 1 shown. And according to the boundary center point (xcenter , y center ), and the scaling factor scale_factor, where the scaling factor takes values of 0.5 and 1.5, and the inner and outer boundaries are generated proportionally. As shown in Figure 2 . x is the boundary point, and x new is the generated inner and outer boundary points. The generation formula is as follows:

[0067] x new = x center + scale_factor · (x - x center )

[0068] y new = y center + scale_factor · (y - y center )

[0069] A virtual constraint force field is constructed within the outer boundary. As shown in Figure 3 . It is stipulated that the internal area within the inner boundary range is not affected by the constraint force field, and the system responds to forces normally in this area; the transition area between the inner boundary and the outer boundary is a weak constraint force field, and the force field gradually increases as it approaches the boundary. The system will compensate for part of the force in this area; the outer area outside the boundary and within the outer boundary is a strong constraint force field, and the field strength gradually increases as it moves away from the boundary. This strong field will completely compensate for the given force.

[0070] Further, step 2: Within the large boundary range, discrete points are generated equidistantly along the x and y axes, and the shortest distance (distance min ) from each discrete point to the boundary is calculated, and the field strength of the constraint force field is calculated based on this distance. The field strength corresponds to the compensation rate, and the mapping from discrete points to compensation rates is completed. Let the compensation rate be c, and define distance maxmin as the maximum value of the shortest distances from all points in the transition area to the boundary. The compensation rate calculation formula is as follows:

[0071]

[0072] When the point is located in the external strong field area, its compensation rate is greater than 1. If the compensation condition is met, the system will compensate 100% of the input force. The core of this virtual constraint force field is to only compensate the input force acting on the end of the robot and does not introduce repulsive forces. Compared with the method of modifying the damping parameter or introducing repulsive forces to constrain the movement, this mechanism flexibly adjusts the compensation force by controlling the compensation of the input force, avoiding the sudden force feedback or rigid reaction force generated by the former, while improving the stability and safety of the system, maintaining a natural human - machine interaction experience, and reducing the impact on the operator's operation.

[0073] Step 3: The hip and knee osteotomy robot obtains the force information at its end through a six-dimensional force sensor, calculates the position at the next moment using admittance control, and sends it to the robot, enabling the end to move following the applied input force. During the osteotomy experiment on the model, the virtual boundary will change dynamically. If the robot end is outside the updated virtual boundary, the operator needs to move it inside the updated boundary. It is necessary to compare the compensation rate between the current position and the position at the next moment of the robot end to judge the direction of the applied force. The position at the next moment is obtained through the normal admittance formula.

[0074] As Figure 4 shown, it is the schematic diagram of the virtual constraint motion control method of the present invention. When calculating the force compensation rate, four points near the current position will be indexed to calculate the average compensation rate corresponding to the points, ensuring the robustness of the compensation. If the compensation rate at the next moment is greater than the compensation rate at the current moment, when inside the boundary, it means that the robot end is gradually approaching the boundary; when outside the boundary, it means that the robot end is gradually moving away from the boundary. On the contrary, if the compensation rate decreases, it indicates that the robot end is gradually moving away from the boundary when inside the boundary and gradually approaching the boundary when outside the boundary. According to the change trend of the compensation rate, a preliminary motion control strategy is formulated: when the compensation rate increases, the system should perform corresponding force compensation to maintain the boundary constraint, and when the compensation rate decreases, to achieve the effect of compliant dragging, the force compensation should be stopped; if the compensation rate remains unchanged, it is processed according to the trend of the previous moment.

[0075] According to the change trend of the compensation rate, this strategy can dynamically adjust the compensation process to ensure that when the system faces the change of the virtual constraint plane, it can flexibly adjust and meet the new virtual constraint.

[0076] Step 4: During the actual operation of the system, due to the complex structure of the osteotomy plane polygon, the discreteness of the path points, and the errors existing in the process of calculating the average compensation rate of adjacent discrete points, when the robot end moves relative to the boundary, the distance information at some moments may not conform to the overall movement trend, which may lead to potential instability in the compensation process. To address the above problems, especially the situation where the compensation rate continues to increase but there is a sudden local decrease, an attenuation function f(x) is introduced to correct the above control strategy and suppress the wrong decisions and resulting wrong compensation behaviors caused by local fluctuations. Let x be the number of consecutive decreases in the compensation rate, and the expression of the attenuation function is as follows:

[0077]

[0078] As Figure 5 shown, it is the curve graph of the attenuation function designed by the present invention;

[0079] x takes only integer values. To avoid real-time calculation of f(x), f(x) is discretized: calculate the values of f(x) when x takes integer values from 1 to 30 and store them in an array. When x ≤ 30, directly index the array, and in other cases, assign a value of 0. The above processing can improve the calculation efficiency during the movement process.

[0080] The specific formula for admittance control introducing a control strategy and a decay function is as follows:

[0081]

[0082] where x t+1 is the position of the next moment actually sent to the robot. M, B, and K are the inertia parameter, damping parameter, and stiffness parameter of admittance control respectively. c t is the compensation rate corresponding to the current position, and c t+1 is the compensation rate corresponding to the position of the next moment calculated by the normal admittance formula.

[0083] The decay function and the control strategy only act on the modified admittance formula to calculate the actually sent position, and the above decay function and control strategy are not introduced when judging the movement trend.

[0084] Introducing a control strategy modified by a decay function can ensure that when the compensation rate of the system is continuously increasing, if the distance trend suddenly changes, the system can suppress the response to the improper compensation caused by local mutations and operate stably according to the overall trend. Only when the overall movement trend is stable, the system will gradually approach the initial control strategy, enabling the system to smoothly transition to a new state and reducing the impact of incorrect decisions. The function characteristics of the decay function enable the system to effectively handle sudden changes in the distance information trend, thereby enhancing its safety, stability, and robustness.

[0085] Step 5: Conduct a simulation experiment on the above control strategy in MATLAB. In the experiment, a constant force of 30 N is applied by hand, and the arrow points to the direction of the force. The initial position of the robot end is given. The actual position of the next moment at the end is calculated by the admittance formula introducing the control strategy and the decay function. Given Δt = 0.07, the admittance control parameters are: M = 1, B = 10, K = 0. According to the initial position and the direction of the force, the situation is divided into the following four categories, and the running trajectories are as Figure 6 shown:

[0086] Record the relevant force information for each situation and draw curves, as Figure 7 shown:

[0087] Each situation corresponds to two figures, which respectively draw the compensation force curve without introducing the decay function during the movement process and the compensation force and resultant force curves introducing the decay function. According to Figure 6 and Figure 7It can be seen that the above control strategy can effectively restrict the movement range of the robot end-effector; when facing sudden changes in the movement trend, this strategy can suppress the response to incorrect compensation behaviors or smoothly transition to a new state, as specifically shown in the third case; and when the initial position of the robot end-effector is outside the boundary, this strategy can bring the robot end-effector back within the virtual constraint boundary and maintain the boundary constraint, as specifically shown in the second case.

[0088] Those skilled in the art can understand that the above description is only the preferred embodiment of the present invention. The features described in each embodiment and / or claim of the present disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in the present disclosure. It is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0089] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present invention. Obviously, those skilled in the art can make various changes and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A hip and knee osteotomy robot motion control method based on a virtual constraint force field, characterized in that It includes the following steps: 1) Generate an initial constraint boundary based on the bone point cloud data, generate inner and outer boundaries based on the boundary center point and the scaling factor, and construct a virtual constraint force field within the outer boundary range; 2) Calculate the field strength according to the distance information of the boundary obtained in step 1), the field strength corresponds to the compensation rate, and then dynamically compensate the input force through the compensation rate; 3) Real-time obtain the three-dimensional force / torque information at the end of the robot through a six-dimensional force sensor, calculate the target position at the next moment in combination with the admittance control algorithm, and judge the movement trend of the end of the robot based on the comparison between the compensation rate at the current moment and the compensation rate at the next moment, and dynamically adjust the force compensation intensity; 4) Introduce an attenuation function to correct the motion control strategy in step 3) to suppress the control instability caused by the sudden change of local distance information; 5) Verify the control strategy through Matlab simulation experiments to ensure that when the initial position of the end of the robot is outside the boundary, it is pulled back into the boundary and its motion trajectory is constrained within the virtual boundary, and dynamically adapt to the boundary change.

2. The hip and knee osteotomy robot motion control method based on virtual binding force field according to claim 1, characterized in that, The virtual constraint force field is divided into an internal region, a transition region, and an external strong field region, where the internal region is not affected by the force field, the field strength in the transition region gradually increases as it approaches the boundary, and the field strength in the external strong field region gradually increases as it moves away from the boundary.

3. The hip and knee osteotomy robot motion control method based on virtual binding force field according to claim 1, wherein In step 1), the generation of the inner and outer boundaries based on the boundary center point and the scaling factor is specifically as follows: According to the boundary center point (x center , y center ) and the scaling factor scale_factor, generate the inner and outer boundaries proportionally, that is: x new = x center + scale_factor·(x - x center ) y new = y center + scale_factor·(y - y center ) where x is the boundary point, x new is the generated inner and outer boundary points, and the scaling factors take values of 0.5 and 1.5, which are used to generate the shrunk and expanded boundaries respectively.

4. The hip and knee osteotomy robot motion control method based on a virtual binding force field according to claim 1, characterized in that, In step 1), the construction of the virtual constraint force field within the outer boundary range is specifically as follows: It is stipulated that the internal region within the inner boundary range is not affected by the constraint force field, and the system normally responds to forces in this region; The transition region between the inner boundary and within the boundary is a weak constraint force field, and this force field gradually increases as it approaches the boundary, and the system will compensate for part of the force in this region; The external region outside the boundary and within the outer boundary is a strong constraint force field, and the field strength gradually increases as it moves away from the boundary, and this strong field will completely compensate the given force.

5. The method for controlling the movement of a hip and knee osteotomy robot based on a virtual binding force field according to claim 1, wherein The specific content of step 2) is as follows: Within the outer boundary, discrete points are generated equidistantly along the x and y axes, and the shortest distance distance from each discrete point to the boundary is calculated min , and the field strength of the binding force field is calculated based on this distance. The field strength corresponds to the compensation rate, and the mapping from discrete points to the compensation rate is completed; Let the compensation rate be c, and define distance maxmin as the maximum value among the shortest distances from all points in the transition regions to the boundary, then the compensation rate is: When the point is located in the external strong field region, its compensation rate is greater than 1. If the compensation condition is met, the robot system will compensate 100% of the input force.

6. The method for controlling the movement of a hip and knee osteotomy robot based on a virtual binding force field according to claim 1, wherein In step 3), the adjustment of the force compensation based on the change trend of the compensation rate between the current moment and the next moment is specifically as follows: Index four discrete points near the current position of the robot end, and calculate their average compensation rate to determine the current compensation rate c t ; According to the compensation rate c corresponding to the position at the next moment t+1 and c t to determine the movement trend of the robot end: if c t+1 >c t and the robot end is within the boundary, it is determined to be approaching the boundary and force compensation needs to be enhanced; If c t+1 > c t and the end of the robot is located outside the boundary, it is determined to be far from the boundary, and the input force needs to be fully compensated; If c t+1 <c t and the end of the robot is located within the boundary, it is determined to be far from the boundary and the force compensation is stopped; If c t+1 <c t And if the end of the robot is located outside the boundary, it is determined to be approaching the boundary and the force compensation is stopped.

7. The method for controlling the movement of a hip and knee osteotomy robot based on a virtual binding force field according to claim 1, characterized in that, In step 4), the specific content of the attenuation function is as follows: When the compensation rate continues to decrease, the amplitude mutation is suppressed by introducing the attenuation function f(x). The expression of the attenuation function f(x) is: Where x is the continuous number of times of the decrease in the compensation rate, and only takes integer values; The attenuation values for x = 1 to 30 are pre-calculated and stored in an array, and directly indexed during real-time call; when x > 30, it is assigned 0 to improve the calculation efficiency.

8. The method for controlling the movement of a hip and knee osteotomy robot based on a virtual binding force field according to claim 1, wherein The introduction of the attenuation function to correct the motion control is specifically as follows: The specific formula of the admittance control introducing the control strategy and the attenuation function is: where x t+1 is the position of the next moment actually sent to the robot, and M, B, and K are the inertia parameter, damping parameter, and stiffness parameter of admittance control respectively, and c t is the compensation rate corresponding to the current position, and c t+1 is the compensation rate corresponding to the position of the next moment calculated by the normal admittance formula.

9. The method for controlling the movement of a hip and knee osteotomy robot based on a virtual binding force field according to claim 1, wherein The specific content of step 5) is as follows: Apply a constant force of 30N by hand, with the arrow pointing in the direction of the force, given the initial position of the end of the robot, and the actual position of the end at the next moment is calculated by the above admittance formula introducing the control strategy and the attenuation function; Given Δt = 0.07, admittance control parameters: M = 1, B = 10, K = 0; according to the initial position and the direction of the force, the relevant force information of the four cases of the control strategy is used to plot the compensation force curve without introducing the attenuation function and the compensation force and resultant force curves with the attenuation function during the motion process, and comparison and verification are carried out.

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