Medical mechanical arm precise force control method and system for ultrasonic probe auxiliary grasping

Abstract

The invention discloses a medical mechanical arm precise force control method and system for ultrasonic probe auxiliary grasping, and belongs to the technical field of medical equipment.The method comprises the steps that multi-dimensional pressure intensity distribution data and high-frequency tremor waveform data of a probe contact surface are obtained, and a real-time impedance matching model is established; dynamic pressure parameters of a mechanical arm actuator are regulated and controlled, the vibration direction and strength distribution of the mechanical arm are monitored in real time, and instantaneous rigidity parameters of the mechanical arm actuator are regulated; and when the pressure value of the contact surface of the probe exceeds a first early warning value, executing graded hydraulic damping intervention operation, generating a safety protection signal, activating an orientation parameter locking mechanism, and storing current spatial positioning data. The multi-dimensional pressure intensity distribution data and high-frequency tremor waveform data fusion technology is adopted, an impedance matching model is established in real time, the rigidity and pressure parameters of the mechanical arm are dynamically adjusted, and high-precision force control and all-around safety guarantee of the mechanical arm can be achieved in combination with a graded hydraulic damping intervention mechanism and an orientation parameter locking mechanism.

Description

Technical Field

[0001] This invention relates to the field of medical device technology, and in particular to a precise force control method and system for a medical robotic arm used for assisting in the gripping of an ultrasound probe. Background Technology

[0002] A medical robotic arm is a robotic device used in medical surgery or diagnosis, capable of high-precision force control. Ultrasonic probe-assisted grasping refers to the use of a robotic arm to grasp and position an ultrasound probe, enabling precise examination or manipulation of specific areas of the patient. The core technologies of this device include signal acquisition, real-time impedance matching modeling, pressure compensation control, and safety protection mechanisms.

[0003] Currently, the application of medical robotic arms in ultrasound probe-assisted gripping mainly relies on single-dimensional pressure sensors and fixed gain control strategies. By collecting force distribution data at the probe contact surface, existing systems typically use simple PID (proportional-integral-derivative) controllers to adjust the actuator parameters of the robotic arm. Meanwhile, some systems attempt to absorb robotic arm vibrations by fixing stiffness parameters to achieve stable probe contact.

[0004] However, existing technical solutions have the following drawbacks: First, a single-dimensional pressure sensor cannot fully reflect the multi-dimensional pressure distribution on the probe contact surface, making it difficult for the robotic arm to accurately identify changes in the patient's position and tissue impedance characteristics. Second, fixed gain control strategies and stiffness parameters often fail to achieve precise force distribution adjustment when faced with complex positional changes and high-frequency tremors, easily leading to uneven distribution of probe contact force. In addition, in terms of safety, existing systems typically lack a graded damping intervention mechanism, making it impossible to adjust damping parameters in real time when pressure exceeds limits, resulting in a high risk of device malfunction and potentially causing unnecessary damage to the patient. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a precise force control method and system for a medical robotic arm used for assisting in the gripping of ultrasound probes. It employs a multi-dimensional pressure distribution data and high-frequency vibration waveform data fusion technology to establish an impedance matching model in real time and dynamically adjust the stiffness and pressure parameters of the robotic arm. Simultaneously, by combining a graded hydraulic damping intervention mechanism and an orientation parameter locking mechanism, it can achieve high-precision force control of the robotic arm and comprehensive safety assurance.

[0006] The above objectives can be achieved through the following approach:

[0007] A precise force control method for a medical robotic arm used for ultrasound probe-assisted grasping includes: acquiring multi-dimensional pressure distribution data and high-frequency vibration waveform data of the probe contact surface to generate a composite sensing signal; extracting body displacement and resistance change characteristics based on the composite sensing signal to establish a real-time impedance matching model; adjusting the dynamic pressure parameters of the robotic arm actuator based on the real-time impedance matching model to generate a pressure compensation command; responding to the pressure compensation command and monitoring the direction and intensity distribution of the robotic arm vibration in real time, adjusting the instantaneous stiffness parameters of the robotic arm actuator based on the monitoring results; based on the adjustment results, when the pressure value of the probe contact surface exceeds a preset first warning value, performing a graded hydraulic damping intervention operation to generate a safety protection signal; and activating an orientation parameter locking mechanism based on the safety protection signal to save the current spatial positioning data.

[0008] Optionally, the step of acquiring multi-dimensional pressure distribution data and high-frequency vibration waveform data of the probe contact surface to generate a composite sensing signal includes: acquiring force distribution data in various directions of the probe contact surface to obtain multi-dimensional pressure distribution data; acquiring high-frequency vibration signals of the probe to obtain high-frequency vibration waveform data; and fusing the multi-dimensional pressure distribution data and the high-frequency vibration waveform data to generate a composite sensing signal with time phase correction characteristics.

[0009] Optionally, the step of identifying body displacement and resistance change characteristics based on the composite sensor signal and establishing a real-time impedance matching model includes: acquiring a preset physiological dynamic feature template library, the template library containing soft tissue compression characteristic parameters under different body postures; extracting body displacement and resistance change characteristics from the composite sensor signal to obtain a body position offset component and a local impedance anomaly component; calling the corresponding soft tissue compression characteristic parameters based on the body position offset component to generate a pressure adjustment base value; performing current ripple inversion calculation on the local impedance anomaly component and outputting a dynamic correction coefficient; and using the pressure adjustment base value and the dynamic correction coefficient to generate a real-time impedance matching model.

[0010] Optionally, the step of controlling the dynamic pressure parameters of the robotic arm actuator based on the real-time impedance matching model to generate a pressure compensation command includes: decomposing the real-time impedance matching model into a stable pressure component and a transient compensation component; calculating a set of reference parameter values ​​for the PID controller based on the stable pressure component; driving the robotic arm actuator with the transient compensation component to generate a micro-displacement compensation amount; and fusing the set of reference parameter values ​​and the micro-displacement compensation amount to generate a pressure compensation command.

[0011] Optionally, adjusting the instantaneous stiffness parameters of the robotic arm actuator based on the monitoring results includes: establishing the motion trajectory equations of the redundant degrees of freedom of the robotic arm actuator; monitoring the direction vector and energy gradient of the robotic arm vibration in real time; dynamically allocating the redundant degrees of freedom to four quadrants based on the direction vector; and adjusting the instantaneous stiffness parameters of the robotic arm actuator according to the energy gradient.

[0012] Optionally, the step of performing graded hydraulic damping intervention and generating a safety protection signal when the probe contact surface pressure value exceeds a preset first warning value includes: when the probe contact surface pressure value exceeds the preset first warning value, determining it as a pressure over-limit event, and judging the magnitude of the probe contact surface pressure value compared with a preset second warning value and a preset third warning threshold; if the current probe contact surface pressure value is less than or equal to the second warning value, triggering a micro-pressure relief operation in the buffer oil circuit; if the current probe contact surface pressure value is greater than the second warning value and less than or equal to the third warning value, absorbing the remaining energy after pressure relief; if the current probe contact surface pressure value is greater than the third warning value, initiating the locking state of the hydraulic circuit and stopping the power supply to the robotic arm actuator.

[0013] Optionally, the activation of the orientation parameter locking mechanism includes: when the pressure over-limit event occurs, acquiring the current spatial positioning data, which includes the probe attitude Euler angles and the centroid coordinates of the contact surface; saving the control parameter set of the current pressure-position dual-loop control loop to obtain a dynamic freeze snapshot, which includes the current pressure reference value; generating a timestamped security verification code and binding it to the dynamic freeze snapshot for storage.

[0014] Optionally, the method further includes: after receiving a reset command from an external input, calling the dynamic freeze snapshot to adjust the position of the robotic arm; using a preset zero-position calibration program to adjust the contact surface pressure value of the probe; and when the contact surface pressure value is detected to reach the reference value recorded by the dynamic freeze snapshot, releasing the hydraulic circuit lock-up state.

[0015] Optionally, after receiving a reset command from an external input, calling the dynamic freeze snapshot to adjust the position of the robotic arm includes: generating a compensation vector field using the difference between the spatial positioning data and the current actual position; decomposing the compensation vector field into translational and rotational components with redundant degrees of freedom; and adjusting the position of the robotic arm in segments according to the translational and rotational components.

[0016] Based on the same inventive concept, this invention also provides a precision force control system for a medical robotic arm used for ultrasound probe-assisted grasping. The system includes: a signal acquisition module for acquiring multi-dimensional pressure distribution data and high-frequency vibration waveform data of the probe contact surface, generating a composite sensing signal; a model building module for extracting body displacement and resistance change characteristics based on the composite sensing signal, establishing a real-time impedance matching model; a pressure compensation module for adjusting the dynamic pressure parameters of the robotic arm actuator based on the real-time impedance matching model, generating a pressure compensation command; and a vibration absorption module for responding to the pressure compensation command and monitoring the direction and intensity distribution of the robotic arm vibration in real time, and based on the monitoring results... The system includes: an adjustment module for the instantaneous stiffness parameters of the robotic arm actuator; a safety warning module for performing graded hydraulic damping intervention and generating a safety protection signal when the probe contact surface pressure value exceeds a preset first warning value; a reset parameter locking module for activating the orientation parameter locking mechanism and saving the current spatial positioning data based on the safety protection signal; a reset control module for receiving an external reset command and calling the dynamic freeze snapshot to adjust the robotic arm position; adjusting the probe contact surface pressure value using a preset zero-position calibration program; and releasing the hydraulic circuit lock state when the contact surface pressure value reaches the pressure reference value recorded by the dynamic freeze snapshot.

[0017] Compared with the prior art, the present invention has the following advantages:

[0018] 1. This invention integrates multi-dimensional pressure distribution data and high-frequency vibration waveform data to establish an impedance matching model in real time, dynamically adjust the stiffness and pressure parameters of the robotic arm actuator, effectively suppress the influence of high-frequency vibration on probe contact, and significantly improve the operational stability of the robotic arm in complex environments.

[0019] 2. By combining the physiological dynamic feature template library, this invention can adaptively adjust the pressure parameters of the robotic arm according to the soft tissue compression characteristics under different body postures, ensuring the uniform distribution of the contact surface between the probe and the patient, avoiding discomfort or operational deviation caused by changes in body position, and significantly improving the patient's examination comfort and operational accuracy.

[0020] 3. This invention employs a graded hydraulic damping intervention mechanism and an orientation parameter locking mechanism. When a pressure over-limit event is detected, it achieves precise control and safety protection of the robotic arm through hierarchical damping control and dynamic freeze snapshot technology, effectively avoiding loss of control of the robotic arm or injury to the patient due to operational errors or accidental impacts.

[0021] 4. By monitoring the direction and intensity distribution of robotic arm vibration in real time and combining it with the dynamic allocation of redundant degrees of freedom, this invention can intelligently adjust the instantaneous stiffness parameters of the robotic arm, effectively absorb and control high-frequency vibrations, and significantly improve the intelligence level and adaptability of the robotic arm force control system.

[0022] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating the precise force control method for a medical robotic arm used for ultrasound probe-assisted grasping, according to an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram of the process of graded hydraulic damping intervention operation according to an embodiment of the present invention.

[0026] Figure 3 This is a structural schematic diagram of a precision force control system for a medical robotic arm used for ultrasound probe-assisted gripping, according to an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Reference Figure 1 One embodiment of the present invention proposes a precise force control method for a medical robotic arm used for ultrasound probe-assisted gripping. It employs a multi-dimensional pressure distribution data and high-frequency vibration waveform data fusion technology to establish an impedance matching model in real time and dynamically adjust the stiffness and pressure parameters of the robotic arm. Combined with a graded hydraulic damping intervention mechanism and an orientation parameter locking mechanism, it can achieve high-precision force control of the robotic arm and comprehensive safety assurance.

[0029] The method described in this embodiment specifically includes:

[0030] Acquire multi-dimensional pressure distribution data and high-frequency vibration waveform data of the probe contact surface to generate composite sensing signals;

[0031] Specifically, the process begins by acquiring multi-dimensional pressure distribution data and high-frequency vibration waveform data at the probe's contact surface. This process requires a three-dimensional force sensor array and a miniature inertial measurement unit. The three-dimensional force sensor array captures force distribution data in various directions on the contact surface through distributed multi-point detection, including force values ​​in the three orthogonal directions of x, y, and z. The output signal of each sensor reflects the pressure in a local area, and integrating these signals yields the multi-dimensional pressure distribution data.

[0032] Meanwhile, miniature inertial measurement units (MMUs) typically include accelerometers and gyroscopes, mounted on probes or robotic arms to acquire high-frequency vibration signals in real time. This sensor can capture minute frequency changes and waveform characteristics at a high-frequency sampling rate, which is crucial for subsequent analysis of high-frequency tremors caused by body position.

[0033] Next, a composite sensing signal is generated by fusing multidimensional pressure distribution data and high-frequency vibration waveform data using a nonlinear coupling algorithm. The key to this process lies in processing the time phase correction of the data to ensure signal synchronization and consistency. Nonlinear coupling algorithms typically employ methods such as adaptive filtering or wavelet transform. The three-dimensional force field data is F(t) = [F...]. x (t),F y (t),F z The high-frequency tremor data is G(t)], where G(t) = [G x (t),G y (t),G z If [(t)], then for the composite signal S(t) at time t, we have:

[0034] S(t)=W1*F(t)+U1*G(t)+V1,

[0035] In the formula, W1 is the weight matrix for the three-dimensional force field data, U1 is the weight matrix for the high-frequency tremor data, and V1 is the nonlinear correction vector. Depending on specific needs, the weight matrix and nonlinear correction vector can be adaptively adjusted based on the training data. For example, when using this method in abdominal ultrasound examinations, the weight matrix will automatically adjust to better focus on acquiring lateral force signals based on changes in patient position caused by respiration.

[0036] For example, when the probe contacts the patient's skin, the synchronous acquisition function of the three-dimensional force sensor array and the miniature inertial measurement unit is activated; force field data and vibration waveforms are continuously acquired, with the sampling rate typically set above 500Hz to ensure the accuracy of capturing high-frequency vibrations; then, a nonlinear coupling algorithm is used for signal fusion, while time phase adjustment is performed to eliminate time delay differences between sensors, and the generated composite sensor signal serves as the basis for subsequent processing. Through deep fusion of multi-dimensional data, the mechanical state of the probe contact surface can be identified more accurately, improving the robotic arm's adaptability to different body position changes, thereby enhancing the stability and diagnostic accuracy of ultrasound examinations, effectively reducing operational difficulty, and improving the patient's examination experience.

[0037] Based on the composite sensor signals, body displacement and resistance change characteristics are extracted, and a real-time impedance matching model is established.

[0038] Specifically, the first step is to acquire a pre-defined physiological dynamic feature template library. This library contains soft tissue compression characteristic parameters under different body postures, such as the elastic modulus and viscoelastic coefficient of soft tissues in different positions (supine, lateral, standing, etc.). These parameters are established based on human anatomy and a large amount of experimental data, and are used to describe the mechanical properties of soft tissues in different body positions.

[0039] Then, the postural displacement component and the local impedance anomaly component are extracted from the composite sensing signal. The postural displacement component reflects the displacement caused by changes in the patient's position, such as breathing or movement, while the local impedance anomaly component reflects changes in the mechanical properties of the contact surface, such as the elasticity or stiffness of soft tissue. The extraction of these components typically requires signal processing algorithms such as frequency domain filtering and time domain integration. For the postural displacement component ΔP(t), we have:

[0040]

[0041] In the formula, W p This is the weighting matrix for the body position offset component, used to weight the contributions of different sensors; for the local impedance anomaly component ΔZ(t), we have:

[0042] ΔZ(t)=S(t)*W z ―S(t―1)*W z ,

[0043] In the formula, W z It is the weight matrix of the impedance anomaly components, used to extract local impedance changes.

[0044] Next, based on the postural offset component ΔP(t), the corresponding soft tissue compressibility parameters from the physiological dynamic feature template library are called to generate a pressure adjustment base value. This process is usually implemented through a lookup matching algorithm to ensure that the pressure adjustment base value is consistent with the mechanical properties of the current postural state.

[0045] Simultaneously, current ripple inversion calculations are performed on the local impedance anomaly components to output dynamic correction coefficients. Current ripple inversion is an algorithm based on sensor signal feature extraction, used to quantify the impact of local impedance changes on the overall force control of the robotic arm, and to output dynamic correction coefficients C. dyn (t), we have:

[0046]

[0047] In the formula, α1 and β1 are pre-calibrated coefficients used to balance the contributions of static impedance change and dynamic impedance change; This indicates the rate of change of impedance.

[0048] Finally, the pressure adjustment base value and the dynamic correction coefficient are superimposed to form a real-time impedance matching model M(t), which is used for subsequent force control adjustment of the robotic arm. The functional expression of the real-time impedance matching model is as follows:

[0049] M(t)=P base (t)+C dyn (t),

[0050] In the formula, P base (t) represents the pressure adjustment base value.

[0051] For example, in conjunction with a medical robotic arm assisting in ultrasound probe grasping, the robotic arm contacts the abdominal soft tissue during an abdominal ultrasound examination. The acquired composite sensor signals are used to extract the positional offset component and the impedance anomaly component. Based on the positional offset component, the soft tissue compression characteristics of the corresponding position are searched in a template library to calculate the pressure adjustment base value. Simultaneously, the impedance anomaly component is inverted to obtain a dynamic correction coefficient. Finally, the pressure adjustment base value and the dynamic correction coefficient are superimposed to form a real-time impedance matching model, which is used to adjust the force control parameters of the robotic arm. By extracting positional offset and impedance anomaly features and combining them with a template library for dynamic matching, the real-time impedance matching model can more accurately reflect the mechanical properties of the current contact surface. This allows the robotic arm to automatically adjust the force control parameters under different patient positions, ensuring the stability and uniformity of the force between the probe and the contact surface, thereby improving ultrasound image quality and examination comfort.

[0052] Based on the real-time impedance matching model, the dynamic pressure parameters of the robotic arm actuator are adjusted to generate pressure compensation commands.

[0053] First, the real-time impedance matching model is decomposed into a steady-state pressure component and a transient compensation component. The steady-state pressure component reflects the current steady-state force at the contact surface, while the transient compensation component dynamically adjusts for minute pressure changes in the robotic arm. The decomposition process is typically based on the model's characteristic frequencies or equilibrium point analysis, and can be expressed as:

[0054] M(t)=P stable (t)+P transient (t),

[0055] In the formula, P stable (t) represents the steady-state pressure component at the contact surface of the robotic arm, P transient (t) represents the transient pressure change that needs to be compensated in real time.

[0056] Next, the set of reference parameter values ​​for the PID controller is calculated based on the steady-state pressure component. The PID controller is a common feedback control algorithm used to achieve closed-loop pressure regulation. The reference parameters include the proportional gain K. p Integral coefficient K i and differential coefficient K d The formula for calculating the baseline parameters is:

[0057] K p =a*P stable (t),

[0058] K i =b*P stable (t),

[0059] K d =c*P stable (t),

[0060] In the formula, a, b, and c are pre-calibrated coefficients.

[0061] Simultaneously, a transient compensation component is used to drive the actuator, generating a micro-displacement compensation amount. The actuator adjusts the micro-displacement by changing its preload. For the micro-displacement compensation amount ΔD(t), we have:

[0062] ΔD(t)=d*P transient (t),

[0063] Where d is the sensitivity coefficient of the actuator.

[0064] Finally, the set of baseline parameter values ​​{K} p ,K i ,K d The pressure compensation command is generated by fusing the micro-displacement compensation amount ΔD(t). This command is used to adjust the actuator parameters of the robotic arm to achieve precise control of dynamic pressure. For the pressure compensation command C(t), we have:

[0065] C(t) = [K p ,K i ,K d ,ΔD(t)].

[0066] By decomposing the real-time impedance matching model into steady-state and transient components, and combining it with a PID controller and actuator, precise control of both steady-state and dynamic pressure can be achieved. This allows the robotic arm to quickly respond and adjust the pressure when the mechanical properties of the contact surface change, avoiding ultrasonic probe vibration or poor contact caused by pressure fluctuations, thereby improving operational stability and inspection results.

[0067] The system responds to the pressure compensation command and monitors the direction and intensity distribution of the robotic arm's vibration in real time, adjusting the instantaneous stiffness parameters of the robotic arm's actuator based on the monitoring results.

[0068] Specifically, the redundant degrees of freedom motion trajectory equation of the robotic arm's end effector forms the basis of the displacement buffer control mode. Redundant degrees of freedom refer to the additional degrees of freedom the robotic arm can use for attitude adjustment while performing its primary task, such as maintaining probe contact, thereby absorbing external disturbances such as patient movement or vibration. The motion trajectory equation for redundant degrees of freedom is typically expressed as:

[0069] q(t)=q main (t)+q redundant (t),

[0070] Where q(t) is the position vector of all joints of the robotic arm; q main (t) represents the joint position for the primary task (maintaining probe contact); q redundant (t) represents the joint position with redundant degrees of freedom.

[0071] Next, the direction vector and energy gradient of the flutter absorption control quantity are monitored in real time. The direction vector reflects the direction of flutter motion, and the energy gradient reflects the intensity distribution of the flutter. The monitoring process is typically implemented based on sensor data (such as accelerometers or force sensors) and signal processing algorithms.

[0072] Then, redundant degrees of freedom are dynamically allocated across four quadrants based on the direction vector. This four-quadrant dynamic allocation is a control strategy based on motion direction and energy distribution, formally expressed as:

[0073] q′ redundant (t)=K0*v(t),

[0074] Where K0 is the dynamic allocation coefficient matrix, reflecting the degree of response of each redundant degree of freedom to the direction vector; q′ redundant v(t) is the joint position adjustment amount for redundant degrees of freedom, and v(t) is the direction vector.

[0075] Finally, the instantaneous stiffness parameter of the actuator is adjusted according to the energy gradient. The actuator absorbs vibration energy by changing its stiffness parameter. For the instantaneous stiffness parameter k(t), we have:

[0076] k(t) = Kbase +K dyn *E(t),

[0077] where K base is the preset stiffness base value; K dyn is the dynamic stiffness coefficient matrix; E(t) is the energy gradient.

[0078] Exemplarily, according to the generated pressure compensation instruction, start the displacement buffer control mode, and establish the redundant degree of freedom motion trajectory equation of the end effector of the robotic arm. Real-time monitor the direction vector and energy gradient of the tremor absorption control amount. Based on the direction vector, perform four-quadrant dynamic allocation, and calculate the joint position adjustment amount of the redundant degree of freedom. Adjust the instantaneous stiffness parameter of the actuator according to the energy gradient, and output the tremor absorption control amount. Through the displacement buffer control mode, it is possible to effectively absorb external disturbances such as high-frequency tremors caused by patient body movements, and maintain stable contact between the probe and the contact surface. The dynamic allocation of the redundant degree of freedom and the adaptive adjustment of the stiffness parameter enable the robotic arm to achieve precise tremor absorption under different vibration amplitudes and directions, significantly improving the stability and accuracy of ultrasonic examinations.

[0079] According to the adjustment result, when it is detected that the pressure value on the probe contact surface exceeds the preset first warning value, perform a hierarchical hydraulic damping intervention operation to generate a safety protection signal;

[0080] Specifically, as Figure 2 shown, first, when the pressure value P current on the probe contact surface is greater than the first-level warning value, it is a pressure overlimit event. Detecting the pressure overlimit event is usually completed by comparing the current pressure value with the preset three-level warning thresholds. The preset three-level warning thresholds are the first-level warning value P1, the second-level warning value P2, and the third-level warning value P3, where P1 < P2 < P3. When the real-time monitored pressure value P current exceeds these warning values, trigger the corresponding hierarchical damping intervention operation. When P1 < P current ≤ P2, trigger a first-level warning. When P2 < P current ≤ P3, trigger a second-level warning. When P3 < P current , trigger a third-level warning.

[0081] Next, according to the triggered warning level, perform the corresponding hierarchical hydraulic damping intervention operation. The first-level warning is to trigger the micro-pressure relief operation of the buffer oil circuit. The micro-pressure relief of the buffer oil circuit is to release some hydraulic oil by adjusting the throttle valve of the hydraulic system to reduce the pressure. For the micro-pressure relief flow rate Q leak in the micro-pressure relief operation, there is:

[0082] Q leak = k0 * (P current ― P1),

[0083] Where k0 is the pressure relief flow coefficient; P current —P1 is the over-limit pressure difference. A reverse kinetic energy absorption module is activated during the second-level warning. The reverse kinetic energy absorption module absorbs additional energy through the hydraulic system's energy accumulator to prevent further pressure increase. Regarding the absorbed energy E... absorbed ,have:

[0084]

[0085] Where C is the stiffness coefficient of the energy storage device; P current —P2 is the over-limit pressure difference. The overall hydraulic circuit locking mechanism is activated upon a level 3 warning. The locking mechanism quickly closes the hydraulic valve, cutting off the power supply to the actuator and preventing further pressure increase. Its controlled locking time ΔT lock ,have:

[0086] ΔT lock =α2*(P current —P3),

[0087] Where α2 is the locking time coefficient; P current —P3 represents the pressure differential exceeding the limit. In the above operation, safety is enhanced through a multi-stage damping intervention mechanism, while simultaneously generating safety protection signals to alert operators or initiate further safety measures.

[0088] For example, after outputting a pressure compensation command, the system monitors a gradual increase in pressure at the probe contact surface. When the pressure exceeds the first-level warning value, a micro-pressure relief operation is triggered in the buffer oil circuit to release some pressure. If the pressure continues to rise and exceeds the second-level warning value, a reverse kinetic energy absorption module is loaded to further absorb energy. If the pressure is still not effectively controlled and continues to rise and exceeds the third-level warning value, the overall hydraulic circuit locking mechanism is activated to completely cut off the power supply. During this process, a safety protection signal is generated to alert the operator to intervene. Through a graded hydraulic damping intervention mechanism, the system can gradually take different damping measures according to different levels of pressure over-limit events, thereby maintaining the normal operation of the robotic arm as much as possible while ensuring safety. For example, during a first-level warning, micro-pressure relief is used to alleviate pressure, avoiding immediate locking of the robotic arm and resulting operational interruption. In more severe pressure over-limit situations, complete locking ensures absolute safety. This graded control strategy improves system safety and reduces the possibility of misoperation.

[0089] The orientation parameter locking mechanism is activated based on the security protection signal to save the current spatial positioning data.

[0090] Specifically, after a pressure over-limit event occurs, graded hydraulic damping intervention is executed, and a safety protection signal is generated. To further ensure operational safety, the robotic arm's orientation parameters need to be locked to prevent unauthorized operation or accidental movements. First, spatial positioning data is extracted from the safety protection signal to obtain the orientation parameters. This data includes the probe's Euler angles {φ,θ,ψ} and the centroid coordinates of the contact surface. Next, a control loop freeze snapshot is established, achieved by dynamically freezing the current pressure-position dual-loop control loop parameters. This includes freezing the current PID controller parameters {K...} p ,K i ,K d The data includes the actuator state, which comprises spatial positioning data and control parameter sets. Next, to prevent accidental operations, a timestamped security verification code is generated and bound to the frozen snapshot data for storage. For the security verification code C... verify (t) can be represented as:

[0091] C verify (t)=H(D position (t),D force (t)),

[0092] Among them, D position (t) represents the current location data; D force (t) is the current force data; H is the hash function that generates a unique checksum.

[0093] For example, during the ultrasonic probe gripping process, if excessive pressure is detected, a safety protection signal is generated. The system extracts the current probe orientation {30°, 45°, 60°} and the centroid coordinates of the contact surface (0.2, 0.1, 0.15) from the safety protection signal. The current PID parameters {K} are frozen. p =0.5,K i =0.2,K d =0.3} and executor status. Generate security verification code C verify (t), and stored in the system along with the frozen data. Through the orientation parameter locking mechanism, the system can accurately save the current operating state when a pressure over-limit event occurs, preventing safety accidents caused by accidental operation. Furthermore, the generation of verification codes ensures data integrity and security, avoiding unauthorized modification or misoperation, thus improving the system's reliability and security.

[0094] Optionally, the method further includes:

[0095] After receiving a reset command from an external source, the position of the robotic arm is adjusted by calling the dynamically frozen snapshot.

[0096] Specifically, after receiving an external reset command, the command needs to be parsed and the reset process triggered. The dynamic freeze snapshot contains previously saved spatial positioning data, control parameter sets, and security verification codes. The validity of the reset command is checked, including verification code validation. The stored dynamic freeze snapshot is retrieved, and the probe attitude Euler angles, contact surface centroid coordinates, and PID control parameters are parsed. The security verification code is decrypted and verified to ensure the data has not been tampered with. After calling the dynamic freeze snapshot, reverse trajectory planning is performed. The goal of reverse trajectory planning is to generate a compensation vector field from the current robotic arm position to the position recorded in the dynamic freeze snapshot. First, the difference between the current position of the robotic arm end effector and the position recorded in the freeze snapshot is calculated to obtain the compensation vector field. For the compensation vector field ΔV(t), we have:

[0097] ΔV(t)=Pcurrent-Pfreeze,

[0098] In the formula, P current Let P be the current position vector of the robotic arm's end effector. freeze The position vector recorded in the frozen snapshot is used. Then, the compensation vector field is decomposed into redundant degrees of freedom (translational and rotational components) to avoid system resonance during the robot arm's movement. For the translational component V... shift (t) and rotational component V rotatc (t), we have:

[0099] V shift (t)=k s *ΔV(t),

[0100] V rotatc (t)=k r *ΔV(t),

[0101] In the formula, k s Let k be the weight matrix of the translation components. r This is the weight matrix for the rotation components.

[0102] Finally, the entire adjustment process is divided into several time periods or stages, each corresponding to a sub-target position. Within each stage, an asymptotic approximation algorithm, such as polynomial interpolation or piecewise linear approximation, is applied to fit the control signals of each joint from its current state to the sub-target state. For example, for each stage, the rate of change of the joint angle is defined. for:

[0103]

[0104] In the formula, k i P is the adjustment coefficient for stage i. iLet θ(t) be the target position for stage i, and θ(t) be the rate of change of the current joint angle. Considering the dynamic characteristics of the robotic arm at each stage, the control signal is optimized to avoid vibration and overshoot. Each joint actuator executes adjustment actions sequentially according to the optimized control signal, ensuring that resonance or excessive load on the system is not caused simultaneously. After each stage, the actual execution results are fed back, and the control parameters for the next stage are fine-tuned to ensure gradual approximation to the target position. When the difference between the robotic arm's end-effector position and the target position is less than a preset threshold, the adjustment process stops, and normal operation resumes.

[0105] The contact surface pressure value of the probe is adjusted using a preset zero-point calibration program.

[0106] Specifically, the zero-point calibration procedure aims to restore the initial state of the robotic arm's end effector, including position and force references. The specific steps are: initializing the actuator and zeroing its current state value; activating the micro-pressure sensor to monitor minute pressure changes on the contact surface in real time; and adjusting the actuator's preload using a closed-loop control algorithm to gradually bring the contact surface pressure value closer to the pressure reference value recorded in the frozen snapshot.

[0107] When the pressure value of the contact surface reaches the pressure reference value recorded in the dynamic freeze snapshot, the hydraulic circuit lock-up state is released.

[0108] Specifically, the micro-pressure preloading operation at the contact surface is used to ensure stable contact between the robotic arm's end effector and the contact surface, while avoiding damage caused by overpressure. First, a preload pressure command F is generated based on the pressure baseline value recorded in the frozen snapshot. preload (t):

[0109] F preload (t)=F base (t)+β2*ΔF(t),

[0110] In the formula, F base (t) represents the pressure reference value recorded in the frozen snapshot, ΔF(t) is the difference between the current contact surface pressure value and the reference value, and β2 is the preload coefficient. Then, a PID controller (using the parameter set {K} recorded in the frozen snapshot) is used. p ,K i ,K d This system achieves closed-loop pressure control, adjusting the actuator's output force. When the pressure value detected at the probe contact surface reaches the pressure reference value recorded in the dynamic freeze snapshot, the hydraulic circuit lock-up state is released, restoring normal operation of the robotic arm. For example, it triggers a hydraulic circuit unlock command to restore power supply; and monitors the system's stability to ensure that both pressure and position are within safe ranges.

[0111] For example, suppose that during an abdominal ultrasound examination, the robotic arm experiences excessive pressure due to patient movement, triggering a level three warning and locking the orientation parameters. After the examination, the operator inputs a reset command. First, the reset command is parsed, the safety verification code is verified, a dynamic freeze snapshot is retrieved, and the probe orientation {30°, 45°, 60°} and the contact surface centroid coordinates (0.2, 0.1, 0.15) are analyzed. Then, the difference between the current robotic arm end-effector position and the position recorded in the freeze snapshot is calculated, generating a compensation vector field. The compensation vector field is decomposed into translational and rotational components, and the redundant degrees of freedom of the robotic arm are gradually adjusted. A zero-position calibration program is initiated, adjusting the actuator's preload to gradually increase the contact surface pressure from 0.3 atmospheres to the pressure reference value of 0.25 atmospheres recorded in the dynamic freeze snapshot. When the contact surface pressure reaches 0.25 atmospheres, the hydraulic circuit lock is released, and the robotic arm resumes normal operation. Through reverse trajectory planning of the dynamic freeze snapshot and the zero-position calibration program, the robotic arm's operating state can be quickly restored, ensuring a safe and accurate reset process. The system employs graded locking and precise pressure closed-loop control to avoid potential overpressure damage or positional deviations during the reset process, thereby improving system reliability and operational stability. Simultaneously, combined with fine control of redundant degrees of freedom, it effectively suppresses mechanical vibrations during the reset process, further enhancing the accuracy of the ultrasonic probe's gripping.

[0112] Based on the same inventive concept, such as Figure 3 As shown, the present invention also provides a precision force control system for a medical robotic arm used for assisted gripping of an ultrasound probe, the system comprising:

[0113] The signal acquisition module is used to acquire multi-dimensional pressure distribution data and high-frequency vibration waveform data of the probe contact surface, and generate composite sensing signals;

[0114] The model building module is used to extract body displacement and resistance change characteristics based on the composite sensor signals and build a real-time impedance matching model.

[0115] The pressure compensation module is used to adjust the dynamic pressure parameters of the robotic arm actuator based on the real-time impedance matching model and generate pressure compensation commands.

[0116] The vibration absorption module is used to respond to the pressure compensation command and monitor the direction and intensity distribution of the robotic arm vibration in real time, and adjust the instantaneous stiffness parameters of the robotic arm actuator according to the monitoring results;

[0117] The safety warning module is used to generate a safety protection signal when the pressure value of the probe contact surface exceeds the preset first warning value, based on the adjustment results;

[0118] The reset parameter locking module is used to activate the orientation parameter locking mechanism according to the safety protection signal and save the current spatial positioning data;

[0119] The reset control module is used to receive a reset command from an external source, call the dynamic freeze snapshot to adjust the position of the robotic arm, adjust the contact surface pressure value of the probe using a preset zero-position calibration program, and release the hydraulic circuit lock state when the contact surface pressure value reaches the pressure reference value recorded by the dynamic freeze snapshot.

[0120] It should be noted that the formulas described above, through the principle of dimensional consistency and mathematical standardization methods (such as normalization, dimensionless parameter conversion, or unit system unification), can translate physical quantities with different properties into unitless standard values ​​or parameters that can be superimposed in the same dimension. This eliminates the interference of different dimensions on the computational logic, allowing the formulas to retain the original data distribution characteristics while possessing mathematical rationality and adaptability to objective laws. These are conventional technical methods and will not be elaborated further. The electrical connections between the various units described above do not necessarily represent direct or indirect connections; any indirect connection method is applicable to the embodiments of this invention as long as it achieves the purpose of this invention. The above descriptions are merely exemplary embodiments of this invention and should not be construed as limiting the scope of this invention.

[0121] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A precise force control method for a medical robotic arm used for assisting in the grasping of an ultrasound probe, characterized in that, The method includes: Acquire multi-dimensional pressure distribution data and high-frequency vibration waveform data of the probe contact surface to generate composite sensing signals; Based on the composite sensor signals, body displacement and resistance change characteristics are extracted, and a real-time impedance matching model is established. Based on the real-time impedance matching model, the dynamic pressure parameters of the robotic arm actuator are adjusted to generate pressure compensation commands. The system responds to the pressure compensation command and monitors the direction and intensity distribution of the robotic arm's vibration in real time, adjusting the instantaneous stiffness parameters of the robotic arm's actuator based on the monitoring results. Based on the adjustment results, when the pressure value of the probe contact surface exceeds the preset first warning value, a graded hydraulic damping intervention operation is performed to generate a safety protection signal. The orientation parameter locking mechanism is activated based on the security protection signal to save the current spatial positioning data.

2. The precise force control method for a medical robotic arm used for assisted gripping of an ultrasound probe according to claim 1, characterized in that, The process of acquiring multi-dimensional pressure distribution data and high-frequency vibration waveform data of the probe contact surface to generate a composite sensing signal includes: Force distribution data in all directions of the probe contact surface are collected to obtain multi-dimensional pressure distribution data; The high-frequency vibration signal of the probe is collected to obtain high-frequency vibration waveform data; By integrating the multi-dimensional pressure distribution data and the high-frequency vibration waveform data, a composite sensing signal with time phase correction characteristics is generated.

3. The precise force control method for a medical robotic arm used for assisted gripping of an ultrasound probe according to claim 1, characterized in that, The step of identifying body displacement and resistance change characteristics based on the composite sensing signal and establishing a real-time impedance matching model includes: Obtain a preset physiological dynamic feature template library, which contains soft tissue compressibility parameters under different body postures; From the composite sensing signal, body displacement and resistance change features are extracted to obtain the body position offset component and the local impedance anomaly component. Based on the body position offset component, the corresponding soft tissue compression characteristic parameters are called to generate a pressure adjustment base value; The local impedance anomaly component is subjected to current ripple inversion calculation, and dynamic correction coefficients are output. A real-time impedance matching model is generated using the pressure adjustment base value and the dynamic correction coefficient.

4. The precise force control method for a medical robotic arm used for assisted gripping of an ultrasound probe according to claim 1, characterized in that, The step of adjusting the dynamic pressure parameters of the robotic arm actuator based on the real-time impedance matching model and generating pressure compensation commands includes: The real-time impedance matching model is decomposed into a steady pressure component and a transient compensation component. The set of reference parameter values ​​for the PID controller is calculated based on the stable pressure components. The actuator of the robotic arm is driven by the transient compensation component to generate a micro-displacement compensation amount; The pressure compensation command is generated by integrating the set of reference parameter values ​​and the micro displacement compensation amount.

5. The precise force control method for a medical robotic arm used for assisted gripping of an ultrasound probe according to claim 1, characterized in that, The adjustment of the instantaneous stiffness parameters of the robotic arm actuator based on the monitoring results includes: Establish the motion trajectory equations for the redundant degrees of freedom of the robotic arm actuator; Real-time monitoring of the direction vector and energy gradient of robotic arm tremors; Based on the aforementioned direction vector, redundant degrees of freedom are dynamically allocated across four quadrants. The instantaneous stiffness parameters of the robotic arm actuator are adjusted according to the energy gradient.

6. The precise force control method for a medical robotic arm used for assisted gripping of an ultrasound probe according to claim 1, characterized in that, When the pressure value at the probe contact surface exceeds a preset first warning value, a graded hydraulic damping intervention operation is performed to generate a safety protection signal, including: When the pressure value of the probe contact surface exceeds the preset first warning value, it is determined as a pressure over-limit event, and the magnitude of the probe contact surface pressure value is compared with the preset second warning value and the preset third warning threshold. If the current probe contact surface pressure value is less than or equal to the second warning value, the buffer oil circuit micro-pressure relief operation is triggered; If the current pressure value of the probe contact surface is greater than the second warning value and less than or equal to the third warning value, then the remaining energy after depressurization will be absorbed. If the current probe contact surface pressure value is greater than the third warning value, the hydraulic circuit is locked to stop the power supply to the robotic arm actuator.

7. The precise force control method for a medical robotic arm used for assisted gripping of an ultrasound probe according to claim 6, characterized in that, The activation azimuth parameter locking mechanism includes: When the pressure over-limit event occurs, the current spatial positioning data is acquired, which includes the probe attitude Euler angles and the centroid coordinates of the contact surface; Save the current set of control parameters for the pressure-position dual-loop control loop to obtain a dynamic freeze snapshot. The set of control parameters includes the current pressure reference value. Generate a timestamped security verification code and bind it to the dynamically frozen snapshot for storage.

8. The precise force control method for a medical robotic arm used for assisted gripping of an ultrasound probe according to claim 7, characterized in that, The method further includes: After receiving a reset command from an external source, the position of the robotic arm is adjusted by calling the dynamically frozen snapshot. The contact surface pressure value of the probe is adjusted using a preset zero-point calibration program. When the pressure value of the contact surface reaches the pressure reference value recorded in the dynamic freeze snapshot, the hydraulic circuit lock-up state is released.

9. The precise force control method for a medical robotic arm used for assisted gripping of an ultrasound probe according to claim 8, characterized in that, After receiving a reset command from an external input, the process of invoking the dynamic freeze snapshot to adjust the robotic arm position includes: A compensation vector field is generated using the difference between the spatial positioning data and the current actual location; The compensation vector field is decomposed into translational and rotational components with redundant degrees of freedom. The position of the robotic arm is adjusted in segments based on the translation component and the rotation component.

10. A precision force control system for a medical robotic arm used for assisting in the grasping of an ultrasound probe, applied to the precision force control method for a medical robotic arm used for assisting in the grasping of an ultrasound probe as described in any one of claims 1-9, characterized in that, The system includes: The signal acquisition module is used to acquire multi-dimensional pressure distribution data and high-frequency vibration waveform data of the probe contact surface, and generate composite sensing signals; The model building module is used to extract body displacement and resistance change characteristics based on the composite sensor signals and build a real-time impedance matching model. The pressure compensation module is used to adjust the dynamic pressure parameters of the robotic arm actuator based on the real-time impedance matching model and generate pressure compensation commands. The vibration absorption module is used to respond to the pressure compensation command and monitor the direction and intensity distribution of the robotic arm vibration in real time, and adjust the instantaneous stiffness parameters of the robotic arm actuator according to the monitoring results; The safety warning module is used to generate a safety protection signal when the pressure value of the probe contact surface exceeds the preset first warning value, based on the adjustment results; The reset parameter locking module is used to activate the orientation parameter locking mechanism according to the safety protection signal and save the current spatial positioning data; The reset control module is used to receive a reset command from an external source, call the dynamic freeze snapshot to adjust the position of the robotic arm, adjust the contact surface pressure value of the probe using a preset zero-position calibration program, and release the hydraulic circuit lock state when the contact surface pressure value reaches the pressure reference value recorded by the dynamic freeze snapshot.

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