CN112247898A - Robot non-rigid body assembling method based on deflection analysis - Google Patents
Robot non-rigid body assembling method based on deflection analysis Download PDFInfo
- Publication number
- CN112247898A CN112247898A CN202010985581.4A CN202010985581A CN112247898A CN 112247898 A CN112247898 A CN 112247898A CN 202010985581 A CN202010985581 A CN 202010985581A CN 112247898 A CN112247898 A CN 112247898A
- Authority
- CN
- China
- Prior art keywords
- shaft
- contact
- assembly
- contact state
- deflection
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J19/00—Accessories fitted to manipulators, e.g. for monitoring, for viewing; Safety devices combined with or specially adapted for use in connection with manipulators
- B25J19/007—Means or methods for designing or fabricating manipulators
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25B—TOOLS OR BENCH DEVICES NOT OTHERWISE PROVIDED FOR, FOR FASTENING, CONNECTING, DISENGAGING OR HOLDING
- B25B27/00—Hand tools, specially adapted for fitting together or separating parts or objects whether or not involving some deformation, not otherwise provided for
Landscapes
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Robotics (AREA)
- Automatic Assembly (AREA)
- Manipulator (AREA)
Abstract
A robot non-rigid body assembly method based on deflection analysis is characterized in that a contact force sense model provided based on deflection analysis accords with contact deformation characteristics of non-rigid body parts in the assembly process, and the deflection angle theta and the deflection amount d of a shaft can be well predictedx,dy(ii) a Based on the contact state recognition model and the contact force sense model, the current contact state and the current posture of the part can be better reflected, and the part assembly can be realized more quickly.
Description
Technical Field
The invention belongs to the field of robot assembly and control, and is suitable for the field of robot non-rigid body part assembly. In particular to a robot non-rigid body assembling method based on deflection analysis.
Background
In recent years, with rapid development of industrial robot industry and related technologies, industrial robots are widely used in various fields such as automated assembly, manufacturing, and the like. In the field of automatic assembly, the materials of the assembly parts are gradually diversified, and the assembly parts are not limited to metal materials with high rigidity and hard texture, such as non-rigid materials of plastics, rubber and the like, and are widely applied. Unlike rigid bodies, non-rigid body parts are prone to elastic deformation during assembly contact, and such deformation can cause inaccurate force feedback information to affect overall assembly, so that it is necessary to study the assembly method of the non-rigid body parts of the robot.
The existing robot part assembling method is to combine a contact force sense model and robot inverse kinematics analysis to realize the assembling task of parts. However, the existing contact force sense model is only suitable for rigid body parts, that is, deformation of the parts is negligible in the assembling and contacting process, and is not suitable for non-rigid body parts which generate non-negligible deformation in the assembling and contacting process.
Disclosure of Invention
In order to solve the problem that a contact force sense model is not suitable for a non-rigid part in the existing robot assembly method, the invention provides a non-rigid part assembly method of a robot based on deflection analysis.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a robot non-rigid body assembling method based on deflection analysis comprises the following steps:
step 1: the shaft hole assembly is divided into three states: a single point contact state, a two point contact state, and a planar contact state. Acquiring force sense data aiming at different contact states, analyzing the force sense data characteristics of the different states, and establishing a non-rigid body assembling contact body state recognition model by combining a machine learning algorithm and statics characteristics;
step 2: designing a non-rigid contact force sense model based on deflection analysis according to different contact states,
single-point contact state and two-point contact state:
Mx=-G(0.5sinθl-lg)-Ff(cosθl+sinθr-lv+δ1)
+Fn(sinθl-cosθr+lh+δ2)
(1)
lg=cosθWg (2)
lv=-sinθ(cosθer+We-r)+cosθsinθer (3)
lh=cosθ(cosθer+We-r)+sinθsinθer (4)
object=|Mx+G(0.5sinθl-lg)+Ff(cosθl+sinθr-lv+δ1)
-Fn(sinθl-cosθr+lh+δ2)|
(6)
G=max(|tanθe-θe|-σ1,0)+max(θe-θ,0)+max(|lh|-σ2,0) (7)
fitness(i)=object+(C×i)γG (8)
in the formula: mxMoment on the shaft, G gravity on the shaft, FfThe friction force acting on the shaft, FnThe supporting force applied to the shaft, l is the length of the shaft, r is the radius of the shaft, theta is the deflection angle of the shaft relative to the vertical line, lgIs the lateral offset of the center of gravity of the shaft relative to the clamping end,/vLongitudinal offset of the shaft contact relative to the clamping end,/hIs the transverse offset, δ, of the shaft contact with respect to the clamping end1,δ2For local deformation of the contact point, WgDeflection of the midpoint of the shaft, WeIs the deflection of the free end of the shaft, thetaeIs a corner of the free end face of the shaft, G1,G2,G3Are all single point contact state constraints, σ1,σ2To constrain the constant object to be the objective function of the model, G to be the constraint function of the model, fitness to be the fitness function, C,gamma is a punishment constant of a dynamic punishment function, i is the iteration number of the optimization algorithm, the constraint function can limit variables in a feasible domain, and the dynamic punishment function can fully search the whole feasible domain so as to find an optimal value in the feasible domain;
predicting a deviation angle theta of the shaft relative to the vertical line by combining the formulas (1) - (8) and a parameter optimization algorithm, and providing a deviation correction parameter for subsequent assembly;
plane contact state:
Mz=F1(y1+εy)-F2(x1+εx) (11)
fitness(i)=|Mz-F1(y1+εy)+F2(x1+εx)| (12)
in the formula: x is the number of1,y1The centroid position of the portion of the shaft end face in contact with the bore plane, F1,F2,FnForces acting on the shaft in all directions at the centroid location, Mx,My,MzRespectively the moments, e, of the axes about the x, y, z axesx,εyFor compensation of the centroid due to elastic deformation of the shaft, dx,dyAlpha is the relative distance and the relative deflection angle of the shaft end face center and the hole center respectively, and the centroid compensation quantity, namely epsilon, is predicted by combining the formulas (9) - (12) and a parameter optimization algorithmx,εyCombining formulae (13) - (15) and ∈x,εyThe relative distance between the center of the end face of the shaft and the center of the hole, namely the offset distance d, is obtained by calculating the predicted valuex,dyProviding deviation correction parameters for subsequent assembly;
and step 3: the robot starts to execute the assembly task to move downwards after clamping the non-rigid body part, and the shaft holes are contacted and reach the longitudinal force FzThe threshold value of (1) is quickly lifted to separate the shaft holes, and meanwhile, the next step of instruction is waited;
and 4, step 4: transmitting force sense data acquired in the shaft hole contact process to a PC (personal computer) end, calling corresponding software and a contact state identification model by the PC end, and judging the contact state according to the data characteristics;
and 5: calling corresponding contact force sense models according to different contact states, if the part is in the stage of single-point contact and two-point contact, predicting to obtain an included angle theta between the axis and the vertical line through contact force sense model formulas (1) - (5) and an optimization algorithm, and if the part is in the stage of plane contact, predicting to obtain a relative distance between the center of the end face of the axis and the center of the hole, namely an offset d through contact force sense model formulas (13) - (15) and the optimization algorithmx,dy;
Step 6: and (4) performing inverse kinematics analysis of the robot according to the prediction result, and calling different robot deviation rectifying programs to achieve the purpose of adjusting the posture and the position of the shaft. After the posture or the position of the shaft is adjusted, the assembly action is carried out again;
and 7: and (4) judging whether the assembly condition is met, continuing to operate until the assembly is finished after the assembly condition is met, and otherwise, executing the step 4.
The invention has the following beneficial effects: 1. the contact force sense model based on deflection analysis accords with the contact deformation characteristic of the non-rigid body part in the assembling process, and can better predict the deflection angle theta and the offset d of the shaftx, dy. 2. Based on the contact state recognition model and the contact force sense model, the current contact state and the current posture of the part can be better reflected, and the part assembly can be realized more quickly.
Drawings
FIG. 1 is a single point contact statics analysis.
Fig. 2 is a non-rigid body deflection analysis.
Fig. 3 is a plane contact statics analysis.
FIG. 4 is a flat contact interface analysis.
Fig. 5 is a flow chart of the overall assembly of non-rigid body parts.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1 to 5, a robot non-rigid body assembling method based on deflection analysis includes the following steps:
step 1: the shaft hole assembly is divided into three states: a single point contact state, a two point contact state, and a planar contact state. Acquiring force sense data aiming at different contact states, analyzing the force sense data characteristics of the different states, and establishing a non-rigid body assembling contact body state recognition model by combining a machine learning algorithm and statics characteristics;
step 2: non-rigid contact force sense model established based on deflection analysis and designed for different contact states
The single point contact state is shown in FIGS. 1 and 2, where Fy,FzThe force borne by the fixed end of the shaft, M and F are respectively the moment and the force equivalent to the center of the end face;
Mx=-G(0.5sinθl-lg)-Ff(cosθl+sinθr-lv+δ1)
+Fn(sinθl-cosθr+lh+δ2)
(1)
lg=cosθWg (2)
lv=-sinθ(cosθer+We-r)+cosθsinθer (3)
lh=cosθ(cosθer+We-r)+sinθsinθer (4)
object=|Mx+G(0.5sinθl-lg)+Ff(cosθl+sinθr-lv+δ1)
-Fn(sinθl-cosθr+lh+δ2)|
(6)
G=max(|tanθe-θe|-σ1,0)+max(θe-θ,0)+max(|lh|-σ2,0) (7)
fitness(i)=object+(C×i)γG (8)
in the formula: mxMoment on the shaft, G gravity on the shaft, FfThe friction force acting on the shaft, FnThe supporting force applied to the shaft, l is the length of the shaft, r is the radius of the shaft, theta is the deflection angle of the shaft relative to the vertical line, lgIs the lateral offset of the center of gravity of the shaft relative to the clamping end,/vLongitudinal offset of the shaft contact relative to the clamping end,/hIs the transverse offset, δ, of the shaft contact with respect to the clamping end1,δ2The local deformation of the contact point has a value range of 0, 0.5r],[0,r],WgDeflection of the midpoint of the shaft, WeIs the deflection of the free end of the shaft, thetaeIs a corner of the free end face of the shaft, G1,G2,G3Are all single point contact state constraints, σ1,σ2The constraint constants are respectively 0.001 and 0.002, the penalty constants are respectively 0.5 and 2 for C and gamma;
carrying out parameter optimization by adopting a gray wolf algorithm (GWO), setting initial parameters including a wolf group number Searchgents as 20, an individual dimension dim as 3 and an iteration number Max _ iteration as 100, establishing a contact force sense model by combining formulas (1) to (5), and calculating the fitness of each individual by combining formulas (6) to (8)And (4) a function value is responded, so that the optimal individual of the current iteration is obtained. Obtaining optimal parameters theta, delta through multiple iterations1,δ2Thereby predicting the deviation angle theta of the shaft relative to the vertical line and providing deviation correction parameters for subsequent assembly;
the contact force sensation model in the two-point contact state is similar to that in the single-point contact state, and is not repeated;
the planar contact state is shown in FIGS. 3 and 4, h, p1The center of the hole, the center of the end face of the shaft and the centroid of the contact part of the shaft and the platform, respectively, the shaft should be deformed in fig. 3, which deformation is not shown in the figure.
Mz=F1(y1+εy)-F2(x1+εx) (11)
fitness(i)=|Mz-F1(y1+εy)+F2(x1+εx)| (12)
In the formula: x is the number of1,y1The centroid position of the portion of the shaft end face in contact with the bore plane, F1,F2,FnFor all directions to which the axis is subjected at the centroid positionForce, Mx,My,MzRespectively the moments, e, of the axes about the x, y, z axesx,εyThe value range of the centroid compensation quantity caused by the elastic deformation of the shaft is [ -0.01, 0.01 [)],dx,dyAnd alpha is the relative distance and the relative deflection angle of the center of the shaft end surface and the center of the hole respectively
The gray wolf algorithm (GWO) is adopted to carry out parameter optimization, initial parameters are set to include the wolf group quantity Searchgents which is 20, the individual dimension dim which is 3 and the iteration times Max _ iteration which is 100, and by combining the formulas (9) - (12), the centroid compensation quantity, namely epsilonx,εy. Combining formulae (13) - (15) and ∈x,εyThe relative distance between the center of the end face of the shaft and the center of the hole, namely the offset distance d, is obtained by calculating the predicted valuex,dyProviding deviation correction parameters for subsequent assembly;
and step 3: the robot starts to execute the assembly task to move downwards after clamping the non-rigid body part, and the shaft holes are contacted and reach the longitudinal force FzThe threshold value of (1) is quickly lifted to separate the shaft holes, and meanwhile, the next step of instruction is waited;
and 4, step 4: transmitting force sense data acquired in the shaft hole contact process to a PC (personal computer) end, calling corresponding software and a contact state identification model by the PC end, and judging the contact state according to the data characteristics;
and 5: and calling the corresponding contact force sense model according to different contact states. If the part is in the single-point contact and two-point contact stages, the included angle theta between the axis and the vertical line is predicted by the contact force sense model formulas (1) - (5) and the optimization algorithm GWO, and if the part is in the plane contact stage, the relative distance between the center of the end face of the axis and the center of the hole, namely the offset d, is predicted by the contact force sense model formulas (13) - (15) and the optimization algorithm GWOx, dyProviding deviation correction parameters for subsequent assembly;
step 6: performing inverse kinematics analysis of the robot according to the prediction result, calling different robot deviation rectifying programs to achieve the purpose of adjusting the posture and the position of the shaft, and performing assembly again after the posture or the position of the shaft is adjusted;
and 7: and (4) judging whether the assembly condition is met, continuing to operate until the assembly is finished after the assembly condition is met, and otherwise, executing the step 4.
The foregoing lists merely illustrate specific embodiments of the invention. It is obvious that the invention is not limited to the above embodiments, but that many variations are possible. All modifications which can be derived or suggested directly from the disclosure of the invention by a person skilled in the art are to be considered within the scope of the invention.
Claims (1)
1. A robot non-rigid body assembling method based on deflection analysis is characterized by comprising the following steps:
step 1: the shaft hole assembly is divided into three states: the method comprises the steps of acquiring force sense data aiming at different contact states, analyzing force sense data characteristics of different states and establishing a non-rigid body assembly contact state recognition model by combining a machine learning algorithm and statics characteristics under the conditions of a single-point contact state, a two-point contact state and a plane contact state;
step 2: designing a non-rigid contact force sense model based on deflection analysis according to different contact states, and establishing a single-point contact state and a two-point contact state:
Mx=-G(0.5sinθl-lg)-Ff(cosθl+sinθr-lv+δ1)+Fn(sinθl-cosθr+lh+δ2)
(1)
lg=cosθWg (2)
lv=-sinθ(cosθer+We-r)+cosθsinθer (3)
lh=cosθ(cosθer+We-r)+sinθsinθer (4)
object=|Mx+G(0.5sinθl-lg)+Ff(cosθl+sinθr-lv+δ1)-Fn(sinθl-cosθr+lh+δ2)|
(6)
G=max(|tanθe-θe|-σ1,0)+max(θe-θ,0)+max(|lh|-σ2,0) (7)
fitness(i)=object+(C×i)γG (8)
in the formula: mxMoment on the shaft, G gravity on the shaft, FfThe friction force acting on the shaft, FnThe supporting force applied to the shaft, l is the length of the shaft, r is the radius of the shaft, theta is the deflection angle of the shaft relative to the vertical line, lgIs the lateral offset of the center of gravity of the shaft relative to the clamping end,/vLongitudinal offset of the shaft contact relative to the clamping end,/hIs the transverse offset, δ, of the shaft contact with respect to the clamping end1,δ2For local deformation of the contact point, WgDeflection of the midpoint of the shaft, WeIs the deflection of the free end of the shaft, thetaeIs a corner of the free end face of the shaft, G1,G2,G3Are all single point contact state constraints, σ1,σ2Is a constraint constant
object is an objective function of the model, G is a constraint function of the model, fit is an adaptive function, C and gamma are penalty constants of dynamic penalty functions, i is the iteration times of an optimization algorithm, the constraint function can limit variables in a feasible domain, and the dynamic penalty function can fully search the whole feasible domain, so that an optimal value is found in the feasible domain;
predicting a deviation angle theta of the shaft relative to the vertical line by combining the formulas (1) - (8) and a parameter optimization algorithm, and providing a deviation correction parameter for subsequent assembly;
plane contact state:
Mz=F1(y1+εy)-F2(x1+εx) (11)
fitness(i)=|Mz-F1(y1+εy)+F2(x1+εx)| (12)
in the formula: x is the number of1,y1The centroid position of the portion of the shaft end face in contact with the bore plane, F1,F2,FnForces acting on the shaft in all directions at the centroid location, Mx,My,MzRespectively the moments, e, of the axes about the x, y, z axesx,εyFor compensation of the centroid due to elastic deformation of the shaft, dx,dyAlpha is the relative distance and the relative deflection angle of the shaft end face center and the hole center respectively, and the centroid compensation quantity, namely epsilon, is predicted by combining the formulas (9) - (12) and a parameter optimization algorithmx,εyCombining formulae (13) - (15) and ∈x,εyThe relative distance between the center of the end face of the shaft and the center of the hole, namely the offset distance d, is obtained by calculating the predicted valuex,dyProviding deviation correction parameters for subsequent assembly;
and step 3: the robot starts to execute the assembly task after clamping the non-rigid body partThe shaft hole is contacted and reaches a longitudinal force F when the shaft hole moves downwardszThe threshold value of (1) is quickly lifted to separate the shaft holes, and meanwhile, the next step of instruction is waited;
and 4, step 4: transmitting force sense data acquired in the shaft hole contact process to a PC (personal computer) end, calling corresponding software and a contact state identification model by the PC end, and judging the contact state according to the data characteristics;
and 5: calling corresponding contact force sense models according to different contact states, if the part is in the stage of single-point contact and two-point contact, predicting to obtain an included angle theta between the axis and the vertical line through contact force sense model formulas (1) - (5) and an optimization algorithm, and if the part is in the stage of plane contact, predicting to obtain a relative distance between the center of the end face of the axis and the center of the hole, namely an offset d through contact force sense model formulas (13) - (15) and the optimization algorithmx,dy;
Step 6: performing inverse kinematics analysis of the robot according to the prediction result, calling different robot deviation rectifying programs to achieve the purpose of adjusting the posture and the position of the shaft, and performing assembly again after the posture or the position of the shaft is adjusted;
and 7: and (4) judging whether the assembly condition is met, continuing to operate until the assembly is finished after the assembly condition is met, and otherwise, executing the step 4.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010985581.4A CN112247898B (en) | 2020-09-18 | 2020-09-18 | Robot non-rigid body assembling method based on deflection analysis |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010985581.4A CN112247898B (en) | 2020-09-18 | 2020-09-18 | Robot non-rigid body assembling method based on deflection analysis |
Publications (2)
Publication Number | Publication Date |
---|---|
CN112247898A true CN112247898A (en) | 2021-01-22 |
CN112247898B CN112247898B (en) | 2022-04-08 |
Family
ID=74232877
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202010985581.4A Active CN112247898B (en) | 2020-09-18 | 2020-09-18 | Robot non-rigid body assembling method based on deflection analysis |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN112247898B (en) |
Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108161934A (en) * | 2017-12-25 | 2018-06-15 | 清华大学 | A kind of method for learning to realize robot multi peg-in-hole using deeply |
CN108161991A (en) * | 2018-01-26 | 2018-06-15 | 北京邮电大学 | A kind of robotic asssembly automatic seeking hole method based on force feedback |
JP2018144149A (en) * | 2017-03-03 | 2018-09-20 | 本田技研工業株式会社 | Abnormal contact detection method and contact region identification method for mobile robot |
CN109531560A (en) * | 2019-01-09 | 2019-03-29 | 中国工程物理研究院激光聚变研究中心 | Big L/D ratio peg-in-hole assembly analysis system and method |
CN109543732A (en) * | 2018-11-09 | 2019-03-29 | 山东大学 | Assembly system and method based on category feature knowledge base |
CN109670538A (en) * | 2018-12-03 | 2019-04-23 | 浙江工业大学 | A kind of assembly contact condition recognition methods of non-rigid part |
CN110363214A (en) * | 2019-05-31 | 2019-10-22 | 浙江工业大学 | A kind of contact condition recognition methods of the robotic asssembly based on GWA-SVM |
-
2020
- 2020-09-18 CN CN202010985581.4A patent/CN112247898B/en active Active
Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2018144149A (en) * | 2017-03-03 | 2018-09-20 | 本田技研工業株式会社 | Abnormal contact detection method and contact region identification method for mobile robot |
CN108161934A (en) * | 2017-12-25 | 2018-06-15 | 清华大学 | A kind of method for learning to realize robot multi peg-in-hole using deeply |
CN108161991A (en) * | 2018-01-26 | 2018-06-15 | 北京邮电大学 | A kind of robotic asssembly automatic seeking hole method based on force feedback |
CN109543732A (en) * | 2018-11-09 | 2019-03-29 | 山东大学 | Assembly system and method based on category feature knowledge base |
CN109670538A (en) * | 2018-12-03 | 2019-04-23 | 浙江工业大学 | A kind of assembly contact condition recognition methods of non-rigid part |
CN109531560A (en) * | 2019-01-09 | 2019-03-29 | 中国工程物理研究院激光聚变研究中心 | Big L/D ratio peg-in-hole assembly analysis system and method |
CN110363214A (en) * | 2019-05-31 | 2019-10-22 | 浙江工业大学 | A kind of contact condition recognition methods of the robotic asssembly based on GWA-SVM |
Non-Patent Citations (1)
Title |
---|
夏研春等: "柔性轴孔装配策略研究", 《中国机械工程》 * |
Also Published As
Publication number | Publication date |
---|---|
CN112247898B (en) | 2022-04-08 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2020207219A1 (en) | Non-model robot control method for multi-shaft-hole assembly optimized by environmental prediction | |
CN110539302B (en) | Industrial robot overall dynamics modeling and dynamics parameter identification method | |
CN110103225B (en) | Data-driven method and device for controlling repeated motion of mechanical arm | |
US11648664B2 (en) | Method for controlling a robot and robot controller | |
CN110363214B (en) | GWA-SVM-based contact state recognition method for robot assembly | |
CN110936382A (en) | Data-driven industrial robot energy consumption optimization method | |
CN113341706B (en) | Man-machine cooperation assembly line system based on deep reinforcement learning | |
Ye et al. | High-accuracy prediction and compensation of industrial robot stiffness deformation | |
CN111872937A (en) | Control method for uncertain mechanical arm in task space | |
CN111673733A (en) | Intelligent self-adaptive compliance control method of robot in unknown environment | |
Chen et al. | Evolutionary robot calibration and nonlinear compensation methodology based on GA-DNN and an extra compliance error model | |
CN112247898B (en) | Robot non-rigid body assembling method based on deflection analysis | |
Hsiao et al. | Positioning accuracy improvement of industrial robots considering configuration and payload effects via a hybrid calibration approach | |
CN109670538B (en) | Method for identifying assembly contact state of non-rigid part | |
Akhmetzyanov et al. | Deep Learning with Transfer Learning Method for Error Compensation of Cable-driven Robot. | |
CN113459104A (en) | Robot positioning error prediction method and prediction device based on support vector regression | |
CN113799137A (en) | Mobile processing robot rapid visual positioning method based on neural network | |
Heyn et al. | Application of artificial neural networks in force-controlled automated assembly of complex shaped deformable components | |
CN112894814B (en) | Mechanical arm DH parameter identification method based on least square method | |
WO2022051973A1 (en) | Milling robot multi-modal frequency response prediction method based on small-sample transfer learning | |
CN116652939A (en) | Calibration-free visual servo compliant control method for parallel robot | |
CN109397293B (en) | Ground level error modeling and compensating method based on mobile robot | |
CN111546035B (en) | Online rapid gear assembly method based on learning and prediction | |
Hu et al. | Model-based contact detection and accommodation for soft bending actuators: An integrated direct/indirect adaptive robust approach | |
CN115026828B (en) | Robot arm grabbing control method and system |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |