Working dog and robot collaborative operation system

Abstract

The invention relates to the technical field of special operation, in particular to a working dog and robot collaborative operation system. The system comprises the steps of receiving a task point position information set and obtaining robot loading starting point position information, generating a to-be-selected execution path based on the robot loading starting point position information and the task point position information set, and calculating an execution prediction feature value of the to-be-selected execution path, and judging whether the to-be-selected execution path meets a path execution condition based on the execution prediction feature value, obtaining coordinates of each task point position on the execution path, calculating an execution driving angle between the positions of the adjacent task points, and controlling the robot to drive between the adjacent task points in the execution path according to the execution driving angle. The angle error value is calculated and corrected in the running process, the robot is controlled to adjust the running angle according to the angle error value, the biological characteristics of the working dog and the scientific and technological advantages of the robot can be brought into full play, and efficient and flexible special operation tasks are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of special operations, and particularly to a cooperative operation system for working dogs and robots. Background Art

[0002] Working dogs (such as police dogs, search and rescue dogs, military dogs, etc.) play an important role in the fields of public security, anti-terrorism, rescue, etc. However, working dogs have some limitations when performing tasks, such as limited load-bearing capacity and insufficient tolerance in extreme environments. Quadruped robots have good mobility and load-bearing capacity, can carry a variety of scientific and technological devices (such as video acquisition devices, sensors, detectors, positioning modules, capture nets, weapons, etc.), and can walk stably on complex terrains. However, quadruped robots lack the sense of smell and autonomous judgment ability of biological dogs and need to rely on manual operation or preset programs.

[0003] At present, the applications of working dogs and robot dogs are mostly carried out independently, and no effective cooperative operation mode has been formed, so the advantages of both cannot be fully utilized. When carrying the load required at the task point, the path planning is not accurate enough, resulting in low cooperative operation efficiency.

[0004] Therefore, there is an urgent need for a cooperative operation system for working dogs and robots to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide a cooperative operation system for working dogs and robots: aiming to organically combine working dogs and quadruped robots to achieve efficient and flexible special operation tasks through accurate path planning.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A cooperative operation system for working dogs and robots, the system includes:

[0008] A working dog task positioning module, used to mark the position information of the task points based on the positioning system on the working dog, form a set of task point position information, and send the set of task point position information to the path planning module;

[0009] A path planning module, used to receive the set of task point position information and obtain the starting position information of the robot carrying the load, generate a candidate execution path based on the starting position information of the robot carrying the load and the set of task point position information, calculate the execution prediction feature value of the candidate execution path, and judge whether the candidate execution path meets the path execution condition based on the execution prediction feature value. If so, use the candidate execution path as the execution path and send it to the robot execution module;

[0010] A robot execution module, used to receive the execution path and complete the task requirements according to the execution path: obtain the coordinates of each task point position on the execution path , calculate the execution travel angle between adjacent task point positions , according to the execution travel angle Control the robot to travel between adjacent task points on the execution path. During the travel, calculate the correction angle error value, and control the robot to adjust the travel angle according to the angle error value.

[0011] Furthermore, generating a number of candidate execution paths based on the robot's load starting position information and the set of task point position information specifically includes the following process:

[0012] Set the load weight at the robot's load starting point as the starting transportation volume, set the load weight required at the task point position as the task demand volume, and accumulate the task demand volume and record it as the path demand volume.

[0013] If the starting transportation volume is greater than or equal to the path demand volume, then connect the task points in sequence according to the distance between the task points and the robot's load position to form a candidate execution path;

[0014] If the starting transportation volume is less than the path demand volume, then obtain the candidate execution path in the way of rough combination.

[0015] Furthermore, obtaining the candidate execution path in the way of rough combination specifically includes the following process:

[0016] Step 1: Set as the rough combination parameter, set a navigation point at each task point, and perform rough combination on each navigation point according to the sampling order of the sampling interval value to obtain a number of rough combination groups;

[0017] Step 2: Combine the first navigation point and the second navigation point of each rough combination group to form a reference vector , and combine the remaining th point with the th navigation point to form a vector , where , calculate the angle between and m , where the calculation formula of the angle

[0018] ;

[0019] Step 3: Compare with the threshold angle . If the of the th point is greater than the threshold angle , then subdivide the navigation points arranged in front of the th point into a group, and arrange the navigation points arranged in the For the navigation points in front of each point, repeat Steps 2 and 3 to complete the subdivision of the rough combination, and connect the navigation points in each subdivided group to form a candidate execution path.

[0020] Further, calculating the execution prediction eigenvalue of the candidate execution path specifically includes the following process:

[0021] Obtain the target information of the first task point, the target information of the second task point, and so on until the target information of the Gth task point on the candidate execution path; wherein, the target information of the first task point includes the first computing power redundancy, the first computing power failure trigger frequency, and the first load transmission distance; the target information of the Gth task point includes the Gth computing power redundancy, the Gth computing power failure trigger frequency, and the Gth load transmission distance; wherein, the computing power redundancy is the computing power redundancy allocated by the path planning module for the task point, the computing power failure trigger frequency is the probability that the robot fails to reach the task point, and the load transmission distance is the load transmission distance from the load starting position of the robot to the task point.

[0022] Add the first computing power redundancy, the first computing power failure trigger frequency, and the first load transmission distance to obtain the execution prediction coefficient of the first task point; until the execution prediction coefficient of the Gth task point is calculated.

[0023] Record the sum of all execution prediction coefficients as the execution prediction eigenvalue of the candidate execution path.

[0024] Further, judging whether the candidate execution path meets the path execution condition based on the execution prediction eigenvalue specifically includes the following process:

[0025] Load the execution prediction eigenvalue threshold, the value of which is set by the system and stored in the system, and judge whether the execution prediction eigenvalue is greater than the execution prediction eigenvalue threshold. If so, it is determined that the candidate execution path meets the path execution condition; if not, it is determined that the candidate execution path does not meet the path execution condition.

[0026] Further, calculate the execution driving angle between adjacent task point positions Specifically includes the following process:

[0027] ;

[0028] Wherein, , , is adjacent to coordinates of the adjacent task point.

[0029] Further, judging whether the robot needs to turn at the task point specifically includes the following process:

[0030] For the first task point of the execution path, first calculate the angle formed by the connection line between it and the second task point of the execution path : ; Among them, the coordinates of the first task point are , and the coordinates of the second task point are ; Calculate the execution travel angle for the robot to reach the first task point, calculate the angle and the difference from the execution travel angle, and adjust the turning angle based on the difference;

[0031] For the last task point of the execution path, the task can be regarded as completed after reaching the end point, and no turning is required;

[0032] For other task points in the execution path, respectively calculate the angles formed by the lines connecting them to adjacent task points 、 : ; ; Among them, the coordinates of other task points in the execution path are , and the coordinates of the adjacent task points are respectively 、 , calculate and difference, judge whether the difference exceeds the preset angle threshold, if so, judge that turning is required, if not, judge that turning is not required.

[0033] Further, calculating the correction angle error value during driving specifically includes the following process:

[0034] Obtain the current position of the robot , the adjacent task point , according to the calculation formula , obtain the correction coefficient curve , where is the sampling time interval value corresponding sampling time serial number, , and is a positive integer; Based on the correction curve calculate the correction angle error value : ; Among them, is a constant, set by the system, is the correction coefficient corresponding to the sampling time serial number , is the correction coefficient corresponding to the sampling time serial number .

[0035] Compared with the existing scheme, the beneficial effects achieved by the present invention:

[0036] Precise Task Location: The task location module of the working dog uses the positioning system on the working dog to mark the location information of the task points, forming an accurate set of task point location information. This method of field positioning based on the working dog can make full use of the flexibility of the working dog and its ability to adapt to complex environments, quickly and accurately determine the location of the task points, providing reliable basic data for subsequent operations. It is especially suitable for some task scenarios where it is difficult for humans to directly reach or the environment is complex.

[0037] Efficient Path Planning: The execution path generated by the present invention is not only feasible, but also optimized in terms of execution efficiency, resource consumption, etc., effectively improving the efficiency and success rate of the robot in performing tasks, and reducing unnecessary path exploration and time waste.

[0038] Precise Robot Execution: During the driving process, the angular error value is calculated and corrected in real time, and the driving angle is adjusted accordingly, ensuring the accuracy and stability of the robot's driving. Even in complex terrains or in the presence of interference, it can ensure that the robot accurately completes the task requirements according to the predetermined path, improving the reliability and stability of the entire collaborative operation system.

[0039] Advantages of Collaborative Operation: The working dog is responsible for quickly and accurately locating the task points, and the robot efficiently executes the tasks according to the planned path, giving full play to the flexibility of the working dog and the precise execution ability of the robot. The advantages of the two complement each other, greatly improving the operation efficiency and quality, expanding the operation scope and application scenarios, and can be widely applied to various fields that require operations in complex environments. Brief Description of the Drawings

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0041] Figure 1 It is a system block diagram of a collaborative operation system of a working dog and a robot according to an embodiment of the present invention;

[0042] Figure 2 It is a working flow chart of a collaborative operation system of a working dog and a robot according to an embodiment of the present invention;

[0043] Figure 3 It is another working flow chart of a collaborative operation system of a working dog and a robot according to an embodiment of the present invention. Detailed Embodiment

[0044] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0045] In addition, the described features, structures or characteristics may be combined in any suitable manner in one or more exemplary embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the exemplary embodiments of the present disclosure. However, those skilled in the art will realize that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, steps, etc. may be adopted. In other cases, well-known structures, methods, implementations or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0046] This embodiment provides a collaborative operation system for working dogs and robots. Figure 1 It is a system block diagram of a collaborative operation system for working dogs and robots according to an embodiment of the present invention. As Figure 1 shown, the system includes:

[0047] A working dog task positioning module, which is used to mark the position information of task points based on the positioning system on the working dog, form a set of task point position information, and send the set of task point position information to the path planning module;

[0048] A path planning module, which is used to receive the set of task point position information and obtain the starting position information of the robot carrying goods, generate a candidate execution path based on the starting position information of the robot carrying goods and the set of task point position information, calculate the execution prediction feature value of the candidate execution path, and judge whether the candidate execution path meets the path execution condition based on the execution prediction feature value. If so, use the candidate execution path as the execution path and send it to the robot execution module;

[0049] A robot execution module, which is used to receive the execution path and complete the task requirements according to the execution path: obtain the coordinates of each task point position on the execution path , calculate the execution driving angle between adjacent task point positions , and control the robot to drive between adjacent task points on the execution path according to the execution driving angle ;

[0050] A turning adjustment module, which judges whether the robot needs to turn at a task point during driving. If so, executes a turning instruction, calculates a correction angle error value, and controls the robot to adjust the driving angle according to the angle error value.

[0051] In summary, the present invention receives the task point position information set and obtains the starting position information of the robot carrying goods. Based on the starting position information of the robot carrying goods and the task point position information set, a candidate execution path is generated, the execution prediction feature value of the candidate execution path is calculated, and it is judged whether the candidate execution path meets the path execution condition based on the execution prediction feature value. If so, the execution path is received and the task requirements are completed according to the execution path: obtaining the coordinates of each task point position on the execution path, calculating the execution driving angle between adjacent task point positions, controlling the robot to drive between adjacent task points on the execution path according to the execution driving angle, calculating the correction angle error value during the driving process, and controlling the robot to adjust the driving angle according to the angle error value, which can give full play to the biological characteristics of the working dog and the technological advantages of the robot to achieve efficient and flexible special operation tasks.

[0052] In some embodiments, Figure 2 is the workflow diagram of a working dog and robot collaborative operation system according to an embodiment of the present invention. As Figure 2 shown, generating a number of candidate execution paths based on the starting position information of the robot carrying goods and the task point position information set specifically includes the following processes:

[0053] Step S201: Set the carrying weight of the starting point of the robot carrying goods as the starting transportation volume, set the carrying weight required at the task point position as the task demand volume, and accumulate the task demand volume and record it as the path demand volume;

[0054] Step S202: If the starting transportation volume is greater than or equal to the path demand volume, then connect the task points in turn according to the distance between the task points and the position of the robot carrying goods to form a candidate execution path;

[0055] Step S203: If the starting transportation volume is less than the path demand volume, then obtain the candidate execution path in a rough combination manner.

[0056] In some embodiments, obtaining the candidate execution path in a rough combination manner specifically includes the following processes:

[0057] Step one: Set as the rough combination parameter, set a navigation point at each task point, and perform rough combination on each navigation point according to the sampling order of the sampling interval value to obtain a number of rough combination groups;

[0058] Step two: Combine the first navigation point and the second navigation point of each rough combination group to form a reference vector , and combine the remaining th point with the th navigation point to form a vector , where , calculate The angle between and m , where the angle is calculated as follows:

[0059] ;

[0060] Step 3: Compare with the threshold angle . If the th point's is greater than the threshold angle , then subdivide the navigation points in front of the th point into a group, repeat Step 2 and Step 3 for the navigation points in front of the th point to complete the subdivision of the rough combination, and connect the navigation points in each subdivided group into a candidate execution path.

[0061] In some embodiments, Figure 3 is the flowchart of another working dog and robot collaborative operation system according to an embodiment of the present invention. As shown in Figure 3 , calculating the execution prediction feature value of the candidate execution path specifically includes the following process:

[0062] Step S301: Obtain the target information of the first task point, the target information of the second task point, and so on until the target information of the Gth task point on the candidate execution path;

[0063] Among them, the target information of the first task point includes the first computing power redundancy, the first computing power failure trigger frequency, and the first load transmission distance; the target information of the Gth task point includes the Gth computing power redundancy, the Gth computing power failure trigger frequency, and the Gth load transmission distance; where the computing power redundancy is the computing power redundancy allocated by the path planning module for the task point, the computing power failure trigger frequency is the probability that the robot does not reach the task point, and the load transmission distance is the load transmission distance from the load starting position of the robot to the task point;

[0064] Step S302: Add the first computing power redundancy, the first computing power failure trigger frequency, and the first load transmission distance to obtain the execution prediction coefficient of the first task point; until the execution prediction coefficient of the Gth task point is calculated;

[0065] Step S303: Denote the sum of all execution prediction coefficients as the execution prediction feature value of the candidate execution path.

[0066] In some embodiments, judging whether the candidate execution path meets the path execution condition based on the execution prediction feature value specifically includes the following process:

[0067] Load and execute the predicted feature value threshold, whose value is set by the system and stored in the system. Determine whether the executed predicted feature value is greater than the executed predicted feature value threshold. If so, determine that the candidate execution path meets the path execution condition. If not, determine that the candidate execution path does not meet the path execution condition.

[0068] In some embodiments, calculate the execution driving angle between adjacent task point positions Specifically, it includes the following process:

[0069] ;

[0070] Among them, , , is the coordinate of the task point adjacent to the adjacent task point coordinates.

[0071] In some embodiments, determining whether the robot needs to turn at a task point specifically includes the following process:

[0072] For the first task point of the execution path, first find the angle formed by the line connecting it and the second task point of the execution path : ; Among them, the coordinate of the first task point is , and the coordinate of the second task point is ; Calculate the execution driving angle when the robot reaches the first task point, calculate the difference between the angle and the execution driving angle, and adjust the turning angle based on the difference;

[0073] For the last task point of the execution path, the task can be regarded as completed after reaching the end point, and no turning is required;

[0074] For other task points in the execution path, find the angles , formed by the lines connecting them to the adjacent task points respectively: ; ; Among them, the coordinates of other task points in the execution path are , and the coordinates of the adjacent task points are respectively , , calculate the difference between and , and determine whether the difference exceeds the preset angle threshold. If so, determine that turning is required. If not, determine that turning is not required.

[0075] In some embodiments, control the robot to travel between adjacent task points in the execution path according to the execution driving angle. Calculating the correction angle error value during travel specifically includes the following process:

[0076] Obtain the current position of the robot , the adjacent task points , according to the calculation formula , the correction coefficient curve is obtained , where is the sampling time interval value corresponding sampling time serial number, , and is a positive integer; based on the correction curve calculate the corrected angular error value : ; where is a constant, set by the system, is the sampling time serial number corresponding correction coefficient, is the sampling time serial number corresponding correction coefficient.

[0077] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that includes one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0078] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present application.

[0079] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0080] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only for some logical function divisions, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0081] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0082] As described above, the above are only specific implementation manners of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all of them should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A cooperative operation system for working dogs and robots, characterized in that, The system includes: A working dog task positioning module, which is used to mark the position information of task points based on the positioning system on the working dog, form a set of task point position information, and send the set of task point position information to the path planning module; A path planning module, which is used to receive the set of task point position information and obtain the starting position information of the robot carrying goods, generate candidate execution paths based on the starting position information of the robot carrying goods and the set of task point position information, calculate the execution prediction feature value of the candidate execution paths, and judge whether the candidate execution paths meet the path execution conditions based on the execution prediction feature value. If so, the candidate execution paths are used as the execution paths and sent to the robot execution module; The robot execution module is used to receive the execution path and complete the task requirements according to the execution path: obtain the coordinates of the position of each task point on the execution path , calculate the execution driving angle between the positions of adjacent task points , according to the execution driving angle control the robot to drive between adjacent task points in the execution path; A turning adjustment module, which judges whether the robot needs to turn at the task point during driving. If so, it executes the turning instruction, calculates the corrected angle error value, and controls the robot to adjust the driving angle according to the angle error value.

2. The collaborative operation system of a working dog and a robot according to claim 1, wherein, Generating several candidate execution paths based on the starting position information of the robot carrying goods and the set of task point position information specifically Includes the following processes: Set the load weight at the starting point of the robot carrying goods as the starting delivery volume, set the load weight required at the task point position as the task demand volume, and accumulate the task demand volume and record it as the path demand volume. If the starting delivery volume is greater than or equal to the path demand volume, connect the task points in turn according to the distance between the task points and the position of the robot carrying goods to form a candidate execution path; If the starting delivery volume is less than the path demand volume, obtain the candidate execution path in the way of rough combination.

3. The collaborative operation system of a working dog and a robot according to claim 2, characterized in that, Obtaining the candidate execution path in the way of rough combination specifically includes the following processes: Step 1: Set as the rough combination parameter, set a navigation point at each task point, and perform rough combination on each navigation point according to the sampling order of the sampling interval value to obtain several rough combination groups; Step 2: Combine the first navigation point and the second navigation point of each rough combination group to form a reference vector , and combine the remaining points with the th navigation point to form a vector . Among them, , calculate the and the angle between m, where the calculation formula of the angle is as follows: ； Step 3: Compare with the threshold angle . If the th point's is greater than the threshold angle , then subdivide the navigation points in front of the th point into a group, repeat Step 2 and Step 3 for the navigation points in front of the th point to complete the subdivision of the rough combination, and connect the navigation points in each subdivided group into a candidate execution path.

4. The collaborative operation system of a working dog and a robot according to claim 1, wherein Calculating the execution prediction feature value of the candidate execution path specifically Includes the following processes: Obtain the target information of the first task point, the target information of the second task point until the target information of the Gth task point on the candidate execution path; among them, the target information of the first task point includes the first computing power redundancy, the first computing power failure trigger frequency and the first load transmission distance; the target information of the Gth task point includes the Gth computing power redundancy, the Gth computing power failure trigger frequency and the Gth load transmission distance; among them, the computing power redundancy is the computing power redundancy allocated by the path planning module for the task point, the computing power failure trigger frequency is the probability that the robot does not reach the task point, and the load transmission distance is the load transmission distance from the starting position of the robot carrying goods to the task point; Add the first computing power redundancy, the first computing power failure trigger frequency and the first load transmission distance to get the execution prediction coefficient of the first task point; until the execution prediction coefficient of the Gth task point is calculated; Record the sum value of all execution prediction coefficients as the execution prediction feature value of the candidate execution path.

5. The collaborative operation system of a working dog and a robot according to claim 1, characterized in that, Judging whether the candidate execution path meets the path execution conditions based on the execution prediction feature value specifically Includes the following processes: Load the execution prediction feature value threshold, the value of which is set by the system and stored in the system, and judge whether the execution prediction feature value is greater than the execution prediction feature value threshold. If so, it is determined that the candidate execution path meets the path execution conditions. If not, it is determined that the candidate execution path does not meet the path execution conditions.

6. The collaborative operation system for a working dog and a robot according to claim 1, characterized in that, Calculate the execution driving angle between the positions of adjacent task points Specifically, it includes the following processes: ； Among them, , , are the coordinates of the task points adjacent to .

7. The collaborative operation system for a working dog and a robot according to claim 1, wherein, Judging whether the robot needs to turn at the task point specifically includes the following processes: For the first task point of the execution path, first calculate the angle formed by its connection line with the second task point of the execution path : ; among them, the coordinates of the first task point are , and the coordinates of the second task point are ; calculate the execution driving angle for the robot to reach the first task point, calculate the difference between the angle and the execution driving angle, and adjust the turning angle based on the difference; For the last task point of the execution path, the task can be regarded as completed after reaching the end point without the need to turn; For other task points in the execution path, calculate the angles formed by the lines connecting them to adjacent task points respectively. , : ; ; Among them, the coordinates of other task points in the execution path are , and the coordinates of adjacent task points are , respectively. Calculate and . Determine whether the difference exceeds a preset angle threshold. If so, it is determined that a turn is required. If not, it is determined that no turn is required.

8. The collaborative operation system for a working dog and a robot according to claim 1, wherein, During driving, calculating the correction angle error value specifically includes the following processes: Obtain the current position of the robot , the adjacent task point , according to the calculation formula , obtain the correction coefficient curve , where is the sampling time interval value corresponding sampling time serial number, , and is a positive integer; based on the correction curve calculate the corrected angle error value : ; where is a constant set by the system, is the sampling time serial number corresponding correction coefficient, is the sampling time serial number corresponding correction coefficient.

Images