Six-axis robot arm device and control method based on terahertz detection

Abstract

The present invention discloses a six-axis robot arm device and control method based on terahertz detection, which relates to the field of robotic arm technology. The method comprises obtaining motion trajectory planning data between an axis joint and a target operand; generating a motion control instruction set based on the motion trajectory planning data; establishing an association mapping relationship between the motion control instruction set and the target operand; and dynamically adapting the computing power allocation strategy of each axis joint of the six-axis robot arm device to the real-time motion parameters based on the triggering of the association mapping relationship. The present invention constructs a unified three-dimensional spatial coordinate system, integrates the positional relationship of the six-axis robot device with environmental information, identifies potential motion conflict areas, and introduces the concept of interference probability calculation based on conflict density and spatial proximity, quantitatively assessing the motion interference risk of each area segment, so that trajectory segments with high interference probability can be automatically sorted and avoided during operation.

Description

Technical Field

[0001] The present invention relates to the technical field of robotic arms, and in particular to a six-axis robotic arm device and a control method based on terahertz detection. Background Art

[0002] The six-axis robot is a widely used industrial robot at this stage. The design of its six joint axes gives it highly flexible movement capabilities, enabling it to perform various delicate operations in complex spatial environments. The combination of terahertz detection technology and a six-axis robot arm device has improved the robot's work efficiency and accuracy in industrial inspection, material identification and other fields. In addition, the unique penetrating and spectral characteristics of terahertz waves enable the six-axis robot arm to quickly obtain internal structural information of an object without contacting the target object, thus playing an important role in non-destructive testing.

[0003] After searching, the Chinese invention patent application with publication number "CN119217356A" proposed a "robot main hand control method, device, robot main hand, medium and product". When performing redundant control, the angle change of the redundant joint is first calculated based on the initial angle of each joint, and then the target angle of the redundant joint is determined according to the angle change; the target angle of the redundant joint obtained by this method can achieve continuous change of the redundant joint, avoiding the adverse effects of joint mutation on the user.

[0004] In addition, the Chinese invention patent application with publication number "CN112140092A" proposes "a micro-robot based on terahertz wave induction". By utilizing the characteristic that terahertz wave radiation can cause material deformation, the motion control of the micro-robot is achieved by arranging absorbing devices with different response frequencies.

[0005] In complex industrial manufacturing scenarios, especially those involving surface processing of transparent materials (such as glass and acrylic) or simultaneous operation of heterogeneous targets (such as workpieces of different sizes or materials) by multiple six-axis robots, the above-mentioned disclosed device methods and similar existing device methods will lead to a significant increase in the probability of interference during robot movement and increased response delay of key axes due to problems such as "difficulty in accurately identifying target features, frequent spatial interaction interference, and uneven scheduling of multi-axis computing power" in path planning and confirmation during actual operation, thereby affecting the efficiency of collaborative operations and the final processing quality of the product. Summary of the Invention

[0006] The purpose of the present invention is to provide a six-axis robot arm device and a control method based on terahertz detection to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] First, a control method for a six-axis robotic arm device based on terahertz detection is proposed, including:

[0009] Obtain motion trajectory planning data between the axis joint and the target object;

[0010] Generate motion control instruction set based on motion trajectory planning data;

[0011] Establishing an association mapping relationship between the action control instruction set and the target operand;

[0012] Based on the triggering of the associated mapping relationship, the computing power allocation strategy of each axis joint of the six-axis robot arm device is dynamically adapted to the real-time motion parameters;

[0013] The motion control instruction set adjusts the trigger parameters based on the computing power allocation strategy, thereby enhancing the response speed of the motion axis;

[0014] The dynamic adaptation method includes:

[0015] Integrate the computing units of the six-axis servo drive through the Ethernet real-time communication protocol and establish a multi-axis collaborative computing resource pool;

[0016] Allocate initial processing power based on trajectory characteristic parameters of the motion control instruction set;

[0017] The historical load data of the corresponding axis joints in the six-axis robot arm device are integrated, and the correction coefficient is constructed based on the ratio of the historical load data to the real-time load data;

[0018] The correction coefficient and the initial processing power are associated to complete the distribution of computing power to the driven axis.

[0019] As a further preferred embodiment of the present technical solution, the correction coefficient is dynamically regulated by quantifying the load change trend during the movement of the axis joint;

[0020] When the correction coefficient is greater than the unit value, it indicates that the current load level exceeds the historical average, and the initial processing computing power needs to be incrementally configured based on the absolute difference between the correction coefficient and the unit value.

[0021] When the correction coefficient is less than the unit value, a dynamic reduction strategy of computing power resources is implemented for the initial processing computing power based on the absolute difference between the unit value and the correction coefficient.

[0022] As a further preferred embodiment of the present technical solution, a method for determining motion trajectory planning data includes:

[0023] Based on the spatial position relationship between the six-axis robotic arm device and the target object, a three-dimensional spatial coordinate system is established;

[0024] Based on the architecture of the operation instruction set and the spatial distance data, the trajectory data of the motion axis within the axis joint is defined;

[0025] According to the environmental feature data, the trajectory data of the motion axis is adjusted and the motion trajectory planning data is generated;

[0026] The method for adjusting the trajectory data of the motion axis includes:

[0027] Identify potential motion conflict areas, which are used to represent the spatial range where interference occurs during the motion of the six-axis robot arm device;

[0028] Based on the evaluation rules, discriminant analysis is performed on each segment within the potential motion conflict area to determine the motion trajectory planning data;

[0029] The execution of the evaluation rules first needs to calculate the interference probability of each area segment in the potential motion conflict area through the interference probability calculation formula, and then sort the interference probabilities of each area segment, and use the lowest sorted value as the motion trajectory planning data.

[0030] As a further preferred embodiment of the present technical solution, the interference factors of the actual operation of the motion axis trajectory data include:

[0031] The first interference factor is the motion interference between multiple six-axis robot arm devices;

[0032] The second interference factor is the dynamic spatial interference generated by multiple target operators during collaborative operation;

[0033] The motion conflict area is used to represent the spatial range in which interference occurs during the motion of the six-axis robot arm device, and the spatial range of interference is formed based on the first interference factor and the second interference factor.

[0034] As a further preferred embodiment of this technical solution,

[0035] The calculation formula for interference probability is: ;

[0036] Used to represent the interference probability of the i-th region segment, The number of six-axis robot arm devices used to represent potential conflict behaviors in the i-th area segment, It is used to indicate the number of six-axis robot arm devices that participate in the motion in the i-th segment. It is used to represent the minimum spatial distance between the motion paths of the six-axis robot arm device in the i-th area segment. Used to indicate the average safety distance of the six-axis robot arm device in a non-interference state. is the conflict density coefficient weight, which is used to evaluate the impact of the proportion of conflicting behaviors on the overall interference probability. Its value range is [0, 1]. is the spatial proximity weight, which is used to reflect the contribution of the minimum spatial distance between the six-axis robot arm devices to the possibility of interference, and its value range is [0, 1]. and The sum is 1, which is used to maintain the normalization of probability weighting.

[0037] As a further preferred embodiment of this technical solution, It is used to measure the proportion of six-axis robot arm devices in the i-th area segment that have potential motion interference. The higher the ratio, the higher the risk of motion interference in the i-th region. Used to measure the spatial distance between six-axis robot arm devices. The lower the ratio, the closer the spatial distance between the six-axis robot arm devices is, and the greater the possibility of interference.

[0038] As a further preferred embodiment of this technical solution, and The determination is made by setting the dynamic coefficient weight adjustment rules based on the application scenario. The dynamic coefficient weight adjustment rules include:

[0039] Establish a weight priority mapping table based on real-time production environment working condition parameters;

[0040] Construct a weight correction coefficient matrix based on the historical interference event database;

[0041] Implement the weight correction coefficient matrix to dynamically adjust the distribution ratio of the conflict density coefficient weight and the spatial proximity weight.

[0042] Secondly, in order to improve the above technical solutions, a six-axis robotic arm device based on terahertz detection is proposed, which includes:

[0043] The terahertz detection module is used to collect surface feature information and spatial distance data of the target object;

[0044] A three-dimensional space modeling module is used to build a unified three-dimensional coordinate system between the six-axis robot arm device and the target object;

[0045] The motion trajectory planning module is used to generate original motion trajectory data based on the operation instructions;

[0046] The trajectory correction module is used to identify the interference area and correct the trajectory based on environmental information;

[0047] Interference probability assessment and weight adjustment module, used to assess the risk of each area based on real-time interference conditions and dynamically adjust the path correction priority;

[0048] The motion instruction set generation and execution module is used to generate motion instructions for each joint according to the final trajectory and control the execution;

[0049] The computing power allocation coordination module is used to dynamically optimize the computing resource allocation of each axis joint to achieve multi-axis coordinated motion control.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] A six-axis robotic arm device and control method based on terahertz detection, during the path planning phase, constructs a unified three-dimensional spatial coordinate system, integrates the positional relationship of the six-axis robotic device with environmental information, identifies potential motion conflict areas, and introduces the concept of interference probability calculation based on conflict density and spatial proximity. This quantitatively assesses the motion interference risk of each segment, allowing the automatic sorting and avoidance of trajectory segments with high interference probabilities during operation, effectively reducing the risk of spatial collisions during multi-robot collaboration and improving the robustness and safety of trajectory planning.

[0052] Furthermore, during the execution control phase, servo drive computing resources are integrated via Ethernet communication to build a multi-axis collaborative computing resource pool. The system allocates initial computing power based on motion trajectory planning data, constructs correction coefficients based on the proportional relationship between each axis's historical and real-time loads, and dynamically adjusts the computing power allocation plan to achieve precise allocation of driven axes and prioritize resources for key motion axes, thereby improving overall response efficiency.

[0053] Finally, by introducing the weight priority mapping table and the interference event database to construct a weight correction coefficient matrix, the conflict density weight and spatial proximity weight in the interference probability model are adaptively adjusted, enabling self-learning and optimization capabilities under different working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 The present invention discloses an assembly diagram of a terahertz sensor array in a six-axis robot arm device;

[0055] Figure 2 A flowchart of the steps of the method disclosed in the present invention;

[0056] Figure 3 It is an auxiliary illustration of the interference probability calculation formula of the present invention.

[0057] Figure 4 This is an auxiliary illustration of the dynamic coefficient adjustment rule of the present invention;

[0058] Figure 5 This is a diagram showing the modular composition of the disclosed device of the present invention. DETAILED DESCRIPTION

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0060] Before understanding the technical solution proposed in this application, it should be made clear that the six-axis robot arm device is an industrial robot that is currently widely used in this technical field. The six joint axes contained in it enable it to flexibly complete various operational tasks in a complex spatial environment. In the existing technology, there have been research attempts to combine terahertz detection technology with the six-axis robot arm device, so as to achieve non-contact detection and perception of the target object during the operation of the robot.

[0061] refer to Figure 1 It can be seen that in the technical solution proposed in this application, the terahertz sensor array is installed at the end effector of the six-axis robot arm device, so that before performing the control operation, it can collect the environmental characteristic data of the surface of the target operation object and the spatial distance data between the end effector in real time, and then control the six-axis robot arm device based on the environmental characteristic data and the spatial distance data. It is worth noting that the technical solution proposed in this application is suitable for scenarios where the target operation object is made of non-metallic materials.

[0062] In addition, it should be added that the principle of the terahertz sensor array in the present application for acquiring environmental feature data is as follows: by emitting a terahertz beam to illuminate the surface of the target operating object and receiving its reflected wave signal, the topography of the target surface is reconstructed according to the phase information and intensity distribution of the reflected wave. The acquisition of spatial distance data is first achieved by emitting a terahertz pulse beam to the surface of the target operating object through the terahertz sensor array, and then setting a high-precision receiving module near the terahertz sensor array to capture the reflected wave signal. Since the propagation speed of terahertz waves in the air is close to the speed of light, the distance between the terahertz sensor array and the surface of the target operating object is calculated by measuring the time delay between the transmitted and received signals and combining the propagation speed, and then constructing the three-dimensional spatial coordinates. It is worth noting that in the technical solution proposed in the present application, the formula for calculating the distance is: , where d represents the spatial distance between the terahertz sensor array and the target operation object surface, c represents the propagation speed of the terahertz wave in the medium, and Δt represents the time delay of the round-trip propagation of the terahertz wave. It should be added that since the propagation speed of the terahertz wave in the air is close to the speed of light, .

[0063] As a preferred embodiment, this embodiment is based on installing a terahertz sensor array at the base position of the end effector of the six-axis robot arm device, so that before performing the control operation, it can collect the environmental feature data of the external features of the target operation object and the spatial distance data between it and the end effector in real time, and then control the six-axis robot arm device based on the environmental feature data and the spatial distance data. A specific six-axis robot arm device control method based on terahertz detection is proposed, such as Figure 2 The process shown includes: step S100 to step S500.

[0064] Specifically, step S100: obtaining motion trajectory planning data between the axis joint and the target operation object.

[0065] It should be noted that the axis joint referred to in step S100 specifically refers to the six independent degrees of freedom motion units in the six-axis industrial robot motion system. The specific composition of the motion unit is: the horizontal rotation joint J1 axis installed on the base, the shoulder pitch joint J2 axis that drives the planar motion of the upper arm, the elbow rotation joint J3 axis that adjusts the spatial position of the forearm, and the wrist composite joint J4-J6 axis that realizes the three-dimensional posture control of the end effector. Each joint unit operates in coordination with the servo drive motor and the harmonic reducer. In addition, the kinematic parameters of the motion unit are calibrated as follows: the J1 axis has a full-circle rotation degree of freedom of ±185°, the motion range of the J2 axis is limited to +150° / −95° pitch freedom, the J3 axis swing angle is configured to +75° / −190°, and the J4-J6 axes have ±355° rotation freedom respectively.

[0066] It should be noted that the basis for determining the target operand in step S100 comes from the operation instruction set issued to the six-axis robot arm device. In a typical industrial application scenario, the six-axis robot arm device and the target operand are located in the same production area spatial coordinate system. It is worth emphasizing that the operation instruction set includes: the spatial posture data of the target operand and the operation action data, wherein the operation action data is mainly used to enable the six-axis robot arm device to perform actions that interact with the target operand. After the operation instruction set is entered by the six-axis robot arm device, the motion trajectory planning data of the axis joint will be derived.

[0067] As a preferred implementation scheme, in this implementation scheme, determining the motion trajectory planning data includes: steps S101 to S103.

[0068] Step S101: establishing a three-dimensional space coordinate system based on the spatial position relationship between the six-axis robotic arm device and the target operation object.

[0069] It is worth noting that the horizontal coordinate of the established three-dimensional space coordinate system is used to represent the horizontal displacement of the six-axis robot arm, the vertical coordinate is used to reflect its height change in the vertical direction, and the vertical coordinate reflects the position change of the six-axis robot arm in the depth direction. Through the three-dimensional space coordinate system, it can provide an accurate spatial positioning basis for the planning of subsequent action trajectories. It should be noted that the six-axis robot arm device is at the origin position in the three-dimensional coordinate system.

[0070] Step S102: Based on the architecture of the operation instruction set and the spatial distance data, the trajectory data of the motion axis in the axis joint is defined.

[0071] It is worth noting that the architecture of the operation instruction set in step S102 represents the content of the operation instruction set, and the motion axis in the axis joint refers to the axis that needs to be operated among the J1 axis to the J6 axis in the axis joint when executing the operation instruction set.

[0072] It should be added that the method for defining the motion axis in the axis joint through the architecture of the operation instruction set in step S102 is to obtain the trajectory data required for each joint axis to achieve the target operation, that is, the motion range and angle, by parsing the spatial posture data of the target operation object in the operation instruction set (that is, the spatial parameters in the three-dimensional space coordinate system) and the operation action data.

[0073] For example, if the operation instruction requires the six-axis robot arm device to grasp the top surface of the target object, the X=1200mm, Y=650mm, and Z=300mm coordinate parameters of the target object in the three-dimensional space coordinate system are obtained through the terahertz sensor array. Combined with the current posture parameters of the J1-J6 axes, the inverse kinematics algorithm in the existing technology is used to solve the angle change of each joint axis, and then determine the motion axis in the axis joint.

[0074] Step S103: adjusting the trajectory data of the motion axis according to the environmental characteristic data and generating motion trajectory planning data.

[0075] It should be pointed out in particular that the application scenario of the environmental characteristic data in step S103 has the following particularity, namely, the six-axis robot arm devices deployed in the factory have multi-body distribution characteristics and are assembled in the assembly line in a uniform cross manner. Under this working condition, the actual operation of the motion axis trajectory data needs to comprehensively consider the interference factors: the first interference factor is the motion interference between multiple six-axis robot arm devices; the second interference factor is the dynamic spatial interference generated by multiple target operators during collaborative operations.

[0076] As a preferred implementation scheme, this implementation scheme is used to explain step S103. It is worth noting that during the specific implementation of this implementation scheme, step S101 needs to be supplemented. Specifically, since there are multiple six-axis robot arm devices in the assembly line, the origin of the three-dimensional space coordinate system is determined by treating the entire assembly line system as a whole and setting a unified global three-dimensional space coordinate system, where the origin in the global three-dimensional space coordinate system is set to the first six-axis robot arm device in the assembly line.

[0077] In this embodiment, the specific method of adjusting the trajectory data of the motion axis and generating the motion trajectory planning data includes: step S103A-step S103B.

[0078] Step S103A: Identify potential motion conflict areas.

[0079] It should be noted that the motion conflict area is used to represent the spatial range in which interference occurs during the motion of the six-axis robot arm device, and the spatial range of interference is formed based on the first interference factor and the second interference factor.

[0080] Specifically, the identification of the motion conflict area is first based on the original motion axis trajectory data of the six-axis robot arm device in the global three-dimensional space coordinate system (i.e., obtained in step S102), and the original motion axis trajectory data is combined with the spatial position of the six-axis robot arm device in the global three-dimensional space coordinate system to derive the motion area of ​​the motion axis in the global three-dimensional space coordinate system. By comparing the motion area with the motion areas of other six-axis robot arm devices and target operations in the global three-dimensional space coordinate system, if there is an area overlap, the overlapping area is marked as a potential motion conflict area.

[0081] Step S103B: Based on the evaluation rules, discriminant analysis is performed on each region segment within the potential motion conflict region to determine motion trajectory planning data.

[0082] It should be clear that in step S103B, the execution of the evaluation rule first needs to calculate the interference probability of each area segment in the potential motion conflict area, and sort the interference probabilities of each area segment, and use the one with the lowest sorting value as the motion trajectory planning data.

[0083] As a preferred implementation scheme, this implementation scheme is used to explain the content of "calculating the interference probability in each area segment within the potential motion conflict area" in step S103B.

[0084] The calculation formula for the interference probability is: ;

[0085] It should be noted that Used to represent the interference probability of the i-th region segment, The number of six-axis robot arm devices used to represent potential conflict behaviors in the i-th area segment, It is used to indicate the number of six-axis robot arm devices that participate in the motion in the i-th segment. It is used to represent the minimum spatial distance between the motion paths of the six-axis robot arm device in the i-th area segment. Used to indicate the average safety distance of the six-axis robot arm device in a non-interference state. is the conflict density coefficient weight, which is used to evaluate the impact of the proportion of conflicting behaviors on the overall interference probability. Its value range is [0, 1]. is the spatial proximity weight, which is used to reflect the contribution of the minimum spatial distance between the six-axis robot arm devices to the possibility of interference, and its value range is [0, 1]. and The sum is 1, which is used to maintain the normalization of probability weighting.

[0086] As a supplement to the interference probability calculation formula, a specific implementation plan is proposed. In this implementation plan, 5 six-axis robot arms are deployed in the automated production line to undertake precision operations between different workstations. Due to layout restrictions and beat requirements, there is a potential spatial interference problem between the robot arms in different workstations. For example, when the No. 3 robot arm needs to perform the cross-station material transfer task within the beat cycle, when it is detected that there are 7 spatial overlaps between its motion path and the polishing path of the No. 2 robot arm, the interference probability calculation formula is set to =0.6, =0.4 Obtain each area segment through terahertz real-time positioning =2, =3, =150mm, =300mm, at this time reference Figure 3 and calculate =0.6×(2 / 3)+0.4×(1-150 / 300)=0.4+0.2=0.6.

[0087] It is worth noting that the interference probability calculation formula proposed in this embodiment uses To measure the proportion of six-axis robot arm devices in the i-th area segment with potential motion interference, the higher the ratio, the higher the motion interference risk of the i-th area segment. To measure the spatial distance between the six-axis robot arm devices, when this ratio is lower, it means that the spatial distance between the six-axis robot arm devices is closer, and the possibility of interference is greater. Considering these two factors comprehensively, and through Conflict density coefficient weight and Weighting the spatial proximity weights can more accurately evaluate the interference probability of the six-axis robot arm device in different area segments, provide a basis for the path planning and collaborative operation of the six-axis robot arm device, and effectively avoid potential interference risks during actual operation, thereby improving work efficiency and safety.

[0088] It should be further added that, in this embodiment and The determination is made by setting the dynamic coefficient weight adjustment rules based on the application scenario.

[0089] Specifically, the content of the dynamic coefficient weight adjustment rule includes: step S103C to step S103E.

[0090] Step S103C: establishing a weight priority mapping table based on real-time production environment working condition parameters.

[0091] The construction basis of the weight priority mapping table is: when the number of six-axis robot arm devices running in the global three-dimensional space coordinate system exceeds 65% of the total number of six-axis robot arm devices, the conflict density coefficient weight Increased to 0.7, at this time The spatial proximity weight is 0.3. If the target object is in high-speed motion (moving speed ≥ 1.2m / s) and the spatial distance data update frequency is higher than 30Hz, the spatial proximity weight Dynamically adjusted to 0.8, the conflict density coefficient weight The value is 0.2.

[0092] Step S103D: Constructing a weight correction coefficient matrix based on the historical interference event database.

[0093] Specifically, the historical interference event database is determined by extracting interference events recorded in the operation log of the six-axis robot arm device within three months. By extracting the interference event characteristic parameters in the historical interference event database, including the number, position, movement speed and spatial distance of the six-axis robot arm motion joint axes when the interference occurs, a weight correction coefficient matrix is ​​constructed. The weight correction coefficient matrix in the existing technology is generated through machine learning algorithm training, and can dynamically adjust the distribution ratio of the conflict density coefficient weight and the spatial proximity weight according to the real-time working condition parameters, so as to achieve more accurate interference probability assessment.

[0094] Step S103E: Implement the weight correction coefficient matrix to dynamically adjust the distribution ratio of the conflict density coefficient weight and the spatial proximity weight.

[0095] It is also worth noting that the reference Figure 4 As can be seen from the content, the dynamic coefficient adjustment rules are determined based on and The change is not a linear increase or decrease, but a relatively step-by-step change.

[0096] Step S200: Generate a motion control instruction set based on the motion trajectory planning data.

[0097] It should be noted that in step S200, the motion control instruction set acts on the motion axis in the six-axis robot arm device. In addition, it should be added that the method for generating the motion control instruction set is implemented based on the motion control instruction generation method based on inverse kinematics solution and servo interpolation algorithm in the prior art. Specifically, the inverse kinematics solution is used to convert the spatial trajectory position points into the angle values ​​of each motion axis, and the servo interpolation algorithm generates time step control instructions that can be executed by the drive system according to the target angle, such as target position, speed and acceleration parameters. This type of method has been widely used in the field of industrial robot control and provides a reliable basis for the present invention.

[0098] Step S300: establishing an association mapping relationship between the motion control instruction set and the target operand.

[0099] It should be pointed out that the core function of the association mapping relationship established in this application is: when the six-axis robot arm device recognizes the same type of detection sample, it quickly matches the pre-stored six-axis robot arm device motion control mode, thereby ensuring the efficiency of the repeated detection process.

[0100] In the specific implementation process, it should be noted that before initialization in step S300, the six-axis robot arm device needs to complete the following preprocessing: first, a standardized motion control instruction set is generated, and the six-axis robot arm device then records the physical characteristic parameters of the target operation object and creates a classified memory directory in the storage system. The classified memory directory generates a memory directory branch with a hierarchical structure by parsing the spatial posture parameters (including three-dimensional coordinates and Euler angles) and motion trajectory data set of the target operation object contained in the operation instruction set. Each branch corresponds to the associated control instruction set that stores a specific operation object category.

[0101] Step S400: Based on the triggering of the association mapping relationship, the computing power allocation strategy of each axis joint of the six-axis robot arm device is dynamically adapted to the real-time motion parameters.

[0102] It should be noted that the real-time motion parameters in step S400 are derived based on the motion control instruction set in step S200.

[0103] As a preferred implementation scheme, this implementation scheme is mainly used to supplement the dynamic adaptation method in step S400.

[0104] The dynamic adaptation method includes: steps S401 to S404.

[0105] Step S401: Establish a multi-axis collaborative computing resource pool.

[0106] It should be noted that the computing resource pool integrates the computing units of the six-axis servo drive through the Ethernet real-time communication protocol, among which the J1-J3 axes constitute the basic motion computing group, and the J4-J6 axes form the terminal posture computing group. The computing power between the basic motion computing group and the terminal posture computing group is not interconnected, but the computing power of the axis joints in the same group is interconnected.

[0107] Step S402: allocating initial processing power based on the trajectory characteristic parameters of the motion control instruction set.

[0108] It is worth noting that step S402 allocates floating-point computing resources in real time according to the distance between the motion trajectory of each axis and the origin. The farther the distance, the more initial computing power is allocated.

[0109] Step S403: fusing historical load data of corresponding axis joints in the six-axis robot arm device, and constructing correction coefficients based on the ratio of historical load data to real-time load data.

[0110] It should be noted that the correction coefficient defined in step S403 is represented by the ratio of the historical load data to the real-time load data.

[0111] Step S404: Associating the correction coefficient with the initial processing computing power to complete the computing power distribution to the driven axis.

[0112] It should be noted that the correction coefficient realizes dynamic regulation by quantifying the load change trend during the movement of the axis joint. Specifically, when the correction coefficient is greater than the unit value, it indicates that the current load level exceeds the historical average, and the initial processing computing power needs to be incrementally configured with computing power resources according to the absolute difference between the correction coefficient and the unit value. Conversely, when the correction coefficient is less than the unit value, the initial processing computing power is implemented with a dynamic reduction strategy of computing power resources according to the absolute difference between the unit value and the coefficient. It should be added that the unit value is 1 in this application.

[0113] Step S500: The motion control instruction set adjusts the trigger parameters according to the computing power allocation strategy, thereby enhancing the response speed of the motion axis.

[0114] It should be added that, during actual operation, step S500 mainly performs dynamic parameter adjustment based on the PID control algorithm in the prior art to ensure stable dynamic response performance under different working conditions.

[0115] refer to Figure 5It can be seen that in order to improve the above technical solution, the present invention also proposes a six-axis robot arm device based on terahertz detection, including: a terahertz detection module for collecting surface feature information and spatial distance data of the target operation object, a three-dimensional space modeling module for constructing a unified three-dimensional coordinate system between the six-axis robot arm device and the target object, a motion trajectory planning module for generating original motion trajectory data based on operation instructions, a trajectory correction module for identifying interference areas and correcting trajectories based on environmental information, an interference probability assessment and weight adjustment module for assessing the risks of each area based on real-time interference conditions and dynamically adjusting the path correction priority, an action instruction set generation and execution module for generating action instructions for each joint according to the final trajectory and controlling the execution, and a computing power allocation coordination module for dynamically optimizing the computing resource allocation of each axis joint to achieve multi-axis collaborative motion control.

[0116] Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is limited by the accompanying embodiments and their equivalents.

Claims

1. A six-axis robot arm device control method based on terahertz detection, characterized in that: include: Obtain motion trajectory planning data between the axis joint and the target object; Generate motion control instruction set based on motion trajectory planning data; Establishing an association mapping relationship between the action control instruction set and the target operand; Based on the triggering of the associated mapping relationship, the computing power allocation strategy of each axis joint of the six-axis robot arm device is dynamically adapted to the real-time motion parameters; The motion control instruction set adjusts the trigger parameters based on the computing power allocation strategy, thereby enhancing the response speed of the motion axis; The dynamic adaptation method includes: Integrate the computing units of the six-axis servo drive through the Ethernet real-time communication protocol and establish a multi-axis collaborative computing resource pool; Allocate initial processing power based on trajectory characteristic parameters of the motion control instruction set; The historical load data of the corresponding axis joints in the six-axis robot arm device are integrated, and the correction coefficient is constructed based on the ratio of the historical load data to the real-time load data; Associate the correction coefficient with the initial processing power to complete the power distribution to the driven axis; The correction coefficient is dynamically regulated by quantifying the load change trend during the movement of the axis joint; When the correction coefficient is greater than the unit value, it indicates that the current load level exceeds the historical average. In this case, the initial processing computing power will be incrementally allocated based on the absolute difference between the correction coefficient and the unit value. When the correction coefficient is less than the unit value, a dynamic reduction strategy of computing power resources is implemented for the initial processing computing power based on the absolute difference between the unit value and the correction coefficient.

2. The method for controlling a six-axis robot arm device based on terahertz detection according to claim 1, characterized in that: Methods for determining motion trajectory planning data include: Based on the spatial position relationship between the six-axis robotic arm device and the target object, a three-dimensional spatial coordinate system is established; Based on the architecture of the operation instruction set and the spatial distance data, the trajectory data of the motion axis within the axis joint is defined; According to the environmental feature data, the trajectory data of the motion axis is adjusted and the motion trajectory planning data is generated; The method for adjusting the trajectory data of the motion axis includes: Identify potential motion conflict areas, which are used to represent the spatial range where interference occurs during the motion of the six-axis robot arm device; Based on the evaluation rules, discriminant analysis is performed on each segment within the potential motion conflict area to determine the motion trajectory planning data; The execution of the evaluation rules first needs to calculate the interference probability of each area segment in the potential motion conflict area through the interference probability calculation formula, and then sort the interference probabilities of each area segment, and use the lowest sorted value as the motion trajectory planning data.

3. The method for controlling a six-axis robot arm device based on terahertz detection according to claim 2, characterized in that: The actual operation of the motion axis trajectory data is subject to interference factors including: The first interference factor is the motion interference between multiple six-axis robot arm devices; The second interference factor is the dynamic spatial interference generated by multiple target operators during collaborative operation; The motion conflict area is used to represent the spatial range in which interference occurs during the motion of the six-axis robot arm device, and the spatial range of interference is formed based on the first interference factor and the second interference factor.

4. The method for controlling a six-axis robot arm device based on terahertz detection according to claim 2, wherein: The calculation formula for interference probability is: ; It is used to represent the interference probability of the i-th region segment, The number of six-axis robot arm devices used to represent potential conflict behaviors in the i-th area segment, It is used to indicate the number of six-axis robot arm devices involved in the motion in the i-th segment. It is used to represent the minimum spatial distance between the motion paths of the six-axis robot arm device in the i-th area segment. Used to indicate the average safety distance of the six-axis robot arm device in a non-interference state. is the conflict density coefficient weight, which is used to evaluate the impact of the proportion of conflicting behaviors on the overall interference probability. Its value range is [0,1]. is the spatial proximity weight, which is used to reflect the contribution of the minimum spatial distance between the six-axis robot arm devices to the possibility of interference, and its value range is [0, 1]. and The sum is 1, which is used to maintain the normalization of probability weighting.

5. The method for controlling a six-axis robot arm device based on terahertz detection according to claim 4, characterized in that: It is used to measure the proportion of six-axis robot arm devices in the i-th area segment that have potential motion interference. The higher the ratio, the higher the risk of motion interference in the i-th region. Used to measure the spatial distance between six-axis robot arm devices. The lower the ratio, the closer the spatial distance between the six-axis robot arm devices is, and the greater the possibility of interference.

6. The method for controlling a six-axis robot arm device based on terahertz detection according to claim 4, characterized in that: and The determination is made by setting the dynamic coefficient weight adjustment rules based on the application scenario. The dynamic coefficient weight adjustment rules include: Establish a weight priority mapping table based on real-time production environment working condition parameters; Construct a weight correction coefficient matrix based on the historical interference event database; Implement the weight correction coefficient matrix to dynamically adjust the distribution ratio of the conflict density coefficient weight and the spatial proximity weight.

7. A six-axis robot arm device based on terahertz detection, using the six-axis robot arm device control method based on terahertz detection according to any one of claims 1 to 6, characterized in that: include: The terahertz detection module is used to collect surface feature information and spatial distance data of the target object; A three-dimensional space modeling module is used to build a unified three-dimensional coordinate system between the six-axis robot arm device and the target object; The motion trajectory planning module is used to generate original motion trajectory data based on the operation instructions; The trajectory correction module is used to identify the interference area and correct the trajectory based on environmental information; Interference probability assessment and weight adjustment module, used to assess the risk of each area based on real-time interference conditions and dynamically adjust the path correction priority; The motion instruction set generation and execution module is used to generate motion instructions for each joint according to the final trajectory and control the execution; The computing power allocation coordination module is used to dynamically optimize the computing resource allocation of each axis joint to achieve multi-axis coordinated motion control.

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