Guide rail type mechanical arm laser cleaning method and device

Abstract

The invention provides a guide rail type mechanical arm laser cleaning method and device, and relates to the technical field of robot automation. The method comprises the steps that at least one cleaning area determined for a to-be-cleaned workpiece in advance and target poses of a mechanical arm at all cleaning points of the to-be-cleaned workpiece are obtained; generating a plurality of random stations for each cleaning area, and for each station, according to the pose of the mechanical arm at the current station and the target pose of the mechanical arm at each cleaning point in the cleaning area to which the current station belongs, determining the coverage rate of the cleaning point corresponding to the current station in the cleaning area to which the current station belongs, the standing position is the position of the mechanical arm on the guide rail; according to the coverage rate of the cleaning points, target stations corresponding to all the cleaning areas are obtained through screening in the multiple stations; and controlling the mechanical arm to clean the to-be-cleaned workpiece according to the target station. According to the method, the problem that in the prior art, cleaning efficiency is low or cleaning cost is high in cleaning operation can be effectively solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of robot automation, and in particular to a guide rail type mechanical arm laser cleaning method and device. BACKGROUND

[0002] In the field of robot automation cleaning, compared with the traditional handheld scheme, the method of using a laser carried by the end of a mechanical arm to clean can accurately control the distance between the mechanical arm and the cleaning surface, and the cleaning is uniform and effective, and is widely used in the cleaning industry of metal plates. For small workpieces, the robot usually adopts a fixed base installation method, and through reasonable placement, the robot can cover the entire cleaning area. However, for large workpieces (such as large parts in the aerospace industry), a fixed mechanical arm cannot cover the entire cleaning area. To solve this problem, the following methods are usually used in the prior art: first, the mechanical arm is installed on a movable trolley, and experienced workers are designated to fixed stations for cleaning; second, a flow line type is used, and the mechanical arm is still fixed, but multiple lasers are used for cleaning. However, the first method requires reliance on human experience and has low work efficiency, and the second method requires multiple devices and has high cleaning cost. SUMMARY

[0003] The present application provides a guide rail type mechanical arm laser cleaning method and device to solve the problems of low cleaning efficiency or high cleaning cost existing in the prior art.

[0004] The present application provides a guide rail type mechanical arm laser cleaning method, comprising the following steps: obtaining at least one cleaning area determined in advance for a workpiece to be cleaned, and a target pose of a mechanical arm at each cleaning point of the workpiece to be cleaned; generating a plurality of random stations for each cleaning area, and for each station, determining the coverage rate of the cleaning point corresponding to the current station in the cleaning area to which the current station belongs according to the pose of the mechanical arm at the current station and the target pose of the mechanical arm at each cleaning point in the cleaning area to which the current station belongs, the station being the position of the mechanical arm on the guide rail; According to the coverage rate of the cleaning point, the target station corresponding to each cleaning area is selected from the plurality of stations; According to the target station, the mechanical arm is controlled to clean the workpiece to be cleaned.

[0005] According to the guide rail type mechanical arm laser cleaning method provided by the present application, the determination of the coverage rate of the cleaning point corresponding to the current station in the cleaning area to which the current station belongs according to the pose of the mechanical arm at the current station and the target pose of the mechanical arm at each cleaning point in the cleaning area to which the current station belongs for each station comprises: For each of the stations, according to the pose of the robot arm at the current station and the target poses of the robot arm at each cleaning point in the cleaning area to which the current station belongs, determine the cleaning points in the cleaning area to which the current station belongs that can be reached by the end effector of the robot arm; According to the ratio of the reachable cleaning points to the total number of cleaning points in the cleaning area to which the current station belongs, determine the coverage rate of the corresponding cleaning point of the current station in the cleaning area.

[0006] According to the guide rail type robot arm laser cleaning method provided by the present application, for each of the stations, according to the pose of the robot arm at the current station and the target poses of the robot arm at each cleaning point in the cleaning area to which the current station belongs, determine the cleaning points in the cleaning area to which the current station belongs that can be reached by the end effector of the robot arm, comprising: Obtain the initial pose of the robot arm; Determine the interpolation trajectory of the robot arm from the initial pose to each of the target poses through each of the stations, and eliminate the stations with collision risk on the interpolation trajectory among the multiple stations to obtain the remaining stations; For each of the remaining stations, according to the pose of the robot arm at the current station and the target poses of the robot arm at each cleaning point in the cleaning area to which the current station belongs, determine the cleaning points in the cleaning area to which the current station belongs that can be reached by the end effector of the robot arm.

[0007] According to the guide rail type robot arm laser cleaning method provided by the present application, each of the cleaning points in the cleaning area to which the current station belongs includes a target cleaning point, and the determination of the cleaning points in the cleaning area to which the current station belongs that can be reached by the end effector of the robot arm according to the pose of the robot arm at the current station and the target poses of the robot arm at each cleaning point in the cleaning area to which the current station belongs comprises: Determine the relative pose of the target pose of the target cleaning point in the cleaning area to which the current station belongs and the pose of the robot arm at the current station; According to the relative pose, solve the joint angles of the robot arm through the inverse kinematics model of the robot arm; If at least one set of joint angles is determined to be solved, it is determined that the end effector of the robot arm can reach the target cleaning point in the cleaning area to which the current station belongs.

[0008] According to the guide rail type robot arm laser cleaning method provided by the present application, the inverse kinematics model is determined by the following steps: Obtain the unified robot description format file of the robot arm; According to the unified robot description format file, obtain geometric parameters and kinematic parameters of the mechanical arm; According to the geometric parameters and the kinematic parameters, construct an inverse kinematics model of the mechanical arm.

[0009] According to the guide rail type mechanical arm laser cleaning method provided in the application, the station positions with collision risks on the interpolation trajectory are eliminated from the plurality of station positions to obtain remaining station positions, including: Obtain a bounding box hierarchy tree corresponding to the mechanical arm, and a plurality of directional bounding box models are stored in the bounding box hierarchy tree, the plurality of directional bounding box models being generated in advance for each joint, connecting rod, end effector of the mechanical arm and fixed obstacles in a working area where the mechanical arm is located; Based on the bounding box hierarchy tree, a flexible collision detection library is used to determine the station positions with collision risks on the interpolation trajectory; The station positions with collision risks are eliminated from the plurality of station positions to obtain the remaining station positions.

[0010] According to the guide rail type mechanical arm laser cleaning method provided in the application, the cleaning area includes adjacent first and second cleaning areas, and an overlap area is arranged between the first and second cleaning areas, the overlap area including at least one cleaning point in the first cleaning area and at least one cleaning point in the second cleaning area, and for each station position, the coverage rate of the corresponding cleaning point in the cleaning area to which the current station position belongs is determined according to the pose of the mechanical arm at the current station position and the target pose of each cleaning point in the cleaning area to which the current station position belongs, including: For each station position of the plurality of station positions generated for the first cleaning area, the coverage rate of the corresponding cleaning point in the first cleaning area is determined according to the pose of the mechanical arm at the current station position and the target pose of each cleaning point in the first cleaning area and the overlap area; For each station position of the plurality of station positions generated for the second cleaning area, the coverage rate of the corresponding cleaning point in the second cleaning area is determined according to the pose of the mechanical arm at the current station position and the target pose of each cleaning point in the second cleaning area and the overlap area.

[0011] According to the guide rail type mechanical arm laser cleaning method provided in the application, the mechanical arm is controlled to clean the workpiece to be cleaned according to the target station position, including: Based on the travel constraint information of the guide rail and the distribution constraint information of the station position, a target station position sequence is generated according to the target station positions; determine a cleaning action sequence of the mechanical arm at each of the target stations; control the mechanical arm to move on the guide rail according to the target station sequence, and control the mechanical arm to clean the workpiece to be cleaned according to the cleaning action sequence.

[0012] According to the guide rail type mechanical arm laser cleaning method provided in the present application, the at least one cleaning area is determined by the following steps: obtain boundary constraint information of a cleaning area input by a user; determine the at least one cleaning area according to the boundary constraint information and target poses of the mechanical arm at each cleaning point of the workpiece to be cleaned.

[0013] The present application also provides a guide rail type mechanical arm laser cleaning device, comprising the following modules: an obtaining module, configured to obtain at least one cleaning area determined in advance for a workpiece to be cleaned, and target poses of a mechanical arm at each cleaning point of the workpiece to be cleaned; a determining module, configured to generate a plurality of stations for each of the cleaning areas at random, and for each of the stations, determine a coverage rate of a cleaning point corresponding to the station in a cleaning area to which the station belongs according to a pose of the mechanical arm at the station and target poses of the mechanical arm at each cleaning point in the cleaning area to which the station belongs, the station being a position of the mechanical arm on a guide rail; a screening module, configured to screen target stations corresponding to each of the cleaning areas from the plurality of stations according to the coverage rates of the cleaning points; a control module, configured to control the mechanical arm to clean the workpiece to be cleaned according to the target stations.

[0014] The present application also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements a guide rail type mechanical arm laser cleaning method according to any one of the above when executing the computer program.

[0015] The present application also provides a non-transitory computer readable storage medium having a computer program stored thereon, and the computer program is executable on a processor to implement a guide rail type mechanical arm laser cleaning method according to any one of the above.

[0016] The present application also provides a computer program product comprising a computer program, and the computer program is executable on a processor to implement a guide rail type mechanical arm laser cleaning method according to any one of the above.

[0017] The application provides a guide rail type mechanical arm laser cleaning method, first, at least one cleaning area determined in advance for a to-be-cleaned workpiece is acquired, and a target pose of the mechanical arm at each cleaning point of the to-be-cleaned workpiece is acquired; then, a plurality of random stations are generated for each cleaning area, and for each station, according to the pose of the mechanical arm at the current station and the target pose of the mechanical arm at each cleaning point in the cleaning area to which the current station belongs, the coverage rate of the current station at the corresponding cleaning point in the cleaning area is determined; then, according to the coverage rate of the cleaning point, the target station corresponding to each cleaning area is screened from the plurality of stations; finally, according to the target station, the mechanical arm is controlled to clean the to-be-cleaned workpiece. In the application, the fixed station for cleaning the workpiece is no longer manually specified, which can significantly improve the cleaning efficiency. In addition, the same mechanical arm can move on the guide rail according to the plurality of target stations planned in advance and realize the cleaning operation, without carrying multiple lasers, that is, without configuring more cleaning equipment, which can significantly reduce the cleaning cost. Therefore, the method of the application can effectively solve the problems of low cleaning efficiency or high cleaning cost in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0019] Figure 1 is a flowchart of a guide rail type mechanical arm laser cleaning method according to an embodiment of the application.

[0020] Figure 2 is a cleaning area division result schematic diagram according to an embodiment of the application.

[0021] Figure 3 is a coordinate system change schematic diagram according to an embodiment of the application.

[0022] Figure 4 is a flowchart of a collision detection algorithm according to an embodiment of the application.

[0023] Figure 5 is a whole flowchart of a mechanical arm cleaning operation according to an embodiment of the application.

[0024] Figure 6 is a structural block diagram of a guide rail type mechanical arm laser cleaning device according to an embodiment of the application.

[0025] Figure 7is a schematic diagram of an entity structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] For the purpose, technical solutions and advantages of the present application to be clearer, the technical solutions in the present application will be described clearly and completely below with reference to the drawings in the present application. Obviously, the described embodiments are some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0027] To solve the defects of low cleaning efficiency or high cleaning cost of the cleaning operation scheme of the mechanical arm in the prior art, the present application provides a guide rail type mechanical arm laser cleaning method. The execution subject of the method can be any control device with data processing and communication functions. The control device first plans a guide rail station position suitable for the mechanical arm, and then controls the mechanical arm to move on the guide rail according to the planned guide rail station position and clean the workpiece to be cleaned.

[0028] Figure 1 is a flowchart of a guide rail type mechanical arm laser cleaning method according to an embodiment of the present application. With reference to Figure 1 The method of the present application can include the following steps: Step 101, obtaining at least one cleaning area determined in advance for the workpiece to be cleaned, and the target pose of the mechanical arm at each cleaning point of the workpiece to be cleaned.

[0029] The type of the workpiece to be cleaned can be a small workpiece or a large workpiece.

[0030] The number of workpieces to be cleaned can be set according to actual needs. For example, when the workpieces to be cleaned are multiple small workpieces, all the small workpieces can be placed in a spliced manner to realize simultaneous cleaning of multiple workpieces, thereby improving the cleaning efficiency.

[0031] In the present application, at least one cleaning area can be determined in advance by the following method: Obtaining boundary constraint information of the cleaning area input by the user; According to the boundary constraint information and the target pose of the mechanical arm at each cleaning point of the workpiece to be cleaned, at least one cleaning area is determined.

[0032] In the present application, the user can determine the constraint information required to be satisfied by the boundary of the entire cleaning work area in advance according to the on-site situation of the cleaning work area, such as the range of the area available for the movement of the mechanical arm, the range occupied by the workpiece to be cleaned, and various factors, and input the boundary constraint information to the control device.

[0033] Secondly, the user also needs to pre-design the cleaning process point set of the work to be cleaned, which records the positions of all cleaning points in the workpiece to be cleaned and the poses that the robot arm should assume when reaching each cleaning point. Hereinafter, the position and pose information will be collectively referred to as the pose.

[0034] In this application, a cleaning point refers to a point on the workpiece to be cleaned that needs to be cleaned. These points will be pre-determined and recorded in the cleaning process point set. A cleaning area refers to an area formed by at least one cleaning point on the workpiece to be cleaned.

[0035] Next, the control device reads the cleaning process point set and boundary constraint information, and automatically plans at least one cleaning area in the cleaning work area according to the boundary constraint information and the positions of all cleaning points recorded in the cleaning process point set.

[0036] In this application, the control device divides the fixed guide rail length into regions according to the distribution of cleaning points and the artificially divided cleaning area boundary constraints. The division effect can be as shown in Figure 2 . Figure 2 is a schematic diagram of a cleaning area division result according to an embodiment of the present application. In Figure 2 , the four irregular shaded parts represent the cleaning areas obtained by division, and the two vertical dashed lines on the left and right represent the boundary constraint information. The area between the two vertical dashed lines represents the entire cleaning work area. In Figure 2 , the arrow to the right represents the moving direction of the guide rail.

[0037] In this application, the control device divides the entire cleaning work area dynamically and adaptively, which means that the cleaning of the workpiece is no longer dependent on the fixed number of stations, but is achieved through an intelligent processing flow: first, receive the cleaning process point set and the boundary constraints set by the user, then use a small-to-large division strategy to preliminarily divide the cleaning work area into at least one cleaning area (each cleaning area corresponds to potentially multiple stations) according to the pre-set boundary. Next, check whether each cleaning area obtained by division contains at least two cleaning points to verify its validity. Cleaning areas that do not contain any cleaning points are considered invalid cleaning areas and are removed. Finally, the remaining cleaning areas are used as the at least one cleaning area in step 101, and subsequent station planning will also be based on the remaining cleaning areas. The method of the present application is particularly suitable for workpieces with large size differences (e.g., large single parts) or complex layouts (e.g., small multiple parts), and can significantly improve the efficiency and resource utilization of robot cleaning operations.

[0038] Step 102, generating a plurality of random stations for each cleaning area, and for each station, determining the coverage rate of the cleaning point corresponding to the station in the cleaning area to which the station belongs according to the pose of the robot arm at the current station and the target pose of each cleaning point in the cleaning area to which the current station belongs. The station is the position of the robot arm on the guide rail.

[0039] In the present application, since there are multiple stations for the robot arm to clean the cleaning points in a cleaning area, in order to find the best station, the present application first randomly generates a plurality of stations for each cleaning area. Then, according to whether the pose of the robot arm at a certain station can reach the target pose of each cleaning point in the cleaning area to which the station belongs, the coverage rate of the cleaning point corresponding to the station in the cleaning area to which the station belongs is counted, that is, the reachability of the cleaning point is verified for each generated random station, and then the coverage rate of the cleaning point is obtained.

[0040] For example, cleaning area one has four cleaning points, including cleaning points one to four, and two random candidate stations one and two are designed for cleaning area one. Then for candidate station one, assuming that the robot arm is located at candidate station one on the guide rail, it is checked whether the robot arm at the pose of candidate station one can smoothly reach the target pose of each of cleaning points one to four, and assuming that it can reach the target pose of each of cleaning points one and two, then the coverage rate of the cleaning point corresponding to candidate station one in cleaning area one is 50%. Similarly, for candidate station two, assuming that the robot arm is located at candidate station two on the guide rail, it is checked whether the robot arm at the pose of candidate station two can smoothly reach the target pose of each of cleaning points one to four, and assuming that it can reach the target pose of each of cleaning points one to three, then the coverage rate of the cleaning point corresponding to candidate station two in cleaning area one is 75%.

[0041] Step 103, screening the target station corresponding to each cleaning area from the plurality of stations according to the coverage rate of the cleaning point.

[0042] In the present application, the higher the coverage rate of the cleaning point corresponding to a certain station, the better the station. Therefore, for a plurality of stations corresponding to a certain cleaning area, the station with the highest coverage rate of the cleaning point can be screened from at least one station with a coverage rate of the cleaning point greater than a preset coverage rate threshold, as the best station, i.e. the target station.

[0043] Of course, in actual implementation, the target station corresponding to each cleaning area can also be determined according to the coverage rate of the cleaning point according to other strategies, which is not limited in the present application.

[0044] Step 104, controlling the robot arm to clean the workpiece to be cleaned according to the target station.

[0045] In the present application, after the target station positions of the respective cleaning areas are determined, the robot arm can be controlled to reach the respective target station positions to clean the cleaning points in the corresponding cleaning areas. For example, the cleaning areas include cleaning areas 1-3, cleaning area 1 corresponds to target station position 1, cleaning area 2 corresponds to target station position 2, and cleaning area 3 corresponds to target station position 3. Then, the robot arm can be controlled to reach target station position 1 by moving the guide rail, and the cleaning points in cleaning area 1 can be cleaned according to the poses of the cleaning points in the process point set corresponding to cleaning area 1. The robot arm can be controlled to reach target station position 2 by moving the guide rail, and the cleaning points in cleaning area 2 can be cleaned according to the poses of the cleaning points in the process point set corresponding to cleaning area 2. The robot arm can be controlled to reach target station position 3 by moving the guide rail, and the cleaning points in cleaning area 3 can be cleaned according to the poses of the cleaning points in the process point set corresponding to cleaning area 3.

[0046] In the present application, if there are multiple target station positions, the order in which the robot arm reaches the respective target station positions can be set according to actual needs, and the present application does not limit this.

[0047] In the method of the present application, first, at least one cleaning area determined in advance for the workpiece to be cleaned and the target pose of the robot arm at each cleaning point of the workpiece to be cleaned are obtained. Then, a plurality of random station positions are generated for each cleaning area, and for each station position, the coverage rate of the cleaning points corresponding to the current station position in the cleaning area to which the current station position belongs is determined according to the pose of the robot arm at the current station position and the target pose of the robot arm at each cleaning point in the cleaning area to which the current station position belongs. Then, according to the coverage rate of the cleaning points, the target station positions corresponding to the respective cleaning areas are selected from the plurality of station positions. Finally, the robot arm is controlled to clean the workpiece to be cleaned according to the target station positions. In the present application, the cleaning of the workpiece is no longer manually specified at fixed station positions, which can significantly improve the efficiency of the cleaning operation. In addition, the same robot arm can move on the guide rail according to the plurality of target station positions planned in advance and implement the cleaning operation, without the need to carry multiple lasers, i.e., without the need to configure more cleaning equipment, which can significantly reduce the cost of the cleaning operation. Therefore, the method of the present application can effectively solve the problems of low cleaning efficiency or high cleaning cost in the prior art.

[0048] In combination with the above embodiments, in one implementation, step 104 can include: Step 1041, generating a target station position sequence according to the respective target station positions based on the travel constraint information of the guide rail and the distribution constraint information of the station positions.

[0049] In the present application, after determining the optimal station positions of all cleaning areas, the control device comprehensively considers the stroke constraint information of the guide rail (for example, the limited length of the guide rail) and the distribution constraint information of the station positions (for example, the constraint information set for realizing the continuity between the station positions), and generates a complete and optimal guide rail station position sequence, that is, a target station position sequence.

[0050] Step 1042, determining a cleaning action sequence of the mechanical arm at each target station position.

[0051] In the present application, the control device also generates a target sequence of the mechanical arm corresponding to each target station position, that is, a sequence of specific cleaning actions that the mechanical arm needs to perform at the target station position, that is, a cleaning action sequence.

[0052] Step 1043, controlling the mechanical arm to move on the guide rail according to the target station position sequence, and controlling the mechanical arm to clean the workpiece to be cleaned according to the cleaning action sequence.

[0053] In the present application, each selected target station position has undergone strict reachability verification to ensure that it can completely cover all cleaning points in the corresponding cleaning area.

[0054] In the present application, once the target station position sequence is generated and verified, the control device controls the mechanical arm to move to each target station position in turn according to the planned target station position sequence. At each target station position, the mechanical arm accurately completes the cleaning work of the corresponding cleaning area according to the preset laser cleaning program. The whole process is completely automated and does not require human intervention.

[0055] In the present application, the cleaning of the workpiece is no longer manually specified at fixed station positions, which can significantly improve the efficiency of the cleaning work. In addition, the same mechanical arm can move on the guide rail according to multiple target station positions planned in advance and implement the cleaning work, without the need to configure many cleaning devices, which can significantly reduce the cost of the cleaning work.

[0056] In combination with the above embodiments, in an implementation, step 102 can include: Step 1021, for each station position, determining the cleaning points in the cleaning area corresponding to the current station position that can be reached by the end effector of the mechanical arm according to the pose of the mechanical arm at the current station position and the target poses of the cleaning points in the cleaning area corresponding to the current station position.

[0057] Step 1022, determining the coverage rate of the cleaning points in the cleaning area corresponding to the current station position according to the ratio of the reachable cleaning points to the total amount of the cleaning points in the cleaning area corresponding to the current station position.

[0058] In actual implementation, if all cleaning points in the cleaning area to which the current station belongs include the target cleaning point, and the target cleaning point is any one of all cleaning points in the cleaning area to which the previous station belongs, step 1021 can include: determining a relative pose of the target pose of the mechanical arm at the target cleaning point in the cleaning area to which the current station belongs and the pose of the mechanical arm at the current station; solving the joint angles of the mechanical arm according to the relative pose through an inverse kinematics model of the mechanical arm; if at least one set of joint angles is determined to be solved, it is determined that the end effector of the mechanical arm can reach the target cleaning point in the cleaning area to which the current station belongs.

[0059] In the present application, for each dynamically divided cleaning area, the present application uses a Monte Carlo algorithm to perform detailed reachability analysis on the station to determine the optimal station. The specific steps are as follows: First step: random station generation. In each cleaning area, a series of potential stations are randomly generated.

[0060] Second step: coordinate system transformation and reachability evaluation. For each generated potential station, perform corresponding coordinate system transformation to map the mechanical arm workspace under the station to the workpiece coordinate system.

[0061] Third step: using the inverse kinematics model of the mechanical arm, calculate whether the end effector of the mechanical arm under the station can reach all cleaning points in the cleaning area, i.e., determine whether there is at least one reachable joint configuration for each cleaning point. If at least one reachable joint configuration is determined, i.e., at least one set of joint angles can be solved, it is determined that the end effector of the mechanical arm can reach the cleaning point in the cleaning area to which the current station belongs.

[0062] In the present method, the reachability of the cleaning point is the core index of station optimization and is evaluated in a quantitative manner. The pros and cons of a potential station are evaluated by generating a reachability score through the key indicator of cleaning point coverage. The cleaning point coverage of a certain station represents the number of cleaning points in the cleaning area to which the station belongs that the mechanical arm can successfully reach, as a proportion of the total number of cleaning points in the cleaning area to which the station belongs. The higher, the stronger the cleaning point coverage ability of the station, and the higher the reachability score. F is as follows:

[0063] wherein, represents the number of cleaning points in a single cleaning area, The number of cleaning points in the current cleaning area that the robot arm can successfully reach.

[0064] In solving the optimal station, by repeating the above process of randomly generating a station, performing reachability evaluation and score calculation, the Monte Carlo algorithm iteratively finds the station with the highest reachability score greater than the preset score, and takes it as the best station.

[0065] In actual implementation, the target station can be directly screened according to the coverage of the cleaning point, or the reachability score can be obtained according to the coverage of the cleaning point, and then the target station can be screened according to the reachability score. The coverage of the cleaning point is directly proportional to the reachability score.

[0066] In this application, by analyzing the reachability of the cleaning point for each randomly generated station, the best station suitable for each cleaning area can be screened, and the robot arm can be controlled to move on the guide rail and perform cleaning work according to the best station, which can significantly improve the efficiency of cleaning work.

[0067] In combination with the above embodiments, in an implementation, when analyzing the reachability of the cleaning point for each randomly generated station, the collision risk also needs to be considered. Specifically, for each station, according to the pose of the robot arm at the current station and the target pose of each cleaning point in the cleaning area to which the current station belongs, the cleaning points that the end effector of the robot arm can reach in the cleaning area to which the current station belongs are determined, including: obtaining an initial pose of the robot arm; determining the interpolation trajectory of the robot arm from the initial pose to each target pose through each station, and eliminating the stations with collision risk on the interpolation trajectory among the multiple stations to obtain the remaining stations; for each station in the remaining stations, according to the pose of the robot arm at the current station and the target pose of each cleaning point in the cleaning area to which the current station belongs, the cleaning points that the end effector of the robot arm can reach in the cleaning area to which the current station belongs are determined.

[0068] Among them, the initial pose of the robot arm refers to the pose of the robot arm when it has not reached any target station, that is, the pose of the robot arm when it is waiting on the guide rail.

[0069] For example, before the reachability analysis of the cleaning points for a certain station P, it is necessary to determine whether there is a collision risk on the interpolation trajectory of the robot arm from the initial pose to the target pose of each cleaning point in the cleaning area to which the station P belongs via the station P. If there is a collision risk on the interpolation trajectory of the robot arm from the initial pose to the target pose of a certain cleaning point M in the cleaning area to which the station P belongs via the station P, the station P is directly excluded, and other stations are evaluated, and finally the remaining stations without collision risk are obtained.

[0070] Then, the reachability analysis of the cleaning points is performed for each of the remaining stations, which is described in detail above.

[0071] The interpolation trajectory refers to a continuous, smooth and time-parameterized motion path that satisfies certain kinematic constraints, which is generated by a specific mathematical interpolation algorithm (such as spherical linear interpolation and polynomial interpolation) according to a series of given discrete path points (usually a sequence of poses). The interpolation trajectory precisely defines the pose, velocity and acceleration state of the robot end effector or each joint thereof at each instantaneous moment during task execution from the starting point to the target point.

[0072] Therefore, when evaluating each station in the present application, real-time collision detection is performed, i.e., the interpolation trajectory of the robot arm from the initial pose to the target pose of each cleaning point is detected, and the station with the interpolation trajectory having a collision risk is automatically excluded, which can further ensure the safety and feasibility of the selected station.

[0073] In combination with the above embodiments, in an implementation, before performing step 101, the control device of the present application also needs to perform the following work: obtain a Unified Robot Description Format (URDF) file of the robot arm; obtain geometric parameters and kinematic parameters of the robot arm according to the Unified Robot Description Format file; construct an inverse kinematics model of the robot arm according to the geometric parameters and the kinematic parameters.

[0074] The URDF file describes all physical properties of the robot arm, such as the length, weight, motion limit, coordinate system relationship, etc. of each joint.

[0075] In the present application, the control device first acquires the URDF file of the robot arm, then parses the file, extracts the joint information, link length, joint limit and other geometric parameters and kinematic parameters of the robot arm, and then automatically constructs the forward kinematics model and inverse kinematics model of the robot arm based on these parameters. Among them, the forward kinematics model is used to calculate the pose of the end effector under the given joint angle, and the inverse kinematics model is used to calculate the joint angle required for the end effector to reach the specified pose.

[0076] In the present application, when obtaining the interpolation trajectory of the robot arm from the initial pose to a certain cleaning point, the forward kinematics model can be used.

[0077] In addition, the control device can also use a special program library KDL (Kinematics and Dynamics Library) to parse the robot arm URDF file and establish the forward and inverse kinematics model, to ensure the accuracy and universality of the model, and the coordinate system transformation relationship of the whole system is as shown in Figure 3 Figure 3 is a coordinate system change diagram according to an embodiment of the present application.

[0078] In Figure 3 , a plurality of coordinate systems are mentioned, and each coordinate system will be briefly introduced below.

[0079] World coordinate system: It is an absolute, fixed global reference frame. In the entire workstation, its origin and coordinate axis direction are unique and always unchanged.

[0080] Guide rail coordinate system: It is a coordinate system attached to the guide rail, and the guide rail coordinate system itself does not move. When the base of the robot arm moves along the guide rail, the robot arm coordinate system moves with the guide rail. One of its axes will usually be along the moving direction of the guide rail, as shown in Figure 2 . This coordinate system is used to describe the position of the robot arm on the guide rail.

[0081] Robot arm base coordinate system: This coordinate system is fixed on the base of the robot arm, and it moves with the guide rail. For the robot arm itself, this is its own origin. The robot arm base coordinate system is the basis for all self-motion calculations of the robot arm. The end tool position calculated by the forward and inverse kinematics model of the robot arm is relative to this base coordinate system.

[0082] ​Tool frame of robot: This coordinate system is fixed on the end of the robot wrist, that is, the place where the laser cleaning head is installed. When the joints of the robot move, the position and posture of this coordinate system will change accordingly, so it is also called the end effector coordinate system (or robot end coordinate system). The tool frame directly defines the working point and orientation of the tool. For example, for laser cleaning, it is necessary to accurately control the distance between the origin of this coordinate system (laser exit point) and the workpiece surface, and the Z-axis (laser exit direction) perpendicular to the workpiece surface. Its pose is determined relative to the robot base coordinate system.

[0083] Camera frame: It is a coordinate system attached to the camera lens. If a vision camera is installed, this coordinate system can be used to describe the world seen by the camera.

[0084] Robot joint frame, used to describe the final solution. It represents not a spatial coordinate, but a set of angle values. Through inverse kinematics operation, according to the set end target, the specific angle of each joint is calculated. This set of angle values will eventually be sent as instructions to the servo motors of the robot to drive the robot to complete the action.

[0085] Wherein, the conversion process of the cleaning point in each coordinate system can refer to the prior art, which is not limited in the present application.

[0086] In the present application, the forward kinematics model and the inverse kinematics model of the robot are established according to the URDF file of the robot, which provides a technical basis for subsequent determination of the interpolation trajectory and implementation of collision detection of the robot.

[0087] In combination with the above embodiments, in an implementation manner, in the plurality of stations, the station with collision risk on the interpolation trajectory is eliminated, and the remaining stations are obtained, which can include: An oriented bounding box (OBB) is a compact and accurate bounding box representation that can effectively describe the shape of a complex three-dimensional object. Based on the bounding box hierarchy tree, the station with collision risk on the interpolation trajectory is determined through a flexible collision detection library. In the plurality of stations, the station with collision risk is eliminated, and the remaining stations are obtained.

[0088] In the present application, for each joint, link, end effector of the robot, and obstacles that may exist in the working environment, the oriented bounding box (Oriented Bounding Box, OBB) technology is used for geometric fitting. The oriented bounding box is a compact and accurate bounding box representation that can effectively describe the shape of a complex three-dimensional object.

[0089] In this application, the control device generates OBB models for each link, joint, end effector of the robot arm, and fixed obstacles in the work area, which will be stored in the collision detection tree structure, i.e., the bounding box hierarchical tree.

[0090] When performing collision detection, the Flexible Collision Library (FCL) is used to achieve efficient real-time collision detection.

[0091] The real-time collision detection process based on OBB models can include: Input joint state: the algorithm proposes a set of possible joint angles.

[0092] Update OBB pose: according to the set of joint angles and the forward kinematics model, the latest pose of the OBB box on each link of the robot arm is calculated. The OBB of the environment remains unchanged.

[0093] Perform collision detection: query the FCL engine: "Please check whether there is any overlap between the set of OBBs representing the robot arm and the set of OBBs representing the environment in the current state?" Return result: FCL uses its tree structure to make a judgment and returns a simple Boolean value: true (collision occurs) or false (no collision).

[0094] Decision: the algorithm decides according to the returned result, if there is no collision, the joint state (set of joint angles) is available; if there is a collision, the joint state is immediately discarded.

[0095] Figure 4 is a flowchart of a collision detection algorithm according to an embodiment of the present application.

[0096] As shown in Figure 4 , the control device, on the one hand, obtains the STL files of the joints and links of the robot arm, and accurately fits them into OBBs (oriented bounding boxes), and on the other hand, generates corresponding OBB bounding boxes for the obstacles according to the known obstacle pose information. After completing the collision model construction of both sides, starting from a known initial pose of the robot arm, the trajectory interpolation for the motion task to be performed is performed, so as to obtain a series of continuous motion poses from the starting point to the ending point. Finally, the system performs continuous collision detection between the moving robot arm OBB model and the static environment OBB model throughout the entire interpolation trajectory to determine whether there is any interference in any instantaneous state during the motion process, thereby ensuring the absolute safety of the motion trajectory.

[0097] In the present application, collision detection is realized based on the oriented bounding box technology, which helps to improve the quality of the target station obtained by screening, and further improves the efficiency of the cleaning operation of the robot arm. In addition, compared with the simple Axis-Aligned Bounding Box (AABB) technology, the use of the oriented bounding box technology to realize collision detection can more accurately reflect the actual shape of the object, reduce false positives, and improve the accuracy and efficiency of collision detection.

[0098] In combination with the above embodiments, in an implementation, the cleaning area includes adjacent first and second cleaning areas, and an overlap area is arranged between the first and second cleaning areas. The overlap area includes at least one cleaning point in the first cleaning area and at least one cleaning point in the second cleaning area.

[0099] In the present application, the overlap area can be arranged between part of the adjacent cleaning areas according to actual needs. The following description of the arrangement of the overlap area between the first and second cleaning areas is only exemplary.

[0100] Correspondingly, for each station, the coverage rate of the corresponding cleaning point of the current station in the cleaning area to which the current station belongs is determined according to the pose of the robot arm at the current station and the target pose of the robot arm at each cleaning point in the cleaning area to which the current station belongs, including: For each of the multiple stations generated for the first cleaning area, the coverage rate of the corresponding cleaning point of the current station in the first cleaning area is determined according to the pose of the robot arm at the current station and the target pose of the robot arm at each cleaning point in the first cleaning area and the overlap area. For each of the multiple stations generated for the second cleaning area, the coverage rate of the corresponding cleaning point of the current station in the second cleaning area is determined according to the pose of the robot arm at the current station and the target pose of the robot arm at each cleaning point in the second cleaning area and the overlap area.

[0101] In the present application, since the laser cleaning points are usually discrete, and the cleaning trajectory may span different cleaning areas. In order to ensure the continuity of the cleaning trajectory, when a cleaning point group spans two or more cleaning areas, the present application sets a repeated coverage area, i.e. the overlap area. The size of the area is set to be k times the spacing between each cleaning point in the cleaning process point set, where the value of k ranges from 1.5 to 2.0. The length of the overlap area aims to ensure that the key cleaning points can be completely covered by at least two adjacent stations between adjacent cleaning areas, so as to seamlessly connect when the station switches, ensure the integrity of the cleaning trajectory, and at the same time, without involving the modification and interpolation of the original cleaning process point set.

[0102] In the present application, if at least two cleaning points contained in a cleaning area A are all located in the overlapping area between the cleaning area A and an adjacent cleaning area B, the original planned station position corresponding to the cleaning area A will be eliminated. During cleaning, the mechanical arm is directly controlled to the station position corresponding to the cleaning area B through the movement guide rail to achieve cleaning.

[0103] Figure 5 is a whole flowchart of a mechanical arm cleaning operation according to an embodiment of the present application.

[0104] As shown in Figure 5 , the control device receives the input of the URDF file and establishes the forward and inverse kinematics model of the mechanical arm based on the input. Then, the OBB technology is used to accurately geometrically fit each joint, connecting rod, end effector and working environment of the mechanical arm to construct a collision detection model. At the same time, the control device receives the input of the cleaning process point set and the input of the region boundary defined by the user, performs cleaning area division according to the two pieces of information, and performs continuity guarantee processing on the cleaning points at the boundaries of the cleaning areas in the division result. After the division of the cleaning areas and the boundary point processing are completed, the optimal station position calculation link is entered, and a best station position is solved for each cleaning area. This calculation process is closely coupled with a core feedback loop: the control device will make a real-time judgment on whether the mechanical arm collides with the environment, and if the judgment result is "yes", it means that the currently calculated station position has a collision risk, and the process will return to the optimal station position calculation step to re-solve or select other safe station positions. Only when a station position passes the collision detection, i.e. the judgment result is "no", the station position is confirmed as valid. After all the cleaning areas have found verified collision-free optimal station positions, the control device finally generates a complete target station position sequence and a cleaning action sequence.

[0105] The present application realizes the full-process automation of station position planning in the guide rail type mechanical arm laser cleaning operation, effectively solves the problems of relying on manual experience, low planning efficiency, easy collision and unstable operation quality in the prior art, etc. Through dynamic partitioning, overlapping area processing and deep fusion of real-time collision detection Monte Carlo optimization algorithm, the present application can quickly and accurately generate an optimal station position sequence that is continuous in path and safe without collision.

[0106] In summary, the method proposed in the present application has the following technical effects: 1. High automation and flexible adaptability: only a small number of key parameters need to be input, and the entire station position planning process can be automatically completed without manual intervention. The dynamic area division mechanism can flexibly adapt to various types of mechanical arms and workpieces of different sizes and quantities, effectively dealing with the scene of small size parts without fixed station position quantity.

[0107] II. Strong process adaptability and planning independence: By directly processing discrete cleaning points without modifying the original trajectory, the method of the present application exhibits strong adaptability, capable of meeting the cleaning needs of workpieces of different sizes and quantities, while maintaining the independence of station planning and cleaning point generation.

[0108] III. High efficiency and high safety: Special optimization is performed for the constraint conditions of the limited length guide rail, capable of quickly generating a safe and reliable station sequence, with a calculation efficiency fully meeting the requirements of online planning, ensuring collision-free operation of the entire cleaning operation process, and significantly improving production safety and efficiency.

[0109] A guide rail type mechanical arm laser cleaning device provided by the present application will be described below. The guide rail type mechanical arm laser cleaning device described below can be mutually corresponding with the guide rail type mechanical arm laser cleaning method described above.

[0110] Figure 6 is a structural block diagram of a guide rail type mechanical arm laser cleaning device according to an embodiment of the present application. Referring to Figure 6 , the guide rail type mechanical arm laser cleaning device 600 of the present application can include: The acquisition module 601 is configured to acquire at least one cleaning area determined in advance for a workpiece to be cleaned, and a target pose of a mechanical arm at each cleaning point of the workpiece to be cleaned. The determination module 602 is configured to generate a plurality of random stations for each cleaning area, and for each station, determine a coverage rate of a cleaning point corresponding to the current station in the cleaning area to which the current station belongs according to the pose of the mechanical arm at the current station and the target pose of the mechanical arm at each cleaning point in the cleaning area to which the current station belongs. The station is the position of the mechanical arm on the guide rail. The screening module 603 is configured to screen a target station corresponding to each cleaning area from the plurality of stations according to the coverage rate of the cleaning point. The control module 604 is configured to control the mechanical arm to clean the workpiece to be cleaned according to the target station.

[0111] According to the guide rail type mechanical arm laser cleaning device 600 of the present application, the determination module 602 is specifically configured to: For each station, determine the cleaning points in the cleaning area to which the current station belongs that can be reached by an end effector of the mechanical arm according to the pose of the mechanical arm at the current station and the target pose of the mechanical arm at each cleaning point in the cleaning area to which the current station belongs. Determine the coverage rate of the cleaning point corresponding to the current station in the cleaning area to which the current station belongs according to the ratio of the reachable cleaning points to the total number of cleaning points in the cleaning area to which the current station belongs.

[0112] According to the rail type mechanical arm laser cleaning device 600 provided in the application, the determination module 602 is specifically used for: obtaining an initial pose of the mechanical arm; determining an interpolation trajectory of the mechanical arm from the initial pose to each target pose through each station, and eliminating a station with a collision risk on the interpolation trajectory among the stations to obtain a remaining station; for each station in the remaining station, determining a cleaning point in a cleaning area to which the current station belongs and which can be reached by an end effector of the mechanical arm according to a pose of the mechanical arm at the current station and target poses of each cleaning point in the cleaning area to which the current station belongs.

[0113] According to the rail type mechanical arm laser cleaning device 600 provided in the application, each cleaning point in the cleaning area to which the current station belongs includes a target cleaning point, and the determination module 602 is specifically used for: determining a relative pose of the target cleaning point in the cleaning area to which the current station belongs and the pose of the mechanical arm at the current station; solving joint angles of the mechanical arm according to the relative pose through an inverse kinematics model of the mechanical arm; if at least one set of joint angles can be solved, it is determined that the end effector of the mechanical arm can reach the target cleaning point in the cleaning area to which the current station belongs.

[0114] According to the rail type mechanical arm laser cleaning device 600 provided in the application, the determination module 602 is specifically used for: obtaining a bounding box hierarchical tree corresponding to the mechanical arm, and a plurality of directional bounding box models are stored in the bounding box hierarchical tree, the plurality of directional bounding box models being generated in advance for each joint, connecting rod, end effector of the mechanical arm and fixed obstacles in a working area in which the mechanical arm is located; based on the bounding box hierarchical tree, determining a station with a collision risk on the interpolation trajectory through a flexible collision detection library; eliminating the station with the collision risk among the stations to obtain the remaining station.

[0115] According to the rail type mechanical arm laser cleaning device 600 provided in the application, the cleaning area includes adjacent first and second cleaning areas, an overlapping area is arranged between the first and second cleaning areas, the overlapping area includes at least one cleaning point in the first cleaning area and at least one cleaning point in the second cleaning area, and the determination module 602 is specifically used for: For each of the multiple stations generated for the first cleaning area, determining a coverage rate of the cleaning points corresponding to the current station in the first cleaning area based on the posture of the robot arm at the current station and the target posture of the robot arm at each cleaning point in the first cleaning area and the overlapping area; For each of the multiple stations generated for the second cleaning area, the coverage rate of the cleaning points corresponding to the current station in the second cleaning area is determined according to the posture of the robotic arm at the current station and the target posture of the robotic arm at each cleaning point in the second cleaning area and the overlapping area.

[0116] According to a guide rail type robotic arm laser cleaning device 600 of the present application, the control module 604 is specifically used for: Based on the travel constraint information of the guide rail and the distribution constraint information of the stations, generating a target station sequence according to the respective target stations; Determining a cleaning action sequence of the robotic arm at each of the target positions; The robot arm is controlled to move on the guide rail according to the target station sequence, and is controlled to clean the workpiece to be cleaned according to the cleaning action sequence.

[0117] Figure 7 FIG. 1 is a schematic diagram of the physical structure of an electronic device according to an embodiment of the present application. Figure 7 As shown, the electronic device may include: a processor 710, a communications interface 720, a memory 730, and a communication bus 740. The processor 710, the communications interface 720, and the memory 730 communicate with each other via the communication bus 740. The processor 710 may call logic instructions in the memory 730 to execute a guide rail type robotic arm laser cleaning method.

[0118] In addition, the logic instructions in the memory 730 described above can be implemented in the form of software function units and sold or used as independent products, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0119] In another aspect, the present application also provides a computer program product, which comprises a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program is executed by a processor, so that the computer can execute a guide rail type mechanical arm laser cleaning method provided by the above-mentioned methods.

[0120] In another aspect, the present application also provides a non-transitory computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement a guide rail type mechanical arm laser cleaning method provided by the above-mentioned methods.

[0121] The device embodiments described above are only schematic, wherein 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 distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment. Those skilled in the art can understand and implement without creative labor.

[0122] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software plus necessary universal hardware platforms, and of course, can also be realized by hardware. Based on such understanding, the above technical solutions essentially or the parts that contribute to the prior art can be embodied in the form of a software product. The computer software product can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0123] It should be noted that the above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit the same. Although the present application has been described in detail with reference to the foregoing examples, it should be understood by those skilled in the art that the technical solutions recorded in the foregoing examples can still be modified, or some technical features thereof can be replaced by equivalent replacements. Such modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A rail-type robot laser cleaning method, characterized by, The method comprises the following steps: acquiring at least one cleaning area previously determined for a workpiece to be cleaned, and a target pose of a mechanical arm at each cleaning point of the workpiece to be cleaned; generating a plurality of random stations for each cleaning area, and for each station, determining a coverage rate of a corresponding cleaning point in a cleaning area to which the current station belongs according to a pose of the mechanical arm at the current station and target poses of the mechanical arm at each cleaning point in the cleaning area to which the current station belongs, the station being a position of the mechanical arm on a guide rail; screening target stations corresponding to each cleaning area from the plurality of stations according to the coverage rates of the cleaning points; controlling the mechanical arm to clean the workpiece to be cleaned according to the target stations.

2. The rail-type robot laser cleaning method of claim 1, wherein, The step of determining, for each station, a coverage rate of a corresponding cleaning point in a cleaning area to which the current station belongs according to a pose of the mechanical arm at the current station and target poses of the mechanical arm at each cleaning point in the cleaning area to which the current station belongs, comprises the following steps: for each station, determining cleaning points that can be reached by an end effector of the mechanical arm in the cleaning area to which the current station belongs according to a pose of the mechanical arm at the current station and target poses of the mechanical arm at each cleaning point in the cleaning area to which the current station belongs; determining the coverage rate of the corresponding cleaning point in the cleaning area to which the current station belongs according to a ratio of the cleaning points that can be reached to a total amount of cleaning points in the cleaning area to which the current station belongs.

3. The rail-type robot laser cleaning method of claim 2, wherein, The step of determining, for each station, cleaning points that can be reached by an end effector of the mechanical arm in the cleaning area to which the current station belongs according to a pose of the mechanical arm at the current station and target poses of the mechanical arm at each cleaning point in the cleaning area to which the current station belongs, comprises the following steps: acquiring an initial pose of the mechanical arm; determining an interpolation trajectory of the mechanical arm from the initial pose to each target pose via each station, and removing stations with collision risks on the interpolation trajectory from the plurality of stations to obtain remaining stations; for each station in the remaining stations, determining cleaning points that can be reached by an end effector of the mechanical arm in the cleaning area to which the current station belongs according to a pose of the mechanical arm at the current station and target poses of the mechanical arm at each cleaning point in the cleaning area to which the current station belongs.

4. The rail-type robot laser cleaning method of claim 3, wherein, Each cleaning point in the cleaning area to which the current station belongs comprises a target cleaning point, and the step of determining, for each station, cleaning points that can be reached by an end effector of the mechanical arm in the cleaning area to which the current station belongs according to a pose of the mechanical arm at the current station and target poses of the mechanical arm at each cleaning point in the cleaning area to which the current station belongs, comprises the following steps: determining a relative pose of the target pose of the target cleaning point in the cleaning area to which the current station belongs and the pose of the mechanical arm at the current station; solving joint angles of the mechanical arm according to the relative pose through an inverse kinematics model of the mechanical arm. If it is determined that at least a set of joint angles can be solved, it is determined that the end effector of the robot arm can reach the target cleaning point in the cleaning area to which the current station belongs.

5. The rail-type robot laser cleaning method of claim 4, wherein, The inverse kinematics model is determined by the following steps: Obtain a unified robot description format file of the robot arm; According to the unified robot description format file, obtain the geometric parameters and kinematics parameters of the robot arm; According to the geometric parameters and the kinematics parameters, construct the inverse kinematics model of the robot arm.

6. The rail-type robot laser cleaning method of claim 4, wherein, The remaining stations are obtained by removing the stations with collision risks on the interpolation trajectory from the plurality of stations, comprising: Obtain the bounding box hierarchy tree corresponding to the robot arm, and the bounding box hierarchy tree stores a plurality of directional bounding box models, wherein the plurality of directional bounding box models are generated in advance for each joint, link, end effector of the robot arm and fixed obstacles in the working area where the robot arm is located; Based on the bounding box hierarchy tree, determine the stations with collision risks on the interpolation trajectory through a flexible collision detection library; Remove the stations with collision risks from the plurality of stations to obtain the remaining stations.

7. The rail-type robot laser cleaning method of claim 1, wherein, The cleaning area includes adjacent first and second cleaning areas, and an overlap area is arranged between the first and second cleaning areas, wherein the overlap area includes at least one cleaning point in the first cleaning area and at least one cleaning point in the second cleaning area, and the coverage rate of the corresponding cleaning point in the cleaning area to which the current station belongs is determined according to the pose of the robot arm at the current station and the target pose of each cleaning point in the cleaning area to which the current station belongs for each station. For each station of the plurality of stations generated for the first cleaning area, the coverage rate of the corresponding cleaning point in the first cleaning area is determined according to the pose of the robot arm at the current station and the target pose of each cleaning point in the first cleaning area and the overlap area. For each station of the plurality of stations generated for the second cleaning area, the coverage rate of the corresponding cleaning point in the second cleaning area is determined according to the pose of the robot arm at the current station and the target pose of each cleaning point in the second cleaning area and the overlap area.

8. The rail robot laser cleaning method of claim 1, wherein, According to the target station, the robot arm cleans the workpiece to be cleaned, comprising: Generating a target station sequence based on the stroke constraint information of the guide rail and the distribution constraint information of the station according to the target station; Determining the cleaning action sequence of the robot arm at each target station; According to the target station sequence, the robot arm moves on the guide rail, and according to the cleaning action sequence, the robot arm cleans the workpiece to be cleaned.

9. The rail robot laser cleaning method of claim 1, wherein, The at least one cleaning area is determined by the following steps: Obtain the boundary constraint information of the cleaning area input by the user; According to the boundary constraint information and target poses of the mechanical arm at each cleaning point of the workpiece to be cleaned, at least one cleaning area is determined.

10. A rail-type robot laser cleaning device, characterized by, Comprise: An acquisition module is configured to acquire at least one cleaning area determined in advance for a workpiece to be cleaned, and target poses of a mechanical arm at each cleaning point of the workpiece to be cleaned; A determination module is configured to generate a plurality of random stations for each cleaning area, and for each station, determine a coverage rate of a cleaning point corresponding to the station in a cleaning area to which the station belongs according to a pose of the mechanical arm at the current station and target poses of the mechanical arm at each cleaning point in the cleaning area to which the station belongs, the station being a position of the mechanical arm on a guide rail; A screening module is configured to screen target stations corresponding to each cleaning area from the plurality of stations according to the coverage rates of the cleaning points; A control module is configured to control the mechanical arm to clean the workpiece to be cleaned according to the target stations.

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