CN115890659B - Method for optimizing dexterity of continuum robot - Google Patents
Method for optimizing dexterity of continuum robot Download PDFInfo
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- CN115890659B CN115890659B CN202211384554.7A CN202211384554A CN115890659B CN 115890659 B CN115890659 B CN 115890659B CN 202211384554 A CN202211384554 A CN 202211384554A CN 115890659 B CN115890659 B CN 115890659B
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- 238000000034 method Methods 0.000 title claims abstract description 21
- 210000000056 organ Anatomy 0.000 claims abstract description 23
- 238000013178 mathematical model Methods 0.000 claims abstract description 19
- 238000005452 bending Methods 0.000 claims description 15
- 239000011159 matrix material Substances 0.000 claims description 10
- 238000005457 optimization Methods 0.000 claims description 8
- 230000009466 transformation Effects 0.000 claims description 7
- 238000001514 detection method Methods 0.000 claims description 5
- 230000017105 transposition Effects 0.000 claims description 5
- 210000001035 gastrointestinal tract Anatomy 0.000 description 7
- 230000000968 intestinal effect Effects 0.000 description 6
- 238000001356 surgical procedure Methods 0.000 description 5
- 210000002435 tendon Anatomy 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 206010005003 Bladder cancer Diseases 0.000 description 1
- 206010008111 Cerebral haemorrhage Diseases 0.000 description 1
- 208000007097 Urinary Bladder Neoplasms Diseases 0.000 description 1
- 210000002255 anal canal Anatomy 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 210000001731 descending colon Anatomy 0.000 description 1
- 238000002674 endoscopic surgery Methods 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 235000001968 nicotinic acid Nutrition 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 210000000664 rectum Anatomy 0.000 description 1
- 238000004904 shortening Methods 0.000 description 1
- 210000001599 sigmoid colon Anatomy 0.000 description 1
- 230000008733 trauma Effects 0.000 description 1
- 201000005112 urinary bladder cancer Diseases 0.000 description 1
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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- Y02T10/10—Internal combustion engine [ICE] based vehicles
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Abstract
The invention relates to a method for optimizing the dexterity of a continuum robot, which comprises the steps of establishing a point cloud map model of tissue organs, establishing a positive kinematics formula of the continuum robot according to a constant curvature model of the continuum robot, establishing a mathematical model of a reachable space of the continuum robot, constructing a mathematical model of a dexterity index of the continuum robot by calculating the occupation ratio of the reachable space in a task space, and optimizing the dimension parameters of the continuum robot by taking the mathematical model of the dexterity index of the continuum robot as an objective function, so that the objective function meets constraint conditions: the end point of the continuum robot reaches the target point, and the minimum distance between the end point and the edge of the tissue organ is larger than a set distance threshold. The invention ensures that the robot meets geometric constraint and anatomical constraint based on the operation task, improves the dexterity of the continuum robot, avoids collision contact between the continuum robot and tissues and organs, and reduces pain of patients.
Description
Technical Field
The invention belongs to the technical field of artificial intelligence, relates to a continuum robot, and in particular relates to a method for optimizing the dexterity of the continuum robot.
Background
The inspiration of continuum robots comes from bionics, typically made of flexible materials with low elastic modulus. The continuum robot may utilize the flexibility of the material to change its natural shape, which change in shape enables the robot to reach a narrow space.
Single hole access surgery (SPAS), natural lumen endoscopic surgery, and cellular surgery are modern surgical procedures aimed at shortening recovery time, reducing trauma to healthy tissue, and continuum robots can well meet this requirement. Examples of some applications include cerebral hemorrhage clearance, bladder cancer, laryngeal airway surgery, where continuum robots can bypass obstacles and enter a closed environment due to their high flexibility and compliance. Thus, they may be used to detect tasks, relief or surgery.
Most continuum robots use a single backbone to pass through the actuator, tendons are common actuators that model the continuum robot, such as a trunk-like multisection continuum robot. One of the backbones of the multi-backbone continuum robot is considered the primary backbone and the other backbones are considered the secondary backbone. The concentric tube robot is another continuum robot, which uses a tube as a backbone and has potential for miniaturization. The length of each section of one continuum robot must be properly designed to ensure that the robot meets geometric and anatomical constraints based on the task of operation.
Therefore, the study of parameter optimization of the continuum robot is of great significance for improving the dexterity of the continuum robot in the operation space.
Disclosure of Invention
The invention aims to provide an optimization method for the dexterity of a continuum robot, so as to improve the dexterity of the continuum robot in operation or detection tasks.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a method of optimizing the dexterity of a continuum robot, comprising:
s1: a point cloud map model of the tissue organ is established,
S2: a positive kinematic formula of the continuum robot is established according to a constant curvature model of the continuum robot,
S3: a mathematical model of the reachable space of the continuum robot is established,
S4: by calculating the occupancy ratio of the reachable space in the task space and constructing a mathematical model of the dexterity index of the continuum robot,
S5: optimizing the size parameters of the continuum robot by taking a mathematical model of the dexterity index of the continuum robot as an objective function, so that the objective function meets constraint conditions: the continuum robot end point (x end,yend) reaches the target point P target, and the minimum distance mindis from the tissue organ edge is greater than the set distance threshold.
In the above technical solution, preferably, in S1, a point cloud map model of the tissue and organ is established by using python.
Further preferably, establishing the point cloud map model of the tissue organ includes: and constructing a tissue organ stl file by using the three-dimensional reconstruction of the CT file, converting the stl file format into a ply format, and generating a tissue organ edge information point cloud map through an open3d library.
In the above technical solution, preferably, in S2, the pose of the continuum robot and the position coordinates of the head of the continuum robot are calculated by substituting the parameters of the bending angle of the continuum robot into a positive kinematic formula, and the constant curvature model is:
the positive kinematic model is:
Wherein: x n and y n are n-th node end point coordinates, n epsilon (1, N), l 1 is a first node length, theta 1 is a first node bending angle, R m-1 is a rotation transformation matrix from the m-1 th node to the m-1 th node, l m is an m-th node length, and theta m' is a bending angle difference value between the m-th node and the m-1 th node.
In the above technical solution, preferably, in S3: the left mathematical model of the reachable space is:
Wherein, T 1,T2 and T 3 are respectively transformation matrixes of corresponding joints, θ 1,θ2,θ3 and θ i are respectively bending angles of the corresponding joints, T is matrix transposition, and a mathematical model of a right space is obtained by symmetry.
In the above-described aspect, preferably, in S4, constructing a mathematical model of the dexterity of the continuum robot includes: setting a surgical task space in the point cloud map, dividing the reachable space area in the task space by the set surgical task space area, and taking the obtained value as a dexterity index of the continuum robot, wherein the dexterity index formula of the continuum robot is as follows:
DT＝Saccessible/ST
ST＝a×b
Wherein: s accessible is the reachable area of the continuum robot in the set surgical task space, S T is the area of the set surgical task space, and a and b are the length and width of the set surgical task space, respectively.
In the above technical solution, preferably, in S5, the objective function uses the length of each segment of the continuum robot as a design variable, and the total length and the dexterity index of the continuum robot are multiplied by the weights respectively and then added as the objective function:
Min F＝1/DT
wherein: d T is a continuum robot dexterity index.
Further preferably, the optimal design variables of the objective function are:
L＝(l1,l2,...,lN)T
Wherein: and l i is the length of the ith section of the continuum robot, N is the total number of sections of the continuum robot, T is the matrix transposition, and the variable quantity in the objective function is determined by optimizing the design variable.
In the above technical solution, preferably, in S5, the constraint condition is:
wherein: θ i is the bending angle of the ith section, and l i is the length of the ith section.
In the above technical scheme, preferably, in S5, the minimum distance detection uses point cloud map data to build a kd tree, and the minimum distance is obtained by a nearest neighbor search method, so as to judge whether the continuum robot collides with the tissue organ, if the distance between the continuum robot and the tissue organ meets the threshold requirement, and the optimization function result meets the requirement, the optimal size parameter is met; if collision contact with the tissue organ occurs or the result of the optimization function does not meet the requirement, the currently set number of the nodes of the continuum robot does not meet the requirement, the number of the nodes needs to be increased, and the number of variables is correspondingly increased in the objective function.
Due to the application of the technical scheme, compared with the prior art, the invention has the following advantages:
The invention uses the positive kinematics of the continuum robot to optimize the parameters of the continuum robot by establishing the objective function, ensures that the robot meets geometric constraint and anatomical constraint based on the operation task, improves the dexterity of the continuum robot, avoids the collision contact between the continuum robot and the tissue organ, and reduces the pain of patients.
Drawings
Figure 1 is a schematic block diagram of the flow of the method in this embodiment,
Figure 2 is an image of the intestinal stl in this embodiment,
Figure 3 is a map image of the intestinal point cloud obtained by python processing in this embodiment,
Figure 4 is a schematic view of a tendon-driven continuum robot (unbent) in this embodiment,
Figure 5 is a schematic view of a tendon-driven continuum robot (S-bend) in this embodiment,
Fig. 6 is a schematic diagram of the reachable space of the continuum robot in this embodiment.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, a method for optimizing the dexterity of a continuum robot, in which a tissue organ is an example of an intestinal tract, includes the following steps:
first, a point cloud map model of the intestinal tract (anal canal, rectum, sigmoid colon, descending colon) is established by CT files. And (3) establishing an intestinal canal point cloud map, using python, constructing an intestinal canal stl file by using a CT file three-dimensional reconstruction, converting the stl file format into a ply format, and generating an intestinal canal edge information point cloud map through an open3d library, wherein the figures 2 and 3 are shown.
Then, a continuous robot positive kinematics formula is established according to a continuous robot constant curvature model, wherein: the common curvature model of the continuous robot is to consider each section of the robot as a continuously curved arc, and calculate the coordinates of each section through a rotation transformation matrix. And calculating the pose of the continuum robot and the position coordinates of the head of the continuum robot by substituting the bending angle parameters of the continuum robot into a positive kinematic formula, as shown in fig. 4 and 5. Wherein:
The constant curvature model is:
the positive kinematic model is:
Wherein: x n and y n are n-th node end point coordinates, n epsilon (1, N), l 1 is a first node length, theta 1 is a first node bending angle, R m-1 is a rotation transformation matrix from the m-1 th node to the m-1 th node, l m is an m-th node length, and theta m' is a bending angle difference value between the m-th node and the m-1 th node.
Then, establishing a mathematical model of the reachable space of the continuum robot, wherein the mathematical model of the reachable space of the continuum robot is calculated by a positive kinematic formula of the continuum robot, and the mathematical model of the left side of the reachable space is as follows:
The right space can be derived from symmetry, where T 1,T2 and T 3 are the transformation matrices of the corresponding joint, respectively, and θ 1,θ2,θ3 and θ i are the bending angles of the corresponding joint, respectively, and T is the matrix transpose.
Next, a mathematical model of the dexterity index of the continuum robot is constructed by calculating the occupancy ratio of the reachable space in the task space. The method specifically comprises the following steps: setting a surgical task space in the point cloud map, dividing the reachable space area in the task space by the set surgical task space area, and taking the obtained value as a dexterity index of the continuum robot, wherein the dexterity index formula of the continuum robot is as follows:
DT＝Saccessible/ST
wherein: s accessible is the reachable area of the continuum robot in the set operation task space, S accessible is calculated by a reachable space calculation formula, S T is the area of the set operation task space, a doctor sets the area in an image, the area is cylindrical, and the area is displayed as a rectangle in the image.
ST＝a×b
Wherein a and b are the length and width of the set surgical task space, respectively.
Finally, taking a mathematical model of the dexterity index of the continuum robot as an objective function, and enabling the objective function to meet constraint conditions: the continuum robot distal point (x end,yend) reaches the target point P target, and the minimum distance mindis from the intestinal rim is greater than the set distance threshold. The minimum distance detection is carried out by using point cloud map data to establish a kd tree, obtaining the minimum distance through a nearest neighbor search method, judging whether the continuum robot is in collision contact with the intestinal tract, and if the distance between the continuum robot and the intestinal tract meets the threshold requirement, and the optimization function result meets the requirement, then the optimal size parameter is met; if collision contact with intestinal tracts occurs or the result of the optimization function does not meet the requirement, the currently set number of the nodes of the continuum robot does not meet the requirement, the number of the nodes needs to be increased, and the number of variables is correspondingly increased in the objective function.
Specific: the objective function takes the length of each section of the continuum robot as a design variable, and the total length and the dexterity index of the continuum robot are multiplied by the weights respectively and then added as the objective function, so that the maximum dexterity is required to be obtained:
Min F＝1/DT
wherein: d T is a continuum robot dexterity index.
The optimal design variables of the objective function determine the variable quantity in the objective function, and the variable quantity is as follows:
L＝(l1,l2,...,lN)T
wherein: l i is the length of the ith section of the continuum robot, N is the total number of sections of the continuum robot, and T is the matrix transposition.
The following formula is a constraint:
wherein: θ i is the bending angle of the ith section, and l i is the length of the ith section.
According to the embodiment, the structural parameters of the continuum robot are optimized, so that better motion performance is obtained, human intestinal tract detection and operation tasks can be better performed, and collision contact between the continuum robot and the intestinal tract is avoided.
The above embodiments are provided to illustrate the technical concept and features of the present invention and are intended to enable those skilled in the art to understand the content of the present invention and implement the same, and are not intended to limit the scope of the present invention. All equivalent changes or modifications made in accordance with the spirit of the present invention should be construed to be included in the scope of the present invention.
Claims (9)
1. The method for optimizing the dexterity of the continuum robot is characterized by comprising the following steps of: comprising the following steps:
s1: a point cloud map model of the tissue organ is established,
S2: a positive kinematic formula of the continuum robot is established according to a constant curvature model of the continuum robot,
S3: a mathematical model of the reachable space of the continuum robot is established,
S4: constructing a mathematical model of the dexterity of the continuum robot by calculating an occupancy ratio of the reachable space in the task space comprises: setting a surgical task space in the point cloud map, dividing the reachable space area in the task space by the set surgical task space area, and taking the obtained value as a dexterity index of the continuum robot, wherein the dexterity index formula of the continuum robot is as follows:
DT＝Saccessible/ST
ST＝a×b
wherein: s accessible is the reachable area of the continuum robot in the set surgical task space, S T is the area of the set surgical task space, a and b are the length and width of the set surgical task space respectively,
S5: optimizing the size parameters of the continuum robot by taking a mathematical model of the dexterity index of the continuum robot as an objective function, so that the objective function meets constraint conditions: the continuum robot end point (x end,yend) reaches the target point P target, and the minimum distance mindis from the tissue organ edge is greater than the set distance threshold.
2. The method of optimizing the dexterity of a continuum robot of claim 1, wherein: in S1, a point cloud map model of a tissue organ is established by using python.
3. The method of optimizing the dexterity of a continuum robot of claim 2, wherein: the establishing of the point cloud map model of the tissue organ comprises the following steps: and constructing a tissue organ stl file by using the three-dimensional reconstruction of the CT file, converting the stl file format into a ply format, and generating a tissue organ edge information point cloud map through an open3d library.
4. The method of optimizing the dexterity of a continuum robot of claim 1, wherein: in S2, the pose of the continuum robot and the position coordinates of the head of the continuum robot are calculated by substituting the bending angle parameters of the continuum robot into a positive kinematic formula, and a constant curvature model is as follows:
the positive kinematic model is:
Wherein: x n and y n are n-th node end point coordinates, n epsilon (1, N), l 1 is a first node length, theta 1 is a first node bending angle, R m-1 is a rotation transformation matrix from the m-1 th node to the m-1 th node, l m is an m-th node length, and theta m' is a bending angle difference value between the m-th node and the m-1 th node.
5. The method of optimizing the dexterity of a continuum robot of claim 1, wherein: in S3: the left mathematical model of the reachable space is:
Wherein, T 1,T2 and T 3 are respectively transformation matrixes of corresponding joints, θ 1,θ2,θ3 and θ i are respectively bending angles of the corresponding joints, T is matrix transposition, and a mathematical model of a right space is obtained by symmetry.
6. The method of optimizing the dexterity of a continuum robot of claim 1, wherein: in S5, the objective function is to multiply the total length of the continuum robot and the dexterity index by the weights, respectively, using the length of each segment of the continuum robot as a design variable, and then add the multiplied weights to obtain an objective function:
Min F＝1/DT
wherein: d T is a continuum robot dexterity index.
7. The method of optimizing the dexterity of a continuum robot of claim 6, wherein: the optimal design variables of the objective function determine the number of variables in the objective function, which is:
L＝(l1,l2,...,lN)T
wherein: l i is the length of the ith section of the continuum robot, N is the total number of sections of the continuum robot, and T is the matrix transposition.
8. The method of optimizing the dexterity of a continuum robot of claim 1, wherein: in S5, the constraint is:
wherein: θ i is the bending angle of the ith section, and l i is the length of the ith section.
9. The method of optimizing the dexterity of a continuum robot of claim 1, wherein: in S5, the minimum distance detection uses point cloud map data to establish a kd tree, the minimum distance is obtained through a nearest neighbor search method, whether the continuum robot is in collision contact with a tissue organ or not is judged, and if the distance between the continuum robot and the tissue organ meets the threshold requirement and the optimization function result meets the requirement, the optimal size parameter is met; if collision contact with the tissue organ occurs or the result of the optimization function does not meet the requirement, the currently set number of the nodes of the continuum robot does not meet the requirement, the number of the nodes needs to be increased, and the number of variables is correspondingly increased in the objective function.
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| WO2013184192A2 (en) * | 2012-05-12 | 2013-12-12 | Massachusetts Institute Of Technology | Continuum style manipulator actuated with phase change media |
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| JP6921602B2 (en) * | 2017-04-21 | 2021-08-18 | キヤノン株式会社 | Continuum robot control system, its control method, and program |
| CN111618853B (en) * | 2020-05-25 | 2021-06-15 | 清华大学深圳国际研究生院 | Continuous robot deviation correction kinematics equivalent method and control device |
| CN111941394A (en) * | 2020-08-07 | 2020-11-17 | 北京交通大学 | Flexible dexterous manipulator based on parallel connection continuum mechanism |
| US20220105627A1 (en) * | 2020-10-05 | 2022-04-07 | Autodesk, Inc. | Singularity-free kinematic parameterization of soft robot manipulators |
| CN112828893B (en) * | 2021-01-26 | 2022-05-13 | 南京理工大学 | Multi-segment linear drive continuum mechanical arm dynamics modeling method |
| CN113033065B (en) * | 2021-04-02 | 2023-06-09 | 南京理工大学 | Inverse kinematics solving method for two-joint rope-driven continuous mechanical arm |
| CN113752253B (en) * | 2021-08-16 | 2022-11-11 | 常州大学 | Parameter optimization method for continuum robot |
| CN114938996A (en) * | 2022-04-01 | 2022-08-26 | 天津大学 | Flexible joint and flexible endoscope robot for minimally invasive surgery |
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