CN111338334A - Assembly path planning method based on multi-factor cost guidance in virtual assembly simulation - Google Patents
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Abstract
The invention discloses an assembly path planning method based on multi-factor cost guidance in virtual assembly simulation, which comprises the following steps: 1. establishing a virtual assembly three-dimensional environment, setting a search space boundary of an assembly path, determining a range for limiting the search of the assembly path, and setting planning initial parameters; 2. in the assembly path search space, taking the initial state of the part to be assembled as a root node, taking the multi-factor path cost as guidance, and combining a rapid search random tree algorithm to search and expand the assembly path; 3. and expanding the path tree into a near space of the target state of the part to be assembled, finishing the path search, and obtaining a complete assembly path from the initial state to the target state. The method comprises the steps of constructing an assembly path multi-factor cost model by taking path distance, path inflection corners and rotation motion angles of parts to be assembled as main factors, and guiding partial assembly paths in each step to grow towards the direction with the relatively lowest cost by using multi-factor path cost, so that an overall optimized assembly path is obtained.
Description
Technical Field
The invention relates to the field of virtual assembly path planning, in particular to an assembly path planning method based on multi-factor cost guidance.
Background
The method comprises the steps of planning an assembly path of parts of an assembly body in a virtual assembly environment, generating a collision-free and effective motion path from an assembly starting point to an assembly final point, and applying the path planning technology to the fields of product structure design, assembly process design (for example, whether the assembly body structure design is reasonable or not and whether an assembly sequence planning result is feasible or not is verified), operation training and the like. In the assembly path planning with rotation constraint requirements between adjacent postures, in order to obtain a smooth assembly motion path, a phenomenon of 'sharp turning' should be avoided, wherein the 'sharp turning' includes two layers of meanings: firstly, the 'sharp turn' caused by translational motion, and secondly, the 'sharp turn' caused by the rotation motion of parts around the axes of the parts. The calculated path points not only contain three-dimensional coordinate information of the parts in the space, but also contain three-dimensional rotation information, and the included angle between the translation components of adjacent motions is ensured to be within a set range, and meanwhile, the rotation variation between adjacent poses is ensured to be within an allowed range.
Among many planning algorithms, a rapid searching random tree method (RRT for short) processes a C space by using randomness without accurately calculating a state space, and is fast in searching speed and widely applied to a motion planning problem in a complex high-dimensional environment. However, in the existing literature records, there is no assembly path planning method that comprehensively considers and simultaneously solves the two types of 'sharp turns', and the difficulty is mainly reflected in two aspects: first, a "sharp turn" caused by translational motion can be improved by pruning a path, reducing path nodes, but the adjacent pose changes compared to the path before pruning, and instead, a second "sharp turn" is caused. Second, the 'sharp turn' caused by large rotation variation between adjacent poses can be improved by increasing the weight of the rotation component in the distance metric generated by the guide path, and meanwhile, the path searching efficiency is reduced, the path nodes are increased, the path length is increased, and the 'sharp turn' caused by translational motion is easy to occur.
Disclosure of Invention
The invention aims to provide an assembly path planning method based on multi-factor cost guidance, which improves the quality of an assembly path solution of an assembly part and the stability of assembly movement in a virtual assembly environment.
In order to achieve the purpose, the invention constructs a path cost model taking the path distance, the path inflection point corner and the rotating motion angle of the piece to be assembled as main factors, and combines the model with an RRT algorithm to form an assembly path planning method based on multi-factor cost guidance.
In order to achieve the purpose, the method adopted by the invention is as follows: an assembly path planning method based on multi-factor cost guidance in virtual assembly simulation comprises the following steps:
s1, establishing a virtual assembly three-dimensional environment, determining an assembly path search space, and setting initial parameters of assembly path planning:
establishing a three-dimensional model of a virtual assembly environment, wherein the assembly environment can be the whole assembly workshop environment or a local assembly environment in which the to-be-assembled part is located, and determining an assembly path search space of the to-be-assembled part;
determining an initial state and a target state of a part to be assembled, wherein the initial state and the target state comprise the position and the posture of the part to be assembled in a search space;
taking the virtual assembly environment, the assembly path search space, the initial state and the target state of the part to be assembled as initial parameters of assembly path planning;
s2, in the assembly path search space, taking the initial state of the parts to be assembled as a root node, taking the cost of the multi-factor path as guidance, and combining the rapid search random tree algorithm to search and expand the assembly path, specifically:
s201, carrying out uniform random sampling in an assembly path search space and obtaining M sampling points;
s202, with the minimum Euclidean distance of a three-dimensional space as a measurement target, searching father nodes for the sampling points in the constructed path tree, and performing interpolation calculation from the father nodes to the corresponding sampling points to obtain corresponding candidate leaf nodes;
s203, performing multi-factor path cost estimation on each candidate leaf node, sorting according to the cost estimation value, and adding into a candidate leaf node list;
s204, selecting the candidate leaf node with the minimum path cost in the free space as an expansion target of the current local path;
s205, if the current local path meets the collision detection constraint, adding the leaf node into the path tree, assembling the path to complete one-time growth, and entering the step S3;
if the current local path does not satisfy the collision detection constraint, discarding the leaf node, updating the candidate leaf node list, and reselecting the extension target of the current local path until the assembly path completes one growth or the candidate leaf node list is empty after updating, and then entering step S3;
and S3, repeating the step S2 until the leaf node newly added into the path tree falls into the adjacent space of the target state of the part to be assembled, and directly connecting the latest leaf node with the target point, thereby obtaining a complete assembly path from the initial state to the target state.
Preferably, in step S1, the entire assembly shop environment is created, and the search space boundary of the assembly route is set, thereby narrowing the search range of the assembly route.
Preferably, in step S201, a plurality of points are sampled each time, each random sampling point has a target pose point with a probability p of being a path, and each random sampling point includes position information and pose information.
As a preferred aspect of the present invention, in step S203, a multi-factor path cost C estimation is performed for each candidate leaf node, and the candidate leaf node list is sorted and added according to the cost estimation value, where the multi-factor path cost C estimation formula is:
C＝wLL+wPP+wAA (1)
in the formula (1), wL，wP，wAL is distance cost, P is path inflection point corner cost, A is rotating motion cost of the to-be-assembled piece, each part of cost L, P and A in the formula (1) is composed of two parts, and the accumulated cost L of the generated path from a wei initial pose to the current candidate leaf nodecum，Pcum，AcumEstimation cost L of path to be generated from two wei current candidate poses to target poseest，Pest，Aest。
Preferably, the distance cost L in the formula (1) is expressed by a euclidean distance value of the path:
in the formula (2), n represents the number of nodes on the path from the initial pose point to the current candidate leaf node, LiRepresenting the euclidean distance between two adjacent nodes on the path.
As a preferable aspect of the present invention, the path inflection angle cost P in the formula (1):
in the formula (3), b1Is constant, making cost A and path distance cost L of the same order, j being the number of path inflection points, αiCorner of inflection point, β1，β2Turning angle α at the turning point of the pathiDividing into three grades, when the turning point corner is not large, 0 is less than or equal to αi≤β1Approximately, the path is not considered to occur, namely, the inflection point cost of the path is considered to be 0, and β is considered to be when the path turns back when the inflection point turn angle is larger2≤αiHere the path inflection cost 2b1β when corner turns are medium1≤αi≤β2Here the path inflection cost b1。
As a preferable aspect of the present invention, the rotation movement cost a of the member to be assembled in formula (1):
in the formula (4), b2And the cost A and the path distance cost L are made to have the same magnitude, n represents the number of nodes on the path from the initial pose point to the current candidate leaf node, and R represents the rotation information of the parts to be assembled at the path point and is expressed by unit quaternion.
Preferably, the specific steps of step S204 are as follows: and sorting the candidate leaf nodes according to the size of the path cost estimated value, adding and updating the candidate leaf node list, selecting the candidate leaf node with the minimum path cost estimated value from the candidate leaf nodes to perform collision detection and local path collision detection, if the collision detection requirement is met, adding the leaf node and the local path into the path tree, if the collision detection requirement is not met, deleting the candidate leaf node, updating the list table and then reselecting until the path number finishes one-time growth or the list table is empty.
Preferably, the adjacent space described in step S3 includes two conditions, and a rotational motion, which is decomposed from a part of the movement of a to-be-assembled part from a new leaf node to a target state point, has a rotational motion angle smaller than a set value, and in this embodiment, the distance between the quaternions of two position and posture point units is smaller than the set value, and the euclidean distance between the two new leaf nodes and the target state point is smaller than the set value, and it is determined that the leaf node falls into the adjacent space of the to-be-assembled part in the target state only if the above two conditions are satisfied simultaneously.
Has the advantages that:
the multi-factor path cost provided by the invention comprises distance cost, path inflection point corner cost and rotating motion cost of the to-be-assembled part, and the generation of each step of partial assembly path is guided by the multi-factor path cost to grow towards the direction with the relatively lowest path cost, so that the overall optimized assembly path is obtained.
Drawings
FIG. 1 is a general flow diagram of an assembly path planning method based on multi-factor cost guidance in virtual simulation according to the present invention;
FIG. 2 is a flow chart of the present invention for searching and expanding an assembly path in an assembly path search space, using an initial state of a component to be assembled as a root node, using multi-factor path cost as guidance, and combining a fast search random tree algorithm;
FIG. 3 is a diagram of corner angles of a path tree according to the present invention;
FIG. 4 is a schematic diagram of the present invention relating to the division of inflection point corner levels of a path tree.
FIG. 5 is a diagram of path cost selection in free space according to the present invention.
Detailed Description
The following description will explain embodiments of the present invention with reference to the accompanying drawings. Modifications may be made to the described embodiments without departing from the spirit and scope of the invention. Accordingly, the drawings and description are illustrative in nature and not intended to limit the scope of the claims. In addition, the drawings are not drawn to scale in this description.
The general flow of the assembly path planning method based on multi-factor cost guidance in virtual assembly simulation provided by the invention is shown in fig. 1:
s1, establishing a virtual assembly three-dimensional environment, determining an assembly path search space, and setting initial parameters of assembly path planning:
establishing a three-dimensional model of a virtual assembly environment, wherein the assembly environment can be the whole assembly workshop environment or a local assembly environment in which the to-be-assembled part is located, and determining an assembly path search space of the to-be-assembled part;
determining the initial state and the target state of the part to be assembled, including the position (three-dimensional coordinate) and the posture of the part to be assembled in a search space;
taking the virtual assembly environment, the assembly path search space, the initial state and the target state of the part to be assembled as initial parameters of assembly path planning;
preferably, in step S1, the entire assembly shop environment is established, and the search range of the assembly route is narrowed by setting the search space boundary of the assembly route. Therefore, when the assembling path planning is carried out on products on different assembling stations, the assembling environment model does not need to be changed, and only the search space boundary needs to be reset, so that the universality of the three-dimensional model of the assembling environment is improved;
the definition search space boundary comprises three-dimensional coordinates of definition path points and poses of the definition path points;
in step S2, in the assembly path search space, the initial state of the component to be assembled is taken as a root node, the multi-factor path cost is taken as guidance, and the rapid search random tree algorithm is combined to search and expand the assembly path, so that the implementation flow is as shown in fig. 2, and the specific steps include:
s201, setting the number M of random sampling points (M is more than or equal to 2, and in the embodiment, M is 15), and performing random uniform sampling in an assembly path search space;
the sampling points represent pose points of the to-be-assembled parts in a search space, each random sampling point has a target pose point with the probability of p as a path, each random sampling point comprises position information and attitude information, in the embodiment, the position information of the sampling points is represented by three-dimensional coordinates, and the attitude information is represented by unit quaternion;
s202, with the minimum Euclidean distance of the three-dimensional space as a measurement target, searching father nodes for the sampling points in the constructed path tree, and performing interpolation calculation from the father nodes to the corresponding sampling points to obtain corresponding candidate leaf nodes;
s203, multi-factor path cost estimation is carried out on each candidate leaf node, and the candidate leaf nodes are sorted according to the estimated cost value and added into a candidate leaf node list;
the multi-factor path cost C comprises a distance cost L, a path inflection point corner cost P and a rotating motion cost A of the piece to be assembled, and the formula C is equal to wLL+wPP+wAA expression (wherein wL，wP，wAWeight coefficients) and each partial cost (L, P, a) is composed of two parts, one is the cumulative cost (L) of the generated path from the initial pose to the current candidate leaf nodecum，Pcum，Acum) And two is the estimated cost (L) of the path to be generated from the current candidate pose to the target poseest，Pest，Aest)；
The distance cost L is represented by the euclidean distance value of the path,
where n represents the number of nodes on the path from the initial pose point to the current candidate leaf node, LiRepresenting the Euclidean distance between two adjacent nodes on the path, the path tree in the embodiment grows by equal step length epsilon, that isLcum＝(n-1)ε，LestThe Euclidean distance from the current candidate leaf node to the target state point is obtained;
the path inflection point corner cost P is represented by the sum of corner costs of the path at each inflection point, and P is equal to Pcum+Pest，
The rotating motion cost A of the to-be-assembled part is represented by the sum of each motion rotation in the path, a unit quaternion is adopted in the embodiment to represent the pose state of the to-be-assembled part at a path pose point, the change of the pose state between adjacent pose points (the change of the unit quaternion) represents that the to-be-assembled part generates motion rotation, the rotating motion cost is measured by the distance of the unit quaternion R of the adjacent pose, and the rotating motion cost of the to-be-assembled part
Wherein b is2Is constant, making cost P and path cost A have the same magnitude, n represents the number of nodes on the path from the initial pose point to the current candidate leaf node, Aest＝b2(1-||Rnew·Rtarget|) represents a rotation distance generated by one-time rotation between the candidate leaf node and the target pose;
according to a multi-factor cost calculation model, for each candidate leaf node, estimating the size of a path cost value of a path formed by the generated path passing through the candidate leaf node to reach a target pose, sorting the candidate leaf nodes according to the size of the path cost estimation value, and adding and updating a candidate leaf node list;
s204, selecting the candidate leaf node with the minimum path cost in the free space as the expansion target of the current local path, wherein the selection process is as shown in FIG. 5;
according to the updated candidate leaf node list in the step S203, selecting a candidate leaf node with the minimum path cost from the list, performing collision detection on the candidate leaf node, if the candidate leaf node is located in a free space, taking the candidate leaf node as an expansion target of the current local path, and entering the step S205, if the candidate leaf node is located in an obstacle space, deleting the candidate leaf node from the list table, updating the list table, and then re-selecting the candidate leaf node until the expansion target of the current local path is determined, if the updated list table is empty, indicating that the candidate leaf nodes are all located in the obstacle space, wherein the path tree in the iteration cannot be expanded, and entering the step S201 to start the next iteration;
s205, local path collision detection and path growth;
if the current local path meets the collision detection constraint, adding the candidate leaf node into the path tree, assembling the path to finish one-time growth, and entering the step S3;
if the current local path does not meet the collision detection constraint, discarding the leaf node, updating the candidate leaf node list, reselecting the expansion target of the current local path until the assembly path finishes one-time growth, and entering step S3, or entering the next iteration if the candidate leaf node list is empty after being updated;
s3 repeating step S2 until the leaf node of the newly added path tree falls into the adjacent space of the target state of the parts to be assembled, directly connecting the latest leaf node with the target point, thereby obtaining a complete assembly path from the initial state to the target state.
The leaf node in the newly added path tree falls into the adjacent space of the target state of the part to be assembled and comprises two layers of meanings, the rotary motion of the part to be assembled, which is decomposed from the local motion of the new leaf node to the target state point, has a rotary motion angle smaller than a set value, the embodiment is represented by the fact that the distance between the quaternions of two position and posture point units is smaller than the set value, the Euclidean distance between the two new leaf nodes and the target state point is smaller than the set value, and the leaf node is judged to fall into the adjacent space of the target state of the part to be assembled only if the two conditions are met simultaneously.
Claims (9)
1. The assembly path planning method based on multi-factor cost guidance in virtual assembly simulation is characterized by comprising the following steps of:
s1, establishing a virtual assembly three-dimensional environment, determining an assembly path search space, and setting initial parameters of assembly path planning:
establishing a three-dimensional model of a virtual assembly environment, wherein the assembly environment can be the whole assembly workshop environment or a local assembly environment in which the to-be-assembled part is located, and determining an assembly path search space of the to-be-assembled part;
determining an initial state and a target state of a part to be assembled, wherein the initial state and the target state comprise the position and the posture of the part to be assembled in a search space;
taking the virtual assembly environment, the assembly path search space, the initial state of the part to be assembled and the target state as initial parameters of assembly path planning;
s2, in the assembly path search space, taking the initial state of the parts to be assembled as a root node, taking the cost of the multi-factor path as guidance, and combining the rapid search random tree algorithm to search and expand the assembly path, specifically:
s201, carrying out uniform random sampling in an assembly path search space and obtaining M sampling points;
s202, with the minimum Euclidean distance of a three-dimensional space as a measurement target, searching father nodes for the sampling points in the constructed path tree, and performing interpolation calculation from the father nodes to the corresponding sampling points to obtain corresponding candidate leaf nodes;
s203, performing multi-factor path cost estimation on each candidate leaf node, sorting according to the cost estimation value, and adding into a candidate leaf node list;
s204, selecting the candidate leaf node with the minimum path cost in the free space as an expansion target of the current local path;
s205, if the current local path meets the collision detection constraint, adding the leaf node into a path tree, assembling the path to finish one-time growth, and entering the step S3;
if the current local path does not satisfy the collision detection constraint, discarding the leaf node, updating the candidate leaf node list, reselecting the expansion target of the current local path until the assembly path finishes one-time growth or the candidate leaf node list is empty after being updated, and then entering step S3;
and S3, repeating the step S2 until the leaf node newly added into the path tree falls into the adjacent space of the target state of the part to be assembled, and directly connecting the latest leaf node with the target point, thereby obtaining a complete assembly path from the initial state to the target state.
2. The assembly path planning method based on multi-factor cost guidance in virtual assembly simulation according to claim 1, characterized in that: in step S1, the entire assembly shop environment is established, and the search range of the assembly route is narrowed by setting the search space boundary of the assembly route.
3. The assembly path planning method based on multi-factor cost guidance in virtual assembly simulation according to claim 1, characterized in that: in step S201, a plurality of points are sampled each time, each random sampling point has a target pose point with a probability p as a path, and each random sampling point includes position information and attitude information.
4. The assembly path planning method based on multi-factor cost guidance in virtual assembly simulation according to claim 1, characterized in that: in step S203, a multi-factor path cost C estimation is performed for each candidate leaf node, and the candidate leaf node list is sorted and added according to the cost estimation value, where the multi-factor path cost C estimation formula is:
C＝wLL+wPP+wAA (1)
in the formula (1), wL，wP，wAIs a weight coefficient, L is a distance cost, P is a path inflection point corner cost, A is a rotating motion cost of the to-be-assembled piece, and each part of the cost L, P and A in the formula (1) is composed of two parts, one is a generated path accumulated cost L from an initial pose to a current candidate leaf nodecum，Pcum，AcumAnd secondly, estimating cost L of the path to be generated from the current candidate pose to the target poseest，Pest，Aest。
5. The assembly path planning method based on multi-factor cost guidance in virtual assembly simulation according to claim 4, characterized in that: the distance cost L in equation (1) is expressed in terms of the euclidean distance value of the path:
in the formula (2), n represents the number of nodes on the path from the initial pose point to the current candidate leaf node, LiRepresenting the euclidean distance between two adjacent nodes on the path.
6. The assembly path planning method based on multi-factor cost guidance in virtual assembly simulation according to claim 4, characterized in that: path inflection angle cost P in equation (1):
in the formula (3), b1Is constant, making cost A and path distance cost L of the same order, j being the number of path inflection points, αiCorner of inflection point, β1，β2Turning angle α at the turning point of the pathiDividing into three grades, when the turning point corner is not large, 0 is less than or equal to αi≤β1Approximately recognizeFor this reason, the path does not occur, namely, the cost of the inflection point of the path is considered to be 0, and β is required when the path turns back when the inflection angle is larger2≤αiHere the path inflection cost 2b1β when corner turns are medium1≤αi≤β2Here the path inflection cost b1。
7. The assembly path planning method based on multi-factor cost guidance in virtual assembly simulation according to claim 4, wherein the cost A of the rotational motion of the to-be-assembled part in the formula (1):
in the formula (4), b2And the cost A and the path distance cost L are made to have the same magnitude, n represents the number of nodes on the path from the initial pose point to the current candidate leaf node, and R represents the rotation information of the parts to be assembled at the path point and is expressed by unit quaternion.
8. The assembly path planning method based on multi-factor cost guidance in virtual assembly simulation according to claim 1, wherein the specific steps of step S204 are: and sorting the candidate leaf nodes according to the size of the path cost estimated value, adding and updating the candidate leaf node list, selecting the candidate leaf node with the minimum path cost estimated value from the candidate leaf nodes to perform collision detection and local path collision detection, adding the leaf node and the local path into the path tree if the collision detection requirement is met, deleting the candidate leaf node if the collision detection requirement is not met, updating the list table and then reselecting until the path number is grown once or the list table is empty.
9. The method for assembling route planning based on multi-factor cost guidance in virtual assembly simulation of claim 1, wherein the close space in step S3 includes two conditions, a rotational motion angle of a part to be assembled, which is decomposed from a part of the motion from a new leaf node to a target state point, is smaller than a predetermined value, in this embodiment, the distance between quaternions of two pose points is smaller than the predetermined value, the euclidean distance between two new leaf nodes and the target state point is smaller than the predetermined value, and it is determined that a leaf node falls into the close space of the target state of the part to be assembled only if the above two conditions are satisfied simultaneously.
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Application publication date: 20200626 |