CN114310917B - Oil pipe transfer robot joint track error compensation method - Google Patents
Oil pipe transfer robot joint track error compensation method Download PDFInfo
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Abstract
The invention belongs to the technical field of joint track error compensation of an oil pipe transfer robot, and particularly relates to a joint track error compensation method of an oil pipe transfer robot. The joint track error compensation method of the oil pipe carrying robot improves the motion precision of the oil pipe carried by the tail end executing mechanism of the carrying robot, improves the processing and repairing efficiency of manufacturing equipment, and reduces the running cost of the manufacturing equipment. The joint track error compensation method of the oil pipe transfer robot comprises the steps of constructing a joint topological graph and a joint angle characteristic matrix of the oil pipe transfer robot, constructing a joint dynamic graph network model of the oil pipe transfer robot, constructing a joint dynamic graph circulating network model of the oil pipe transfer robot, predicting joint angles of future sequences based on joint angles of historical sequences and the like.
Description
Technical Field
The invention belongs to the technical field of joint track error compensation of an oil pipe transfer robot, and particularly relates to a joint track error compensation method of an oil pipe transfer robot.
Background
Oil pipe processing or repairing equipment is widely applied to the procedures of oil pipe processing, repairing, paint spraying, detecting and the like in oil fields. It is worth noting that because oil pipe length is long, weight is big, and artifical material loading is wasted time and energy, consequently adopt robot link mechanism to carry at present stage usually to fundamentally solve the transport difficult problem of oil pipe.
However, after further research, the inventor finds that, because the oil pipe is long and heavy, the robot has many joints, the joints of the robot have slight deviation, the positions of all the joints behind the joints are changed, and all the components are deviated; due to no feedback, even if all joint variables are set, the tail end of the oil pipe transfer robot cannot be accurately positioned at a given position, so that the movement precision is reduced easily in the transfer process, and the equipment processing and repairing precision is directly influenced. With the development of modern science and technology, because the error compensation precision and the joint motion have a direct important relationship and are influenced by various geometric errors and non-geometric errors, the spatial correlation among the joints is highly dynamic and is determined by a real-time joint motion state and a connecting rod topological structure, and meanwhile, because of nonlinear change and periodic cross mixing, the connecting rod motion has strong nonlinear dynamic correlation in space and time dimensions, so that a simple, reliable and accurate error compensation method must be established to improve the motion precision of a carrying robot in the carrying process and reduce the carrying, processing, testing and maintenance costs.
Disclosure of Invention
The invention provides a joint track error compensation method of an oil pipe transfer robot, which improves the motion precision of an end actuating mechanism of the transfer robot for transferring oil pipes, improves the processing and repairing efficiency of manufacturing equipment, and reduces the running cost of the manufacturing equipment.
In order to solve the technical problem, the invention adopts the following technical scheme:
a joint track error compensation method for an oil pipe transfer robot comprises the following steps:
step 1: constructing a joint topological graph and a joint angle characteristic matrix of the oil pipe carrying robot;
and 2, step: constructing a joint dynamic graph network model of the oil pipe carrying robot to obtain dynamic node characteristics; inputting the dynamic node characteristics into graph convolution for dynamic filtering;
and 3, step 3: constructing a joint dynamic graph circulating network model of the oil pipe carrying robot;
and 4, step 4: predicting joint angles of a future sequence based on joint angles of a historical sequence according to a joint dynamic graph circulation network model of the oil pipe carrying robot;
preferably, the method further comprises the following steps:
and 5: and performing deviation correction on a predicted value obtained by the joint dynamic graph circulation network model of the oil pipe transfer robot.
Preferably, the step 5 may be specifically described as:
the correction formula for correcting the deviation of the predicted value is as follows:
wherein, the first and the second end of the pipe are connected with each other,
for the final predicted joint angle at time t after correction,
predicting the joint angle for the final corrected time (t + 1); theta.theta.i(t) is the actual joint angle at time t,
τ is a deviation correction coefficient for the predicted joint angle at time (t + 1).
Preferably, the step of constructing the joint topology map of the pipe handling robot in step 1 may be specifically described as follows:
defining a joint topological graph of the oil pipe transfer robot as follows: g ═ V, E, a; wherein V represents a set of joint nodes of the oil pipe transfer robot, and V ═ V1,V2,…,Vn}; n is the number of nodes of the joint node; e represents the set of edges where the joints are located; a represents the static adjacency matrix between joint nodes.
Preferably, the step of constructing the joint angle feature matrix of the pipe handling robot in step 1 may be specifically described as follows:
defining a joint angle characteristic matrix of the oil pipe transfer robot as follows: theta ^ N Rn×p(ii) a Wherein R is the diameter of the joint node; and p is the attribute characteristic number of the joint node.
Preferably, the step of the network model of the joint dynamic diagram of the oil pipe transfer robot in step 2 may be specifically described as:
At each time step, the series joint angle θtTime t and hidden state Ht-1Obtaining dynamic node characteristics I as input of a joint dynamic graph network modelt(ii) a Wherein, It＝θt||Tt||Ht-1；It∩Rb×n×dB is the block size, n is the number of nodes, d is the characteristic dimension, TtIs the articulation cycle time.
Preferably, the step of inputting the dynamic node characteristics into the graph convolution in step 2 to perform dynamic filtering may be specifically described as:
dynamic node characteristics ItThe input graph is convolved and the output is: DF (Decode-feed)t＝Φ*G(It) (ii) a Wherein the dynamic filtering tensor DFt∩Rb×n×d，Φ*GRepresenting the graph convolution operation, phi is the learned filter parameter.
Preferably, the step 3 can be specifically described as:
the joint dynamic graph circulation network model of the oil pipe transfer robot constructed by adopting the k-hop algorithm is expressed as follows:
wherein HinInputting the parameters for each node in the oil pipe transfer robot, wherein alpha is a control input signal, beta is a joint dynamic diagram parameter, gamma is a predefined joint static diagram parameter,
a degree matrix after node self-loop is introduced; a is a static adjacency matrix and a static adjacency matrix,
the static adjacent matrix in a hidden state;
for a dynamic graph after node self-loop introduction, a degree matrix at t moment is represented by diagonal elements
Is a dynamic adjacent matrix containing node self-information at t moment under a hidden state, DA tWhen the node self-information is not contained, a dynamic adjacent matrix is obtained, and I is an identity matrix;
the output after the dynamic graph convolution process is represented as:
preferably, the step 4 may be specifically described as:
at time t, the angle value (theta) of each joint is obtained1,θ2,…,θi) Giving a training step length M; predicting an output sequence using theta (t-M) to theta (t-1) as training inputs
To
And Q is the network prediction step size.
The invention provides an oil pipe transfer robot joint track error compensation method which comprises the steps of constructing a joint topological graph and a joint angle characteristic matrix of an oil pipe transfer robot, constructing a joint dynamic graph network model of the oil pipe transfer robot, constructing a joint dynamic graph circulating network model of the oil pipe transfer robot, predicting joint angles of future sequences based on joint angles of historical sequences and the like. The joint trajectory error compensation method of the oil pipe transfer robot with the steps and characteristics carries out real-time compensation on joint trajectory errors of the oil pipe transfer robot by establishing a nonlinear dynamic space-time correlation model, reduces repeated motion of repair equipment, improves motion precision of a tail end execution mechanism, increases machining and repair efficiency of the equipment, and reduces operation cost of the equipment.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
FIG. 1 is a schematic flow chart of a joint trajectory error compensation method for an oil pipe transfer robot according to the present invention;
FIG. 2 is a flow diagram of a process for constructing a dynamic joint diagram network model of a tubing handling robot;
FIG. 3 is a schematic diagram convolution flow diagram of a joint dynamics diagram cycle network model;
FIG. 4 is a schematic representation of joint prediction for a joint dynamics graph cycle network model.
Detailed Description
The invention provides a joint track error compensation method of an oil pipe transfer robot, which improves the motion precision of an end actuating mechanism of the transfer robot for transferring oil pipes, improves the processing and repairing efficiency of manufacturing equipment, and reduces the running cost of the manufacturing equipment.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
The invention provides a joint track error compensation method of an oil pipe carrying robot, which specifically comprises the following steps:
step 1: and constructing a joint topological graph and a joint angle characteristic matrix of the oil pipe carrying robot.
As a preferred embodiment of the present invention, the step of constructing the joint topology map of the tubing handling robot in step 1 may be specifically described as follows: defining a joint topological graph of the oil pipe carrying robot as follows: g ═ V, E, a. V represents a set of joint nodes of the oil pipe transfer robot, and V is { V ═ V1,V2,…,Vn}; n is the number of nodes of the joint node; e represents the set of edges where the joints are located; a represents the static adjacency matrix between joint nodes.
It should be noted that, by constructing the joint topological diagram of the oil pipe transfer robot, the definition of the characteristic information of each joint of the oil pipe transfer robot can be realized; and the related characteristic information can be defined as any motion information of the joint, such as joint angle, velocity, acceleration and the like according to requirements, and the aim of the related characteristic information is to finally realize error compensation by predicting the joint angle.
As another preferred embodiment of the present invention, the step of constructing the joint angle feature matrix of the pipe handling robot in step 1 may be specifically described as follows: defining a joint angle characteristic matrix of the oil pipe transfer robot as follows: theta ^ N R n×p(ii) a Wherein R is the diameter of the joint node; and p is the attribute feature number (namely the historical time sequence length of the training learning data set) of the joint node.
After the constructed joint topological graph G and the joint angle characteristic matrix theta are completed, a mapping function f is learned and generated by utilizing a graph convolution network, so that joint angle motion information in a certain period of time in the future can be predicted, and the specific relevant formula can refer to the following formula:
θt+1,θt+2,...,θt+T＝f(G；(θt-n,...,θt-1,θt))。
step 2: constructing a joint dynamic graph network model of the oil pipe transfer robot to obtain dynamic node characteristics; and inputting the dynamic node characteristics into graph convolution for dynamic filtering.
It should be noted that, since the influence of non-geometric errors such as the spatial clearance and friction of each joint of the pipe-handling robot is complicated, in order to ensure that the described result is clear and natural, a dynamic node feature is adopted here, so as to perform description in a dynamic angle (see fig. 2).
Specifically, as a preferred embodiment of the present invention, the step of the network model of the joint dynamic diagram of the oil pipe transfer robot in step 2 may be specifically described as follows:
at each time step, the tandem joint angle θtTime t and hidden state Ht-1Obtaining dynamic node characteristics I as input of joint dynamic graph network model t(ii) a Wherein, It＝θt||Tt||Ht-1；It∩Rb×n×dB is the block size, n is the number of nodes, d is the characteristic dimension, TtIs the articulation cycle time.
The step of inputting the dynamic node characteristics into the graph convolution in the step 2 for dynamic filtering can be specifically described as follows:
dynamic node characteristics ItThe input graph is convolved and the output is: DF (Decode-feed)t＝Φ*G(It) (ii) a Wherein the dynamic filtering tensor DFt∈Rb×n×d，Φ*GRepresenting the graph convolution operation, phi is the learned filter parameter.
It should be noted that, by performing a graph convolution operation on a predefined adjacency matrix a (including a static distance relationship between nodes), message passing on a dynamic node state can be realized, that is, the adjacency matrix is updated.
And step 3: and constructing a joint dynamic graph circulation network model of the oil pipe transfer robot.
In order to improve the accuracy of joint prediction, a joint dynamic graph circulation network model of the oil pipe transfer robot needs to be constructed; in the joint dynamic graph cyclic network model, as shown in fig. 3, when graph convolution is performed, a predefined distance-based static graph and a node attribute-based dynamic graph are combined, and weighted summation of graph convolution results of an input graph signal, the dynamic graph and the predefined static graph is performed on each graph convolution layer.
Specifically, as a preferred embodiment of the present invention, step 3 may be specifically described as:
the joint dynamic graph circulation network model of the oil pipe transfer robot built by adopting the k-jump algorithm is expressed as follows:
wherein HinInputting for each node in the oil pipe transfer robot, alpha is a control input signal, beta is a joint dynamic diagram parameter, gamma is a predefined joint static diagram parameter,
a degree matrix after node self-loop is introduced; a is a static adjacent matrix and A is a static adjacent matrix,
is a static adjacency matrix in a (predefined) hidden state;
for a dynamic graph after node self-loop introduction, a degree matrix at t moment is represented by diagonal elements
Is a dynamic adjacent matrix containing node self-information at t moment under a hidden state, DAtTherefore, a dynamic adjacent matrix without node self-information is obtained, and I is an identity matrix;
the output after the dynamic graph convolution process is represented as:
it is noted that the skilled person may also consider the two-way propagation of the gap errorConsider the use of a bipartite graph convolution, i.e. the output representation can be chosen as: hout＝Φ1*G(Hin,DAt,A)+Φ2*G(Hin,DAt T,AT)。
And 4, step 4: and predicting the joint angle of a future sequence based on the joint angle of the historical sequence according to the joint dynamic graph circulation network model of the oil pipe transfer robot.
Specifically, as a preferred embodiment of the present invention, the step 4 may be specifically described as:
as shown in fig. 4, a dynamic adjacency matrix is synchronously generated at each time step according to the recursive computation of the DGCRN; at time t, the angle value (theta) of each joint is obtained1,θ2,…,θi) Giving a training step length M; predicting an output sequence using θ (t-M) to θ (t-1) as training inputs
To is that
And Q is a network prediction step size.
It is noted that, since the joint dynamic graph loop network model described above mainly makes the predicted angle infinitely close to the actual angle of the joint, in order to prevent overfitting, and at the same time add the L2 regularization term, the specific definition loss function can be as follows:
wherein in the above formula, λ is a regularization hyper-parameter. And (3) updating and training the weight thresholds of various learning parameters of the model by calculating a loss function between the predicted value and the actual value and adopting an adaptive moment estimation optimization algorithm Adam until the model design requirements are met.
In addition, as a preferred embodiment, the method for compensating joint trajectory error of a pipe handling robot according to the present invention further includes step 5: and performing deviation correction on a predicted value obtained by the joint dynamic graph circulation network model of the oil pipe transfer robot.
It should be noted that, in consideration of the prediction deviation of the predicted value itself and the influence caused by time lag during the joint compensation process, in order to improve the accuracy of error compensation, after the training of the joint dynamic graph loop network model in step 4 is completed, during actual compensation, deviation correction may be further performed on the predicted value of the joint dynamic graph loop network model, specifically, the correction formula may refer to:
wherein, the first and the second end of the pipe are connected with each other,
for the final predicted joint angle at time t after correction,
predicting the joint angle for the final corrected time (t + 1); theta.theta.i(t) is the actual joint angle at time t,
the predicted joint angle at time (t +1) is τ, which is a deviation correction coefficient.
Therefore, the joint trajectory error compensation method for the oil pipe transfer robot provided by the invention has the advantages that the joint angle is finely adjusted and compensated through calculating and finally predicting the difference between the joint angle and the target value, and the tail end error is reduced.
The invention provides an oil pipe transfer robot joint track error compensation method which comprises the steps of constructing a joint topological graph and a joint angle characteristic matrix of an oil pipe transfer robot, constructing a joint dynamic graph network model of the oil pipe transfer robot, constructing a joint dynamic graph circulating network model of the oil pipe transfer robot, predicting joint angles of future sequences based on joint angles of historical sequences and the like. The joint track error compensation method of the oil pipe transfer robot with the steps and the characteristics carries out real-time compensation on joint track errors of the oil pipe transfer robot by establishing the nonlinear dynamic space-time correlation model, improves the motion precision of the tail end execution mechanism while reducing the repeated motion of the repair equipment, increases the processing and repair efficiency of the equipment, and reduces the running cost of the equipment.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (3)
1. A joint track error compensation method of an oil pipe transfer robot is characterized by comprising the following steps:
step 1: constructing a joint topological graph and a joint angle characteristic matrix of the oil pipe carrying robot;
the steps of constructing the joint topological graph of the oil pipe transfer robot can be specifically described as follows:
defining a joint topological graph of the oil pipe transfer robot as follows: g ═ V, E, a; wherein V represents a set of joint nodes of the oil pipe transfer robot, and V ═ V1,V2,…,Vn}; n is the number of nodes of the joint node; e represents the set of edges where the joints are located; a represents a static adjacency matrix between joint nodes;
the steps of constructing the joint angle feature matrix of the oil pipe transfer robot can be specifically described as follows:
defining a joint angle characteristic matrix of the oil pipe transfer robot as follows: theta ^ N R n×p(ii) a Wherein R is the diameter of the joint node; p is the attribute characteristic number of the joint node;
and 2, step: constructing a joint dynamic graph network model of the oil pipe carrying robot to obtain dynamic node characteristics; inputting the dynamic node characteristics into graph convolution for dynamic filtering;
the step of the joint dynamic diagram network model of the oil pipe transfer robot in step 2 can be specifically described as follows:
at each time step, the series joint angle θtTime t and hidden state Ht-1Obtaining dynamic node characteristics I as input of joint dynamic graph network modelt(ii) a Wherein, It＝θt||Tt||Ht-1；It∈Rb×n×dB is the block size, n is the number of nodes, d is the characteristic dimension, TtIs the articulation cycle time;
the step of inputting the dynamic node characteristics into the graph convolution in the step 2 for dynamic filtering can be specifically described as follows:
dynamic node characteristics ItThe input graph is convolved and the output is: DF (Decode-feed)t＝Φ*G(It) (ii) a Wherein the dynamic filtering tensor DFt∈Rb ×n×d，Φ*GRepresenting graph convolution operation, phi is the learned filter parameter;
and step 3: constructing a joint dynamic graph circulation network model of the oil pipe transfer robot;
wherein, the step 3 can be specifically described as:
the joint dynamic graph circulation network model of the oil pipe transfer robot constructed by adopting the k-hop algorithm is expressed as follows:
Wherein HinInputting for each node in the oil pipe transfer robot, alpha is a control input signal, beta is a joint dynamic diagram parameter, gamma is a predefined joint static diagram parameter,
a degree matrix after node self-loop is introduced; a is a static adjacent matrix and A is a static adjacent matrix,
is a static adjacent matrix in a hidden state;
for a dynamic graph after node self-loop introduction, a degree matrix at t moment is represented by diagonal elements
Is a dynamic adjacent matrix containing node self-information at t moment in a hidden state, DAtTherefore, a dynamic adjacent matrix without node self-information is obtained, and I is an identity matrix;
the output after the dynamic graph convolution process is represented as:
and 4, step 4: predicting joint angles of a future sequence based on joint angles of a historical sequence according to a joint dynamic graph circulation network model of the oil pipe carrying robot;
wherein, the step 4 can be specifically described as:
at time t, the angle value (theta) of each joint is obtained1,θ2,…,θi) Giving a training step length M; predicting an output sequence using theta (t-M) to theta (t-1) as training inputs
To
And Q is the network prediction step size.
2. The method for compensating for joint trajectory error of a pipe handling robot of claim 1, further comprising the steps of:
And 5: and correcting the deviation of the predicted value obtained by the joint dynamic diagram circulating network model of the oil pipe carrying robot.
3. The method for compensating joint trajectory error of a tubing handling robot according to claim 2, wherein the step 5 can be specifically described as:
the correction formula for correcting the deviation of the predicted value is as follows:
wherein, the first and the second end of the pipe are connected with each other,
for the final predicted joint angle at time t after correction,
predicting the joint angle for the corrected final (t +1) time; theta.theta.i(t) is the actual joint angle at time t,
τ is a deviation correction coefficient for the predicted joint angle at time (t + 1).
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