WO2021042519A1

WO2021042519A1 - Assembly path planning method and related device

Info

Publication number: WO2021042519A1
Application number: PCT/CN2019/117030
Authority: WO
Inventors: 王义文; 王健宗
Original assignee: 平安科技（深圳）有限公司
Priority date: 2019-09-02
Filing date: 2019-11-11
Publication date: 2021-03-11
Also published as: CN110646006A

Abstract

An assembly path planning method and a related device, the method comprising: selecting a sampling region including a start node (101); generating T new pose nodes in the sampling region by means of random numbers (102); selecting a first sampling center from the T new pose nodes, and selecting a new sampling region including the first sampling center (103); repeating the step of acquiring the first sampling center until a preset condition is met, so as to obtain n second sampling centers (104); and connecting the start node, the first sampling center, and the n second sampling centers so as to obtain a first path, and obtaining a target path according to the first path (105). The assembly path planning method greatly accelerates the sampling convergence rate, and solves the problem in which conventional path planning using an RRT (rapidly-exploring random tree) algorithm can easily fall into the trap of locally optimal solutions.

Description

Assembly path planning method and related device

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office, the application number is 2019108245692, and the application name is "Assembly Route Planning Method and Related Devices" on September 2, 2019. The entire content of this application is incorporated into this application by reference. .

Technical field

This application relates to the field of path planning, and specifically relates to an assembly path planning method and related devices.

Background technique

With the rise of large-scale industrial production, digital assembly technology, that is, virtual assembly technology, is increasingly used in actual industrial production. Path planning is the core of virtual assembly technology. Among them, RRT algorithm is a fast random path planning method. Because it uses random sampling planning method, it does not need scene preprocessing, so it can solve the problem of multi-dimensional non-convex space. The path planning problem is widely used to solve the optimal path in the static road network problem. However, because the traditional RRT algorithm adopts a global uniform random sampling strategy, the calculation amount of the algorithm will increase sharply and the efficiency of the algorithm will be reduced; and it cannot get rid of the trouble of local optimal solution, so that the RRT algorithm only has a probability optimal solution.

Summary of the invention

The embodiments of the present application provide an assembly path planning method and related devices, which can accelerate the sampling convergence speed and solve the problem of being easily trapped in a local optimum during the path planning process.

The first aspect of the embodiments of the present application discloses an assembly path planning method, the method includes:

Select the sampling area that contains the starting node;

Generate T new pose nodes through random numbers in the sampling area, where T is a positive integer;

Selecting a first sampling center from the T new pose nodes, and selecting a new sampling area that includes the first sampling center;

Repeat the method of obtaining the first sampling center until a preset condition is met, and n second sampling centers are obtained, and the preset condition includes that the last second sampling center among the n second sampling centers is the target node , Or, n=c-1, c is the preset maximum sampling times, n and c are both positive integers;

Connecting the starting node, the first sampling center and the n second sampling centers to obtain a first path, and obtaining a target path according to the first path.

A second aspect of the present application discloses an assembly path planning device, the assembly path planning device includes:

The sampling unit is used to select a sampling area that includes the starting node, and generate T new pose nodes by random numbers in the sampling area, where T is a positive integer, and select the first from the T new pose nodes A sampling center, selecting a new sampling area that includes the first sampling center, and repeating the method of obtaining the first sampling center until a preset condition is met, and n second sampling centers are obtained, and the preset condition includes The last second sampling center among the n second sampling centers is the target node, or n=c-1, c is the preset maximum number of sampling times, and both n and c are positive integers;

The path obtaining unit is configured to connect the starting node, the first sampling center and the n second sampling centers to obtain a first path, and obtain a target path according to the first path.

The third aspect of the present application discloses an electronic device, including a processor, a memory, a communication interface, and one or more programs. The one or more programs are stored in the memory and configured to be processed by the processor. The program is executed by a device, and the program includes a method for executing any one of the first aspect.

The fourth aspect of the present application discloses a computer non-volatile readable storage medium, the computer storage medium stores a computer program, and the computer program is executed by a processor to implement any one of the first aspect method.

In the solution of the embodiment of the present application, the sampling area including the starting node is selected; T new pose nodes are generated by random numbers in the sampling area; the first sampling center is selected from the T new pose nodes, Select a new sampling area that includes the first sampling center; repeat the method of obtaining the first sampling center until a preset condition is met, and n second sampling centers are obtained; connect the start node and the first sampling center The sampling center and the n second sampling centers obtain the first path, and the target path is obtained according to the first path; this assembly path planning method can greatly accelerate the sampling convergence speed and solve the problem that the path planning of the traditional RRT algorithm tends to be trapped locally Optimal problem.

Description of the drawings

In order to more clearly illustrate the technical solutions in the embodiments of the application or the background art, the following will briefly introduce the drawings involved in the embodiments of the application or the background art.

FIG. 1 is a schematic flowchart of an assembly path planning method provided by an embodiment of the application;

Figure 2 is a schematic diagram of partial sampling provided by an embodiment of the application;

FIG. 3 is a schematic diagram of an expansion strategy based on accurate collision calculation provided by an embodiment of the application;

4 is a schematic structural diagram of an electronic device provided by an embodiment of this application;

Fig. 5 is a schematic structural diagram of an assembly path planning device provided by an embodiment of the application.

detailed description

In order to enable those skilled in the art to better understand the solutions of the application, the technical solutions in the embodiments of the application will be clearly described below in conjunction with the drawings in the embodiments of the application. Obviously, the described embodiments are of the application. Part of the embodiment, but not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative work should fall within the protection scope of this application.

The terms "first", "second", and "third" appearing in the specification, claims, and drawings of this application are used to distinguish different objects, rather than describing a specific sequence. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes unlisted steps or units, or optionally also includes Other steps or units inherent in these processes, methods, products or equipment.

Reference to "embodiments" herein means that a specific feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art clearly and implicitly understand that the embodiments described herein can be combined with other embodiments.

The electronic devices involved in the embodiments of the present application may include various handheld devices with wireless communication functions, in-vehicle devices, wireless headsets, computing devices or other processing devices connected to wireless modems, as well as various forms of user equipment (user equipment). , UE), mobile station (mobile station, MS), terminal device (terminal device), etc. The electronic device may be, for example, a smart phone, a tablet computer, a headset box, and so on. For ease of description, the devices mentioned above are collectively referred to as electronic devices.

The following describes the embodiments of the present application in detail.

Please refer to FIG. 1. FIG. 1 is a schematic flowchart of an assembly path planning method provided by an embodiment of the application, including:

S101. Select a sampling area that includes a starting node.

Among them, in the path planning process, because the traditional RRT algorithm tends to fall into the local optimal solution and the convergence is slow, this application improves the RRT algorithm and proposes an assembly path planning method based on the improved RRT algorithm, which can solve the problem of traditional RRT Algorithm problem.

Knowing a feasible solution space containing the starting node q _s and the target node q _e (that is, the pre-selected end point), it is necessary to find the closest feasible path from the starting node to the target node in the feasible solution space to The starting node is the sampling center, and an area containing the starting node is selected as the sampling area in the feasible solution space for local sampling, so that a significant local sampling effect can be achieved without falling into the local optimal solution.

S102: Generate T new pose nodes through random numbers in the sampling area, where T is a positive integer.

Among them, in each sampling process, because the random number in the new pose node generation formula is constantly changing, through the new pose node generation formula, T different new pose nodes can be obtained to form an extended random tree, and T can be predetermined Set, for example, 5, 10, 15, 20 and so on.

S103: Select a first sampling center from the T new pose nodes, and select a new sampling area that includes the first sampling center.

Among them, as shown in Figure 2, Figure 2 is a schematic diagram of local sampling. The local sampling strategy is adopted to determine the next sampling area through the current sampling center. In order to shorten the length of the target path as much as possible, the current sampling center and the next _{The distance between the sampling centers, therefore, the node q near that is} closest to the current sampling center is selected from the T new pose nodes as the first sampling center, and the first sampling center is selected according to the method described in S101 Of the new sampling area.

S104. Repeat the method of obtaining the first sampling center until a preset condition is satisfied, and n second sampling centers are obtained, where the preset condition includes that the last second sampling center among the n second sampling centers is The target node, or n=c-1, c is the preset maximum sampling times, and both n and c are positive integers.

Wherein, the method of acquiring the sampling center and the sampling area is the same each time. The sampling area is determined according to the current sampling center, the next sampling center is determined in the sampling area, and the method of obtaining the first sampling center is repeated, until Reach the preset maximum sampling times or reach the target node.

S105. Connect the start node, the first sampling center, and the n second sampling centers to obtain a first path, and obtain a target path according to the first path.

Among them, the first path is obtained by connecting all sampling centers including the start node and the target node. Because the traditional RRT algorithm uses a global uniform random sampling strategy, the algorithm calculation is huge, the algorithm efficiency is low, and it cannot get rid of the local maximum. The problem of the optimal solution is that only the optimal solution can be obtained. In the embodiment of the present application, by combining random sampling, partial sampling and end-point sampling, the success rate of sampling center expansion of the expanded random tree during iterative sampling is improved, and the success rate of sampling center expansion is improved. The convergence speed of the traditional RRT algorithm is solved, and the problem of local optimal solution is solved. Specifically, the possible paths are obtained through random sampling, local sampling and end-point sampling at the same time, and the first path, the second path and the third path are obtained. The above three paths are fused to obtain the final target path.

Among them, in the embodiment of the present application, the assembly path planning method is applied to a six-degree-of-freedom intelligent robot. The six-degree-of-freedom intelligent robot is a core component that relieves industrial production pressure and improves industrial production efficiency. , Forearm, wrist arm and terminal controller, with compact structure, complex parts and other related characteristics. The embodiment of this application uses a six-degree-of-freedom robot as a carrier to verify whether the improved RRT algorithm can successfully complete the path planning task in a virtual environment through simulation. The simulation result shows that the improved RRT algorithm can be successfully used for complex assemblies (six-degrees of freedom robot). The optimal assembly path of the assembly is obtained. At the same time, the successful completion of the six-degree-of-freedom robot assembly path planning proves that the improved RRT algorithm is scientific and effective.

It can be seen that in this embodiment of the application, the sampling area containing the starting node is selected; T new pose nodes are generated by random numbers in the sampling area; the first one is selected from the T new pose nodes. Sampling center, select a new sampling area that includes the first sampling center; repeat the method of obtaining the first sampling center until a preset condition is met, and n second sampling centers are obtained; connect the start node and the The first sampling center and the n second sampling centers obtain the first path, and obtain the target path according to the first path; this assembly path planning method can greatly accelerate the sampling convergence speed and solve the traditional RRT algorithm path planning It is easy to fall into the problem of local optimum.

Optionally, the generating T new pose nodes by random numbers in the sampling area is obtained by the following formula:

q _rand = q _d +f _z f _y (q _s -q _e )

Repeat the formula T times to obtain T new pose nodes, and record T new pose nodes in the form of a list, where f _z is a random number, f _z ∈ (-1, 0)∩(0, 1), f _y is the range factor, f _y ∈ (0, 1), q _s is the starting node, q _e is the target node, q _d is the current sampling center, and q _rand is the new pose node.

Among them, the above formula is the new pose node generation formula, if the current sampling center is the starting node, then q _d =q _s , because _{the value of f y} directly affects the sampling efficiency of the algorithm, if f _y takes a smaller value, then The local solution space is small, which is not conducive to jumping out of the local value. If f _y takes a larger value, the meaning of local sampling is lost, and the local sampling effect is not obvious. Therefore, in this application, in order to ensure a better sampling effect, take f _y ＝0.4, this application _{does not limit the specific value of f y} . Each node parameter in the above formula is a six-dimensional variable, and the six-dimensional includes the X, Y, Z three-dimensional and posture angle coordinate systems in the rectangular space coordinate system The three dimensions of Yaw, Pitch, and Roll, where Yaw is the yaw angle, which rotates around the Z axis, Pitch is the pitch angle, which rotates around the Y axis, and Roll is the roll angle, which rotates around the X axis, which can be determined by these six coordinates The coordinate information and posture information (orientation) of a node parameter. Suppose the coordinates of q _d in the spatial rectangular coordinate system are (x1, y1, z1), and the coordinates of q _s -q _e in the spatial rectangular coordinate system are (x2, y2, z2), then the new pose node q _{rand is} at The possible positions in the spatial rectangular coordinate system consist of the point O1 (x1-0.4x2, y1-0.4y2, z1-0.4z2) and the point O2 (x1+0.4x2, y1+0.4y2, z1+0.4z2) as the body pair Inside the rectangular parallelepiped formed by the corners, the rectangular parallelepiped is the sampling area. Obviously, q _{d is} at the center of the rectangular parallelepiped. It can be seen that when the spatial coordinates of the starting node and target node and the current sampling center are known, the following For a sampling area, in the same way, the posture information (orientation) of the T new posture nodes in the cuboid _{can be determined according to the coordinates of q d} and q _s -q _{e in the posture angle coordinate system.}

Given the current node q _d , repeat the above formula T times, because f _z is a random number, so an expanded random tree composed of T different new pose nodes will be obtained.

It can be seen that the above formula can not only obtain enough new pose nodes to meet the requirements in the current sampling area, but also it is not easy to fall into local values.

Optionally, after the selecting a first sampling center from the T new pose nodes, and selecting a new sampling area containing the first sampling center, the method further includes:

Confirm whether there is a collision point on the line connecting the starting node and the first sampling center;

If the collision point exists, obtain a new sampling direction according to the target node, the first sampling center and the collision point, and determine the sampling step length;

The next sampling center is determined according to the sampling direction and the sampling step length.

Wherein, the obtaining a new sampling direction according to the target node, the first sampling center and the collision point includes:

Confirm the size of the angle ω between the direction vector N1 and the normal vector N2, where the direction vector N1 is the direction vector from the collision point to the target node, and the normal vector N2 is the normal vector of the collision point;

If ω>90°, take the vertical direction of the normal vector N2 as the new sampling direction;

If ω<90°, take the angle bisector direction between the direction vector N1 and the normal vector N2 as the new sampling direction.

Among them, because the traditional RRT algorithm tends to fall into the local optimum during path planning, the embodiment of the present application adopts when the random tree is expanded for expansion, if the expanded node, that is, the first sampling center or the second sampling center is located inside the obstacle area, Then, accurate collision calculation is used to guide the expansion direction of the expanded random tree. As shown in Figure 3, Figure 3 is a schematic diagram of the expansion strategy based on accurate collision calculation, where q _s is the starting node and q _e is the target node. In the extended sampling, q _rand is the current sampling point, and q is obtained by calculation. _Near _{is the point closest to q rand in the} expansion tree, which is used as the point to be expanded in the next step. However, if _{the expansion method from q near} to q _rand is adopted, it will collide with the obstacle area. The collision point is q _c , which will lead to new The pose extension point q _{new is} not successfully extended. Therefore, to obtain the direction vector N1 between _{q c} and q _e _{and the normal vector N2 of the collision point q c} , and let ω be the angle between the direction vector N1 and the normal vector N2, then there is

When ω>90°, as shown in the circle “1” in Figure 3, take _{the vertical direction of the normal vector N2 of the collision point q c} as the new sampling direction N, and N=N ₁ sinω; when ω<90°, As shown in Figure 3 "Ring 2", take the direction of the angle bisector between N1 and N2 as the new sampling direction N, and

Determine the sampling step length, and determine the next sampling center to expand according to the current sampling point q _rand , sampling direction N and sampling step length until the termination condition is reached (a collision with an obstacle or a preset maximum sampling number is reached). This problem is solved by an expansion strategy based on accurate collision calculations and large-stride attempts.

It can be seen that the use of an expansion strategy based on accurate collision calculation and large-stride attempts to move out can shorten the algorithm convergence time and improve the efficiency of algorithm expansion.

Optionally, after the first path is obtained by connecting the start node, the first sampling center, and the n second sampling centers, the method further includes:

Obtain the second path through random sampling;

The third path is obtained by sampling at the end point.

Among them, random sampling is to randomly select sampling points in the feasible solution space, which is the basic sampling strategy of the traditional RRT algorithm. End-point sampling is to sample the target node as the sampling point in the feasible solution space. The end-point sampling strategy can expand the random tree Converge quickly to the target node.

It can be seen that in this application, three strategies of random sampling, end-point sampling, and partial sampling are executed in parallel to obtain feasible paths from the start node to the target node, which are the first path, the second path, and the third path, respectively.

Optionally, the obtaining the target path according to the first path includes:

Perform mathematical processing on the first path, the second path, and the third path to obtain a target path.

Wherein, performing mathematical processing on the first path, the second path, and the third path to obtain the target path includes: selecting k equidistant abscissas on the abscissa of the planar rectangular coordinate system;

Determine the k first ordinates corresponding to the k abscissas on the first path, determine the k second ordinates corresponding to the k abscissas on the second path, and determine the first K third ordinates corresponding to the k abscissas on the three paths;

According to the k first ordinates, the k second ordinates, and the k third ordinates, k target ordinates corresponding to the k abscissas are obtained, and according to the k target ordinates The coordinates obtain k target sampling centers, and the k target sampling centers are connected to obtain the target path.

Among them, the final target path can be obtained by averaging the weighting of the first path, the second path, and the third path. Specifically, in a rectangular coordinate system, k equidistant abscissas ( k1, k2,,, kn), determine the ordinate a1, a2,,, an in the first path corresponding to k1, k2,,, kn, determine the ordinate b1 in the second path corresponding to k1, k2,,, kn , B2,,, bn, determine the ordinates c1, c2,,, cn of the third path corresponding to k1, k2,,, kn, for the first path, the second path, and the third path at each of the above-mentioned abscissas Perform average weighting on the ordinate to get y1=(a1+b1+c1)/3, y2=(a2+b2+c3)/3,,, yn=(an+bn+cn)/3, get k targets Sampling centers m1 (k1, y1), m2 (k2, y2),,, mn (kn, yn), sequentially connect m1, m2,,, mn to obtain a new path, which is the target path.

The target path can also be obtained by processing the above three paths in other ways, which is not limited in this application.

It can be seen that by combining random sampling, local sampling and end-point sampling, the success rate of node expansion during iteration of the expanded random tree is improved, and the improved RRT algorithm has a higher convergence speed.

Please refer to FIG. 4, which is a schematic structural diagram of an electronic device provided by an embodiment of the application. As shown in the figure, it includes a processor, a memory, a communication interface, and one or more programs. In the memory, and configured to be executed by the processor.

Optionally, the program includes instructions for executing the following steps:

Select the sampling area that contains the starting node;

Optionally, after the first sampling center is selected from the T new pose nodes, and a new sampling area containing the first sampling center is selected, the program includes instructions for executing the following steps:

If the collision point exists, obtain a new sampling direction according to the starting node, the first sampling center and the collision point, and determine the sampling step length;

Optionally, in terms of obtaining a new sampling direction according to the starting node, the first sampling center and the collision point, the program includes instructions for executing the following steps:

Optionally, after the first path is obtained by connecting the starting node, the first sampling center, and the n second sampling centers, the program includes instructions for executing the following steps:

Obtain the second path through random sampling;

The third path is obtained by sampling at the end point.

Optionally, in terms of obtaining the target path according to the first path, the program includes instructions for executing the following steps:

Optionally, in terms of performing mathematical processing on the first path, the second path, and the third path to obtain a target path, the program includes instructions for executing the following steps:

Select k equidistant abscissas on the horizontal axis of the rectangular coordinate system;

The foregoing mainly introduces the solution of the embodiment of the present application from the perspective of the method execution process. It can be understood that, in order to implement the above-mentioned functions, the terminal includes hardware structures and/or software modules corresponding to each function. Those skilled in the art should easily realize that in combination with the units and algorithm steps of the examples described in the embodiments provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed by hardware or computer software-driven hardware depends on the specific application and design constraint conditions of the technical solution. Professionals and technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered beyond the scope of this application.

The embodiments of the present application may divide the terminal into functional units according to the foregoing method examples. For example, each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit. The above-mentioned integrated unit can be implemented in the form of hardware or software functional unit. It should be noted that the division of units in the embodiments of the present application is illustrative, and is only a logical function division, and there may be other division methods in actual implementation.

Consistent with the above, please refer to Fig. 5, which is a schematic structural diagram of an assembly path planning device 500 provided by an embodiment of the application. The assembly route planning device 500 includes a sampling unit 501 and a route acquisition unit 502, wherein:

The sampling unit 501 is configured to select a sampling area containing the starting node, and generate T new pose nodes by random numbers in the sampling area, where T is a positive integer, and select from the T new pose nodes A first sampling center, selecting a new sampling area that includes the first sampling center, and repeating the method of obtaining the first sampling center until a preset condition is met, and n second sampling centers are obtained, the preset condition Including the last second sampling center among the n second sampling centers as the target node, or n=c-1, c is the preset maximum number of sampling times, and both n and c are positive integers;

The path obtaining unit 502 is configured to connect the start node, the first sampling center, and the n second sampling centers to obtain a first path, and obtain a target path according to the first path.

Optionally, the sampling unit 501 generates T new pose nodes in the sampling area through the following formula:

q _{rand =} q _d +f _z f _y (q _s -q _e )

Repeat the above formula T times to obtain T new pose nodes, where f _z is a random number, f _z ∈(-1,0)∩(0,1), f _y is a range factor, and f _y ∈( 0, 1), q _s is the starting node, q _e is the target node, q _d is the current sampling center, and q _rand is the new pose node.

Optionally, the assembly path planning device further includes an obstacle avoidance unit 503 for confirming whether there is a collision point on the line between the starting node and the first sampling center, and if the collision point exists, according to The target node, the first sampling center, and the collision point obtain a new sampling direction, determine a sampling step, and determine the next sampling center according to the sampling direction and the sampling step.

Optionally, in terms of obtaining a new sampling direction according to the starting node, the first sampling center and the collision point, the obstacle avoidance unit 503 is specifically configured to:

If ω<90°, take the direction of the angular bisector between the direction vector N1 and the normal vector N2 as the new sampling direction.

Optionally, after the first path is obtained by connecting the starting node, the first sampling center, and the n second sampling centers, the path obtaining unit 502 is specifically configured to:

Obtain the second path through random sampling;

The third path is obtained by sampling at the end point.

Optionally, in terms of obtaining the target path according to the first path, the path obtaining unit 502 is specifically configured to:

Optionally, in terms of performing mathematical processing on the first path, the second path, and the third path, the path obtaining unit 502 is specifically configured to:

The above-mentioned unit may be used to execute the method described in the above-mentioned embodiment. For specific description, please refer to the description of the embodiment, which will not be repeated here.

In the embodiment of the present application, a sampling area containing the starting node is selected; T new pose nodes are generated by random numbers in the sampling area; the first sampling center is selected from the T new pose nodes, and A new sampling area including the first sampling center; repeat the method of obtaining the first sampling center until a preset condition is met, and n second sampling centers are obtained; connect the start node and the first sampling The center and the n second sampling centers obtain the first path, and the target path is obtained according to the first path; through this assembly path planning method, the sampling convergence speed can be greatly accelerated, and the path planning of the traditional RRT algorithm is easily trapped in the local maximum. Excellent question.

The embodiment of the present application also provides a computer non-volatile readable storage medium that stores a computer program for electronic data exchange. The computer program enables the computer to execute any of the assembly path planning methods described in the above method embodiments. Part or all of the steps.

The embodiments of the present application also provide a computer program product. The computer program product includes a non-transitory computer non-volatile readable storage medium storing a computer program. The computer program causes a computer to execute the method described in the foregoing method embodiment. Part or all of the steps of any assembly path planning method.

It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that this application is not limited by the described sequence of actions. Because according to this application, some steps can be performed in other order or at the same time. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application. In the above-mentioned embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in an embodiment, reference may be made to related descriptions of other embodiments.

As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: The technical solutions recorded in the embodiments are modified, or some of the technical features are equivalently replaced; these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

An assembly path planning method, characterized in that the method includes:

Select the sampling area that contains the starting node;

Generate T new pose nodes through random numbers in the sampling area, where T is a positive integer;

Selecting a first sampling center from the T new pose nodes, and selecting a new sampling area that includes the first sampling center;

Repeat the method of obtaining the first sampling center until a preset condition is met, and n second sampling centers are obtained, and the preset condition includes that the last second sampling center among the n second sampling centers is the target node , Or, n=c-1, c is the preset maximum sampling times, n and c are both positive integers;

Connecting the starting node, the first sampling center and the n second sampling centers to obtain a first path, and obtaining a target path according to the first path.
The method according to claim 1, wherein the generating T new pose nodes by random numbers in the sampling area is obtained by the following formula:

q rand = q d +f z f y (q s -q e )

Repeat the formula T times to obtain T new pose nodes, where f z is a random number, f z ∈ (-1, 0) ∩ (0, 1), f y is a range factor, and f y ∈ (0 , 1), q s is the starting node, q e is the target node, q d is the current sampling center, and q rand is the new pose node.
The method according to claim 1, wherein after said selecting a first sampling center from the T new pose nodes and selecting a new sampling area containing the first sampling center, the method further include:

Confirm whether there is a collision point on the line connecting the starting node and the first sampling center;

If the collision point exists, obtain a new sampling direction according to the starting node, the first sampling center and the collision point, and determine the sampling step length;

The next sampling center is determined according to the sampling direction and the sampling step length.
The method according to claim 3, wherein the obtaining a new sampling direction according to the starting node, the first sampling center and the collision point comprises:

Confirm the size of the angle ω between the direction vector N1 and the normal vector N2, where the direction vector N1 is the direction vector from the collision point to the target node, and the normal vector N2 is the normal vector of the collision point;

If ω>90°, take the vertical direction of the normal vector N2 as the new sampling direction;

If ω<90°, take the angle bisector direction between the direction vector N1 and the normal vector N2 as the new sampling direction.
The method according to claim 1, wherein after the first path is obtained by connecting the starting node, the first sampling center, and the n second sampling centers, the method further comprises:

Obtain the second path through random sampling;

The third path is obtained by sampling at the end point.
The method according to claim 5, wherein the obtaining the target path according to the first path comprises:

Perform mathematical processing on the first path, the second path, and the third path to obtain a target path.
The method according to claim 6, wherein said performing mathematical processing on said first path, said second path and said third path to obtain a target path comprises:

Select k equidistant abscissas on the horizontal axis of the rectangular coordinate system;

Determine the k first ordinates corresponding to the k abscissas on the first path, determine the k second ordinates corresponding to the k abscissas on the second path, and determine the first K third ordinates corresponding to the k abscissas on the three paths;

According to the k first ordinates, the k second ordinates, and the k third ordinates, k target ordinates corresponding to the k abscissas are obtained, and according to the k target ordinates The coordinates obtain k target sampling centers, and the k target sampling centers are connected to obtain the target path.
An assembly path planning device, characterized in that the assembly path planning device includes:

The sampling unit is used to select a sampling area that includes the starting node, and generate T new pose nodes by random numbers in the sampling area, where T is a positive integer, and select the first from the T new pose nodes A sampling center, selecting a new sampling area that includes the first sampling center, and repeating the method of obtaining the first sampling center until a preset condition is met, and n second sampling centers are obtained, and the preset condition includes The last second sampling center among the n second sampling centers is the target node, or n=c-1, c is the preset maximum number of sampling times, and both n and c are positive integers;

The path obtaining unit is configured to connect the starting node, the first sampling center and the n second sampling centers to obtain a first path, and obtain a target path according to the first path.
8. The assembly path planning device according to claim 8, wherein the assembly path planning device further comprises:

The obstacle avoidance unit is used to confirm whether there is a collision point on the line between the start node and the first sampling center, and if the collision point exists, according to the target node, the first sampling center and the first sampling center The collision point obtains a new sampling direction, the sampling step is determined, and the next sampling center is determined according to the sampling direction and the sampling step.
The assembly path planning device according to claim 9, wherein, in terms of obtaining a new sampling direction according to the starting node, the first sampling center and the collision point, the obstacle avoidance unit specifically Used for:

Confirm the size of the angle w between the direction vector N1 and the normal vector N2, where the direction vector N1 is the direction vector from the collision point to the target node, and the normal vector N2 is the normal vector of the collision point;

If w>90°, take the vertical direction of the normal vector N2 as the new sampling direction;

If w<90°, take the angle bisector direction between the direction vector N1 and the normal vector N2 as the new sampling direction.
The assembly path planning device according to claim 8, wherein after the first path is obtained by connecting the starting node, the first sampling center, and the n second sampling centers, the path The acquisition unit is specifically used for:

Obtain the second path through random sampling;

The third path is obtained by sampling at the end point.
The assembly path planning device according to claim 11, wherein, in terms of obtaining the target path according to the first path, the path obtaining unit is specifically configured to:

Perform mathematical processing on the first path, the second path, and the third path to obtain a target path.
The assembly path planning device according to claim 12, wherein, in terms of performing mathematical processing on the first path, the second path, and the third path, the path acquisition unit is specifically configured to :

Select k equidistant abscissas on the horizontal axis of the rectangular coordinate system;

Determine the k first ordinates corresponding to the k abscissas on the first path, determine the k second ordinates corresponding to the k abscissas on the second path, and determine the first K third ordinates corresponding to the k abscissas on the three paths;

According to the k first ordinates, the k second ordinates, and the k third ordinates, k target ordinates corresponding to the k abscissas are obtained, and according to the k target ordinates The coordinates obtain k target sampling centers, and the k target sampling centers are connected to obtain the target path.
An electronic device, characterized by comprising a processor, a memory, a communication interface, and one or more programs, the programs are stored in the memory and configured to be executed by the processor, and the programs are The configuration is used to perform the following steps:

Select the sampling area that contains the starting node;

Generate T new pose nodes through random numbers in the sampling area, where T is a positive integer;

Selecting a first sampling center from the T new pose nodes, and selecting a new sampling area that includes the first sampling center;

Repeat the method of obtaining the first sampling center until a preset condition is met, and n second sampling centers are obtained, and the preset condition includes that the last second sampling center among the n second sampling centers is the target node , Or, n=c-1, c is the preset maximum sampling times, n and c are both positive integers;

Connecting the starting node, the first sampling center and the n second sampling centers to obtain a first path, and obtaining a target path according to the first path.
The electronic device according to claim 14, wherein after the selecting a first sampling center from the T new pose nodes and selecting a new sampling area containing the first sampling center, the program Is configured to perform the following steps:

Confirm whether there is a collision point on the line connecting the starting node and the first sampling center;

If the collision point exists, obtain a new sampling direction according to the starting node, the first sampling center and the collision point, and determine the sampling step length;

The next sampling center is determined according to the sampling direction and the sampling step length.
The electronic device according to claim 15, wherein, in terms of obtaining a new sampling direction according to the starting node, the first sampling center and the collision point, the program is configured to execute The following steps:

Confirm the size of the angle ω between the direction vector N1 and the normal vector N2, where the direction vector N1 is the direction vector from the collision point to the target node, and the normal vector N2 is the normal vector of the collision point;

If ω>90°, take the vertical direction of the normal vector N2 as the new sampling direction;

If ω<90°, take the angle bisector direction between the direction vector N1 and the normal vector N2 as the new sampling direction.
The electronic device according to claim 14, wherein after the first path is obtained by connecting the starting node, the first sampling center, and the n second sampling centers, the program is configured Used to perform the following steps:

Obtain the second path through random sampling;

The third path is obtained by sampling at the end point.
The electronic device according to claim 17, wherein, in terms of obtaining the target path according to the first path, the program is configured to perform the following steps:

Perform mathematical processing on the first path, the second path, and the third path to obtain a target path.
The electronic device according to claim 18, wherein in the aspect of performing mathematical processing on the first path, the second path, and the third path to obtain a target path, the program is configured to To perform the following steps:

Select k equidistant abscissas on the horizontal axis of the rectangular coordinate system;

Determine the k first ordinates corresponding to the k abscissas on the first path, determine the k second ordinates corresponding to the k abscissas on the second path, and determine the first K third ordinates corresponding to the k abscissas on the three paths;

According to the k first ordinates, the k second ordinates, and the k third ordinates, k target ordinates corresponding to the k abscissas are obtained, and according to the k target ordinates The coordinates obtain k target sampling centers, and the k target sampling centers are connected to obtain the target path.
A computer non-volatile non-volatile readable storage medium, characterized in that, a computer program is stored on the computer non-volatile non-volatile readable storage medium, and the program is executed by a processor to realize the rights The method of any one of 1 to 7 is required.