A Safety Chamfer Parameter Design and Optimization Method for Elderly Care Robots in Multiple Scenarios

Abstract

This invention discloses a method for designing and optimizing safety chamfer parameters for elderly care robots across multiple scenarios, relating to the field of intelligent control technology. The method includes: initializing and constructing a digital twin model of human-robot collaborative operation; obtaining robot motion commands based on motion trajectory data planned by the robot controller in joint space and obtaining the user's motion intention by capturing the user's joint motion trajectory through a visual sensor; performing advanced simulation to calculate the dynamic risk field strength around the human and robot, and generating a virtual safety boundary; quantifying and deciding on risks based on the virtual safety boundary; monitoring the intrusion of the robot and user's positions relative to the virtual safety boundary in the real world; calculating the risk quantity based on the depth and speed of the intrusion; and outputting a preliminary safety chamfer parameter adjustment strategy based on the current task scenario type. This invention achieves dynamic, accurate, and personalized optimization of safety chamfer parameters throughout the entire lifecycle.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, and in particular to a method for designing and optimizing safety chamfer parameters for elderly care robots in multiple scenarios. Background Technology

[0002] In the safety of elderly care robots, chamfering the edges and corners of the mechanical structure that come into contact with people is the key to passive safety. Existing technologies usually adopt static parameter optimization methods based on preset rules. That is, according to the robot's task type, a fixed chamfer radius is set for its rigid edges and corners through limited experiments or historical data. The impact force during collision is reduced through uniform geometric buffering. The advantage is that the design is simple and the implementation cost is low.

[0003] The actual working scenarios of elderly care robots are complex and varied, ranging from gentle daily companionship to emergency item delivery. The movement status and collision risks vary greatly. A fixed set of chamfer parameters is difficult to achieve the best balance between safety and efficiency in all scenarios. In low-risk scenarios, it may affect the smoothness of the task due to being too conservative, while in high-risk scenarios, it may pose safety hazards due to insufficient protection. It does not take into account the differences in collision tolerance caused by different physiological conditions such as osteoporosis and muscle strength among elderly people. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method for designing and optimizing safety chamfer parameters for elderly care robots in multiple scenarios, which solves the core problem that using fixed chamfer parameters cannot dynamically adapt to changes in risk in multiple task scenarios and individual differences among users.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for designing and optimizing safety chamfer parameters for elderly care robots in multiple scenarios, which includes initializing and constructing a digital twin model of human-machine collaborative operation, obtaining robot motion commands based on motion trajectory data planned by the robot controller in the joint space and obtaining user motion intentions by capturing user joint motion trajectories through visual sensors, performing advanced simulation, calculating the field strength of the dynamic risk field around the human and machine, and solving to generate virtual safety boundaries.

[0008] Risk quantification and decision-making are based on virtual safety boundaries. The intrusion of robots and users in the real world relative to the virtual safety boundaries is monitored. The risk is calculated based on the depth and speed of the intrusion. The initial safety chamfer parameter adjustment strategy is output in combination with the current task scenario type.

[0009] After verifying and optimizing the initial safety chamfer parameter adjustment strategy in the digital twin environment of human-machine collaborative operation, the strategy is then deployed and executed. The initial safety chamfer parameter adjustment strategy is then simulated and iteratively optimized in the digital twin model of human-machine collaborative operation to obtain biomechanical index data. Finally, the optimal safety chamfer parameters are deployed to the variable chamfer actuator to obtain entity interaction data.

[0010] The evolution of digital twin models driven by entity interaction data for human-machine collaborative operation involves collecting entity robot interaction data and comparing it with biomechanical index data to obtain comparative differences, and then performing parameter self-calibration of the digital twin model based on these differences.

[0011] As a preferred embodiment of the safety chamfer parameter design optimization method for elderly care robots in multiple scenarios according to the present invention, the method includes the following steps: Initializing and constructing a digital twin model for human-machine collaborative operation.

[0012] By analyzing the robot's computer-aided files and URDF descriptions to load the robot's geometric, kinematic, and dynamic properties, and using depth image information acquired by a depth camera, a detailed human skeleton model of the user is reconstructed and generated.

[0013] The system loads the user's personalized biomechanical profile from a cloud database, integrates the robot's geometric, kinematic, and dynamic attributes, the user's detailed human skeletal model, and the user's personalized biomechanical profile, and constructs a digital twin model for human-robot collaborative operation.

[0014] As a preferred embodiment of the safety chamfer parameter design optimization method for elderly care robots in multiple scenarios according to the present invention, the method includes the following steps: obtaining robot motion commands based on motion trajectory data planned by the robot controller in the joint space and obtaining user motion intentions by capturing user joint motion trajectories through visual sensors, performing advanced simulation, calculating the dynamic risk field strength around the human-machine interface, and generating virtual safety boundaries.

[0015] The robot controller reads the planned future motion trajectory data in the joint space in real time and defines it as robot motion commands. The user's joint motion trajectory is captured by the vision sensor.

[0016] The user's joint movement trajectory is matched with a personalized behavioral primitive library to obtain the user's movement intention;

[0017] Based on robot motion commands and user motion intentions, advanced simulations are performed in a digital twin model of human-machine collaborative operation to calculate robot kinetic energy, predict the vulnerability of human tissue at contact points, and the dynamic risk field strength caused by the combined effect of relative speed.

[0018] Based on the dynamic risk field strength surrounding the human-machine interface, a virtual safety boundary is calculated and generated.

[0019] As a preferred embodiment of the safety chamfer parameter design optimization method for elderly care robots in multiple scenarios according to the present invention, the method includes: risk quantification and decision-making based on virtual safety boundaries, and monitoring the intrusion of the robot and user's positions relative to the virtual safety boundaries in the real world, comprising the following steps:

[0020] Acquire real-time spatial coordinate data of the virtual safety boundary, and obtain the real-time position coordinates of the robot and the user in the real world through a depth sensor;

[0021] The spatial geometric transformation algorithm is used to calculate the relative positional relationship between the real-time position coordinates of the robot and the user and the real-time spatial coordinate data of the virtual safety boundary, and to determine the intrusion status of the robot and the user's real-time position coordinates in the area defined by the real-time spatial coordinate data of the virtual safety boundary.

[0022] Monitor intrusions by robots and users into the virtual security boundary.

[0023] As a preferred embodiment of the safety chamfer parameter design and optimization method for elderly care robots in multiple scenarios according to the present invention, the method includes the following steps: calculating the risk based on the depth and speed of intrusion, and outputting a preliminary safety chamfer parameter adjustment strategy in conjunction with the current task scenario type.

[0024] The Euclidean distance method is used to quantify the depth of the area defined by the real-time spatial coordinate data of the robot and user's intrusion into the virtual security boundary;

[0025] The finite difference method is used to calculate the instantaneous velocity of the robot and user intruding into the area defined by the real-time spatial coordinate data of the virtual safety boundary;

[0026] Based on the intrusion depth and the instantaneous intrusion speed, a comprehensive risk level is calculated to identify the current task scenario type;

[0027] Based on the comprehensive risk level and the current task scenario type, the preset decision mapping relationship is queried, and a preliminary safety chamfer parameter adjustment strategy is output.

[0028] As a preferred embodiment of the multi-scenario safety chamfer parameter design and optimization method for elderly care robots of this invention, the method includes: verifying and optimizing the initial safety chamfer parameter adjustment strategy in a human-machine collaborative digital twin environment and then issuing it for execution; performing simulation verification and iterative optimization of the initial safety chamfer parameter adjustment strategy in a human-machine collaborative digital twin model to obtain biomechanical index data; and issuing the optimal safety chamfer parameters to the variable chamfer actuator to obtain entity interaction data. This includes the following steps:

[0029] The initial safety chamfer parameter adjustment strategy is input into the digital twin model of human-machine collaborative operation, and the contact process after applying the initial safety chamfer parameter adjustment strategy is simulated in the digital twin model of human-machine collaborative operation.

[0030] In the simulated contact process of the digital twin model of human-machine collaborative operation, biomechanical index data are calculated to determine the degree to which the biomechanical index data meets the biomechanical safety constraints set for the peak force and pressure values ​​that the bones and soft tissues of the elderly can withstand.

[0031] When the biomechanical index data do not meet the biomechanical safety constraints, the initial safety chamfer parameter adjustment strategy is iteratively corrected.

[0032] When the biomechanical index data meet the biomechanical safety constraints, the initial safety chamfer parameter adjustment strategy that meets the conditions is set as the optimal safety chamfer parameter;

[0033] The optimal safety chamfer parameters are sent to the variable chamfer actuator, which then adjusts the optimal safety chamfer parameters.

[0034] The force sensor collects physical interaction data generated by the variable chamfer actuator after adjustment and contact with the user.

[0035] As a preferred embodiment of the safety chamfer parameter design optimization method for elderly care robots in multiple scenarios according to the present invention, the method includes the following steps: Based on the evolution of a digital twin model driven by entity interaction data for human-machine collaborative operation, collecting entity robot interaction data and comparing it with biomechanical index data to obtain comparative differences.

[0036] Collect physical robot interaction data, compare the physical robot interaction data with biomechanical index data, and calculate the difference between the physical robot interaction data and biomechanical index data using relative error.

[0037] As a preferred embodiment of the safety chamfer parameter design optimization method for elderly care robots in multiple scenarios according to the present invention, the method includes the following steps: Self-calibration of parameters in the digital twin model based on comparison differences.

[0038] Analyze the differences between physical robot interaction data and biomechanical index data;

[0039] Based on the difference between the interaction data of the physical robot and the biomechanical index data, the mechanical parameters of the human skeleton model in the digital twin model of human-robot collaborative operation are adjusted.

[0040] Based on the difference between the interaction data of the physical robot and the biomechanical index data, the dynamic parameters of the robot body in the digital twin model of human-robot collaborative operation are adjusted.

[0041] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed by the processor, implements any step of the method for designing and optimizing safety chamfer parameters for elderly care robots in multiple scenarios as described in the first aspect of the present invention.

[0042] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the method for designing and optimizing safety chamfer parameters for elderly care robots for multiple scenarios as described in the first aspect of the present invention.

[0043] The beneficial effects of this invention are as follows: By constructing a digital twin model of human-machine collaborative operation, advanced simulation is performed based on robot motion commands and user motion intentions to generate a dynamic risk field and solve the virtual safety boundary; then, the intrusion of the human-machine relative position into the boundary in the real world is monitored, and the risk quantity is calculated by combining the intrusion depth, speed and task scenario type, and a preliminary safety parameter adjustment strategy is output; the strategy is simulated, verified and iteratively optimized in the digital twin environment, and after obtaining the optimal parameters that meet biomechanical safety constraints, they are sent to the variable chamfer actuator for execution, and the entity interaction data is collected to drive the self-calibration of the digital twin model parameters, realizing the dynamic, accurate and personalized optimization of the safety chamfer parameters throughout the entire life cycle. Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 A flowchart illustrating the optimization method for safety chamfer parameters of elderly care robots for multiple scenarios.

[0046] Figure 2 This is a schematic diagram for calculating the field strength of a dynamic risk field.

[0047] Figure 3 This is a schematic diagram of virtual security boundary intrusion monitoring.

[0048] Figure 4 A schematic diagram verifying the strategy for adjusting safety chamfer parameters. Detailed Implementation

[0049] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0050] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0051] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0052] Reference Figures 1-4 This is one embodiment of the present invention, which provides a method for designing and optimizing safety chamfer parameters for elderly care robots in multiple scenarios, including the following steps:

[0053] S1. Initialize and build a digital twin model of human-machine collaborative operation.

[0054] S1.1 By parsing the robot's computer-aided files and URDF description, the geometric, kinematic, and dynamic properties of the robot body are loaded. Using depth image information acquired by a depth camera, a detailed human skeleton model of the user is reconstructed and generated.

[0055] Furthermore, the computer-aided design files of the robot are analyzed to obtain the precise three-dimensional geometric dimensions and mass distribution of each rigid component of the robot body. The unified robot description format files are analyzed to obtain the kinematic chain structure relationship of each joint of the robot body, the limit range of joint rotation or movement, and dynamic parameters such as mass and inertia tensors. Depth cameras deployed on the robot body or in the working environment acquire depth image sequences containing the user's body contour. Skeletal joint point recognition and three-dimensional coordinate reconstruction are performed on the depth image sequences to generate a refined human skeleton model composed of the coordinates of the center points of the main joints and the skeletal links.

[0056] S1.2 Load the user's personalized biomechanical profile from the cloud database, integrate the robot's geometric, kinematic and dynamic attributes, the user's detailed human skeleton model and the user's personalized biomechanical profile, and construct a digital twin model of human-machine collaborative operation.

[0057] Furthermore, the user's personalized biomechanical profile is retrieved and downloaded from a cloud database via a secure network protocol. This profile includes bone density distribution, maximum muscle strength of key muscle groups, and mechanical constraint parameters for typical joint range of motion, derived from the user's previous medical imaging data and physical fitness assessment reports. The robot's geometric, kinematic, and dynamic properties, along with the user's detailed skeletal model, are then fused with the user's personalized biomechanical profile loaded from the cloud database, and their coordinate systems are unified to construct a digital twin model of human-robot collaborative operation that includes complete geometric, kinematic, dynamic, and biomechanical constraint information.

[0058] S2. Obtain robot motion commands based on motion trajectory data planned by the robot controller in joint space and obtain user motion intentions by capturing user joint motion trajectories through visual sensors. Perform advanced simulation to calculate the dynamic risk field strength around the human and machine and generate virtual safety boundaries.

[0059] S2.1 Read the planned future motion trajectory data in the joint space from the robot controller in real time, define it as robot motion commands, and capture the user's joint motion trajectory through the vision sensor.

[0060] Furthermore, the robot controller's internal application programming interface reads in real time the sequence data of joint angles planned in the joint space for a future period of time. The joint angle sequence data is the future motion trajectory data planned by the robot controller in the joint space. The future motion trajectory data stream is defined as the robot's motion command. The visual sensors deployed on the robot body or in the working environment continuously capture two-dimensional image sequences containing the user's main joint points at a fixed sampling frequency. The two-dimensional image sequences are processed by a human posture estimation algorithm to extract the time series coordinate data of the user's joint motion trajectory.

[0061] S2.2 Match the user's joint movement trajectory with the personalized behavior primitive library to obtain the user's movement intention.

[0062] Furthermore, the real-time time-series data of the user's joint movement trajectory captured by the visual sensor is compared with the standardized behavioral pattern trajectory stored in the user's personalized behavioral primitive library, either locally or in the cloud, to perform similarity matching calculations. The matching calculation can use time-series similarity measurement algorithms such as dynamic time warping. When the real-time joint movement trajectory and a certain behavioral primitive in the personalized behavioral primitive library, such as the standard trajectory of a quick stand-up or a steady walk, reach the highest similarity, the user's current movement intention is determined.

[0063] S2.3. Based on the robot's motion commands and the user's motion intentions, advanced simulation is performed in the digital twin model of human-machine collaborative operation to calculate the robot's kinetic energy, predict the vulnerability of human tissue at the contact points, and the dynamic risk field strength caused by the combined effect of relative speed.

[0064] The expression for the dynamic risk field strength is:

[0065] ;

[0066] in, For dynamic risk, each scenario is strong. This is a scene adjustment coefficient. Let the robot's equivalent mass be the mass in the predicted collision direction. To predict the magnitude of the relative velocity between the robot and the user at the point of contact. This represents the vulnerability coefficient of human tissues.

[0067] Furthermore, by taking the robot's motion commands and the current user's motion intentions as inputs, a digital twin model for human-robot collaborative operation is driven to perform millisecond-level advanced dynamic simulation. During the advanced simulation, half of the product of the robot's equivalent mass in the predicted collision direction and the square of the relative velocity is calculated, which is the kinetic energy term. The kinetic energy term is then multiplied by the human tissue vulnerability coefficient corresponding to the predicted contact area obtained from the user's personalized biomechanical profile, and multiplied by a nonlinear function related to the relative velocity. A scene adjustment coefficient is introduced for normalization and weight adjustment to calculate the dynamic risk field strength.

[0068] It should be noted that by inputting robot motion commands and user motion intentions into a digital twin model for human-robot collaborative operation, advanced simulation was performed, enabling comprehensive quantitative calculation of the field strength of dynamic risk fields. Its beneficial effect lies in dynamically coupling the robot's kinetic energy, the vulnerability of specific human tissues based on personalized biomechanical profiles, and real-time relative speed with physiological factors. This generates a comprehensive index that reflects the potential collision risk under the current human-robot interaction state, transforming safety assessment from static geometric parameter design to dynamic risk perception based on real-time state and individual differences. This provides a quantitative basis for core safety decisions that possesses both physical realism and personalized characteristics.

[0069] S2.4. Based on the dynamic risk field strength surrounding the human-machine interface, calculate and generate a virtual safety boundary.

[0070] Furthermore, the numerical value of the dynamic risk field strength surrounding the human-machine interface is mapped to an isoelectric surface. The position and shape of the isoelectric surface in three-dimensional space are composed of points with equal field strength values, and the isoelectric surface is defined as a virtual safety boundary.

[0071] S3. Quantify and make decisions based on virtual security boundaries, and monitor the intrusion of robots and users in the real world relative to the virtual security boundaries.

[0072] S3.1 Obtain real-time spatial coordinate data of the virtual safety boundary, and obtain the real-time position coordinates of the robot and the user in the real world through the depth sensor.

[0073] Furthermore, the real-time coordinate point set data of the boundary surface in three-dimensional space is extracted from the mathematical model of the virtual safety boundary, defining the spatial range of the virtual safety boundary. Depth sensors deployed in the working environment collect depth images containing the contours of the robot and the user at a fixed frequency. Point cloud segmentation and target recognition processing are performed on the depth images, and the three-dimensional coordinates of the predetermined monitoring points on the robot body and the three-dimensional coordinates of the predetermined joints on the user body are extracted respectively, which together constitute the real-time position coordinates of the robot and the user in the real world.

[0074] S3.2. Use a spatial geometric transformation algorithm to calculate the relative positional relationship between the real-time position coordinates of the robot and the user and the real-time spatial coordinate data of the virtual safety boundary, and determine the intrusion status of the robot and the user's real-time position coordinates in the area defined by the real-time spatial coordinate data of the virtual safety boundary.

[0075] Furthermore, the real-time position coordinates of the robot and user in the real world are uniformly transformed to the coordinate system of the real-time spatial coordinate data of the virtual safety boundary through a coordinate transformation matrix; the shortest Euclidean distance from the robot's real-time position coordinates to the virtual safety boundary surface is calculated, and the shortest Euclidean distance from the user's real-time position coordinates to the same virtual safety boundary surface is also calculated; when the calculated result of the shortest Euclidean distance is negative, it is determined that the real-time position coordinates of the robot or the user have invaded the area defined by the real-time spatial coordinate data of the virtual safety boundary, and the absolute value of the negative value is the invasion depth; when the calculated result of the shortest Euclidean distance is positive, it is determined that there is no invasion. The abstract virtual boundary and physical position are transformed into quantifiable spatial relationship indicators to accurately determine the invasion status.

[0076] S3.3 Monitor the intrusion of the robot and user's positions relative to the virtual security boundary.

[0077] Furthermore, based on the intrusion status results, the robot and user's positions relative to the virtual safety boundary are continuously recorded in each sampling period, along with the intrusion depth values ​​and intrusion or non-intrusion status flags. This data is stored and monitored as a time series, enabling continuous and real-time monitoring of safety boundary violations. This provides complete time-series data input for subsequent risk calculations based on intrusion depth and velocity.

[0078] S4. Calculate the risk based on the depth and speed of the intrusion, and output a preliminary safety chamfer parameter adjustment strategy in combination with the current task scenario type.

[0079] S4.1 Use the Euclidean distance method to quantify the depth value of the region defined by the real-time position coordinates of the robot and the user intruding into the virtual security boundary.

[0080] Furthermore, based on the shortest Euclidean distance from the real-time position coordinates of the robot and the user to the virtual safety boundary surface, the distance value is the absolute value of the intrusion depth. When the intrusion is determined, the calculated result of the shortest Euclidean distance is negative, and its absolute value is taken as the intrusion depth value. When the intrusion is determined, the intrusion depth value is recorded as zero.

[0081] S4.2 Use the finite difference method to calculate the instantaneous velocity of the robot and user's real-time position coordinates intruding into the area defined by the real-time spatial coordinate data of the virtual safety boundary.

[0082] The expression for instantaneous velocity is:

[0083] ;

[0084] in, Instantaneous velocity For the current moment The relative position vector between the robot and the user The previous sampling time The relative position vector between the robot and the user For time intervals, For the current moment, This refers to the previous sampling time.

[0085] Furthermore, by calculating the relative instantaneous velocity between the robot and the user using the differential method, the dynamic trend of intrusion into the safety boundary is accurately quantified. It not only reflects the spatial depth of the intrusion, but more importantly, it captures the temporal urgency of the intrusion. A high-speed intrusion means a shorter available safety response time window and a higher risk level. By incorporating the rate of change of spatial displacement into the risk assessment mechanism, safety decisions can be made ahead of the actual occurrence of the collision, upgrading from static spatial relationship judgment to dynamic risk warning.

[0086] S4.3. Based on the intrusion depth and the instantaneous intrusion speed, calculate the comprehensive risk level and identify the current task scenario type.

[0087] The expression for the comprehensive risk level is:

[0088] ;

[0089] in, To assess the overall risk level, Risk coefficient for the task scenario. This represents the depth of intrusion.

[0090] Furthermore, the quantified invasion depth is multiplied by the instantaneous velocity to obtain a preliminary physical risk product term; the currently executing task identifier is read from the robot task scheduler, and the task scenario risk coefficient corresponding to the current task scenario type is determined according to the preset mapping table of task scenario type and task scenario risk coefficient; the task scenario risk coefficient is multiplied by the preliminary physical risk product term to obtain the comprehensive risk amount.

[0091] S4.4 Based on the comprehensive risk level and the current task scenario type, query the preset decision mapping relationship and output the preliminary safety chamfer parameter adjustment strategy.

[0092] Furthermore, a decision mapping table is constructed with the comprehensive risk level and the current task scenario type as joint input keys and the recommended safety chamfer parameter adjustment amount as output. Using the comprehensive risk level and the identified current task scenario type as query keys, the decision mapping table is searched or interpolated to output the corresponding preliminary safety chamfer parameter adjustment strategy, which specifically indicates the adjustment instructions for the variable chamfer actuator.

[0093] S5. After verifying and optimizing the initial safety chamfer parameter adjustment strategy in the digital twin environment of human-machine collaborative operation, the strategy is then deployed for execution. The initial safety chamfer parameter adjustment strategy is then simulated and iteratively optimized in the digital twin model of human-machine collaborative operation to obtain biomechanical index data. The optimal safety chamfer parameters are then deployed to the variable chamfer actuator to obtain entity interaction data.

[0094] S5.1 Input the preliminary safety chamfer parameter adjustment strategy into the digital twin model of human-machine collaborative operation, and simulate the contact process after applying the preliminary safety chamfer parameter adjustment strategy in the digital twin model of human-machine collaborative operation.

[0095] Furthermore, the parameter values ​​specified by the initial safety chamfer parameter adjustment strategy, such as the target chamfer radius and material compliance, are assigned to the corresponding robot body contact surface geometry and material properties in the digital twin model of human-robot collaborative operation. The digital twin model driving human-robot collaborative operation performs dynamic simulation based on the predicted robot motion commands and user motion intentions, simulating the complete physical process of the robot coming into contact with the user's human skeleton model after the application of the new safety chamfer parameters.

[0096] S5.2 Calculate biomechanical index data during the simulated contact process of the digital twin model of human-machine collaborative operation, and determine the degree to which the biomechanical index data meets the biomechanical safety constraints set for the peak force and pressure values ​​that the bones and soft tissues of the elderly can withstand.

[0097] The expression for biomechanical index data is as follows:

[0098] ;

[0099] in, For biomechanical index data, For contact stiffness coefficient, For the maximum contact deformation, The damping coefficient is... The relative velocity is the velocity at the instant of the collision.

[0100] Based on the principle of energy conservation during collision, in an idealized linear spring collision model, the kinetic energy at the moment of collision is completely converted into the potential energy stored by the elastic deformation of the contact interface. Setting the kinetic energy equal to the elastic potential energy, multiplying both sides of the equation by 2 and dividing by the stiffness coefficient, and taking the square root of both sides, yields the maximum contact deformation. The formula establishes the initial conditions and maximum shape of the collision.

[0101] The physical relationship between them.

[0102] Furthermore, compared with existing technologies, the core difference of this approach lies in transforming safety verification from a retrospective, qualitative assessment based on geometric experience and physical prototype collision tests to a priori, quantitative, and precise prediction based on biomechanical models and digital simulations. Existing technologies typically rely on preset fixed chamfer dimensions or a limited number of physical collision tests to roughly assess safety, failing to accurately quantify the actual mechanical load generated by collisions on the bones and soft tissues of a specific elderly population. By establishing a dynamic model that includes stiffness, damping, and the law of conservation of energy, biomechanical indicators such as peak contact force are calculated in a digital twin environment and directly compared with the tolerance thresholds of elderly tissues obtained in clinical medicine. This enables precise prediction and safety assurance in virtual space, avoiding the high cost, long cycle, and ethical risks of physical testing.

[0103] S5.3 When the biomechanical index data does not meet the biomechanical safety constraints, the initial safety chamfer parameter adjustment strategy shall be iteratively corrected.

[0104] Furthermore, the safety threshold is set based on the critical damage force and pressure values ​​that the bones and soft tissues of the elderly can withstand, as measured in clinical biomechanical studies, combined with a safety factor. When it is determined that the biomechanical index data does not meet the biomechanical safety constraints, the parameter values ​​in the preliminary safety chamfer parameter adjustment strategy are corrected according to the magnitude and direction of the biomechanical index data exceeding the safety threshold, using the gradient descent method, to increase the chamfer radius or improve the material compliance. The corrected parameters are then re-input into the digital twin model of human-machine collaborative operation until the biomechanical index data meets the biomechanical safety constraints.

[0105] S5.4 When the biomechanical index data meets the biomechanical safety constraints, the initial safety chamfer parameter adjustment strategy that meets the conditions shall be set as the optimal safety chamfer parameter.

[0106] Furthermore, when it is determined that the biomechanical index data meets the biomechanical safety constraints, the parameter combination of the preliminary safety chamfer parameter adjustment strategy that enables the biomechanical index data to meet the standards is formally determined and marked as the optimal safety chamfer parameter.

[0107] S5.5 The optimal safety chamfer parameters are sent to the variable chamfer actuator, and the variable chamfer actuator performs the adjustment of the optimal safety chamfer parameters.

[0108] Furthermore, the optimal safety chamfer parameters are sent to the controller of the variable chamfer actuator in the physical world via a communication bus; after receiving the instruction, the controller of the variable chamfer actuator drives its internal actuator, the electroactive polymer driver, to achieve the physical shape of the chamfer.

[0109] S5.6. Collect physical interaction data generated by the variable chamfer actuator after adjustment and contact with the user through a force sensor.

[0110] Furthermore, after the variable chamfer actuator performs the adjustment, when the robot comes into actual contact with the user in a real environment, the force sensors integrated on or near the surface of the variable chamfer actuator collect the three-dimensional force and torque data generated during the contact process; the three-dimensional force and torque data are recorded in real time as physical interaction data.

[0111] S6. Evolution of a digital twin model for human-machine collaborative operation driven by entity interaction data: collect entity robot interaction data and compare it with biomechanical index data to obtain comparative differences.

[0112] S6.1 Collect physical robot interaction data, compare the physical robot interaction data with biomechanical index data, and calculate the difference between the physical robot interaction data and biomechanical index data using relative error.

[0113] The expression for the difference value is:

[0114] ;

[0115] in, For the difference value, For physical robot interaction data vectors, This is a vector of biomechanical index data.

[0116] Furthermore, interaction data of the physical robot is collected from the force sensors integrated on the variable chamfer actuator and the torque sensors of the robot's joints, including three-dimensional contact force, contact torque, and joint drive current. These data are arranged dimensionally to form a physical robot interaction data vector. The corresponding biomechanical index data vector is obtained from the previous simulation results of the digital twin model of human-robot collaborative operation, including data of the corresponding dimensions such as three-dimensional contact force and contact torque obtained from simulation calculations. The physical robot interaction data vector and the biomechanical index data vector are subtracted from the values ​​of the biomechanical index data vector in each corresponding dimension to obtain the absolute deviation vector. Each element in the absolute deviation vector is divided by the value of the biomechanical index data vector in the corresponding dimension to obtain the relative deviation vector. Finally, the L2 norm of the relative deviation vector is calculated, that is, the square root of the sum of the squares of each element of the relative deviation vector is obtained to obtain the difference value in scalar form.

[0117] S7. Perform parameter self-calibration on the digital twin model based on the comparison differences.

[0118] S7.1 Analyze the differences between the interaction data and biomechanical index data of the physical robot.

[0119] Furthermore, we obtain the interaction data vector and biomechanical index data vector of the physical robot, calculate the relative error of the interaction data vector and biomechanical index data vector in each corresponding data dimension, construct a relative deviation vector from the relative errors of all dimensions, calculate the L2 norm of the relative deviation vector, and obtain a scalar difference value that characterizes the overall difference between the interaction data and biomechanical index data of the physical robot.

[0120] S7.2 Adjust the mechanical parameters of the human skeleton model in the digital twin model of human-robot collaborative operation based on the difference between the interaction data of the physical robot and the biomechanical index data.

[0121] Furthermore, by analyzing the main sources of the discrepancies, if the discrepancies are primarily dominated by deviations in the contact force or torque data dimensions, it is determined that there are errors in the mechanical parameters of the human skeleton model in the digital twin model of human-robot collaborative operation. Optimization algorithms such as gradient descent are then used to adjust the mechanical parameters of the human skeleton model in the digital twin model of human-robot collaborative operation, such as the elastic modulus of soft tissue or the damping characteristics of joints, so that the biomechanical index data vector output by the adjusted model simulation approximates the interaction data vector of the physical robot, thereby reducing the discrepancies.

[0122] S7.3 Adjust the dynamic parameters of the robot body in the digital twin model of human-robot collaborative operation based on the difference between the interaction data of the physical robot and the biomechanical index data.

[0123] Furthermore, by analyzing the main sources of the discrepancies, if the discrepancies are primarily dominated by deviations in motion acceleration or joint torque data, it is determined that there are errors in the dynamic parameters of the robot body in the digital twin model of human-robot collaborative operation. By employing parameter identification algorithms, the dynamic parameters of the robot body in the digital twin model of human-robot collaborative operation are adjusted, such as the mass properties of links, the friction coefficient of joints, or transmission efficiency, so that the robot motion state calculated by the model simulation after adjustment is more consistent with the actual motion state of the physical robot, thereby reducing the discrepancies.

[0124] This embodiment also provides a computer device applicable to the design and optimization method for safety chamfer parameters of elderly care robots for multiple scenarios, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the design and optimization method for safety chamfer parameters of elderly care robots for multiple scenarios proposed in the above embodiment.

[0125] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0126] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the method for optimizing the safety chamfer parameters of elderly care robots for multiple scenarios, as proposed in the above embodiment. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0127] In summary, this invention constructs a digital twin model of human-machine collaborative operation, performs advanced simulation based on robot motion commands and user motion intentions, generates a dynamic risk field, and calculates virtual safety boundaries. It then monitors the intrusion of the human-machine relative positions into the boundaries in the real world, calculates the risk quantity based on intrusion depth, speed, and task scenario type, and outputs preliminary safety parameter adjustment strategies. The strategies are then simulated, verified, and iteratively optimized in the digital twin environment. After obtaining the optimal parameters that satisfy biomechanical safety constraints, these parameters are sent to the variable chamfer actuator for execution. The collected entity interaction data drives the self-calibration of the digital twin model parameters, achieving dynamic, accurate, and personalized optimization of safety chamfer parameters throughout the entire lifecycle.

[0128] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for designing and optimizing safety chamfer parameters for elderly care robots in multiple scenarios, characterized in that: This includes initializing and building a digital twin model for human-machine collaborative work, comprising the following steps: By analyzing the robot's computer-aided files and URDF descriptions to load the robot's geometric, kinematic, and dynamic properties, and using depth image information acquired by a depth camera, a detailed human skeleton model of the user is reconstructed and generated. The user's personalized biomechanical profile is loaded from the cloud database, and the robot's geometric, kinematic and dynamic properties, the user's detailed human skeleton model and the user's personalized biomechanical profile are integrated to build a digital twin model of human-machine collaborative operation. Based on the motion trajectory data planned by the robot controller in the joint space, the robot motion commands are obtained, and the user's motion intention is obtained by capturing the user's joint motion trajectory through vision sensors. Then, advanced simulation is performed to calculate the dynamic risk field strength around the human and machine, and virtual safety boundaries are generated. This includes the following steps: The robot controller reads the planned future motion trajectory data in the joint space in real time and defines it as robot motion commands. The user's joint motion trajectory is captured by the vision sensor. The user's joint movement trajectory is matched with a personalized behavioral primitive library to obtain the user's movement intention; Based on robot motion commands and user motion intentions, advanced simulations are performed in a digital twin model of human-machine collaborative operation to calculate robot kinetic energy, predict the vulnerability of human tissue at contact points, and the dynamic risk field strength caused by the combined effect of relative speed. Based on the dynamic risk field strength surrounding the human-machine interface, a virtual safety boundary is calculated and generated. Risk quantification and decision-making are based on virtual safety boundaries. The intrusion of robots and users in the real world relative to the virtual safety boundaries is monitored. The risk is calculated based on the depth and speed of the intrusion. The initial safety chamfer parameter adjustment strategy is output in combination with the current task scenario type. After verifying and optimizing the initial safety chamfer parameter adjustment strategy in the digital twin environment of human-machine collaborative operation, the strategy is then deployed and executed. The initial safety chamfer parameter adjustment strategy is then simulated and iteratively optimized in the digital twin model of human-machine collaborative operation to obtain biomechanical index data. Finally, the optimal safety chamfer parameters are deployed to the variable chamfer actuator to obtain entity interaction data. The evolution of digital twin models driven by entity interaction data for human-machine collaborative operation involves collecting entity robot interaction data and comparing it with biomechanical index data to obtain comparative differences, and then performing parameter self-calibration of the digital twin model based on these differences.

2. The method for designing and optimizing safety chamfer parameters for elderly care robots oriented towards multiple scenarios as described in claim 1, characterized in that: Risk quantification and decision-making based on virtual security boundaries, monitoring the intrusion of robots and users in the real world relative to the virtual security boundaries, includes the following steps: Acquire real-time spatial coordinate data of the virtual safety boundary, and obtain the real-time position coordinates of the robot and the user in the real world through a depth sensor; The spatial geometric transformation algorithm is used to calculate the relative positional relationship between the real-time position coordinates of the robot and the user and the real-time spatial coordinate data of the virtual safety boundary, and to determine the intrusion status of the robot and the user's real-time position coordinates in the area defined by the real-time spatial coordinate data of the virtual safety boundary. Monitor intrusions by robots and users into the virtual security boundary.

3. The method for designing and optimizing safety chamfer parameters for elderly care robots oriented towards multiple scenarios as described in claim 2, characterized in that: Based on the depth and speed of the intrusion, the risk level is calculated, and a preliminary security chamfer parameter adjustment strategy is output in conjunction with the current task scenario type, including the following steps: The Euclidean distance method is used to quantify the depth of the area defined by the real-time spatial coordinate data of the robot and user's intrusion into the virtual security boundary; The finite difference method is used to calculate the instantaneous velocity of the robot and user intruding into the area defined by the real-time spatial coordinate data of the virtual safety boundary; Based on the intrusion depth and the instantaneous intrusion speed, a comprehensive risk level is calculated to identify the current task scenario type; Based on the comprehensive risk level and the current task scenario type, the preset decision mapping relationship is queried, and a preliminary safety chamfer parameter adjustment strategy is output.

4. The method for designing and optimizing safety chamfer parameters for elderly care robots oriented towards multiple scenarios as described in claim 3, characterized in that: After verifying and optimizing the initial safety chamfer parameter adjustment strategy in a human-machine collaborative digital twin environment, the strategy is deployed for execution. The initial safety chamfer parameter adjustment strategy is then simulated and iteratively optimized within the human-machine collaborative digital twin model to obtain biomechanical index data. Finally, the optimal safety chamfer parameters are deployed to the variable chamfer actuator to obtain entity interaction data. This process includes the following steps: The initial safety chamfer parameter adjustment strategy is input into the digital twin model of human-machine collaborative operation, and the contact process after applying the initial safety chamfer parameter adjustment strategy is simulated in the digital twin model of human-machine collaborative operation. In the simulated contact process of the digital twin model of human-machine collaborative operation, biomechanical index data are calculated to determine the degree to which the biomechanical index data meets the biomechanical safety constraints set for the peak force and pressure values ​​that the bones and soft tissues of the elderly can withstand. When the biomechanical index data do not meet the biomechanical safety constraints, the initial safety chamfer parameter adjustment strategy is iteratively corrected. When the biomechanical index data meet the biomechanical safety constraints, the initial safety chamfer parameter adjustment strategy that meets the conditions is set as the optimal safety chamfer parameter; The optimal safety chamfer parameters are sent to the variable chamfer actuator, which then adjusts the optimal safety chamfer parameters. The force sensor collects physical interaction data generated by the variable chamfer actuator after adjustment and contact with the user.

5. The method for designing and optimizing safety chamfer parameters for elderly care robots oriented towards multiple scenarios as described in claim 4, characterized in that: The evolution of a digital twin model for human-robot collaborative operations based on entity interaction data involves collecting entity robot interaction data and comparing it with biomechanical index data to obtain comparative differences. This includes the following steps: Collect physical robot interaction data, compare the physical robot interaction data with biomechanical index data, and calculate the difference between the physical robot interaction data and biomechanical index data using relative error.

6. The method for designing and optimizing safety chamfer parameters for elderly care robots oriented towards multiple scenarios as described in claim 5, characterized in that, The digital twin model undergoes parameter self-calibration based on comparison differences, including the following steps: Analyze the differences between physical robot interaction data and biomechanical index data; Based on the difference between the interaction data of the physical robot and the biomechanical index data, the mechanical parameters of the human skeleton model in the digital twin model of human-robot collaborative operation are adjusted. Based on the difference between the interaction data of the physical robot and the biomechanical index data, the dynamic parameters of the robot body in the digital twin model of human-robot collaborative operation are adjusted.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the method for designing and optimizing safety chamfer parameters for elderly care robots oriented towards multiple scenarios, as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the method for designing and optimizing safety chamfer parameters for elderly care robots for multiple scenarios, as described in any one of claims 1 to 6.

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