Collaborative robot collision control method based on cloud computing

Abstract

The invention discloses a collaborative robot collision control method based on cloud computing, and belongs to the technical field of motion control. Monitoring and analyzing movement of an end effector of the collaborative robot according to the movement design data, and implementing a targeted first collision prevention control scheme for obstacles identified in a risk identification area planned by the end effector of the collaborative robot; performing tracing analysis on a first collision prevention control scheme implemented by an end executor of the collaborative robot, determining whether an obstacle monitored in the risk identification area moves to the first supervision area, and implementing a targeted second collision prevention control scheme; different local reaction processing data of reaction control of the collaborative robot are integrated and analyzed, and an existing collision control scheme of the collaborative robot is dynamically optimized and managed; the technical problems that in an existing scheme, the multi-dimensional supervision processing analysis effect of collaborative robot collision control is poor, and the autonomous optimization management effect is poor are solved.

Description

Technical Field

[0001] The present invention relates to the field of motion control technology, and in particular to a collaborative robot collision control method based on cloud computing. Background Art

[0002] To ensure the safety of human-robot collaboration, collision control is a key component in collaborative robotics technology; the goal of collision control is to immediately stop or slow down the robot's movement when unexpected contact occurs to prevent harm to the operator.

[0003] When implementing existing collaborative robot collision control solutions, most of them still remain at the stage of directly controlling the movement of obstacles detected during the movement of the collaborative robot. There is no analysis and judgment of the abnormal impact of the monitored obstacles and dynamic implementation of multi-dimensional management and control. At the same time, it is also impossible to supervise, analyze and integrate the local reaction processing status of the collaborative robot, and dynamically optimize and update the collision control of the collaborative robot. As a result, the multi-dimensional supervision, processing and analysis of the collaborative robot collision control is not effective, and the autonomous optimization management is not effective. Summary of the invention

[0004] The purpose of the present invention is to provide a collaborative robot collision control method based on cloud computing, which is used to solve the technical problems of poor multi-dimensional supervision, processing and analysis effect of collaborative robot collision control and poor autonomous optimization management effect in existing solutions.

[0005] The purpose of the present invention can be achieved through the following technical solutions: The collaborative robot collision control method based on cloud computing includes: Monitor and analyze the movement of the collaborative robot end effector according to the movement design data, determine whether there are obstacles in the movement process of the collaborative robot end effector, and implement a targeted first collision avoidance control plan for the obstacles identified in the risk identification area planned by the collaborative robot end effector; Conduct a retrospective analysis on the first collision avoidance control scheme implemented by the collaborative robot end effector to determine whether the obstacle monitored in the risk identification area moves to the first supervision area planned by the collaborative robot end effector, and implement a targeted second collision avoidance control scheme; Monitor and process the reaction data of the local reaction effects of the first collision avoidance control scheme and the second collision avoidance control scheme implemented during the movement of the collaborative robot, and process and analyze the overall reaction effects of all local reaction effects obtained by the corresponding calculations of different collision avoidance control schemes, and dynamically control the subsequent implementation of the existing collision control scheme of the collaborative robot based on the processing and analysis results; If the obstacle monitored in the risk identification area moves to the first supervision area, a risk enhancement generation instruction is generated, and the straight-line distance LJ between the monitored obstacle and the end effector of the collaborative robot is obtained according to the risk enhancement generation instruction, and the formula is used to calculate the distance LJ between the monitored obstacle and the end effector of the collaborative robot. Calculate and obtain the risk enhancement coefficient FY of the obstacles monitored in the first supervision area on the operation of the collaborative robot end effector; where RY is the radius corresponding to the first supervision area; A is the risk enhancement classification value; If the risk enhancement impact coefficient is less than 0, a risk enhancement mild impact label is generated and the collaborative robot end effector is controlled to move the risk coordinates of the moving path corresponding to the obstacle monitored in the first supervision area according to the second risk design speed; wherein the second risk design speed is ; Vb is the mobile design speed corresponding to the real-time mobile path coordinates of the collaborative robot end effector; If the risk enhancement impact coefficient is greater than or equal to 0, a risk enhancement severe impact label is generated, and the movement of the risk coordinates of the moving path corresponding to the obstacle monitored by the collaborative robot end effector in the first supervision area is controlled to be suspended until the risk enhancement impact coefficient is less than 0.

[0006] Preferably, the end effector of the collaborative robot is obtained and set as a dot, and the corresponding first supervision area and second supervision area are constructed according to the dot and the preset first supervision radius and second supervision radius, and the area where the second supervision area overlaps with the first supervision area is eliminated to obtain a risk identification area; The movement design direction, movement design path and movement design speed in the movement design data corresponding to the collaborative robot end effector are obtained, and the movement of the collaborative robot end effector according to the movement design direction, movement design path and corresponding movement design speed is supervised and analyzed.

[0007] Preferably, when an obstacle is detected in the risk identification area of ​​the end effector, a risk generation instruction is generated, and the real-time moving path risk coordinates of the end effector are obtained according to the risk generation instruction, and the moving path risk coordinates are subjected to data analysis through a path coordinate risk identification function, and a corresponding path coordinate risk factor WF is output; Among them, the expression of the path coordinate risk identification function is: ;WL is the obtained moving path risk coordinate;Uwl is the moving path risk coordinate set; According to the path coordinate risk factor with a value of 0, the collaborative robot end effector is controlled to move at a preset moving design speed along the moving path risk coordinate.

[0008] Preferably, the collaborative robot end effector is controlled to move at a first risk design speed in the moving path risk coordinate according to the path coordinate risk factor having a value of 1, and a relative position of an obstacle monitored in the risk identification area after the moving speed of the collaborative robot end effector is adjusted is retroactively evaluated; If there is always a monitored obstacle in the risk identification area, the collaborative robot end effector is controlled to continue to move at a first risk design speed according to a movement design direction and a movement design path; If the obstacle monitored in the risk identification area disappears, a risk disappearance instruction is generated, and the collaborative robot end effector is controlled according to the risk disappearance instruction to restore the movement design speed corresponding to the movement path coordinates, and move according to the movement design direction and movement design path.

[0009] Preferably, a real-time state of the collaborative robot end effector moving at the second risk design speed is retroactively analyzed; If the monitored obstacle is always in the first supervision area, the moving speed of the collaborative robot end effector in the first supervision area is controlled according to the analysis result of the risk enhancement influence coefficient; If the monitored obstacle disappears from the first supervision area, a risk enhancement disappearance instruction is generated, and according to the risk enhancement disappearance instruction, it is determined whether the monitored obstacle is in the risk identification area, and the collaborative robot end effector is controlled to move at the corresponding movement design speed or the first risk design speed until the monitored obstacle disappears from the risk identification area and a risk disappearance instruction is generated, and according to the risk disappearance instruction, the collaborative robot end effector is controlled to restore the movement design speed corresponding to the movement path coordinates, and move according to the movement design direction and movement design path.

[0010] Preferably, a first abnormal monitoring timestamp corresponding to when an obstacle is monitored in the risk identification area is obtained, and a first movement control timestamp when the end effector of the collaborative robot is controlled to move with respect to the obstacle monitored in the risk identification area is obtained; Calculating a first reaction time difference between a first movement control timestamp and a first abnormality monitoring timestamp; Sorting and combining the first reaction time differences obtained by processing according to the abnormality monitoring time sequence to obtain a first reaction time difference processing sequence; And, obtaining a second abnormal monitoring timestamp corresponding to when an obstacle is monitored in the first supervision area, and obtaining a second movement control timestamp for controlling the movement of the collaborative robot end effector with respect to the obstacle monitored in the first supervision area; calculating a second reaction time difference between a second movement control timestamp and a second abnormality monitoring timestamp; The plurality of second reaction time differences obtained through processing are sorted and combined according to the abnormality monitoring time sequence to obtain a second reaction time difference processing sequence.

[0011] Preferably, all elements in the first reaction time difference processing sequence and the second reaction time difference processing sequence are sequentially processed by the formula Calculate and obtain the local reaction validity FXi corresponding to the collision control of the collaborative robot; where TFi is the different elements in the first reaction time difference processing sequence and the second reaction time difference processing sequence, i=1, 2, 3, ..., n; n is a positive integer; TFi0 is the standard reaction time difference corresponding to all elements in the first reaction time difference processing sequence and the second reaction time difference processing sequence; [ ] is a rounding function, which means obtaining the maximum integer that does not exceed a real number; The local reaction validity calculated and obtained from all elements in the first reaction time difference processing sequence and the second reaction time difference processing sequence are sorted and combined to obtain a local reaction validity processing sequence.

[0012] Preferably, all elements in the local reaction validity processing sequence are traversed and analyzed to determine the overall reaction effect corresponding to the collaborative robot collision control; If all elements in the local reaction validity processing sequence are 0, the overall reaction effect corresponding to the collaborative robot collision control is generated to be normal, and the subsequent implementation of the existing collision control scheme of the collaborative robot is maintained; If there is an element whose value is not 0 in the local reaction validity processing sequence, an overall reaction effect abnormality corresponding to the collaborative robot collision control is generated, and the reaction type corresponding to the element whose value is not 0 is determined.

[0013] Preferably, if the reaction type corresponding to the element whose value is not 0 is a risk-normal type, the risk-normal local collision control design in the existing collision control scheme of the collaborative robot is optimized and updated; If the reaction type corresponding to the element whose value is not 0 is the risk enhancement type, the risk enhancement local collision control design in the existing collision control scheme of the collaborative robot is optimized and updated; If the reaction types corresponding to the elements whose values ​​are not 0 are the normal risk type and the enhanced risk type, the overall collision control design of the existing collision control scheme of the collaborative robot is optimized and updated.

[0014] Compared with the existing solutions, the present invention achieves the following beneficial effects: The present invention monitors and analyzes the movement of the collaborative robot end effector according to the mobile design data, determines whether there are obstacles in the movement process of the collaborative robot end effector, and implements a targeted first collision avoidance control scheme for the obstacles identified in the risk identification area planned by the collaborative robot end effector, thereby improving the reliability of the data analysis of the abnormal impact of obstacles monitored during the movement of the collaborative robot end effector and the reliability of the safe movement of the collaborative robot end effector.

[0015] The present invention performs retroactive analysis on the first collision avoidance control scheme implemented by the collaborative robot end effector, determines whether the obstacle monitored in the risk identification area moves to the first supervision area planned by the collaborative robot end effector, and implements a targeted second collision avoidance control scheme, thereby realizing retroactive analysis of the real-time abnormal impact of the monitored obstacle, and further dynamically controlling the movement of the collaborative robot end effector according to the retroactive analysis results, thereby further improving the autonomous supervision effect and autonomous tracing effect of the collaborative robot's collision control.

[0016] The present invention integrates, calculates and analyzes different local reaction processing data of the collaborative robot's reaction control, and dynamically optimizes and manages the existing collision control scheme of the collaborative robot based on the analysis results, thereby realizing the expanded utilization of different local reaction processing data in the early stage and improving the multi-dimensional supervision and analysis effect and autonomous optimization management effect of the collaborative robot's collision control. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below in conjunction with the accompanying drawings.

[0018] Figure 1 This is a flowchart of the collaborative robot collision control method based on cloud computing of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0020] like Figure 1 As shown, the present invention is a collaborative robot collision control method based on cloud computing, comprising: Monitor and analyze the movement of the collaborative robot end effector according to the mobile design data, determine whether there are obstacles in the movement process of the collaborative robot end effector, and implement a targeted first collision avoidance control plan for the obstacles identified in the risk identification area planned by the collaborative robot end effector; including: The end effector of the collaborative robot is obtained and set as a dot, and the corresponding first supervision area and second supervision area are constructed according to the dot and the preset first supervision radius and second supervision radius, and the area where the second supervision area overlaps with the first supervision area is eliminated to obtain a risk identification area; The first supervision radius is smaller than the second supervision radius, the first supervision area is a circle, and the risk identification area is a ring; the specific values ​​of the first supervision radius and the second supervision radius can be determined based on the operational design requirement data of the collaborative robot end effector combined with the historical collision big data; In the embodiment of the present invention, by constructing a first supervision area and a risk identification area for the collaborative robot end effector, targeted risk identification and control can be implemented for different collision risks corresponding to obstacles monitored during the subsequent movement of the collaborative robot end effector; Obtain the movement design direction, movement design path and movement design speed in the movement design data corresponding to the collaborative robot end effector, and perform supervision and analysis on the movement of the collaborative robot end effector according to the movement design direction, movement design path and corresponding movement design speed; Among them, the collaborative robot end effector corresponds to the mobile design direction, mobile design path and mobile design speed in the mobile design data, and can be customized according to the actual application scenario and application route; When an obstacle is detected in the risk identification area of ​​the end effector, a risk generation instruction is generated. The monitored obstacles include but are not limited to moving equipment and moving personnel, and the real-time moving path risk coordinates of the end effector are obtained according to the risk generation instruction. The moving path risk coordinates can be realized according to the existing wireless positioning technology, such as using Wi-Fi, Bluetooth or other wireless communication protocols for positioning, and the moving path risk coordinates are analyzed through the path coordinate risk identification function, and the corresponding path coordinate risk factor WF is output; Among them, the expression of the path coordinate risk identification function is: ;WL is the obtained moving path risk coordinate;Uwl is the moving path risk coordinate set, which is composed of several sample moving path risk coordinates, and there are fixed obstacles in the corresponding positions of several sample moving path risk coordinates; The path coordinate risk factor contains a value of 0 or 1. A path coordinate risk factor with a value of 0 indicates that the corresponding moving path coordinate risk is normal; a path coordinate risk factor with a value of 1 indicates that the corresponding moving path coordinate risk is abnormal; In the embodiment of the present invention, by calculating the data of the risk coordinates of the moving path, reliable data support can be provided for the subsequent movement control of the end effector of the collaborative robot; When implementing a targeted first collision avoidance control plan based on the calculated path coordinate risk factor; According to the path coordinate risk factor with a value of 0, the collaborative robot end effector is controlled to move at a preset moving design speed in the moving path risk coordinate; it can be understood that when an obstacle is detected in the risk identification area, the corresponding path coordinate risk factor value is 0, indicating that the corresponding monitored obstacle is a conventional fixed obstacle and will not affect the movement of the collaborative robot end effector; and, controlling the collaborative robot end effector to move at a first risk design speed in a moving path risk coordinate according to a path coordinate risk factor having a value of 1, and retroactively evaluating the relative position of a monitoring obstacle in a risk identification area after the moving speed of the collaborative robot end effector is adjusted; It should be noted that when an obstacle is detected in the risk identification area, the corresponding path coordinate risk factor value is 1, indicating that the corresponding monitored obstacle is an unconventional fixed obstacle, which will affect the movement of the collaborative robot end effector. Therefore, the movement of the collaborative robot end effector needs to be actively controlled; The first risk design speed may specifically be 50% of the moving design speed corresponding to the moving path risk coordinate; If there is always a monitored obstacle in the risk identification area, the collaborative robot end effector is controlled to continue to move at a first risk design speed according to a movement design direction and a movement design path; If the obstacle monitored in the risk identification area disappears, a risk disappearance instruction is generated, and the collaborative robot end effector is controlled according to the risk disappearance instruction to restore the movement design speed corresponding to the movement path coordinates, and move according to the movement design direction and movement design path; Different from the prior art solutions in which obstacles detected during the movement of the collaborative robot are directly controlled, without analyzing and judging the abnormal impact of the monitored obstacles and dynamically implementing control; in the embodiment of the present invention, by monitoring and analyzing the movement of the collaborative robot end effector according to the movement design data, it is determined whether there are obstacles in the movement process of the collaborative robot end effector, and a targeted first collision avoidance control scheme is implemented for the obstacles identified in the risk identification area planned by the collaborative robot end effector, thereby improving the reliability of the data analysis of the abnormal impact of obstacles monitored during the movement of the collaborative robot end effector and the reliability of the safe movement of the collaborative robot end effector.

[0021] Conduct a retrospective analysis on the first collision avoidance control scheme implemented by the collaborative robot end effector to determine whether the obstacle monitored in the risk identification area moves to the first supervision area planned by the collaborative robot end effector, and implement a targeted second collision avoidance control scheme; including: If the obstacle monitored in the risk identification area moves to the first supervision area, a risk enhancement generation instruction is generated, and the straight-line distance LJ between the monitored obstacle and the end effector of the collaborative robot is obtained according to the risk enhancement generation instruction, and the formula is used to calculate the distance LJ between the monitored obstacle and the end effector of the collaborative robot. Calculate and obtain the risk enhancement influence coefficient FY of the obstacles monitored in the first supervision area on the operation of the collaborative robot end effector; where RY is the radius corresponding to the first supervision area; A is the risk enhancement classification value, which can be determined based on the operational safety design requirement data of the collaborative robot end effector, or based on the preliminary test data of the collaborative robot end effector operation; It should be noted that the risk enhancement impact coefficient is used to handle the abnormal impact of the obstacles under calculation monitoring on the movement of the collaborative robot end effector in the first supervision area; When implementing a targeted second collision avoidance control plan based on the calculated risk enhancement impact coefficient; If the risk enhancement impact coefficient is less than 0, a risk enhancement mild impact label is generated and the collaborative robot end effector is controlled to move the risk coordinates of the moving path corresponding to the obstacle monitored in the first supervision area according to the second risk design speed; wherein the second risk design speed is ; Vb is the mobile design speed corresponding to the real-time mobile path coordinates of the collaborative robot end effector; Among them, the movement of the collaborative robot end effector is further controlled according to the risk-enhanced mild impact label. By reducing the movement speed of the collaborative robot end effector, the safety of the movement of the collaborative robot end effector and the reliability of the response can be further improved; and, conducting a retrospective analysis of the real-time status of the collaborative robot end effector moving at the second risk design speed; If the monitored obstacle is always in the first supervision area, the moving speed of the collaborative robot end effector in the first supervision area is controlled according to the analysis result of the risk enhancement influence coefficient; If the monitored obstacle disappears from the first supervision area, a risk enhancement disappearance instruction is generated, and according to the risk enhancement disappearance instruction, it is determined whether the monitored obstacle is in the risk identification area, and the collaborative robot end effector is controlled to move at the corresponding movement design speed or the first risk design speed until the monitored obstacle disappears from the risk identification area and a risk disappearance instruction is generated, and according to the risk disappearance instruction, the collaborative robot end effector is controlled to restore the movement design speed corresponding to the movement path coordinates, and move according to the movement design direction and movement design path; In the embodiment of the present invention, by retrospectively analyzing the subsequent movement of the collaborative robot end effector at the second risk design speed, the subsequent movement of the collaborative robot can be further controlled in a variety of ways, thereby improving the diversity and adaptability of the collaborative robot's movement control.

[0022] If the risk enhancement impact coefficient is greater than or equal to 0, a risk enhancement severe impact label is generated, and the movement of the risk coordinates of the moving path corresponding to the obstacle monitored by the collaborative robot end effector in the first supervision area is controlled to be suspended until the risk enhancement impact coefficient is less than 0; it can be understood that when the monitored obstacle leaves the first supervision area, the collaborative robot end effector can be controlled to re-operate to avoid collision between the collaborative robot end effector and the monitored obstacle; In an embodiment of the present invention, by retrospectively analyzing the first collision avoidance control scheme implemented by the collaborative robot end effector, it is determined whether the obstacle monitored in the risk identification area moves to the first supervision area planned by the collaborative robot end effector, and a targeted second collision avoidance control scheme is implemented. This enables retrospective analysis of the real-time abnormal impact of the monitored obstacle, and further dynamic control of the movement of the collaborative robot end effector based on the retrospective analysis results, thereby further improving the autonomous supervision effect and autonomous tracing effect of the collaborative robot's collision control.

[0023] Monitor and process the reaction data of the local reaction effects of the first collision avoidance control scheme and the second collision avoidance control scheme implemented during the movement of the collaborative robot, and process and analyze the overall reaction effects of all local reaction effects obtained by the corresponding calculations of different collision avoidance control schemes, and dynamically control the subsequent implementation of the existing collision control scheme of the collaborative robot according to the processing and analysis results; including: Obtaining a first abnormal monitoring timestamp corresponding to when an obstacle is detected in the risk identification area, and obtaining a first movement control timestamp when controlling the movement of the collaborative robot end effector with respect to the obstacle monitored in the risk identification area; Calculate the first reaction time difference between the first movement control timestamp and the first abnormality monitoring timestamp; the units of different timestamps and the first reaction time difference are accurate to milliseconds; Sorting and combining the first reaction time differences obtained by processing according to the abnormality monitoring time sequence to obtain a first reaction time difference processing sequence; Among them, the first reaction time difference processing sequence performs data processing of the processing reaction dimension based on the obstacles identified in the risk identification area; And, obtaining a second abnormal monitoring timestamp corresponding to when an obstacle is monitored in the first supervision area, and obtaining a second movement control timestamp for controlling the movement of the collaborative robot end effector with respect to the obstacle monitored in the first supervision area; Calculate the second reaction time difference between the second movement control timestamp and the second abnormality monitoring timestamp; the units of different timestamps and the second reaction time difference are accurate to milliseconds; Sorting and combining the obtained second reaction time differences according to the abnormality monitoring time sequence to obtain a second reaction time difference processing sequence; wherein the second reaction time difference processing sequence performs data processing of the processing reaction dimension based on obstacles identified in the first supervision area; All elements in the first reaction time difference processing sequence and the second reaction time difference processing sequence are sequentially transformed into Calculate and obtain the local reaction validity FXi corresponding to the collision control of the collaborative robot; where TFi is the different elements in the first reaction time difference processing sequence and the second reaction time difference processing sequence, i=1, 2, 3, ..., n; n is a positive integer, which is all the elements in the first reaction time difference processing sequence and the second reaction time difference processing sequence; TFi0 is the standard reaction time difference corresponding to all the elements in the first reaction time difference processing sequence and the second reaction time difference processing sequence, which is determined according to the design requirement data corresponding to the collision control of the collaborative robot; [ ] is a rounding function, which means obtaining the maximum integer that does not exceed a real number; In the embodiment of the present invention, by performing data calculation on all elements in the first reaction time difference processing sequence and the second reaction time difference processing sequence, the local reaction validity corresponding to the collision control of the collaborative robot is obtained, which can provide reliable multi-dimensional local reaction processing data support for the overall reaction effect analysis corresponding to the subsequent collision control of the collaborative robot; The local reaction validity calculated and obtained by all elements in the first reaction time difference processing sequence and the second reaction time difference processing sequence are sorted and combined to obtain a local reaction validity processing sequence; Perform traversal analysis on all elements in the local reaction validity processing sequence to determine the overall reaction effect corresponding to the collaborative robot collision control; If all elements in the local reaction validity processing sequence are 0, the overall reaction effect corresponding to the collaborative robot collision control is generated to be normal, and the subsequent implementation of the existing collision control scheme of the collaborative robot is maintained; If there is an element with a non-zero value in the local reaction validity processing sequence, an overall reaction effect abnormality corresponding to the collaborative robot collision control is generated, and the reaction type corresponding to the element with a non-zero value is determined; If the reaction type corresponding to the element whose value is not 0 is the risk general type, that is, the monitored obstacle is in the risk identification area, the risk general local collision control design in the existing collision control scheme of the collaborative robot is optimized and updated; If the reaction type corresponding to the element whose value is not 0 is the risk enhancement type, that is, the monitored obstacle is in the first supervision area, the risk enhancement local collision control design in the existing collision control scheme of the collaborative robot is optimized and updated; If the reaction type corresponding to the element whose value is not 0 is the risk normal type and the risk enhancement type, the overall collision control design of the existing collision control scheme of the collaborative robot is optimized and updated; Among them, by further conducting a retrospective analysis of the reaction type of the abnormal overall reaction effect corresponding to the collaborative robot's collision control, and adaptively performing a multi-dimensional local collision control design optimization update or an overall collision control design optimization update on the collaborative robot's existing collision control scheme based on the retrospective analysis results, the adaptability and reliability of the collaborative robot's collision control optimization management are improved.

[0024] In an embodiment of the present invention, by integrating, calculating and analyzing different local reaction processing data of the collaborative robot's reaction control, and dynamically optimizing and managing the existing collision control scheme of the collaborative robot based on the analysis results, the expanded utilization of different local reaction processing data in the early stage is achieved, thereby improving the multi-dimensional supervision and analysis effect and the autonomous optimization management effect of the collaborative robot's collision control.

[0025] In addition, the formulas involved in the above are all dimensionless and numerical calculations. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and simulating it with simulation software.

[0026] In the several embodiments provided by the present invention, it should be understood that the disclosed method can be implemented in other ways. For example, the above-described embodiments of the invention are only illustrative, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.

[0027] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0028] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0029] It is obvious to a person skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the essential characteristics of the present invention.

[0030] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A collaborative robot collision control method based on cloud computing, characterized in that: include: Monitor and analyze the movement of the collaborative robot end effector according to the movement design data, determine whether there are obstacles in the movement process of the collaborative robot end effector, and implement a targeted first collision avoidance control plan for the obstacles identified in the risk identification area planned by the collaborative robot end effector; Conduct a retrospective analysis on the first collision avoidance control scheme implemented by the collaborative robot end effector to determine whether the obstacle monitored in the risk identification area moves to the first supervision area planned by the collaborative robot end effector, and implement a targeted second collision avoidance control scheme; Monitor and process the reaction data of the local reaction effects of the first collision avoidance control scheme and the second collision avoidance control scheme implemented during the movement of the collaborative robot, and process and analyze the overall reaction effects of all local reaction effects obtained by the corresponding calculations of different collision avoidance control schemes, and dynamically control the subsequent implementation of the existing collision control scheme of the collaborative robot based on the processing and analysis results; If the obstacle monitored in the risk identification area moves to the first supervision area, a risk enhancement generation instruction is generated, and the straight-line distance LJ between the monitored obstacle and the end effector of the collaborative robot is obtained according to the risk enhancement generation instruction, and the formula is used to calculate the distance LJ between the monitored obstacle and the end effector of the collaborative robot. Calculate and obtain the risk enhancement impact coefficient FY of the obstacles monitored in the first supervision area on the operation of the collaborative robot end effector; where RY is the radius corresponding to the first supervision area; A is the risk enhancement classification value; If the risk enhancement impact coefficient is less than 0, a risk enhancement mild impact label is generated and the collaborative robot end effector is controlled to move the risk coordinates of the moving path corresponding to the obstacle monitored in the first supervision area according to the second risk design speed; wherein the second risk design speed is ; Vb is the mobile design speed corresponding to the real-time mobile path coordinates of the collaborative robot end effector; If the risk enhancement impact coefficient is greater than or equal to 0, a risk enhancement severe impact label is generated, and the movement of the risk coordinates of the moving path corresponding to the obstacle monitored by the collaborative robot end effector in the first supervision area is controlled to be suspended until the risk enhancement impact coefficient is less than 0.

2. The cloud computing-based collaborative robot collision control method according to claim 1, characterized in that: The end effector of the collaborative robot is obtained and set as a dot, and the corresponding first supervision area and second supervision area are constructed according to the dot and the preset first supervision radius and second supervision radius, and the area where the second supervision area overlaps with the first supervision area is eliminated to obtain a risk identification area; The movement design direction, movement design path and movement design speed in the movement design data corresponding to the collaborative robot end effector are obtained, and the movement of the collaborative robot end effector according to the movement design direction, movement design path and corresponding movement design speed is supervised and analyzed.

3. The cloud computing-based collaborative robot collision control method according to claim 2, characterized in that: When an obstacle is detected in the risk identification area of ​​the end effector, a risk generation instruction is generated, and the real-time moving path risk coordinates of the end effector are obtained according to the risk generation instruction. The moving path risk coordinates are analyzed through the path coordinate risk identification function, and the corresponding path coordinate risk factor WF is output; Among them, the expression of the path coordinate risk identification function is: ;WL is the obtained moving path risk coordinate;Uwl is the moving path risk coordinate set; According to the path coordinate risk factor with a value of 0, the collaborative robot end effector is controlled to move at a preset moving design speed along the moving path risk coordinate.

4. The cloud computing-based collaborative robot collision control method according to claim 3, characterized in that: Controlling the collaborative robot end effector to move at a first risk design speed in a moving path risk coordinate according to a path coordinate risk factor having a value of 1, and retroactively evaluating the relative position of the monitored obstacle in the risk identification area after the moving speed of the collaborative robot end effector is adjusted; If there is always a monitored obstacle in the risk identification area, the collaborative robot end effector is controlled to continue to move at a first risk design speed according to a movement design direction and a movement design path; If the obstacle monitored in the risk identification area disappears, a risk disappearance instruction is generated, and the collaborative robot end effector is controlled according to the risk disappearance instruction to restore the movement design speed corresponding to the movement path coordinates, and move according to the movement design direction and movement design path.

5. The cloud computing-based collaborative robot collision control method according to claim 4, characterized in that: Conduct retrospective analysis on the real-time status of the collaborative robot end effector moving at the second risk design speed; If the monitored obstacle is always in the first supervision area, the moving speed of the collaborative robot end effector in the first supervision area is controlled according to the analysis result of the risk enhancement influence coefficient; If the monitored obstacle disappears from the first supervision area, a risk enhancement disappearance instruction is generated, and according to the risk enhancement disappearance instruction, it is determined whether the monitored obstacle is in the risk identification area, and the collaborative robot end effector is controlled to move at the corresponding movement design speed or the first risk design speed until the monitored obstacle disappears from the risk identification area and a risk disappearance instruction is generated, and according to the risk disappearance instruction, the collaborative robot end effector is controlled to restore the movement design speed corresponding to the movement path coordinates, and move according to the movement design direction and movement design path.

6. The cloud computing-based collaborative robot collision control method according to claim 5, characterized in that: Obtaining a first abnormal monitoring timestamp corresponding to when an obstacle is detected in the risk identification area, and obtaining a first movement control timestamp when controlling the movement of the collaborative robot end effector with respect to the obstacle monitored in the risk identification area; Calculating a first reaction time difference between a first movement control timestamp and a first abnormality monitoring timestamp; Sorting and combining the first reaction time differences obtained by processing according to the abnormality monitoring time sequence to obtain a first reaction time difference processing sequence; And, obtaining a second abnormal monitoring timestamp corresponding to when an obstacle is monitored in the first supervision area, and obtaining a second movement control timestamp for controlling the movement of the collaborative robot end effector with respect to the obstacle monitored in the first supervision area; calculating a second reaction time difference between a second movement control timestamp and a second abnormality monitoring timestamp; The plurality of second reaction time differences obtained through processing are sorted and combined according to the abnormality monitoring time sequence to obtain a second reaction time difference processing sequence.

7. The cloud computing-based collaborative robot collision control method according to claim 6, characterized in that: All elements in the first reaction time difference processing sequence and the second reaction time difference processing sequence are sequentially transformed into Calculate and obtain the local reaction validity FXi corresponding to the collision control of the collaborative robot; where TFi is the different elements in the first reaction time difference processing sequence and the second reaction time difference processing sequence, i=1, 2, 3, ..., n; n is a positive integer; TFi0 is the standard reaction time difference corresponding to all elements in the first reaction time difference processing sequence and the second reaction time difference processing sequence; [ ] is a rounding function, which means obtaining the maximum integer that does not exceed a real number; The local reaction validity calculated and obtained from all elements in the first reaction time difference processing sequence and the second reaction time difference processing sequence are sorted and combined to obtain a local reaction validity processing sequence.

8. The cloud computing-based collaborative robot collision control method according to claim 7, characterized in that: Perform traversal analysis on all elements in the local reaction validity processing sequence to determine the overall reaction effect corresponding to the collaborative robot collision control; If all elements in the local reaction validity processing sequence are 0, the overall reaction effect corresponding to the collaborative robot collision control is generated to be normal, and the subsequent implementation of the existing collision control scheme of the collaborative robot is maintained; If there is an element whose value is not 0 in the local reaction validity processing sequence, an overall reaction effect abnormality corresponding to the collaborative robot collision control is generated, and the reaction type corresponding to the element whose value is not 0 is determined.

9. The cloud computing-based collaborative robot collision control method according to claim 8, characterized in that: If the reaction type corresponding to the element whose value is not 0 is a risk-normal type, the risk-normal local collision control design in the existing collision control scheme of the collaborative robot is optimized and updated; If the reaction type corresponding to the element whose value is not 0 is the risk enhancement type, the risk enhancement local collision control design in the existing collision control scheme of the collaborative robot is optimized and updated; If the reaction types corresponding to the elements whose values ​​are not 0 are the normal risk type and the enhanced risk type, the overall collision control design of the existing collision control scheme of the collaborative robot is optimized and updated.

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