Mechanical arm tool parameter calibration method and device based on binary contact signal constraint

Abstract

The application relates to the technical field of industrial robots, and provides a mechanical arm tool parameter calibration method and device based on a binary contact signal constraint, which comprises the following steps: setting a contact detection device based on a preset reference object; generating a plurality of candidate sampling postures based on the motion constraint condition of a mechanical arm; controlling the mechanical arm to move towards the reference object based on the candidate sampling postures, and recording the end posture of the mechanical arm; establishing a constraint equation about a tool center point error vector based on the plane constraint of the reference object and the end posture; calculating the tool center point error vector; and updating the coordinate system parameters of the tool. According to the method, only a contact detection device needs to be set, a high-precision displacement sensor is not needed, and the equipment cost is low; the binary contact signal is not prone to errors; the method is highly compatible with a welding robot, the existing arc contact detection function can be directly reused, and the adaptability is further improved and the cost is reduced.

Description

Technical Field

[0001] This application relates to the field of industrial robot technology, specifically to a method and apparatus for calibrating the parameters of a robotic arm tool based on binary contact signal constraints. Background Technology

[0002] As the core execution equipment in automated processing, welding, grinding, and assembly scenarios, the robotic arm relies on various tools mounted at its end effector to complete precise tasks. Common tools include welding guns, grinding heads, clamping jigs, and cutting tools. The robotic arm body achieves six degrees of freedom of movement in space through multi-joint linkage. The controller is based on the kinematic model of the robotic arm linkage and calculates the spatial pose of the robotic arm flange end effector through joint angle conversion. However, the flange end effector is not the actual point of operation. The real point of operation is the tool center point (TCP), which is the reference contact point where the tool end effector interacts with the workpiece.

[0003] The spatial position of the tool's center point is composed of two superimposed parts: the pose of the robotic arm flange end face and the fixed offset vector from the flange to the tool's center point. This offset vector is the core parameter of the tool coordinate system. Currently, mainstream tool calibration solutions in the industry generally rely on high-precision laser displacement sensors and grating displacement sensors for data acquisition. The core logic of their calibration process is: relying on sensors to continuously acquire the continuous displacement of the tool end face relative to a standard reference, and solving the TCP offset parameter by fitting multiple sets of continuous displacement readings. This type of existing calibration method has several inherent defects: First, the entire system must be equipped with high-precision displacement sensing equipment, signal acquisition modules, and data transmission units, resulting in high hardware procurement and deployment costs; Second, the calibration process requires the sensor to continuously output continuous displacement values, resulting in a lengthy data processing link and a complex hardware and software architecture for the control system; Third, displacement sensors are sensitive to installation posture, environmental vibration, and dust obstruction, and continuous displacement readings are prone to jumps and drifts, resulting in poor data fault tolerance; Fourth, for specialized equipment such as welding robotic arms, the original hardware sensing circuit of the equipment cannot be reused, and an additional independent calibration and detection module must be installed, resulting in low equipment integration.

[0004] In addition, the few alternative solutions using visual calibration require industrial cameras, optical calibration boards, and image processing units. These solutions are subject to stringent requirements regarding ambient lighting and workpiece reflection, and are difficult to use stably under high-temperature, splashing, and oily welding conditions. They also suffer from limited deployment options and high overall costs. In summary, existing robotic arm TCP calibration methods all suffer from high hardware dependence, complex system structures, and poor field adaptability. Summary of the Invention

[0005] This application aims to solve the problems of complex and costly calibration of robotic arm tool parameters in the prior art, and provides a method and device for calibrating robotic arm tool parameters based on binary contact signal constraints.

[0006] To solve the above problems, this application is implemented as follows:

[0007] In a first aspect, this application provides a method for calibrating the parameters of a robotic arm tool based on binary contact signal constraints, the method comprising: A contact detection device is set up based on a preset reference object. The contact detection device is used to detect and output a binary contact signal, which is a trigger signal generated when the end of the tool contacts the reference object. Multiple candidate sampling postures are generated based on the motion constraints of the robotic arm; Based on the candidate sampling posture, the robotic arm is controlled to drive the end of the tool toward the reference object, and the end pose of the robotic arm is recorded when the contact detection device outputs the binary contact signal. Based on the planar constraints of the reference object and the end pose, a constraint equation is established regarding the tool center point error vector of the tool. The constraint equations are solved to obtain the error vector of the tool's center point; The coordinate system parameters of the tool are updated based on the error vector of the tool's center point.

[0008] In one embodiment, establishing the constraint equation for the tool center point error vector of the tool based on the planar constraints of the reference object and the end-effector pose includes: Let the equation of the reference plane be:

[0009] in, For unit normal vector, It is a constant. Let be the position vector of any point in space; For the The contact point between the tool tip and the reference object under each candidate sampling posture, and the actual tool center point position. It satisfies the plane equations and has kinematic relationships, expressed as follows:

[0010] in, Offset of the nominal tool center point Let be the error vector of the tool center point to be calibrated. For the first The position vector of the robotic arm end effector under each candidate sampled posture. For the first Rotation matrix of the robotic arm end effector under candidate sampling postures; The constraint equations satisfy the following expression:

[0011] in, For the first The theoretical tool center point position vector calculated based on the nominal tool center point offset under each candidate sampling pose.

[0012] In one embodiment, solving the constraint equations to obtain the tool center point error vector includes: The constraint equations take the unit normal vector and constant of the reference plane as parameters; When the parameters of the reference plane are known, it is simplified to a linear form; when the parameters of the reference plane are unknown, it is solved by nonlinear least squares. When the parameters of the reference plane are pre-calibrated, an overdetermined linear equation system is constructed and solved by the linear least squares method. When the parameters of the reference plane are unknown, the Levenberg-Marquardt algorithm is used to iteratively solve the problem, with the goal of minimizing the deviation of the actual positions of all contact points from the same plane.

[0013] In one embodiment, after generating multiple candidate sampling poses based on the motion constraints of the robotic arm, the method further includes: The target sampling posture is determined from multiple candidate sampling postures based on the condition number of the coefficient matrix or the observability index. The step of controlling the robotic arm based on the candidate sampled posture to drive the end effector of the tool toward the reference object includes: The robotic arm is controlled based on the target sampling posture, driving the end of the tool to move toward the reference object.

[0014] In one embodiment, after updating the coordinate system parameters of the tool based on the tool center point error vector, the method further includes: The calibration accuracy is evaluated using the verification posture that was not calibrated.

[0015] In one embodiment, the contact detection device is an electrical continuity detection circuit between the tool tip and the reference object.

[0016] Secondly, this application provides a robotic arm tool parameter calibration device, comprising: The setting module is used to set a contact detection device based on a preset reference object. The contact detection device is used to detect and output a binary contact signal, which is a trigger signal generated when the end of the tool contacts the reference object. The candidate pose generation module is used to generate multiple candidate sampled poses based on the motion constraints of the robotic arm. The pose acquisition and control module is used to control the robotic arm based on the candidate sampled pose, drive the end of the tool to move toward the reference object, and record the end pose of the robotic arm when the contact detection device outputs the binary contact signal. The constraint equation construction module is used to establish constraint equations for the tool center point error vector of the tool based on the planar constraints of the reference object and the end pose. The error vector solving module is used to solve the constraint equations and obtain the error vector of the tool center point. The tool parameter update module is used to update the coordinate system parameters of the tool based on the error vector of the tool's center point.

[0017] Thirdly, this application provides a terminal device including a processor and a memory, wherein the memory stores a program or instructions executable on the processor, and the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0018] Fourthly, this application provides a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0019] Fifthly, this application provides a chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the steps of the method described in the first aspect.

[0020] In a sixth aspect, this application provides a computer program product stored in a storage medium, the program product being executed by at least one processor to implement the steps of the method described in the first aspect.

[0021] Compared with the prior art, this application has the following beneficial effects: This application discloses a method for calibrating robotic arm tool parameters based on binary contact signal constraints. The method includes: setting a contact detection device to generate a binary contact signal when the tool end-effector contacts the reference object based on a preset reference object; generating multiple candidate sampling postures based on the motion constraints of the robotic arm; controlling the robotic arm based on the candidate sampling postures to bring the tool end-effector close to the reference object, and recording the robotic arm end-effector pose when the binary contact signal is generated; establishing a constraint equation about the tool center point error vector based on the geometric constraints of the reference object plane and the multiple robotic arm end-effector poses; solving the tool center point error vector; and using the solved tool center point error vector to update the tool coordinate system parameters. This method eliminates the need for high-precision displacement sensors, requiring only a contact detection device, resulting in low equipment costs; and utilizes binary contact signals, eliminating the need for complex processing systems and reducing the likelihood of errors. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating the robotic arm tool parameter calibration method based on binary contact signal constraints disclosed in a preferred embodiment of this application. Figure 2 This is a schematic diagram of the structure of the terminal device disclosed in a preferred embodiment of this application. Detailed Implementation

[0024] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] The terms "first," "second," etc., used in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or apparatuses. Additionally, the use of "and / or" in this application indicates at least one of the connected objects, such as A and / or B and / or C, representing seven possibilities: including A alone, B alone, C alone, and the presence of both A and B, both B and C, both A and C, and the presence of A, B, and C.

[0026] In this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0027] The following describes the method for calibrating the parameters of a robotic arm tool based on binary contact signal constraints provided in this application.

[0028] See Figure 1 , Figure 1 This is a flowchart illustrating a method for calibrating robotic arm tool parameters based on binary contact signal constraints, provided in an embodiment of this application. Figure 1 The method for calibrating robotic arm tool parameters based on binary contact signal constraints shown can be performed by robotic arms, welding robots, and other equipment.

[0029] like Figure 1 As shown, the robotic arm tool parameter calibration method based on binary contact signal constraints provided in this application may include the following steps: Step 101: Set up a contact detection device based on a preset reference object. The contact detection device is used to generate a binary contact signal when the end of the tool contacts the reference object.

[0030] In one embodiment, the contact detection device is an electrical continuity detection circuit between the tool tip and the reference object.

[0031] Using an electrical conduction circuit as a contact detection device, it can output a binary signal simply by relying on the connection between the tool and the reference object, and the hardware structure is simple.

[0032] In some embodiments, the welding robot can reuse existing arc detection hardware.

[0033] The welding robot may be a robot that includes the aforementioned robotic arm.

[0034] Step 102: Generate multiple candidate sampling poses based on the motion constraints of the robotic arm.

[0035] The motion constraints of the robotic arm include, but are not limited to, joint limitation, singular pose avoidance, and generating multiple candidate sampled poses.

[0036] Step 103: Control the robotic arm based on the candidate sampling posture, drive the end of the tool to move toward the reference object, and record the end pose of the robotic arm when the contact detection device outputs the binary contact signal.

[0037] Specifically, the robotic arm is controlled according to the candidate sampling postures, causing the tool tip to gradually approach the reference object and collect contact point data: for each sampling posture, the tool tip gradually approaches the reference object, and when the contact detection device is triggered, the position and pose information of the robotic arm end effector, including the position vector, is recorded. and rotation matrix .

[0038] Step 104: Based on the planar constraints of the reference object and the end pose, establish the constraint equation for the tool center point error vector of the tool.

[0039] In one embodiment, establishing the constraint equation for the tool center point error vector of the tool based on the planar constraints of the reference object and the end-effector pose includes: Let the equation of the reference plane be:

[0040] in, For unit normal vector, It is a constant. Let be the position vector of any point in space; For the The contact point between the tool tip and the reference object under each candidate sampling posture, and the actual tool center point position. It satisfies the plane equations and has kinematic relationships, expressed as follows:

[0041] in, Offset of the nominal tool center point Let be the error vector of the tool center point to be calibrated. For the first The position vector of the robotic arm end effector under each candidate sampled posture. For the first Rotation matrix of the robotic arm end effector under candidate sampling postures; The constraint equations satisfy the following expression:

[0042] in, For the first The theoretical tool center point position vector calculated based on the nominal tool center point offset under each candidate sampling pose.

[0043] Constraint equations are established based on the relationship between plane geometric equations and the kinematics of the robotic arm. The mathematical correlation between the TCP real position, flange pose, and tool offset error is established, and the error vector to be solved is accurately located. The constraint logic is rigorous.

[0044] Step 105: Solve the constraint equation to obtain the error vector of the tool center point.

[0045] The joint solution of plane parameters and tool center point error vector is specifically achieved by collecting... M Contact points ( M (≥6), at least 6 independent poses are required to construct an effective system of equations. The more candidate poses and the stronger the pose differences, the more accurate the least squares solution and the stronger the anti-interference ability. Establish the objective function:

[0046] This embodiment can be solved using nonlinear least squares optimization (such as the Levenberg-Marquardt algorithm). If the plane parameters n and c are pre-calibrated, the problem degenerates into linear least squares.

[0047] Update tool parameters: Update the tool coordinate system parameters in the controller.

[0048] In one embodiment, solving the constraint equations to obtain the tool center point error vector includes: The constraint equations take the unit normal vector and constant of the reference plane as parameters; When the parameters of the reference plane are known, it is simplified to a linear form; when the parameters of the reference plane are unknown, it is solved by nonlinear least squares. A fast calibration method based on linear least squares with known plane parameters is used. When the plane parameters of the reference object are pre-calibrated, an overdetermined system of linear equations is constructed and solved using linear least squares. Specifically: Pre-determine the plane parameters of the iron plate using a tool whose center point has been calibrated. , Then, change the calibration tool, collect 15-20 contact points, and construct a system of linear equations to solve. Update the tool's center point parameters and verify the accuracy.

[0049] A joint calibration method based on nonlinear optimization with unknown plane parameters aims to minimize the deviation of the actual positions of all contact points from the same plane when the plane parameters of the reference object are unknown. This is achieved iteratively using the Levenberg-Marquardt algorithm. Specifically: Collect at least 6 contact points and use the objective function. Perform Levenberg-Marquardt optimization and simultaneously determine the plane parameters and .

[0050] Two scenario-adaptive solution strategies are employed: when the plane parameters are known, linear least squares is used for fast calculation; when they are unknown, the LM algorithm is used to jointly optimize the plane and error parameters, adapting to different field calibration conditions.

[0051] In one embodiment, a welding robot tool calibration method based on arc sensing is also included: A welding torch is mounted at the end of a welding robotic arm. A safety detection voltage is output from the welding power supply, using an iron plate as the workpiece. During calibration, the welding wire contacts the iron plate, generating a trigger signal. The pose is recorded, and calibration is performed using either a fast calibration method based on linear least squares (where plane parameters are known) or a joint calibration method based on nonlinear optimization (where plane parameters are unknown). After calibration, the arc initiation success rate and weld seam tracking accuracy were significantly improved.

[0052] In one embodiment, after generating multiple candidate sampling poses based on the motion constraints of the robotic arm, the method further includes: The optimal calibration point is selected from candidate sampled poses based on the condition number of the coefficient matrix or observability indices. 200-500 candidate poses are generated, and the optimal calibration point is selected based on the condition number of the coefficient matrix. Select the optimal calibration points and perform actual data collection and calculation only on the selected points.

[0053] By relying on condition number and observability indicators to screen postures, ill-conditioned sampling points are eliminated, the degree of ill-conditioning in solving the equation system is reduced, the workload of collecting invalid points is reduced, and the stability of solving the calibration equation system is improved.

[0054] In one implementation, a robust calibration method based on multi-directional convergence is employed: For each sampling posture, approaching contact from both sides of the iron plate plane, the average of the two postures is taken as the posture data of that posture, in order to eliminate systematic errors caused by joint gaps or detection delays.

[0055] After updating the coordinate system parameters of the tool based on the tool center point error vector, the process further includes: The calibration accuracy is evaluated using the verification posture that was not calibrated.

[0056] The calibration results are verified by using an independent verification attitude. The positioning deviation after TCP correction is quantified, which can intuitively determine whether the calibration is qualified and ensure that the tool coordinate system parameters are reliable and usable.

[0057] Step 106: Update the coordinate system parameters of the tool based on the error vector of the tool's center point.

[0058] Preferred solutions in this embodiment include: pre-planar calibration, multi-directional contact acquisition, sampling posture optimization and screening, repeated measurement averaging, robust fitting, compatibility with arc sensing systems, and automated implementation.

[0059] This method for calibrating robotic arm tool parameters based on binary contact signal constraints requires no high-precision displacement sensors, only a metal plate and a simple detection circuit, resulting in low equipment costs. Using binary contact signals eliminates the need for complex processing systems, reducing the risk of errors. It can directly reuse existing arc contact detection functions, ensuring high compatibility with welding robots and further improving adaptability and reducing costs. Furthermore, this method can mitigate the impact of errors through averaging, RANSAC, and other methods, demonstrating strong robustness.

[0060] One embodiment of this application also provides a robotic arm tool parameter calibration device, including: The setting module is used to set a contact detection device based on a preset reference object. The contact detection device is used to detect and output a binary contact signal, which is a trigger signal generated when the end of the tool contacts the reference object. The candidate pose generation module is used to generate multiple candidate sampled poses based on the motion constraints of the robotic arm. The pose acquisition and control module is used to control the robotic arm based on the candidate sampled pose, drive the end of the tool to move toward the reference object, and record the end pose of the robotic arm when the contact detection device outputs the binary contact signal. The constraint equation construction module is used to establish constraint equations for the tool center point error vector of the tool based on the planar constraints of the reference object and the end pose. The error vector solving module is used to solve the constraint equations and obtain the error vector of the tool center point. The tool parameter update module is used to update the coordinate system parameters of the tool based on the error vector of the tool's center point.

[0061] Optionally, the constraint equation construction module is specifically used for: Let the equation of the reference plane be:

[0062] in, For unit normal vector, It is a constant. Let be the position vector of any point in space; For the The contact point between the tool tip and the reference object under each candidate sampling posture, and the actual tool center point position. It satisfies the plane equations and has kinematic relationships, expressed as follows:

[0063] in, Offset of the nominal tool center point Let be the error vector of the tool center point to be calibrated. For the first The position vector of the robotic arm end effector under each candidate sampled posture. For the first Rotation matrix of the robotic arm end effector under candidate sampling postures; The constraint equations satisfy the following expression:

[0064] in, For the first The theoretical tool center point position vector calculated based on the nominal tool center point offset under each candidate sampling pose.

[0065] Optionally, the error vector solving module is specifically used for: The constraint equations take the unit normal vector and constant of the reference plane as parameters; When the parameters of the reference plane are known, it is simplified to a linear form; when the parameters of the reference plane are unknown, it is solved by nonlinear least squares. When the parameters of the reference plane are pre-calibrated, an overdetermined linear equation system is constructed and solved by the linear least squares method. When the parameters of the reference plane are unknown, the Levenberg-Marquardt algorithm is used to iteratively solve the problem, with the goal of minimizing the deviation of the actual positions of all contact points from the same plane.

[0066] Optionally, the device further includes: A determination module is used to determine a target sampling posture from multiple candidate sampling postures based on the condition number of the coefficient matrix or an observability index. The pose acquisition and control module is specifically used to control the robotic arm based on the target sampled pose, driving the end of the tool to move toward the reference object.

[0067] Optionally, the device further includes: The verification module is used to evaluate the calibration accuracy using verification postures that were not calibrated.

[0068] Optionally, the contact detection device is an electrical continuity detection circuit between the tool tip and the reference object.

[0069] The device is capable of achieving the functions described in this application. Figure 1 The various processes in the method embodiments, and the ways to achieve the same beneficial effects, will not be repeated here to avoid repetition.

[0070] One embodiment of this application also provides a robotic arm, including a robotic arm body, a tool mounted at the end of the robotic arm body, and a robotic arm tool parameter calibration device as described above.

[0071] like Figure 2 As shown, this application also provides a terminal device, including a processor 401 and a memory 402. The memory 402 stores a program or instructions that can run on the processor 401. When the program or instructions are executed by the processor 401, they implement the various steps of the embodiment of the robotic arm tool parameter calibration method based on binary contact signal constraints as described above, and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0072] It should be noted that the terminal device in this application can be a terminal or other devices besides a terminal. For example, the terminal device can be a mobile phone, tablet computer, laptop computer, etc., and this application does not make any specific limitation.

[0073] This application also provides a readable storage medium storing a program or instructions that, when executed by a processor, implement the various processes of the above-described embodiment of the robotic arm tool parameter calibration method based on binary contact signal constraints, and achieve the same technical effect. To avoid repetition, these will not be described again here.

[0074] The processor is the processor in the terminal device described in the above embodiments. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (Read-Only Memory). Only memory (ROM), random access memory (RAM), magnetic disks or optical disks, etc.

[0075] This application also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described embodiments of the robotic arm tool parameter calibration method based on binary contact signal constraints, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0076] It should be understood that the chip mentioned in this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0077] This application provides a computer program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of the above-described embodiment of the robotic arm tool parameter calibration method based on binary contact signal constraints, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0078] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0079] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as a read-only memory). The device includes a number of instructions in a ROM (random access memory), RAM (magnetic disk), or optical disk to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0080] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for calibrating robotic arm tool parameters based on binary contact signal constraints, characterized in that, The method includes: A contact detection device is set up based on a preset reference object. The contact detection device is used to detect and output a binary contact signal, which is a trigger signal generated when the end of the tool contacts the reference object. Multiple candidate sampling postures are generated based on the motion constraints of the robotic arm; Based on the candidate sampling posture, the robotic arm is controlled to drive the end of the tool toward the reference object, and the end pose of the robotic arm is recorded when the contact detection device outputs the binary contact signal. Based on the planar constraints of the reference object and the end pose, a constraint equation is established regarding the tool center point error vector of the tool. The constraint equations are solved to obtain the error vector of the tool's center point; The coordinate system parameters of the tool are updated based on the error vector of the tool's center point.

2. The method according to claim 1, characterized in that, The constraint equation for establishing the tool center point error vector of the tool based on the planar constraints of the reference object and the end-effector pose includes: Let the equation of the reference plane be: in, For unit normal vector, It is a constant. Let be the position vector of any point in space; For the The contact point between the tool tip and the reference object under each candidate sampling posture, and the actual tool center point position. It satisfies the plane equations and has kinematic relationships, expressed as follows: in, Offset of the nominal tool center point Let be the error vector of the tool center point to be calibrated. For the first The position vector of the robotic arm end effector under each candidate sampled posture. For the first Rotation matrix of the robotic arm end effector under candidate sampling postures; The constraint equations satisfy the following expression: in, For the first The theoretical tool center point position vector calculated based on the nominal tool center point offset under each candidate sampling pose.

3. The method according to claim 1 or 2, characterized in that, The process of solving the constraint equations to obtain the tool center point error vector includes: The constraint equations take the unit normal vector and constant of the reference plane as parameters; When the parameters of the reference plane are known, it is simplified to a linear form; when the parameters of the reference plane are unknown, it is solved by nonlinear least squares. When the parameters of the reference plane are pre-calibrated, an overdetermined linear equation system is constructed and solved by the linear least squares method. When the parameters of the reference plane are unknown, the Levenberg-Marquardt algorithm is used to iteratively solve the problem, with the goal of minimizing the deviation of the actual positions of all contact points from the same plane.

4. The method according to claim 1, characterized in that, After generating multiple candidate sampling poses based on the motion constraints of the robotic arm, the process also includes: The target sampling posture is determined from multiple candidate sampling postures based on the condition number of the coefficient matrix or the observability index. The step of controlling the robotic arm based on the candidate sampled posture to drive the end effector of the tool toward the reference object includes: The robotic arm is controlled based on the target sampling posture, driving the end of the tool to move toward the reference object.

5. The method according to claim 1, characterized in that, After updating the coordinate system parameters of the tool based on the tool center point error vector, the process further includes: The calibration accuracy is evaluated using the verification posture that was not calibrated.

6. The method according to claim 1, characterized in that, The contact detection device is an electrical continuity detection circuit between the tool tip and the reference object.

7. A tool parameter calibration device for a robotic arm, characterized in that, include: The setting module is used to set a contact detection device based on a preset reference object. The contact detection device is used to detect and output a binary contact signal, which is a trigger signal generated when the end of the tool contacts the reference object. The candidate pose generation module is used to generate multiple candidate sampled poses based on the motion constraints of the robotic arm. The pose acquisition and control module is used to control the robotic arm based on the candidate sampled pose, drive the end of the tool to move toward the reference object, and record the end pose of the robotic arm when the contact detection device outputs the binary contact signal; The constraint equation construction module is used to establish constraint equations for the tool center point error vector of the tool based on the planar constraints of the reference object and the end pose. The error vector solving module is used to solve the constraint equations and obtain the error vector of the tool center point. The tool parameter update module is used to update the coordinate system parameters of the tool based on the error vector of the tool's center point.

8. A terminal device, characterized in that, It includes a processor and a memory, wherein the memory stores a program or instructions executable on the processor, the program or instructions, when executed by the processor, implement the steps of the method as described in any one of claims 1-6.

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