A robot motion verification control method and system

Abstract

The application discloses a kind of robot motion verification control method and system, it is related to intelligent control technical field, the method includes: by the preset work area of robot is carried out multi-feature collection, obtains preset area feature and constructs preset area map;Through trajectory planning module output preset planning path;Through trajectory tracking module monitoring obtains real-time motion path;Through motion verification module analysis obtains the real-time comparison result of preset planning path and real-time motion path and obtains real-time rectification scheme;Wherein real-time rectification scheme is used to rectification control to the real-time motion path of robot.Solved in the prior art by position servo control to robot motion verification control, there is control error, while control output is unstable, and then influence robot work quality technical problem.Implementation of the timely rectification control target of robot motion, reaches the technical effect of improving robot motion control precision.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, and in particular to a motion verification and control method and system for a robot. Background Technology

[0002] Because the motion control accuracy of robots is affected by various factors, it is difficult to establish a precise system model. Furthermore, conventional position servo control is prone to large control errors and unstable control outputs. However, the rise of intelligent algorithms and intelligent optimization algorithms has injected new vitality into robot motion verification and control methods. Using computer intelligent algorithms for path optimization and tracking of robot motion control is of great significance for improving the accuracy of robot motion control.

[0003] However, existing technologies that use position servo control to perform motion verification control on robots suffer from large control errors and unstable control outputs, which in turn affect the quality of robot operations. Summary of the Invention

[0004] The purpose of this invention is to provide a motion verification control method and system for robots, in order to solve the technical problems of large control errors and unstable control output in the prior art when using position servo control to perform motion verification control on robots, which in turn affects the quality of robot operation.

[0005] In view of the above problems, the present invention provides a motion verification and control method and system for robots.

[0006] In a first aspect, the present invention provides a motion verification and control method for a robot, the method being implemented through a motion verification and control system for the robot, wherein the method includes: acquiring multiple features of a preset working area of ​​the robot to obtain preset area features, and constructing a preset area map based on the preset area features; analyzing the preset area map through a trajectory planning module to output a preset planned path; monitoring the real-time motion path of the robot through a trajectory tracking module; analyzing the real-time comparison result between the preset planned path and the real-time motion path through a motion verification module, and obtaining a real-time correction scheme based on the real-time comparison result; wherein the real-time correction scheme is used to perform correction control on the real-time motion path of the robot.

[0007] Secondly, the present invention also provides a motion verification and control system for a robot, used to execute a motion verification and control method for a robot as described in the first aspect, wherein the system includes: a map building module, used to collect multiple features of a preset working area of ​​the robot to obtain preset area features, and to build a preset area map based on the preset area features; a path planning module, used to analyze the preset area map through a trajectory planning module and output a preset planned path; a path tracking module, used to monitor and obtain the real-time motion path of the robot through a trajectory tracking module; a comparison and correction module, used to analyze and obtain a real-time comparison result between the preset planned path and the real-time motion path through the motion verification module, and to obtain a real-time correction scheme based on the real-time comparison result; and a control execution module, wherein the real-time correction scheme is used to perform correction control on the real-time motion path of the robot.

[0008] Thirdly, the present invention also provides an electronic device, comprising:

[0009] At least one processor; and

[0010] A memory communicatively connected to the at least one processor; wherein,

[0011] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in any one of the first aspects above.

[0012] One or more technical solutions provided in this invention have at least the following technical effects or advantages:

[0013] By collecting multiple features from the robot's preset work area, preset area features are obtained, and a preset area map is constructed based on these features. The trajectory planning module analyzes the preset area map and outputs a preset planned path. The trajectory tracking module monitors the robot's real-time motion path. The motion verification module analyzes the real-time comparison between the preset planned path and the real-time motion path, and a real-time correction scheme is derived based on the comparison results. This real-time correction scheme is used to correct the robot's real-time motion path. Collecting features from the robot's preset work area and constructing its preset area map provides a map information foundation for subsequent intelligent planning of the robot's work path. The trajectory planning module automatically analyzes the preset area map to obtain the optimal work path for the robot, i.e., the preset planned path, providing a path reference and foundation for the robot's actual operation. This achieves the technical effect of improving the rationality and scientific nature of robot operation planning, thereby improving the efficiency of intelligent operation. The motion verification module compares and analyzes the robot's actual real-time motion path with its theoretical preset planned path to obtain real-time motion correction for the robot, used for real-time correction control to improve the accuracy and timeliness of the robot's motion control. The preset planned path provides a theoretical path reference for the robot's movement. By comparing and analyzing this path with the robot's real-time movement path, a targeted real-time correction scheme is obtained, achieving the goal of timely correction and control of the robot's movement and improving the technical effect of robot motion control accuracy.

[0014] The above description is merely an overview of the technical solution of the present invention. To better understand the technical means of the present invention and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of the present invention more apparent, specific embodiments of the present invention are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily apparent from the following description. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating a motion verification and control method for a robot according to the present invention.

[0017] Figure 2This is a flowchart illustrating the process of obtaining the preset region map based on the preset grid map in a motion verification and control method for a robot according to the present invention.

[0018] Figure 3 This is a flowchart illustrating the process of using the preset map marking results as the preset area map in a robot motion verification and control method of the present invention.

[0019] Figure 4 This is a schematic diagram of the process for forming the preset map marking result in a robot motion verification and control method of the present invention;

[0020] Figure 5 This is a flowchart illustrating the process of adding the real-time rotation angle compensation value to the real-time correction scheme in a robot motion verification and control method of the present invention.

[0021] Figure 6 This is a schematic diagram of the motion verification and control system for a robot according to the present invention.

[0022] Explanation of reference numerals in the attached figures:

[0023] Map building module 11, path planning module 12, path tracking module 13, comparison and correction module 14, control and execution module 15. Detailed Implementation

[0024] This invention provides a motion verification control method and system for robots, solving the technical problems of large control errors and unstable control outputs in existing technologies that use position servo control for robot motion verification control, thus affecting the quality of robot operations. It achieves the goal of timely correction control of robot motion, thereby improving the accuracy of robot motion control.

[0025] The technical solutions of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. It should also be noted that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, not all of them.

[0026] Example 1

[0027] Please see the appendix Figure 1 This invention provides a motion verification and control method for a robot, wherein the method is applied to a motion verification and control system for a robot, and the method specifically includes the following steps:

[0028] Step S100: Collect multiple features of the robot's preset working area to obtain preset area features, and construct a preset area map based on the preset area features;

[0029] Further details are attached. Figure 2 As shown, step S100 of the present invention includes:

[0030] Step S110: Obtain the physical feature information of the preset work area, and generate a map of a preset scale based on the physical feature information;

[0031] Step S120: Obtain the robot width of the robot, and set the unit length based on the robot width;

[0032] Step S130: Divide the preset scale map into grids based on the unit length to obtain a preset grid map;

[0033] Step S140: Obtain the preset area map based on the preset grid map.

[0034] Further details are attached. Figure 3 As shown, step S140 of the present invention includes:

[0035] Step S141: Obtain the application feature information of the preset work area, and generate a preset hybrid map by combining the physical feature information;

[0036] Step S142: Construct the map correspondence between the preset hybrid map and the preset raster map;

[0037] Step S143: Obtain a preset marking scheme and mark the preset grid map in combination with the map correspondence to obtain the preset map marking result;

[0038] Further details are attached. Figure 4 As shown, step S143 of the present invention further includes the following steps:

[0039] Step S1431: Obtain the first cell of the preset grid map;

[0040] Step S1432: Match the first application feature of the first cell grid based on the map correspondence;

[0041] Step S1433: Determine whether the first application feature is a preset application attribute;

[0042] Step S1434: If yes, mark the first cell grid as occupied; if no, mark the first cell grid as free.

[0043] Step S1435: Based on the occupied markers and the free markers, form the preset map marking result.

[0044] Step S144: Use the preset map marking result as the preset area map.

[0045] Specifically, the aforementioned motion verification and control method for a robot is applied to a motion verification and control system for a robot. By analyzing the robot's actual operational needs and the actual operational area, the optimal operational path for the robot can be generated in a targeted manner. This provides a reference benchmark and verification basis for verifying and controlling the robot's actual operational path, thereby improving the accuracy of the robot's motion control.

[0046] First, targeted data collection and analysis are performed on the robot's actual operating environment to obtain relevant feature information of the preset operating area. For example, physical features of the preset operating area, such as shape and size, are collected. Then, a map model of the preset operating area is constructed based on this physical feature information, resulting in the preset-scale map. Next, basic information about the robot is collected, including its structure and corresponding structural dimensions, such as its shape and the length of its front and rear axes. Then, the width data of various locations on the robot's body are compared, and the longest width is taken as the robot's width. Next, the unit length is set according to the robot's width. The unit length is a unit length used for subsequent division of the preset area map. For example, if the robot width is 80cm, the corresponding unit length is set to 80+5cm to improve the accuracy of path setting while ensuring the robot can smoothly pass through the path of the unit length, thus optimizing the preset planned path. Finally, the preset-scale map is divided into grids based on the unit length to obtain a preset grid map, and the preset area map is obtained based on the preset grid map.

[0047] Further, the application characteristic information of the preset work area, i.e., the actual work area of ​​the robot, is collected, such as the display and placement of items above the corresponding work area, equipment installation, and production planning type. Then, a hybrid map of the preset work area, i.e., the preset hybrid map, is generated by combining the physical characteristic information of this area. Further, a mapping relationship is constructed between the preset hybrid map and the aforementioned preset grid map. Next, based on the map correspondence, the preset grid map is marked using a preset marking scheme to obtain the preset map marking result. Specifically, firstly, a cell is randomly extracted from the preset grid map and designated as the first unit grid. Then, the application information of the corresponding grid in the preset hybrid map is matched according to the map correspondence. Wherein, when the first application characteristic corresponding to the first unit grid is an already applied characteristic, such as mechanical equipment placed on the ground corresponding to the first unit grid, the first unit grid is marked as occupied. Conversely, when the first application characteristic corresponding to the first unit grid is unoccupied, the first unit grid is marked as free. Finally, after sequentially marking all the grids in the preset grid map as occupied or free, the preset map marking result is obtained, which is the preset area map. By collecting features from the robot's preset work area and constructing its preset area map, a map information foundation is provided for subsequent intelligent planning of the robot's work path.

[0048] Step S200: Analyze the preset area map through the trajectory planning module and output a preset planned path;

[0049] Further, step S200 of the present invention includes:

[0050] Step S210: Read the robot's origin and destination;

[0051] Step S220: Combine the preset area map to sequentially determine the origin grid of the origin and the destination grid of the destination;

[0052] Step S230: Obtain the first grid set of the starting grid through a preset search method, wherein the first grid set includes M first grids, and M is an integer greater than or equal to 1;

[0053] Step S240: Extract the target first grid cell from the M first grid cells, and calculate the estimated cost of the target first grid cell and the destination grid cell to obtain the target first estimated cost. The calculation formula for the target first estimated cost is as follows:

[0054]

[0055] d2=xj -x i +y j -y i

[0056] d3 = max(x) j -x i ,y j -y i )

[0057]

[0058] Step S250: Wherein, d1 refers to the Euclidean distance between the target first grid and the destination grid, d2 refers to the Manhattan distance between the target first grid and the destination grid, d3 refers to the Chebyshev distance between the target first grid and the destination grid, d refers to the target first estimated cost, and (x i ,y i ) refers to the coordinates of the first grid cell of the target, where (x) j ,y j () refers to the coordinates of the target grid;

[0059] Step S260: Analyze the first estimated cost of the target and match the optimal first grid according to the analysis results;

[0060] Step S270: Based on the optimal first grid, obtain the preset planned path.

[0061] Further, step S270 of the present invention includes:

[0062] Step S271: Obtain the first actual cost based on the starting grid and the optimal first grid;

[0063] Step S272: Obtain the second grid set of the optimal first grid through the preset search method, wherein the second grid set includes N second grids, and N is an integer greater than or equal to 1;

[0064] Step S273: Extract the target second grid from the N second grids, and calculate the estimated cost of the target second grid and the target grid to obtain the target second estimated cost;

[0065] Step S274: Based on the first actual cost and the second estimated cost of the target, determine the heuristic function, wherein the heuristic function is expressed as follows:

[0066] f(n)=a×f′(n)+b×f″(n),0≤a≤1,0≤b≤1

[0067] Step S275: Wherein, f(n) refers to the heuristic function, a refers to the first weight coefficient, b refers to the second weight coefficient, f′(n) refers to the first actual cost, and f″(n) refers to the second estimated cost of the target;

[0068] Step S276: Determine the preset planning path using the heuristic function.

[0069] Specifically, when the trajectory planning module in the motion verification and control system performs intelligent path planning analysis on the robot's actual operation in the preset work area, it first reads the robot's current actual position, such as the transmission data from the position sensor, and simultaneously reads the location of the robot's destination. That is, it first obtains the robot's starting point and destination, and then, in conjunction with the preset area map, sequentially determines the starting grid of the starting point and the destination grid of the destination. Next, it obtains the first grid set of the starting grid through a preset search method. The first grid set refers to all grids that the robot can move through when it is in the starting grid and starts executing its task; that is, the set of all unoccupied grids adjacent to the starting grid. The first grid set includes M first grids, where M is an integer greater than or equal to 1. Next, a grid is randomly selected from the M first grids, denoted as the target first grid, and the estimated cost of the target first grid and the target grid is calculated to obtain the corresponding target first estimated cost. The calculation formula of the target first estimated cost is as follows:

[0070]

[0071] d2=x j -x i +y j -y i

[0072] d3 = max(x) j -x i ,y j -y i )

[0073]

[0074]

[0075] Wherein, d1 refers to the Euclidean distance between the target first grid and the destination grid, d2 refers to the Manhattan distance between the target first grid and the destination grid, d3 refers to the Chebyshev distance between the target first grid and the destination grid, d refers to the target first estimated cost, and (x)i ,y i ) refers to the coordinates of the first grid cell of the target, where (x) j ,y j The coordinates of the target grid are referred to as . By calculating the distance between the target first grid and the target grid in different ways, and then taking the average of the distances obtained by each calculation method, the distance value is taken as the first estimated cost of the target. This achieves the technical effect of improving the accuracy of the calculation of the first estimated cost of the target, thereby providing a basis for subsequently determining the preset planning path.

[0076] After calculating the estimated costs of each grid in the first grid set and the starting grid, the grid with the lowest estimated cost is selected by comparison and screening; that is, the grid with the shortest robot movement distance is selected as the optimal first grid. Next, all grids between the starting grid and the destination grid are determined sequentially according to the optimal first grid determination scheme, i.e., the preset planned path is obtained based on the optimal first grid. Specifically, firstly, a first actual cost is obtained based on the starting grid and the optimal first grid; that is, after the first grid where the robot moves to the work destination is determined, a precise cost is obtained. Next, a second grid set of the optimal first grid is obtained through the preset search method. The second grid set refers to all adjacent grids that the robot can pass through to reach the destination grid after traveling to the optimal first grid, with the optimal first grid as the starting point; that is, the N second grids, where N is an integer greater than or equal to 1. Then, the target second grid is extracted from the N second grids, and the estimated cost between the target second grid and the destination grid is calculated to obtain the target second estimated cost. Since the second grid cell the robot will actually traverse is not yet determined, an estimated cost is obtained at this point. However, after determining the optimal first grid cell as the first grid cell the robot will traverse, the corresponding cost becomes the determined actual cost. Finally, a heuristic function is determined based on the first actual cost and the target second estimated cost. The heuristic function is expressed as follows:

[0077] f(n)=a×f′(n)+b×f″(n),0≤a≤1,0≤b≤1

[0078] Wherein, f(n) refers to the heuristic function, a refers to the first weight coefficient, b refers to the second weight coefficient, f′(n) refers to the first actual cost, and f″(n) refers to the second estimated cost of the target. Finally, the preset planning path is determined through the heuristic function.

[0079] The trajectory planning module automatically analyzes the preset area map to obtain the robot's optimal operation path, i.e., the preset planned path. This provides a path reference and basis for the robot's actual operation, thereby improving the rationality and scientific nature of robot operation planning and thus enhancing the efficiency of intelligent operation.

[0080] Step S300: The real-time motion path of the robot is obtained by monitoring the trajectory tracking module;

[0081] Step S400: The motion verification module analyzes and obtains the real-time comparison result between the preset planned path and the real-time motion path, and obtains a real-time correction scheme based on the real-time comparison result;

[0082] Further details are attached. Figure 5 As shown, step S400 of the present invention includes:

[0083] Step S410: Obtain the pre-aiming distance;

[0084] Step S420: Obtain the center coordinates of the robot's rear axis, and denot them as the first coordinates;

[0085] Step S430: Draw a target circle with the first coordinate as the center and the pre-aiming distance as the radius;

[0086] Step S440: Obtain the intersection point of the target circle and the preset planned path, and record it as the aiming point;

[0087] Step S450: Analyze the real-time motion path to obtain the real-time heading, and combine it with the pre-aiming point to obtain the real-time heading deviation angle;

[0088] Step S460: Collect the rear axle length and calculate the real-time steering angle compensation value in combination with the real-time heading deviation angle;

[0089] Furthermore, step S460 of the present invention includes:

[0090] Step S461: Obtain the corner compensation formula, wherein the corner compensation formula is as follows:

[0091]

[0092] Step S462: Wherein, δ refers to the real-time steering angle compensation value, θ refers to the real-time heading deviation angle, and L refers to the rear axle length. d This refers to the pre-aiming distance.

[0093] Step S470: Add the real-time angle compensation value to the real-time correction scheme.

[0094] Step S500: Wherein, the real-time correction scheme is used to perform correction control on the real-time motion path of the robot.

[0095] Specifically, the trajectory tracking module in the motion verification control system intelligently monitors the robot's real-time position to obtain its real-time motion path. Then, the motion verification module in the motion verification control system analyzes the real-time comparison between the preset planned path and the real-time motion path, thereby comparing the robot's actual position with the system's preset path to obtain a quantitative deviation in the actual motion. Finally, based on the real-time comparison result, a real-time correction scheme is obtained to correct the robot's actual position. Specifically, firstly, relevant control personnel, considering actual environmental conditions, pre-set the aiming distance. Then, the center coordinates of the robot's rear axis are obtained and recorded as the first coordinate. Next, a target circle is drawn with the first coordinate as the center and the aiming distance as the radius. The intersection point of the target circle and the preset planned path is further obtained and recorded as the aiming point. Finally, the real-time motion path is analyzed to obtain the real-time heading, and the real-time heading deviation angle is obtained by combining the aiming point. The formula for calculating the real-time heading deviation angle by combining the real-time heading with the pre-aiming point is the angle compensation formula, which is as follows:

[0096]

[0097] Wherein, δ refers to the real-time steering angle compensation value, θ refers to the real-time heading deviation angle, and L refers to the rear axle length. d This refers to the pre-aiming distance. Finally, the calculated real-time corner compensation value is added to the real-time correction scheme to correct the robot's real-time motion path. By analyzing the preset planned path, a theoretical path reference for the robot's motion is provided. Comparing this path with the robot's real-time motion path, a targeted real-time correction scheme is derived, achieving the goal of timely correction control of the robot's motion and improving the technical accuracy of robot motion control.

[0098] In summary, the motion verification and control method for robots provided by this invention has the following technical effects:

[0099] By collecting multiple features from the robot's preset work area, preset area features are obtained, and a preset area map is constructed based on these features. The trajectory planning module analyzes the preset area map and outputs a preset planned path. The trajectory tracking module monitors the robot's real-time motion path. The motion verification module analyzes the real-time comparison between the preset planned path and the real-time motion path, and a real-time correction scheme is derived based on the comparison results. This real-time correction scheme is used to correct the robot's real-time motion path. Collecting features from the robot's preset work area and constructing its preset area map provides a map information foundation for subsequent intelligent planning of the robot's work path. The trajectory planning module automatically analyzes the preset area map to obtain the optimal work path for the robot, i.e., the preset planned path, providing a path reference and foundation for the robot's actual operation. This achieves the technical effect of improving the rationality and scientific nature of robot operation planning, thereby improving the efficiency of intelligent operation. The motion verification module compares and analyzes the robot's actual real-time motion path with its theoretical preset planned path to obtain real-time motion correction for the robot, used for real-time correction control to improve the accuracy and timeliness of the robot's motion control. The preset planned path provides a theoretical path reference for the robot's movement. By comparing and analyzing this path with the robot's real-time movement path, a targeted real-time correction scheme is obtained, achieving the goal of timely correction and control of the robot's movement and improving the technical effect of robot motion control accuracy.

[0100] Example 2

[0101] Based on the same inventive concept as the robot motion verification and control method described in the foregoing embodiments, this invention also provides a robot motion verification and control system. Please refer to the appendix. Figure 6 The system includes:

[0102] The map building module 11 is used to collect multiple features of the robot's preset working area, obtain the preset area features, and build a preset area map based on the preset area features.

[0103] The path planning module 12 is used to analyze the preset area map through the trajectory planning module and output a preset planned path;

[0104] The path tracking module 13 is used to monitor and obtain the real-time motion path of the robot through the trajectory tracking module;

[0105] The comparison and correction module 14 is used to analyze the real-time comparison results between the preset planned path and the real-time motion path through the motion verification module, and to obtain a real-time correction scheme based on the real-time comparison results.

[0106] The control execution module 15 is used in which the real-time correction scheme is used to perform correction control on the real-time motion path of the robot.

[0107] Furthermore, the map building module 11 in the system is also used for:

[0108] Obtain the physical feature information of the preset work area, and generate a map of a preset scale based on the physical feature information;

[0109] Obtain the robot width and set the unit length based on the robot width;

[0110] The preset scale map is divided into grids based on the unit length to obtain a preset grid map;

[0111] The preset area map is obtained based on the preset grid map.

[0112] Furthermore, the map building module 11 in the system is also used for:

[0113] Obtain the application feature information of the preset work area, and combine it with the physical feature information to generate a preset hybrid map;

[0114] Construct the map correspondence between the preset hybrid map and the preset raster map;

[0115] Obtain a preset marking scheme and mark the preset grid map in combination with the map correspondence to obtain the preset map marking result;

[0116] The preset map marking results are used as the preset area map.

[0117] Furthermore, the map building module 11 in the system is also used for:

[0118] Obtain the first cell of the preset grid map;

[0119] Based on the map correspondence, match the first application feature of the first unit grid;

[0120] Determine whether the first application feature is a preset application attribute;

[0121] If yes, mark the first cell grid as occupied; otherwise, mark the first cell grid as free.

[0122] The preset map marking result is formed based on the occupied markers and the free markers.

[0123] Furthermore, the path planning module 12 in the system is also used for:

[0124] Read the robot's origin and destination;

[0125] The origin grid of the origin and the destination grid of the destination are determined sequentially by combining the preset area map;

[0126] The first grid set of the starting grid is obtained by a preset search method, wherein the first grid set includes M first grids, and M is an integer greater than or equal to 1;

[0127] Extract the target first grid cell from the M first grid cells, and calculate the estimated cost of the target first grid cell and the destination grid cell to obtain the target first estimated cost. The calculation formula for the target first estimated cost is as follows:

[0128]

[0129] d2=x j -x i +y j -y i

[0130] d3 = max(x) j -x i ,y j -y i )

[0131]

[0132] Wherein, d1 refers to the Euclidean distance between the target first grid and the destination grid, d2 refers to the Manhattan distance between the target first grid and the destination grid, d3 refers to the Chebyshev distance between the target first grid and the destination grid, d refers to the target first estimated cost, and (x) i ,y i ) refers to the coordinates of the first grid cell of the target, where (x) j ,y j () refers to the coordinates of the target grid;

[0133] The first estimated cost of the target is analyzed, and the optimal first grid is matched based on the analysis results;

[0134] Based on the optimal first grid, the preset planned path is obtained.

[0135] Furthermore, the path planning module 12 in the system is also used for:

[0136] The first actual cost is obtained based on the starting grid and the optimal first grid;

[0137] The second grid set of the optimal first grid is obtained through the preset search method, wherein the second grid set includes N second grids, and N is an integer greater than or equal to 1;

[0138] Extract the target second grid from the N second grids, and calculate the estimated cost of the target second grid and the target grid to obtain the target second estimated cost;

[0139] Based on the first actual cost and the second estimated cost of the target, a heuristic function is determined, wherein the heuristic function is expressed as follows:

[0140] f(n)=a×f′(n)+b×f″(n),0≤a≤1,0≤b≤1

[0141] Wherein, f(n) refers to the heuristic function, a refers to the first weight coefficient, b refers to the second weight coefficient, f′(n) refers to the first actual cost, and f″(n) refers to the second estimated cost of the target.

[0142] The preset planning path is determined by the heuristic function.

[0143] Furthermore, the comparison correction module 14 in the system is also used for:

[0144] Obtain the aiming distance;

[0145] Obtain the center coordinates of the robot's rear axis, and denote them as the first coordinates;

[0146] A target circle is drawn with the first coordinate as the center and the pre-aiming distance as the radius.

[0147] Obtain the intersection point of the target circle and the preset planned path, and record it as the aiming point;

[0148] The real-time heading is obtained by analyzing the real-time motion path, and the real-time heading deviation angle is obtained by combining the pre-aiming point;

[0149] The rear axle length is collected, and the real-time steering angle compensation value is calculated by combining it with the real-time heading deviation angle.

[0150] The real-time corner compensation value is added to the real-time correction scheme.

[0151] Furthermore, the comparison correction module 14 in the system is also used for:

[0152] Obtain the corner compensation formula, wherein the corner compensation formula is as follows:

[0153]

[0154] Wherein, δ refers to the real-time steering angle compensation value, θ refers to the real-time heading deviation angle, and L refers to the rear axle length. d This refers to the pre-aiming distance.

[0155] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1 The motion verification control method and specific examples for a robot in Embodiment 1 are also applicable to the motion verification control system for a robot in this embodiment. Through the foregoing detailed description of the motion verification control method for a robot, those skilled in the art will clearly understand the motion verification control system for a robot in this embodiment; therefore, for the sake of brevity, it will not be described in detail here. As for the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant details can be found in the method section.

[0156] The present invention also provides an electronic device, comprising:

[0157] At least one processor; and

[0158] A memory communicatively connected to the at least one processor; wherein,

[0159] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in any one of Embodiment 1.

[0160] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0161] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of this invention and its equivalents, this invention also intends to include these modifications and variations.

Claims

1. A motion verification and control method for a robot, characterized in that, The motion verification control method is applied to a motion verification control system, which includes a trajectory planning module, a trajectory tracking module, and a motion verification module. The motion verification control method includes: Multiple features are collected from the robot's preset working area to obtain the preset area features, and a preset area map is constructed based on the preset area features; The trajectory planning module analyzes the preset area map and outputs a preset planned path. The real-time movement path of the robot is obtained by monitoring the trajectory tracking module. The motion verification module analyzes and obtains the real-time comparison results between the preset planned path and the real-time motion path, and obtains a real-time correction scheme based on the real-time comparison results. The real-time correction scheme is used to correct the real-time motion path of the robot. The step of obtaining a real-time correction scheme based on the real-time comparison results includes: Obtain the aiming distance; Obtain the center coordinates of the robot's rear axis, and denote them as the first coordinates; A target circle is drawn with the first coordinate as the center and the pre-aiming distance as the radius. Obtain the intersection point of the target circle and the preset planned path, and record it as the aiming point; The real-time heading is obtained by analyzing the real-time motion path, and the real-time heading deviation angle is obtained by combining the pre-aiming point; The rear axle length is collected, and the real-time steering angle compensation value is calculated by combining it with the real-time heading deviation angle. Add the real-time corner compensation value to the real-time correction scheme; The step of acquiring the rear axle length and calculating the real-time steering angle compensation value by combining it with the real-time heading deviation angle includes: Obtain the corner compensation formula, wherein the corner compensation formula is as follows: ； Among them, the This refers to the real-time corner compensation value, the This refers to the real-time heading deviation angle, the This refers to the length of the rear axle. This refers to the pre-aiming distance.

2. The motion verification and control method according to claim 1, characterized in that, The step of constructing a preset area map based on the preset area features includes: Obtain the physical feature information of the preset work area, and generate a map of a preset scale based on the physical feature information; Obtain the robot width and set the unit length based on the robot width; The preset scale map is divided into grids based on the unit length to obtain a preset grid map; The preset area map is obtained based on the preset grid map.

3. The motion verification and control method according to claim 2, characterized in that, The step of obtaining the preset region map based on the preset grid map includes: Obtain the application feature information of the preset work area, and combine it with the physical feature information to generate a preset hybrid map; Construct the map correspondence between the preset hybrid map and the preset raster map; Obtain a preset marking scheme and mark the preset grid map in combination with the map correspondence to obtain the preset map marking result; The preset map marking results are used as the preset area map.

4. The motion verification and control method according to claim 3, characterized in that, The process of obtaining a preset marking scheme and marking the preset raster map in conjunction with the map correspondence to obtain a preset map marking result includes: Obtain the first cell of the preset grid map; Based on the map correspondence, match the first application feature of the first unit grid; Determine whether the first application feature is a preset application attribute; If yes, mark the first cell grid as occupied; otherwise, mark the first cell grid as free. The preset map marking result is formed based on the occupied markers and the free markers.

5. The motion verification control method according to claim 4, characterized in that, The step of analyzing the preset area map through the trajectory planning module and outputting a preset planned path includes: Read the robot's origin and destination; The origin grid of the origin and the destination grid of the destination are determined sequentially by combining the preset area map; The first grid set of the starting grid is obtained by a preset search method, wherein the first grid set includes M first grids, and M is an integer greater than or equal to 1; Extract the target first grid cell from the M first grid cells, and calculate the estimated cost of the target first grid cell and the destination grid cell to obtain the target first estimated cost. The calculation formula for the target first estimated cost is as follows: ； ； ； ； Among them, the This refers to the Euclidean distance between the target first grid cell and the destination grid cell. This refers to the Manhattan distance between the target first grid cell and the destination grid cell. This refers to the Chebyshev distance between the target first grid cell and the destination grid cell. This refers to the first estimated cost of the target, the This refers to the coordinates of the first grid cell of the target. This refers to the coordinates of the target raster; The first estimated cost of the target is analyzed, and the optimal first grid is matched based on the analysis results; Based on the optimal first grid, the preset planned path is obtained.

6. The motion verification control method according to claim 5, characterized in that, The process of obtaining the preset planned path based on the optimal first grid includes: The first actual cost is obtained based on the starting grid and the optimal first grid; The second grid set of the optimal first grid is obtained through the preset search method, wherein the second grid set includes N second grids, and N is an integer greater than or equal to 1; Extract the target second grid from the N second grids, and calculate the estimated cost of the target second grid and the target grid to obtain the target second estimated cost; Based on the first actual cost and the second estimated cost of the target, a heuristic function is determined, wherein the heuristic function is expressed as follows: ； Among them, the This refers to the heuristic function, where 'a' refers to the first weight coefficient, 'b' refers to the second weight coefficient, and so on. This refers to the first actual cost, the This refers to the second estimated cost of the target; The preset planning path is determined by the heuristic function.

7. A motion verification and control system for a robot, characterized in that, A motion verification control method for executing the robot according to any one of claims 1 to 6, the motion verification control system comprising a trajectory planning module, a trajectory tracking module, and a motion verification module, the system comprising: The map building module is used to collect multiple features of the robot's preset working area, obtain the preset area features, and build a preset area map based on the preset area features. The path planning module is used to analyze the preset area map through the trajectory planning module and output a preset planned path; A path tracking module is used to monitor and obtain the real-time motion path of the robot through the trajectory tracking module; The comparison and correction module is used to analyze the real-time comparison results between the preset planned path and the real-time motion path through the motion verification module, and to obtain a real-time correction scheme based on the real-time comparison results. A control execution module is provided, wherein the real-time correction scheme is used to perform correction control on the real-time motion path of the robot.

8. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

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