Robot track generation method and system

Abstract

The embodiment of the invention relates to the technical field of robots, and discloses a robot track generation method and system. The track generation method comprises the following steps: receiving an actual track point position at a current moment, and recording the actual track point position as a current position; determining the processed track point position at the current moment based on the historical track point position and the current position, and recording the processed track point position as the current processing position; and generating a robot virtual trajectory curve according to the current processing position. According to the scheme of the embodiment of the invention, high-frequency jitter can be suppressed or even eliminated, and the track quality is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of robot technology, and in particular to a robot trajectory generation method and system. BACKGROUND

[0002] In the application scenario of manual dragging robot teaching, the original trajectory point data generated by the operator due to the inconsistent speed rhythm of manual dragging often contains high-frequency jitter, noise and affects the trajectory quality due to the different proficiency of the operator. These defects, if directly used to drive the robot, will cause the motion of the mechanical arm to be unstable and produce violent vibration, which not only affects the quality of task completion, but also endangers the safety of equipment and personnel.

[0003] The prior art often uses a fixed low-pass filter to smooth the current trajectory, but the filtering parameters are static, and the historical data is not referred to, so the processed trajectory cannot adapt to the operation habits or dynamic changes of the operation speed of different operators, especially when processing high-speed and violent motion and low-speed and fine operation, it is difficult to balance smoothness and real-time performance at the same time, and lacks intelligence and robustness. SUMMARY

[0004] To address the defects, the embodiments of the present application disclose a robot trajectory generation method and system, which can suppress or even eliminate high-frequency jitter and improve trajectory quality.

[0005] The first aspect of the embodiments of the present application discloses a robot trajectory generation method, comprising: receiving an actual trajectory point position at a current time, denoted as a current position; determining a processed trajectory point position at the current time based on the historical trajectory point position and the current position, denoted as a current processed position, which specifically comprises: For any trajectory point , its current processed position is:

[0006] wherein, , , are the coordinate values of the trajectory point in the X-axis, Y-axis, and Z-axis directions, is the actual trajectory point position of the trajectory point at the t-th time, i.e. the current position, and are the actual trajectory point positions of the trajectory point at the (t-1)-th time and the (t-2)-th time, , , are the first weight, the second weight and the third weight, and:

[0007]

[0008]

[0009] wherein, , are the maximum and minimum values of the first weight set respectively, , are the maximum and minimum speed values of the robot set respectively, and b is a constant, which can be set to improve the anti-disturbance ability of the robot, is the trajectory point P j the velocity at the t-th moment, and:

[0010] wherein, is the Euclidean distance between and ; is the sampling period; generating a robot virtual trajectory curve according to the current processing position.

[0011] In the embodiment of the present application, a speed-sensitive weight adjustment mechanism is introduced, which is especially suitable for the drag teaching scene of an unskilled operator. It can reduce the historical weight when moving at high speed and increase the historical weight when moving at low speed, thereby solving the phase distortion problem in the variable speed scene.

[0012] When the robot is detected to move at high speed, the weight of the current position is automatically increased , so that the filter bandwidth increases and the lag decreases, and the robot can respond to the instruction change of the operator more quickly, avoiding the phenomenon of not being able to follow or motion trailing in high dynamic motion, and reducing the lag.

[0013] When low-speed operation or tend to stop, the weight of the current position is automatically reduced , and instead relies more on historical data, which can greatly improve the filtering strength, effectively suppress or even eliminate slight jitter, enhance smoothness, and make the robot end perform exceptionally stable.

[0014] As an optional implementation, in the first aspect of the embodiment of the present application, generating a robot virtual trajectory curve according to the current processing position comprises: determining the joint angles according to the current processing position; calculating the end pose according to the joint angles; generate a robot virtual trajectory curve according to the end position.

[0015] By introducing inverse kinematics to solve each joint angle, it is ensured that each smoothed position point must correspond to a set of feasible joint angles, thereby avoiding dangerous configurations of unattainable positions or nearby singular points in advance, and then verifying and visualizing through forward kinematics to generate a virtual trajectory that is both smooth and fully consistent with the robot's own kinematic constraints.

[0016] As an optional implementation, in the first aspect of the embodiment of the application, determining the joint angles according to the current processing position comprises: determining the joint angles through inverse kinematics based on the current processing position; calculating the end position according to the joint angles, comprising: calculating the end position through a forward kinematics equation:

[0017] wherein, the end position, the joint angle of the i th joint; the homogeneous transformation matrix of the i th joint; generating a robot virtual trajectory curve according to the end position, comprising: generating an end effector trajectory curve of the robot according to the end position:

[0018] wherein, the end effector trajectory curve, and the start time and the end time of the trajectory generation, respectively; displaying the end effector trajectory curve on an operation interface.

[0019] The embodiment of the application deeply binds the filtered output and the kinematic model, verifies the trajectory feasibility through forward kinematics, and visualizes the virtual trajectory to assist real-time correction. The operator can observe the final effect after the system processing in real time, thereby being able to more confidently and accurately demonstrate teaching, and also facilitating offline debugging and program verification, thereby reducing the operation threshold and dependence on proficiency.

[0020] As an optional implementation, in the first aspect of the embodiment of the application, the method further comprises: real-time acquisition of the acceleration of any trajectory point of the robot; when the acceleration is greater than a preset acceleration value, controlling the robot to stop urgently or issuing an alarm signal.

[0021] The embodiment of the present application introduces an active safety protection layer, which significantly improves the functional safety level of the whole system. Real-time acceleration monitoring is the last and most critical insurance for filtering effect. Even in the most extreme case, the acceleration sensor can capture abnormal mutations in the first time. The system can trigger an emergency shutdown within milliseconds before the potential danger occurs, and kill the risk in its infancy.

[0022] As an optional implementation, in the first aspect of the embodiment of the present application, the method further comprises: Saving one or more sets of robot virtual trajectory curves to form teaching data; Teaching or skill reproduction to other robots by using the teaching data.

[0023] The teaching data reuse mode can realize the digitization, standardization and assetization of operation skills. By saving the optimized virtual trajectory as teaching data, it becomes a replicable and spreadable digital asset. On the one hand, ordinary workers can call the teaching data to complete high-quality work; on the other hand, the level difference between different operators and the state fluctuation of the same operator are eliminated. When applied to industrial production, it can ensure that every product is processed under an absolute and optimal trajectory. In addition, it can also realize the rapid replication and unified upgrade of operation process, greatly saving time and cost.

[0024] The second aspect of the embodiment of the present application discloses a robot trajectory generation system, comprising: A receiving unit for receiving an actual trajectory point position at a current time, denoted as a current position; A calculation unit for determining a processed trajectory point position at the current time based on the historical trajectory point position and the current position, denoted as a current processed position, which specifically comprises: For any trajectory point , its current processed position is:

[0025] Wherein, , , are the coordinate values of the trajectory point in the X-axis, Y-axis and Z-axis directions, is the actual trajectory point position of the trajectory point at the t time, i.e. the current position, and are the actual trajectory point positions of the trajectory point at the t-1 time and the t-2 time, , , They are the first weight, the second weight, and the third weight, respectively, and:

[0026]

[0027]

[0028] in, , These are the maximum and minimum values ​​of the first weight, respectively. , These represent the maximum and minimum speed values ​​for the robot, respectively, where b is a constant that can be set. This is to improve the robot's ability to resist disturbances. For trajectory points P j The velocity at time t, and:

[0029] in, for and The Euclidean distance between them; The sampling period; The generation unit is used to generate a virtual trajectory curve for the robot based on the current processing position.

[0030] This invention uses a filtering algorithm combined with historical trajectory information to process the current position information, providing the robot with a reliable transformation path from coarse and unreliable drag-and-drop teaching to a smooth, stable, and executable automated trajectory, fundamentally solving the safety hazards and quality defects when using raw trajectory data directly.

[0031] A third aspect of the present invention discloses an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calling the executable program code stored in the memory to execute the robot trajectory generation method disclosed in the first aspect of the present invention.

[0032] A fourth aspect of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program causes a computer to execute the robot trajectory generation method disclosed in the first aspect of the present invention. Attached Figure Description

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. Obviously, the drawings described below only constitute some of the embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0034] Figure 1 is a flow diagram of the robot trajectory generation method disclosed in the embodiments of the present application; Figure 2 is a flow diagram of generating a virtual trajectory curve of a robot according to the embodiments of the present application; Figure 3 is a structural diagram of a robot trajectory generation system provided by the embodiments of the present application; Figure 4 is a structural diagram of an electronic device provided by the embodiments of the present application. DETAILED DESCRIPTION

[0035] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only constitute some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0036] It should be noted that the terms first, second, third, fourth and the like in the specification and claims of the present application are used to distinguish different objects, and are not used to describe a specific sequence. The terms of the embodiments of the present application include and have as well as any transformation thereof, and are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0037] Embodiment one

[0038] Please refer to Figure 1 , Figure 1is a flowchart of a robot trajectory generation method disclosed in embodiments of the present application. Among them, the execution subject of the method described in the embodiments of the present application is an execution subject composed of software or / and hardware, which can receive relevant information through wired or / and wireless means and can send certain instructions. Of course, it can also have certain processing and storage functions. The execution subject can control multiple devices, such as remote physical servers or cloud servers and related software, or it can be a local host or server and related software that performs related operations on a device placed somewhere. In some scenarios, it can also control multiple storage devices, which can be placed in the same place or different places as the devices.

[0039] As shown in Figure 1 , the robot trajectory generation method comprises the following steps: S110, receive the actual trajectory point position at the current time, denoted as the current position.

[0040] The collection of trajectory points can be realized by encoders or related sensors such as Hall elements on the robot executor, or by vision sensors such as binocular cameras, etc. Here, the collection method is not limited.

[0041] The actual trajectory points at each position are collected at the same sampling frequency, wherein the actual trajectory point position at the current time is denoted as the current position, and the trajectory point position at the previous time is denoted as the historical trajectory point position. The data of each historical trajectory point position is saved, and the saved historical trajectory point position can be the actual collected trajectory point position or the trajectory point position after step S120 processing.

[0042] S120, determine the processed trajectory point position at the current time based on the historical trajectory point position and the current position, denoted as the current processed position.

[0043] The embodiments of the present application are mainly aimed at the drag teaching application scenario, in which the problem of inconsistent speed rhythm caused by manual dragging of the operator easily affects the trajectory quality. In order to improve the trajectory quality and achieve better visual effect, the trajectory point position is subjected to smoothing filter processing to suppress or even eliminate high-frequency jitter.

[0044] There are many ways to smooth filter processing, and in the embodiments of the present application, the historical trajectory point position is combined, rather than directly inputting the current position data into the corresponding filter to filter out high-frequency signals. Compared with this direct high-frequency filtering method, the smoothing filter processing combined with the historical trajectory point position has better effect.

[0045] Specifically, the direct high-frequency filtering is to forcibly filter out high-frequency signals in the frequency domain. The higher the order of the filter, the lower the cutoff frequency, the more serious the lag, and the more distorted the signal shape. The filtering combined with the historical trajectory point position is to smooth in the time domain by using weighted average. The real-time performance and smoothness can be accurately balanced by adjusting the weight. The direct high-frequency filtering makes the signal edge "smooth", which causes the steep rising edge / falling edge to become inclined and seriously distorted. The filtering combined with the historical trajectory point position preserves the overall shape and trend of the original signal, and especially maintains the edge of the step signal well.

[0046] Moreover, in the robot context, these noises can also come from the measurement error of the joint encoder, the slight jitter of the transmission mechanism, and the high-frequency oscillation of the current loop control, etc. Filtering high-frequency noise combined with historical trajectory point positions results in a smooth and ideal trajectory that the system expects to execute.

[0047] As an implementation, the second-order Butterworth filter core can be used to achieve efficient noise suppression with fixed weights. The calculation complexity is low, which meets the real-time control requirements and is very suitable for embedded systems with high real-time requirements or robot scenarios requiring high-frequency control.

[0048] Specifically, For any trajectory point , the current processing position is:

[0049] wherein , , are the coordinate values of the trajectory point in the X-axis, Y-axis, and Z-axis directions, is the actual trajectory point position of the trajectory point at the t-th moment, i.e. the current position, and are the actual trajectory point positions of the trajectory point at the (t-1)-th moment and the (t-2)-th moment, , , are the first weight, the second weight, and the third weight, respectively, and .

[0050] By giving the highest weight to the current position and the historical data, for example, the first weight , , can be set.It can effectively filter out high-frequency components introduced by operator hand tremors or sensor noise. At the same time, because its dependence on historical data is limited, the introduced phase lag is minimal, which can fully guarantee the real-time tracking of robot movement and avoid the sluggishness caused by excessive smoothing.

[0051] As another implementation method, a speed-sensitive weight adjustment mechanism is introduced to form an adaptive weighted filtering method, which is especially suitable for drag-and-drop teaching scenarios for operators who are not familiar with the operation. It can reduce the historical weight when moving at high speed and increase the historical weight when moving at low speed, thus solving the phase distortion problem in variable speed scenarios.

[0052] Specifically: For any trajectory point Its current processing position for:

[0053] in, , , These are the trajectory points. The coordinate values ​​in the X, Y, and Z axes. For trajectory points The actual trajectory point position at time t, i.e., the current position. and These are the trajectory points. The actual trajectory point positions at time t-1 and time t-2 , , They are the first weight, the second weight, and the third weight, respectively, and:

[0054]

[0055]

[0056] in, , These are the maximum and minimum values ​​of the first weight, respectively. , These represent the maximum and minimum speed values ​​for the robot, respectively, where b is a constant that can be set. This is to improve the robot's ability to resist disturbances. For trajectory points P j The velocity at time t, and:

[0057] in, is and the Euclidean distance between them; is a sampling period.

[0058] When high-speed motion of the robot is detected, the weight of the current position is automatically increased , for example, to a maximum value of 0.8, so that the filter bandwidth is increased and the hysteresis is reduced, and the robot can respond more quickly to changes in the operator's instructions, avoiding the phenomenon of not being able to follow or motion smearing in high dynamic motion, and reducing hysteresis.

[0059] When low-speed operation or a tendency to stop is detected, the weight of the current position is automatically reduced , for example, to a minimum value of 0.3, and instead more reliance is placed on historical data, which can greatly increase the filtering strength, effectively suppress or even eliminate slight jitter, enhance smoothness, and make the robot end more stable.

[0060] In other embodiments, one or more sets of robot virtual trajectory curves can also be saved to form teaching data, so that they become replicable and propagable digital assets. Using these teaching data, other robots can be taught or skill reproduced, production processes can be quickly replicated and uniformly upgraded, and time and cost can be greatly saved.

[0061] In other embodiments, in order to guarantee the smoothing filtering effect and avoid danger, the acceleration can be limited, that is, the acceleration is limited within a certain range.

[0062] Exemplarily, the acceleration of any trajectory point of the robot is acquired in real time ; when the acceleration is greater than a preset acceleration value , the robot is controlled to stop or an alarm signal is sent.

[0063] Real-time monitoring of acceleration is the last and most critical insurance for the filtering effect. Even in the most extreme case, the acceleration sensor can capture abnormal mutations in the first time. The system can trigger an emergency stop within milliseconds before a potential danger occurs, and kill the risk in its infancy.

[0064] S130, generating a robot virtual trajectory curve according to the current processing position.

[0065] The embodiment of the application deeply binds the filtering output and the kinematic model, verifies the feasibility of the trajectory through forward kinematics, and visualizes the virtual trajectory to assist in real-time correction. The operator can observe the final effect of his operation after system processing in real time, so as to be more confident and more accurate in teaching, and also facilitate offline debugging and program verification, reducing the operation threshold and dependence on proficiency.

[0066] Please refer to Figure 2 As shown in the drawings, it can specifically include the following steps: S131, determining the joint angles according to the current processing position.

[0067] The determination of the joint angles according to the current processing position is realized through inverse kinematics. By introducing inverse kinematics to calculate the joint angles, it is ensured that each smoothed pose point must correspond to a set of feasible joint angles, thereby avoiding the danger of unattainable poses or configurations close to singular points in advance.

[0068] S132, calculating the end pose according to the joint angles.

[0069] The calculation of the end pose according to the joint angles can calculate the end pose through the forward kinematics equation:

[0070] wherein, is the end pose, is the joint angle of the i-th joint; is the homogeneous transformation matrix of the i-th joint.

[0071] S133, generating a virtual trajectory curve of the robot according to the end pose.

[0072] Based on the end pose of step S132, the end effector trajectory curve of the robot can be generated:

[0073] wherein, is the end effector trajectory curve, and are the start time and end time of trajectory generation, respectively; Through verification and visualization by forward kinematics, a virtual trajectory is generated which is both smooth and fully complies with the kinematic constraints of the robot itself, and the end effector trajectory curve can also be displayed in real time on the operation interface.

[0074] The embodiment of the present application uses a filtering algorithm combined with historical trajectory information to process the current position information, providing a reliable conversion path from rough and unreliable drag teaching to smooth, stable and executable automatic trajectory for the robot, fundamentally solving the safety hazards and quality defects when the original trajectory data is directly used.

[0075] Embodiment two

[0076] Please refer to Figure 3 , Figure 3 is a structural schematic diagram of the robot trajectory generation system disclosed by the embodiment of the present application. As shown inFigure 3 As shown, the robot trajectory generation system can include: a receiving unit 210 configured to receive an actual trajectory point position at a current time, denoted as a current position; a calculating unit 220 configured to determine a processed trajectory point position at the current time based on the historical trajectory point positions and the current position, denoted as a current processed position; a generating unit 230 configured to generate a robot virtual trajectory curve according to the current processed position.

[0077] Preferably, the calculating unit 220 can include: for any trajectory point , its current processed position is:

[0078] wherein, , , are coordinate values of the trajectory point in X-axis, Y-axis, and Z-axis directions, is an actual trajectory point position of the trajectory point at the t-th time, i.e., the current position, and are actual trajectory point positions of the trajectory point at the (t-1)-th time and the (t-2)-th time, , , are a first weight, a second weight, and a third weight, respectively, and .

[0079] The calculating unit 220 can also include: for any trajectory point , its current processed position is:

[0080] wherein, , , are coordinate values of the trajectory point in X-axis, Y-axis, and Z-axis directions, is an actual trajectory point position of the trajectory point at the t-th time, i.e., the current position, and are actual trajectory point positions of the trajectory point at the (t-1)-th time and the (t-2)-th time, , , respectively a first weight, a second weight and a third weight, and

[0081]

[0082]

[0083] wherein, , respectively a maximum value and a minimum value of the first weight, , respectively a maximum speed value and a minimum speed value of the robot, b is a constant, is a trajectory point P j a velocity at the t-th moment, and:

[0084] wherein, is a Euclidean distance between and is a sampling period.

[0085] Preferably, the generating unit 230 can comprise: determining the respective joint angles by inverse kinematics based on the current processing position; calculating the end pose by a forward kinematics equation:

[0086] wherein, is an end pose, is a joint angle of the i-th joint; is a homogeneous transformation matrix of the i-th joint; generating an end effector trajectory curve of the robot according to the end pose:

[0087] wherein, is an end effector trajectory curve, and respectively a start time and an end time of the trajectory generation.

[0088] Preferably, the system can further comprise an amplitude limiting unit for acquiring the acceleration of any trajectory point of the robot in real time; and when the acceleration is greater than a preset acceleration value, controlling the robot to stop urgently or sending an alarm signal.

[0089] ​Preferably, the system can further comprise a multiplexing unit for saving one or more sets of robot virtual trajectory curves as teaching data, and using the teaching data to teach or skill reproduce other robots.

[0090] Embodiment three

[0091] Please refer to Figure 4 , Figure 4 is a structural schematic diagram of an electronic device disclosed by the embodiment of the present application. The electronic device can be a computer, a server, and the like, and of course, under certain circumstances, can also be a smart device such as a mobile phone, a tablet computer, and a monitoring terminal, and an image acquisition device with processing function. As shown in the figure, the electronic device can include: Figure 4 a memory 310 storing executable program codes; a processor 320 coupled with the memory 310; The processor 320 calls the executable program codes stored in the memory 310 to execute part or all of the steps in the robot trajectory generation method in embodiment one.

[0092] The embodiment of the present application discloses a computer readable storage medium storing a computer program, wherein the computer program causes a computer to execute part or all of the steps in the robot trajectory generation method in embodiment one.

[0093] The embodiment of the present application further discloses a computer program product, wherein when the computer program product runs on a computer, it causes the computer to execute part or all of the steps in the robot trajectory generation method in embodiment one.

[0094] The embodiment of the present application further discloses an application publishing platform, wherein the application publishing platform is used to publish a computer program product, wherein when the computer program product runs on a computer, it causes the computer to execute part or all of the steps in the robot trajectory generation method in embodiment one.

[0095] In various embodiments of the present application, it should be understood that the size of the serial number of the processes does not mean the inevitable sequence of execution, and the execution sequence of the processes should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0096] The units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or they can be distributed on multiple network units. According to actual needs, part or all of the units can be selected to achieve the purpose of the embodiment of the present application.

[0097] ​In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function unit.

[0098] When the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-accessible memory. Based on this understanding, the technical solutions of the present application, essentially or the part that contributes to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. The computer software product is stored in a memory and includes several parts or all steps of the methods performed by a computer device (which can be a personal computer, a server or a network device, and specifically can be a processor in the computer device) to execute the embodiments of the present application.

[0099] In the embodiments provided by the present application, it should be understood that B corresponding to A means that B is associated with A, and B can be determined according to A. However, it should also be understood that the determination of B according to A does not mean that B is determined only according to A, but B can also be determined according to A and / or other information.

[0100] A person of ordinary skill in the art can understand that some or all of the steps in the various methods of the embodiments can be completed by a program instructing relevant hardware, and the program can be stored in a computer-readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, a magnetic disk storage, a magnetic tape storage, or any other medium that can be used to carry or store data which can be read by a computer.

[0101] The robot trajectory generation method and system disclosed in the embodiments of the present application are described in detail, and the principles and implementation manners of the present application are described by applying specific examples. The above embodiment description is only used to help understand the method of the present application and its core idea. Meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manner and application range will be changed, and the above description should not be understood as a limitation of the present application.

Claims

1. A robot trajectory generation method characterized by, The method comprises the following steps: receiving an actual trajectory point position at a current time, denoted as a current position; determining a processed trajectory point position at the current time based on a historical trajectory point position and the current position, denoted as a current processed position, which specifically comprises: For any trajectory point its current processing position is: in, , , These are the trajectory points. The coordinate values ​​in the X, Y, and Z axes. For trajectory points The actual trajectory point position at time t, i.e., the current position. and These are the trajectory points. The actual trajectory point positions at time t-1 and time t-2 , , They are the first weight, the second weight, and the third weight, respectively, and: wherein, , are a maximum and a minimum value of a set first weight, respectively, , are a maximum and a minimum speed value of the robot, respectively, and b is a constant, is a trajectory point P j a velocity at the t-th time instant, and: wherein is and the Euclidean distance between is the sampling period; generating a robot virtual trajectory curve according to the current processed position.

2. The robot trajectory generation method of claim 1, wherein, The method of generating a robot virtual trajectory curve according to the current processed position comprises: determining joint angles according to the current processed position; calculating an end pose according to the joint angles; generating a robot virtual trajectory curve according to the end pose.

3. The robot trajectory generation method of claim 2, wherein, The method of determining joint angles according to the current processed position comprises: determining the joint angles through inverse kinematics based on the current processed position; The method of calculating an end pose according to the joint angles comprises: calculating the end pose through a forward kinematics equation: wherein, is the end pose, is the joint angle of the ith joint; is the homogeneous transformation matrix of the ith joint; The method of generating a robot virtual trajectory curve according to the end pose comprises: generating an end effector trajectory curve of the robot according to the end pose: wherein, is an end effector trajectory curve, and are a start time and an end time of trajectory generation, respectively; displaying the end effector trajectory curve on an operation interface.

4. The robot trajectory generation method of any one of claims 1-3, wherein, The method further comprises: obtaining acceleration of any trajectory point of the robot in real time; when the acceleration is greater than a preset acceleration value, controlling the robot to stop or sending an alarm signal.

5. The robot trajectory generation method of any one of claims 1-3, wherein, The method further comprises: saving one or more sets of robot virtual trajectory curves to form teaching data; using the teaching data to teach or skill-reproduce other robots.

6. A robot trajectory generation system, characterized by, The method comprises the following steps: a receiving unit configured to receive an actual trajectory point position at a current time, denoted as a current position; a calculating unit configured to determine a processed trajectory point position at the current time based on a historical trajectory point position and the current position, denoted as a current processed position, which specifically comprises: For any trajectory point its current processing position is: in, , , These are the trajectory points. The coordinate values ​​in the X, Y, and Z axes. For trajectory points The actual trajectory point position at time t, i.e., the current position. and These are the trajectory points. The actual trajectory point positions at time t-1 and time t-2 , , They are the first weight, the second weight, and the third weight, respectively, and: wherein, , are respectively a maximum and a minimum value of a first weight set, , are respectively a maximum and a minimum speed value of the robot set, b is a constant, is a trajectory point P j a speed at the t-th time, and: wherein is and the Euclidean distance between is the sampling period; a generating unit configured to generate a robot virtual trajectory curve according to the current processed position.

7. An electronic device, comprising: The method comprises the following steps: a memory storing executable program codes; a processor coupled with the memory; the processor invokes the executable program codes stored in the memory to execute the robot trajectory generation method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, wherein the computer program causes a computer to execute the robot trajectory generation method according to any one of claims 1 to 5.

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