CN110646006A - Assembly path planning method and related device - Google Patents
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Abstract
The embodiment of the invention provides an assembly path planning method and a related device, wherein the method comprises the following steps: selecting a sampling area containing an initial node; generating T new pose nodes in the sampling area through random numbers; selecting a first sampling center from the T new pose nodes, and selecting a new sampling area containing the first sampling center; repeatedly executing the method for obtaining the first sampling center until a preset condition is met, and obtaining n second sampling centers; 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; by the method for planning the assembly path, the sampling convergence speed can be greatly increased, and the problem that the path planning of the traditional RRT (fast search random tree) algorithm is easy to be trapped in local optimization is solved.
Description
Technical Field
The invention relates to the field of path planning, in particular to an assembly path planning method and a related device.
Background
With the rise of large-scale industrial production, the digital assembly technology, i.e. the virtual assembly technology, is increasingly applied to actual industrial production. The RRT algorithm is used as a fast random path planning method, and because the random sampling planning method is adopted, the scene preprocessing is not needed, so that the path planning problem of a multi-dimensional non-convex space can be well solved, and the RRT algorithm is widely applied to solving the optimal path in a static road network problem. However, because the traditional RRT algorithm adopts a global uniform random sampling strategy, the calculation amount of the algorithm is increased sharply, and the efficiency of the algorithm is reduced; and the trouble of local optimal solution can not be got rid of, so that the RRT algorithm only has the probabilistic optimal solution.
Disclosure of Invention
The embodiment of the invention provides an assembly path planning method and a related device, which can accelerate the sampling convergence speed and solve the problem that the path planning process is easy to be affected by local optimization.
The first aspect of the embodiment of the invention discloses an assembly path planning method, which comprises the following steps:
selecting a sampling area containing an initial node;
generating T new pose nodes in the sampling region through random numbers, wherein T is a positive integer;
selecting a first sampling center from the T new pose nodes, and selecting a new sampling area containing the first sampling center;
repeatedly executing the method for obtaining the first sampling center until a preset condition is met, and obtaining n second sampling centers, wherein the preset condition comprises that the last second sampling center in the n second sampling centers is a target node, or n is c-1, c is a preset maximum sampling frequency, and n and c are positive integers;
and 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 second aspect of the present invention discloses an assembly path planning apparatus, which includes:
the sampling unit is used for selecting a sampling area containing an initial node, generating T new pose nodes through random numbers in the sampling area, wherein T is a positive integer, selecting a first sampling center from the T new pose nodes, selecting a new sampling area containing the first sampling center, and repeatedly executing the method for acquiring the first sampling center until a preset condition is met to obtain n second sampling centers, wherein the preset condition comprises that the last second sampling center in the n second sampling centers is a target node, or n is c-1, c is a preset maximum sampling frequency, and n and c are both positive integers;
and the path acquisition unit is used for 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 third aspect of the invention discloses an electronic device comprising a processor, a memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs comprising instructions for performing the method of any of the first aspects.
A fourth aspect of the invention discloses a computer-readable storage medium storing a computer program for execution by a processor to perform the method of any of the first aspects.
In the scheme of the embodiment of the invention, a sampling area containing an initial node is selected; generating T new pose nodes in the sampling area through random numbers; selecting a first sampling center from the T new pose nodes, and selecting a new sampling area containing the first sampling center; repeatedly executing the method for obtaining the first sampling center until a preset condition is met, and obtaining n second sampling centers; 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; by the method for planning the assembly path, the sampling convergence speed can be greatly increased, and the problem that the path planning of the traditional RRT algorithm is easy to be partially optimal is solved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flow chart of an assembly path planning method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a partial sampling provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of an extended strategy based on precise collision calculation according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an assembly path planning apparatus according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present invention better understood by those skilled in the art, the technical solutions in the embodiments of the present invention will be clearly described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The appearances of the phrases "first," "second," and "third," or the like, in the specification, claims, and figures are not necessarily all referring to the particular order in which they are presented. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The electronic device according to the embodiment of the present application may include various handheld devices, vehicle-mounted devices, wireless headsets, computing devices or other processing devices connected to a wireless modem, and various forms of User Equipment (UE), Mobile Stations (MS), terminal devices (terminal devices), and the like, which have a wireless communication function, and the electronic device may be, for example, a smart phone, a tablet computer, a headset box, and the like. For convenience of description, the above-mentioned devices are collectively referred to as electronic devices.
The following describes embodiments of the present application in detail.
Referring to fig. 1, fig. 1 is a schematic flow chart of an assembly path planning method according to an embodiment of the present application, including:
s101, selecting a sampling area containing a starting node.
In the path planning process, because the traditional RRT algorithm is easy to fall into a local optimal solution and is slow in convergence, the RRT algorithm is improved, and then the assembly path planning method based on the improved RRT algorithm is provided, so that the problem of the traditional RRT algorithm can be solved.
Known to contain a start node qsAnd a target node qeA feasible solution space (i.e., a preselected end point) needs to find a closest feasible path from a starting node to a target node in the feasible solution space, and a region containing the starting node is selected as a sampling region in the feasible solution space for local sampling by taking the starting node as a sampling center, so that a significant local sampling effect is obtained on the premise that the feasible solution space does not fall into a locally optimal solution.
S102, generating T new pose nodes in the sampling area through random numbers, wherein T is a positive integer.
In each sampling process, because the random number in the new pose node generation formula is continuously changed, T different new pose nodes can be obtained through the new pose node generation formula to form an extended random tree, and T can be preset and can be set to be 5, 10, 15, 20 and the like.
S103, selecting a first sampling center from the T new pose nodes, and selecting a new sampling area containing the first sampling center.
As shown in fig. 2, fig. 2 is a schematic diagram of local sampling, a local sampling strategy is adopted, a next sampling region is determined through a current sampling center, and in order to shorten the length of a target path as much as possible, the distance between the current sampling center and the next sampling center should be shortened as much as possible, so that a node q closest to the current sampling center is selected from the T new pose nodesnearAnd selecting the new sampling area containing the first sampling center as the first sampling center according to the method in S101.
And S104, repeatedly executing the method for obtaining the first sampling center until a preset condition is met, and obtaining n second sampling centers, wherein the preset condition comprises that the last second sampling center in the n second sampling centers is a target node, or n is c-1, c is a preset maximum sampling frequency, and n and c are positive integers.
The method for acquiring the sampling center and the sampling area each time is the same, the sampling area is determined according to the current sampling center, the next sampling center is determined in the sampling area, and the method for acquiring the first sampling center, namely iterative sampling, is repeatedly executed until the preset maximum sampling frequency is reached or the target node is reached.
And S105, 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.
In the embodiment of the application, random sampling, local sampling and end point sampling are combined, so that the success rate of expansion of the sampling center of an expanded random tree during iterative sampling is improved, the convergence speed of the traditional RRT algorithm is improved, the problem of the local optimal solution is solved, specifically, possible paths are obtained through random sampling, local sampling and end point sampling at the same time, the first path, the second path and the third path are obtained, and the three paths are subjected to fusion processing to obtain a final target path.
In the embodiment of the application, the assembly path planning method is applied to a six-degree-of-freedom intelligent robot, the six-degree-of-freedom intelligent robot is used as a core component for relieving industrial production pressure and improving industrial production efficiency, and the six-degree-of-freedom intelligent robot consists of a base, a waist arm, a large arm, a small arm, a wrist arm and a tail end controller, and has the relevant characteristics of compact structure, complex parts and the like. According to the embodiment of the application, a six-degree-of-freedom robot is used as a carrier, whether the RRT algorithm can be successfully completed in a virtual environment is improved through simulation verification, and a simulation result shows that the optimal assembly path of an assembly body can be successfully obtained by applying the improved RRT algorithm to a complex assembly body (the six-degree-of-freedom robot), and meanwhile, the improved RRT algorithm is proved to have algorithm scientificity and effectiveness through successful completion of the six-degree-of-freedom robot assembly path planning.
It can be seen that, in the embodiment of the present application, a sampling region including a start node is selected; generating T new pose nodes in the sampling area through random numbers; selecting a first sampling center from the T new pose nodes, and selecting a new sampling area containing the first sampling center; repeatedly executing the method for obtaining the first sampling center until a preset condition is met, and obtaining n second sampling centers; 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; by the method for planning the assembly path, the sampling convergence speed can be greatly increased, and the problem that the path planning of the traditional RRT algorithm is easy to be partially optimal is solved.
Optionally, the generating T new pose nodes by random numbers in the sampling region is obtained by the following formula:
qrand＝qd+fzfy(qs-qe)
repeating the formula for T times to obtain T new pose nodes, and recording the T new pose nodes in a list form, wherein fzIs a random number, fz∈(-1，0)∩(0，1)，fyIs a range factor, fy∈(0，1)，qsIs a starting node, qeIs a target node, qdAs the current sampling center, qrandAnd the new pose node is obtained.
The formula is a new pose node generation formula, and if the current sampling center is an initial node, q isd＝qsBecause f isyThe value of (a) directly affects the sampling efficiency of the algorithm, if fyIf the smaller value is taken, the local solution space is smaller, which is not beneficial to jumping out of the local value, if f is smalleryIf a larger value is taken, the significance of local sampling is lost, and the local sampling effect is not obvious, so in the application, in order to ensure a better sampling effect, f is takeny0.4, for fyThe specific values of the node parameters are not limited, each node parameter in the formula is a six-dimensional variable, the six dimensions include X, Y, Z three dimensions in a rectangular space coordinate system and Yaw, Pitch and Roll three dimensions in a posture angle coordinate system, wherein Yaw is a Yaw angle, the Yaw rotates around a Z axis, Pitch is a Pitch angle, the Yaw rotates around a Y axis, and Roll rotates around an X axis, and coordinate information and posture information (orientation) of one node parameter can be determined through the six coordinates. Let q bedThe coordinates in the rectangular space coordinate system are (x1, y1, z1), qs-qeCoordinates in a space rectangular coordinate system are (x2, y2, z2), and then the new pose node q is obtainedrandPossible positions in the rectangular spatial coordinate system are inside a cuboid formed by a point O1(x1-0.4x2, y1-0.4y2, z1-0.4z2) and a point O2(x1+0.4x2, y1+0.4y2, z1+0.4z2) as the body diagonal, the cuboid being the sampling region, and it is obvious that q is the sampling regiondAt the center of the rectangular parallelepipedWhen the spatial coordinates of the starting node, the target node and the current sampling center are known, the next sampling region can be determined, and similarly, the next sampling region can be determined according to qdAnd q iss-qeThe coordinates in the pose angular coordinate system determine the pose information (orientation) of the T new pose nodes inside the cuboid.
Knowing the current node qdRepeat the above formula T times because fzIs a random number, so an extended random tree formed by T different new pose nodes is obtained.
Therefore, through the formula, enough new pose nodes meeting the requirements can be obtained in the current sampling region, and the pose nodes are not easy to fall into local values.
Optionally, after selecting a first sampling center from the T new pose nodes and selecting a new sampling area including the first sampling center, the method further includes:
confirming whether a collision point exists on a connecting line of the starting node and the first sampling center;
if the collision point exists, obtaining a new sampling direction according to the target node, the first sampling center and the collision point, and determining a sampling step length;
and determining the next sampling center according to the sampling direction and the sampling step length.
Wherein the obtaining of the new sampling direction from the first sampling center and the collision point according to the target node comprises:
confirming the size of an included angle omega between a direction vector N1 and a normal vector N2, wherein 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 omega is larger than 90 degrees, taking the vertical direction of the normal vector N2 as a new sampling direction;
and if omega is less than 90 degrees, taking the direction of the angular bisector between the direction vector N1 and the normal vector N2 as a new sampling direction.
Because the traditional RRT algorithm is easy to fall into local optimum during path planning, the embodiment of the application adoptsAn extended strategy based on precise collision computation and large stride attempted move-out is used to solve this problem. When the expanded random tree is expanded, if an expansion node, namely a first sampling center or a second sampling center is positioned in an obstacle area, the expansion direction of the expanded random tree is guided through accurate collision calculation. FIG. 3 is a schematic diagram of an extended strategy based on precise collision calculation, as shown in FIG. 3, wherein q issIs a starting node, qeIn extended sampling, q, for the target noderandFor the current sample point, q is obtained by calculationnearFor extending the distance q in the treerandThe nearest point is taken as the point to be expanded next, but if q is takennearTo qrandWill collide with the obstacle area with a collision point qcThen result in a new pose extension point qnewThe extension was unsuccessful. Thus, q is obtainedcAnd q iseDirection vector N1 between and the collision point qcLet ω be the vector angle between the direction vector N1 and the normal vector N2, then there isWhen ω > 90, the collision point q is taken as indicated by the ring "1" in FIG. 3cThe vertical direction of the normal vector N2 is the new sampling direction N, and N is equal to N1sin omega; when ω < 90 °, as shown in "ring 2" of fig. 3, the bisector direction between N1 and N2 is taken as the new sampling direction N, and
determining sampling step length according to current sampling point qrandThe sampling direction N and the sampling step length determine the next sampling center to expand until the termination condition is reached (collision with an obstacle or a preset maximum number of samples).
Therefore, by adopting an expansion strategy based on accurate collision calculation and large-stride trial shift-out, the algorithm convergence time can be shortened, and the algorithm expansion efficiency can be improved.
Optionally, the obtaining a target path according to the first path includes: and obtaining a second path through random sampling, obtaining a third path through end point sampling, and obtaining a target path through the first path, the second path and the third path.
In the application, three strategies of random sampling, end point sampling and local sampling are executed in parallel to obtain feasible paths from a starting node to a target node, namely a first path, a second path and a third path, the three paths are subjected to average weighting processing to obtain a final target path, specifically, in a plane rectangular coordinate system, equidistant k points (k1, k2, kn) are sequentially selected on an x axis to determine longitudinal coordinates a1, a2, an corresponding to k1, k2, kn in the first path, a corresponding to k1 in the second path, k2,, the ordinate b1, b2,, bn of kn, determine the ordinate c1, c2,, cn of the third path corresponding to k1, k2,, kn, perform average weighting processing on the ordinates of the first path, the second path and the third path at the above points, to obtain y1 ═ a1+ b1+ c1)/3, y2 ═ a2+ b2+ c3)/3, yn ═ an + bn + cn)/3, to obtain k new sampling points 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 may also be obtained by processing the three paths in other manners, which is not limited in this application.
Therefore, by combining random sampling, local sampling and end point sampling, the success rate of node expansion of the expanded random tree in iteration is improved, and the improved RRT algorithm is ensured to have higher convergence rate.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure, which includes a processor, a memory, a communication interface, and one or more programs, where the programs are stored in the memory and configured to be executed by the processor.
Optionally, the program comprises instructions for performing the steps of:
selecting a sampling area containing an initial node;
generating T new pose nodes in the sampling region through random numbers, wherein T is a positive integer;
selecting a first sampling center from the T new pose nodes, and selecting a new sampling area containing the first sampling center;
repeatedly executing the method for obtaining the first sampling center until a preset condition is met, and obtaining n second sampling centers, wherein the preset condition comprises that the last second sampling center in the n second sampling centers is a target node, or n is c-1, c is a preset maximum sampling frequency, and n and c are positive integers;
and 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.
Optionally, after selecting a first sampling center from the T new pose nodes and selecting a new sampling region including the first sampling center, the program includes instructions for performing the following steps:
confirming whether a collision point exists on a connecting line of the starting node and the first sampling center;
if the collision point exists, obtaining a new sampling direction according to the initial node, the first sampling center and the collision point, and determining a sampling step length;
and determining the next sampling center according to the sampling direction and the sampling step length.
Optionally, in the aspect that a new sampling direction is obtained according to the start node, the first sampling center and the collision point, the program includes instructions for performing the following steps:
confirming the size of an included angle omega between a direction vector N1 and a normal vector N2, wherein 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 omega is larger than 90 degrees, taking the vertical direction of the normal vector N2 as a new sampling direction;
and if omega is less than 90 degrees, taking the direction of the angular bisector between the direction vector N1 and the normal vector N2 as a new sampling direction.
Optionally, in the aspect of obtaining the target path according to the first path, the program includes instructions for performing the following steps:
and obtaining a second path through random sampling, obtaining a third path through end point sampling, and obtaining a target path through the first path, the second path and the third path.
The above description mainly introduces the solution of the embodiment of the present application from the perspective of the method implementation process. It is understood that the terminal includes corresponding hardware structures and/or software modules for performing the respective functions in order to implement the above-described functions. Those of skill in the art will readily appreciate that the present application is capable of hardware or a combination of hardware and computer software implementing the various illustrative elements and algorithm steps described in connection with the embodiments provided herein. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiment of the present application, the terminal may be divided into the functional units according to the above method example, 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 integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. It should be noted that the division of the unit in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
In accordance with the above, please refer to fig. 5, fig. 5 is a schematic structural diagram of an assembly path planning apparatus 500 according to an embodiment of the present disclosure. The assembly path planning apparatus 500 includes a sampling unit 501 and a path obtaining unit 502, where:
a sampling unit 501, configured to select a sampling region including an initial node, generate T new pose nodes in the sampling region through a random number, where T is a positive integer, select a first sampling center from the T new pose nodes, select a new sampling region including the first sampling center, and repeatedly execute the method for obtaining the first sampling center until a preset condition is met, so as to obtain n second sampling centers, where the preset condition includes that a last second sampling center of the n second sampling centers is a target node, or n is c-1, c is a preset maximum sampling number, and n and c are both positive integers;
a path obtaining unit 502, 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 region by the following formula:
qrand＝qd+fzfy(qs-qe)
repeating the above formula for T times to obtain T new pose nodes, wherein fzIs a random number, fz∈(-1，0)∩(0，1)，fyIs a range factor, fy∈(0，1)，qsIs a starting node, qeIs a target node, qdAs the current sampling center, qrandAnd the new pose node is obtained.
Optionally, the assembly path planning apparatus further includes an obstacle avoidance unit 503, configured to determine whether a collision point exists on a connection line between the start node and the first sampling center, and if the collision point exists, obtain a new sampling direction according to the target node, the first sampling center, and the collision point, determine a sampling step size, and determine a next sampling center according to the sampling direction and the sampling step size.
Optionally, in the aspect that a new sampling direction is obtained according to the start node, the first sampling center, and the collision point, the obstacle avoidance unit 503 is specifically configured to:
confirming the size of an included angle omega between a direction vector N1 and a normal vector N2, wherein 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 omega is larger than 90 degrees, taking the vertical direction of the normal vector N2 as a new sampling direction;
and if omega is less than 90 degrees, taking the direction of the angular bisector between the direction vector N1 and the normal vector N2 as a new sampling direction.
Optionally, in the aspect of obtaining the target path according to the first path, the path obtaining unit is specifically configured to:
and obtaining a second path through random sampling, obtaining a third path through end point sampling, and obtaining a target path through the first path, the second path and the third path.
The above units may be configured to execute the method described in the above embodiments, and specific description is given in the description of the embodiments, which is not repeated herein.
In the embodiment of the application, a sampling area containing a starting node is selected; generating T new pose nodes in the sampling area through random numbers; selecting a first sampling center from the T new pose nodes, and selecting a new sampling area containing the first sampling center; repeatedly executing the method for obtaining the first sampling center until a preset condition is met, and obtaining n second sampling centers; 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; by the method for planning the assembly path, the sampling convergence speed can be greatly increased, and the problem that the path planning of the traditional RRT algorithm is easy to be partially optimal is solved.
Embodiments of the present application also provide a computer-readable storage medium storing a computer program for electronic data exchange, where the computer program enables a computer to execute part or all of the steps of any one of the assembly path planning methods described in the above method embodiments.
Embodiments of the present application also provide a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program causes a computer to execute part or all of the steps of any one of the assembly path planning methods described in the above method embodiments.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application. In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. An assembly path planning method, characterized in that the method comprises:
selecting a sampling area containing an initial node;
generating T new pose nodes in the sampling region through random numbers, wherein T is a positive integer;
selecting a first sampling center from the T new pose nodes, and selecting a new sampling area containing the first sampling center;
repeatedly executing the method for obtaining the first sampling center until a preset condition is met, and obtaining n second sampling centers, wherein the preset condition comprises that the last second sampling center in the n second sampling centers is a target node, or n is c-1, c is a preset maximum sampling frequency, and n and c are positive integers;
and 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.
2. The method according to claim 1, wherein the generation of the T new pose nodes by random numbers within the sampling region is obtained by the following formula:
qrand＝qd+fzfy(qs-qe)
repeating the formula T times to obtain T new pose nodes, wherein fzIs a random number, fz∈(-1，0)∩(0，1)，fyIs a range factor, fy∈(0，1)，qsIs a starting node, qeIs a target node, qdAs the current sampling center, qrandAnd the new pose node is obtained.
3. The method of claim 1, wherein after said selecting a first sampling center from the T new pose nodes, selecting a new sampling region containing the first sampling center, the method further comprises:
confirming whether a collision point exists on a connecting line of the starting node and the first sampling center;
if the collision point exists, obtaining a new sampling direction according to the initial node, the first sampling center and the collision point, and determining a sampling step length;
and determining the next sampling center according to the sampling direction and the sampling step length.
4. The method of claim 3, wherein the deriving a new sampling direction from the start node, the first sampling center, and the collision point comprises:
confirming the size of an included angle omega between a direction vector N1 and a normal vector N2, wherein 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 omega is larger than 90 degrees, taking the vertical direction of the normal vector N2 as a new sampling direction;
and if omega is less than 90 degrees, taking the direction of the angular bisector between the direction vector N1 and the normal vector N2 as a new sampling direction.
5. The method of claim 1, wherein obtaining the target path according to the first path comprises: and obtaining a second path through random sampling, obtaining a third path through end point sampling, and obtaining a target path through the first path, the second path and the third path.
6. An assembly path planning apparatus, characterized in that the assembly path planning apparatus comprises:
the sampling unit is used for selecting a sampling area containing an initial node, generating T new pose nodes through random numbers in the sampling area, wherein T is a positive integer, selecting a first sampling center from the T new pose nodes, selecting a new sampling area containing the first sampling center, and repeatedly executing the method for acquiring the first sampling center until a preset condition is met to obtain n second sampling centers, wherein the preset condition comprises that the last second sampling center in the n second sampling centers is a target node, or n is c-1, c is a preset maximum sampling frequency, and n and c are both positive integers;
and the path acquisition unit is used for 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.
7. The assembly path planner of claim 6 wherein the sampling unit generates T new pose nodes within the sampling region by:
qrand＝qd+fzfy(qs-qe)
repeating the formula T times to obtain T new pose nodes, wherein fzIs a random number, fz∈(-1，0)∩(0，1)，fyIs a range factor, fy∈(0，1)，qsIs a starting node, qeIs a target node, qdAs the current sampling center, qrandAnd the new pose node is obtained.
8. The assembly path planning apparatus according to claim 6, further comprising:
and the obstacle avoidance unit is used for confirming whether a collision point exists on a connecting line of the starting node and the first sampling center, obtaining a new sampling direction according to the target node, the first sampling center and the collision point if the collision point exists, determining a sampling step length, and determining a next sampling center according to the sampling direction and the sampling step length.
9. The assembly path planning apparatus according to claim 6, wherein in the aspect that a new sampling direction is obtained according to the start node, the first sampling center, and the collision point, the obstacle avoidance unit is specifically configured to:
confirming the size of an included angle w between a direction vector N1 and a normal vector N2, wherein 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 is larger than 90 degrees, taking the vertical direction of the normal vector N2 as a new sampling direction;
and if w is less than 90 degrees, taking the direction of the angle bisector between the direction vector N1 and the normal vector N2 as a new sampling direction.
10. The assembly path planning apparatus according to claim 6, wherein in the aspect of obtaining the target path according to the first path, the path obtaining unit is specifically configured to:
and obtaining a second path through random sampling, obtaining a third path through end point sampling, and obtaining a target path through the first path, the second path and the third path.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111220786A (en) * | 2020-03-09 | 2020-06-02 | 生态环境部华南环境科学研究所 | Method for rapidly monitoring organic pollution of deep water sediments |
| CN112344938A (en) * | 2020-10-31 | 2021-02-09 | 哈尔滨工程大学 | Space environment path generation and planning method based on pointing and potential field parameters |
| CN112987799A (en) * | 2021-04-16 | 2021-06-18 | 电子科技大学 | Unmanned aerial vehicle path planning method based on improved RRT algorithm |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113985884A (en) * | 2021-10-29 | 2022-01-28 | 国网山东省电力公司经济技术研究院 | Power inspection robot path planning method and system and robot |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102708242A (en) * | 2012-05-07 | 2012-10-03 | 上海飞机制造有限公司 | Method and device for solving disassembling path of product pipe piece |
| CN108983780A (en) * | 2018-07-24 | 2018-12-11 | 武汉理工大学 | One kind is based on improvement RRT*The method for planning path for mobile robot of algorithm |
| CN109886279A (en) * | 2019-01-24 | 2019-06-14 | 平安科技（深圳）有限公司 | Image processing method, device, computer equipment and storage medium |
| CN109877836A (en) * | 2019-03-13 | 2019-06-14 | 浙江大华技术股份有限公司 | Paths planning method, device, mechanical arm controller and readable storage medium storing program for executing |
| CN110181515A (en) * | 2019-06-10 | 2019-08-30 | 浙江工业大学 | A kind of double mechanical arms collaborative assembly working path planing method |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102375416B (en) * | 2010-08-13 | 2013-10-23 | 同济大学 | Human type robot kicking action information processing method based on rapid search tree |
| JP6606442B2 (en) * | 2016-02-24 | 2019-11-13 | 本田技研工業株式会社 | Mobile route plan generation device |
| CN106695802A (en) * | 2017-03-19 | 2017-05-24 | 北京工业大学 | Improved RRT<*> obstacle avoidance motion planning method based on multi-degree-of-freedom mechanical arm |
| CN110083165B (en) * | 2019-05-21 | 2022-03-08 | 大连大学 | Path planning method of robot in complex narrow environment |
-
2019
- 2019-09-02 CN CN201910824569.2A patent/CN110646006A/en active Pending
- 2019-11-11 WO PCT/CN2019/117030 patent/WO2021042519A1/en active Application Filing
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102708242A (en) * | 2012-05-07 | 2012-10-03 | 上海飞机制造有限公司 | Method and device for solving disassembling path of product pipe piece |
| CN108983780A (en) * | 2018-07-24 | 2018-12-11 | 武汉理工大学 | One kind is based on improvement RRT*The method for planning path for mobile robot of algorithm |
| CN109886279A (en) * | 2019-01-24 | 2019-06-14 | 平安科技（深圳）有限公司 | Image processing method, device, computer equipment and storage medium |
| CN109877836A (en) * | 2019-03-13 | 2019-06-14 | 浙江大华技术股份有限公司 | Paths planning method, device, mechanical arm controller and readable storage medium storing program for executing |
| CN110181515A (en) * | 2019-06-10 | 2019-08-30 | 浙江工业大学 | A kind of double mechanical arms collaborative assembly working path planing method |
Non-Patent Citations (1)
| Title |
|---|
| 刘密;刘检华;何永熹;尚炜;: "复杂结构条件下的装配路径求解与优化技术", 机械工程学报 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111220786A (en) * | 2020-03-09 | 2020-06-02 | 生态环境部华南环境科学研究所 | Method for rapidly monitoring organic pollution of deep water sediments |
| CN112344938A (en) * | 2020-10-31 | 2021-02-09 | 哈尔滨工程大学 | Space environment path generation and planning method based on pointing and potential field parameters |
| CN112987799A (en) * | 2021-04-16 | 2021-06-18 | 电子科技大学 | Unmanned aerial vehicle path planning method based on improved RRT algorithm |
| CN112987799B (en) * | 2021-04-16 | 2022-04-05 | 电子科技大学 | Unmanned aerial vehicle path planning method based on improved RRT algorithm |
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