CN113954065B - Robot offline teaching platform based on inertial navigation positioning technology and offline teaching method thereof - Google Patents
Robot offline teaching platform based on inertial navigation positioning technology and offline teaching method thereof Download PDFInfo
- Publication number
- CN113954065B CN113954065B CN202111144102.7A CN202111144102A CN113954065B CN 113954065 B CN113954065 B CN 113954065B CN 202111144102 A CN202111144102 A CN 202111144102A CN 113954065 B CN113954065 B CN 113954065B
- Authority
- CN
- China
- Prior art keywords
- axis
- data
- offline teaching
- robot
- main control
- 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.)
- Active
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1694—Programme controls characterised by use of sensors other than normal servo-feedback from position, speed or acceleration sensors, perception control, multi-sensor controlled systems, sensor fusion
-
- 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
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- Robotics (AREA)
- Mechanical Engineering (AREA)
- Manipulator (AREA)
- Numerical Control (AREA)
Abstract
The invention provides a robot offline teaching platform and an offline teaching method based on an inertial navigation positioning technology. The robot offline teaching platform comprises a sensor, a main control chip, a WiFi module, a microSD card slot and a power supply system; the sensor acquires pose data; the main control chip receives data; the WiFi module sends data to the computer in a wireless transmission mode; the microSD card is arranged in the microSD card slot and used for storing and recording; the power supply system supplies power for the sensor, the main control chip, the WiFi module and the microSD card. Aiming at the problem that the requirement of small-batch flexible production cannot be met, the data are collected by operating the pose acquisition system, and the robot receives the data and reproduces the track and the action.
Description
Technical Field
The invention belongs to the field of off-line teaching of industrial robots; in particular to a robot offline teaching platform and an offline teaching method based on inertial navigation positioning technology.
Background
Industrial robots, by virtue of the advantages of high efficiency, reliable processing quality, capability of working in severe environments and the like, are increasingly applied to operations such as automobile manufacturing, machining, welding, loading and unloading, grinding and polishing, carrying and stacking, assembling, spraying and the like. At present, the industrial robot programming method is mainly divided into two types: manual teaching reproduction and off-line programming. Due to the problems of insufficient flexibility, accurate calibration and the like, in the fast-paced modern industrial production, the two methods cannot meet the requirements of small-batch flexible production. The robot is high in repeated positioning precision and low in positioning precision at present.
Disclosure of Invention
The invention provides an off-line teaching platform of a robot and an off-line teaching method thereof based on an inertial navigation positioning technology, which aim at the problem that the requirement of small-batch flexible production cannot be met, and realize that a pose acquisition system is operated to collect data, and the robot receives the data and reproduces tracks and actions.
The invention is realized by the following technical scheme:
the robot offline teaching platform comprises a sensor, a main control chip, a WiFi module, a microSD card slot and a power supply system; the sensor acquires pose data; the main control chip receives data; the WiFi module sends data to the computer in a wireless transmission mode; the microSD card is arranged in the microSD card slot and used for storing and recording; the power supply system supplies power for the sensor, the main control chip, the WiFi module and the microSD card.
Further, the sensor comprises four nine-axis sensors, wherein the nine-axis sensors are a three-axis acceleration sensor, a three-axis gyroscope sensor and a three-axis angle sensor, and the four nine-axis sensors are directly connected with the main control chip through SPI ports.
Further, the four nine-axis sensors are independently powered, and the WiFi module is independently powered.
The power supply system comprises a 5V direct current power supply and a 3.3V direct current power supply, and the 5V direct current power supply is converted into the 3.3V direct current power supply.
Further, the off-line teaching platform of robot appearance is pen-shaped, and its inner circuit board arranges into rectangular shape, distributes four nine-axis modules in the axis both sides simultaneously, and the circuit board is the WiFi module leftmost, and the centre is main control chip, and the rightmost is micro USB interface, and power management module hugs closely micro USB interface, and Tf draw-in groove is in main control chip's left side.
An off-line teaching method of a robot off-line teaching platform based on inertial navigation positioning technology, the off-line teaching method of the robot comprises the following steps:
step 1: acquiring pose data of the robot by using four nine-axis sensors;
step 2: the pose data in the step 1 are transmitted to a main control chip, and the main control chip calculates angle data and displacement data;
step 3: the angle data and the displacement data calculated in the step 2 are transmitted to a computer in a wireless transmission mode, and pose tracks are displayed;
step 4: and the robot receives the pose track of the step 3 and executes the command.
Further, the step 1 is specifically to establish a carrier coordinate system using ox b y b z b Representing the origin as the loadCenter of gravity, x of body b The axis is rightward along the transverse axis of the carrier, y b The axis being directed forward along the longitudinal axis of the carrier, z b The shaft is directed upwards along the vertical axis of the carrier.
Further, in step 2, the collected initial data are all data based on a carrier coordinate system, and the carrier coordinate system is converted into a geographic coordinate system on a main control chip, namely ox is used g y g z g The origin is the center of gravity of the carrier, x g The axis points to the east, i.e., E; y is g The axis is directed north, N; z g The axis points to the sky, namely U;
the data are resolved by complementary filtering to obtain a gravity estimated value g under a carrier coordinate system b Then, calculating a pitch angle and a roll angle of the carrier, wherein g is the local gravity acceleration:
processing data by using a quaternion method, wherein Q (t) is an attitude quaternion;
wherein I is a unit vector, θ is a rotation of a certain rotation by an angle θ around the unit vector, Q (t 0 ) Is t 0 The gesture quaternion of the moment in time,
will be
And->
Finite term computation in series expansion, intercepting finiteTerm, each order approximation algorithm of quaternion is obtained:
first order algorithm:
second order algorithm:
third-order algorithm:
/>
fourth-order algorithm:
the beneficial effects of the invention are as follows:
the invention realizes that the robot breaks away from the demonstrator to teach.
The invention has the function of dragging teaching, and complex mechanical movement can be realized quickly and conveniently by adopting the function.
The invention has low requirement on operators, greatly reduces programming time and improves teaching efficiency.
Drawings
FIG. 1 is a pictorial view of a pcb panel of the present invention.
Fig. 2 is a physical view of the housing of the present invention.
Fig. 3 is a flow chart of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. 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.
The robot offline teaching platform comprises a sensor, a main control chip, a WiFi module, a microSD card slot and a power supply system; the sensor acquires pose data; the main control chip receives data; the WiFi module sends data to the computer in a wireless transmission mode; the microSD card is arranged in the microSD card slot and used for storing and recording; the power supply system supplies power for the sensor, the main control chip, the WiFi module and the microSD card.
Further, the sensor comprises four nine-axis sensors, wherein the nine-axis sensors are a three-axis acceleration sensor, a three-axis gyroscope sensor and a three-axis angle sensor, and the four nine-axis sensors are directly connected with the main control chip through SPI ports.
Further, the four nine-axis sensors are independently powered, and the WiFi module is independently powered.
The power supply system comprises a 5V direct current power supply and a 3.3V direct current power supply, and the 5V direct current power supply is converted into the 3.3V direct current power supply.
Noise caused by other aspects is reduced as much as possible, and power supply of the four nine-axis modules is independently separated out and independently supplied; considering that the WiFi module has larger power consumption, in order to obtain a fast and convenient power supply more easily, 5v is decided to be used as the power supply input voltage, but the nine-axis sensor, the main control chip and the like all need 3.3v power supply.
Further, the off-line teaching platform of robot appearance is pen-shaped, and its inner circuit board arranges into rectangular shape, distributes four nine-axis modules in the axis both sides simultaneously, and the circuit board is the wiFi module leftmost, and the centre is master control chip STM32F7 chip, and the rightmost is micro USB interface, and power management module hugs closely micro USB interface, and Tf draw-in groove is in master control chip's left side.
The main control chip selects STM32F767IGT6 model of ARM architecture, the main frequency is 216MHz, a floating point operation unit is integrated, double-period floating point multiplication operation can be carried out, and the operation performance can reach 1 hundred million times per second of floating point operation.
The data collected by the sensor is converted into a geographic coordinate system by taking the carrier coordinate system as a reference in a main control chip, the data is subjected to quasi-transformation, the data is calculated through complementary filtering, the accumulated error is reduced, and then the data is processed by using a quaternion method, so that the coordinate system of the data is converted into the geographic coordinate system.
As in fig. 3. Considering volume and stability, all components are chip packages.
The method is further improved in that: the holding posture considered in the design of the casing is designed to be a pen-like form with the length of about 240mm, and the pen point is designed at the front end, so that the pen point can conveniently move along a preset track. The overall width of the shell is 36mm, the thickness is 26mm, and the edge is a round corner with the radius of 7 mm. Two hole sites are left on the whole shell, one is the tf clamping groove position, and the other is the micro usb interface position. A gap of 2mm is reserved between the shell and the main board, three mounting holes with the diameter of 3mm are formed in the main board, and hole positions are also designed in corresponding positions on the shell.
An off-line teaching method of a robot off-line teaching platform based on inertial navigation positioning technology, the off-line teaching method of the robot comprises the following steps:
step 1: acquiring pose data of the robot by using four nine-axis sensors;
step 2: the pose data in the step 1 are transmitted to a main control chip, and the main control chip calculates angle data and displacement data;
step 3: the angle data and the displacement data calculated in the step 2 are transmitted to a computer in a wireless transmission mode, and pose tracks are displayed;
step 4: and the robot receives the pose track of the step 3 and executes the command.
Further, the step 1 is specifically to establish a carrier coordinate system using ox b y b z b The origin is the center of gravity of the carrier, x b The axis is rightward along the transverse axis of the carrier, y b The axis being directed forward along the longitudinal axis of the carrier, z b The shaft is upwards along the vertical shaft of the carrier; the dragging control pen collects gesture information in the motion process.
Further, in step 2, the collected initial data are all data based on a carrier coordinate system, and the carrier coordinate system is converted into a geographic coordinate system on a main control chip, namely ox is used g y g z g The origin is the center of gravity of the carrier, x g The axis points to the east, i.e., E;y g the axis is directed north, N; z g The axis points to the sky, namely U;
the data are resolved by complementary filtering to obtain a gravity estimated value g under a carrier coordinate system b Then, calculating a pitch angle and a roll angle of the carrier, wherein g is the local gravity acceleration:
processing data by using a quaternion method, wherein Q (t) is an attitude quaternion;
wherein I is a unit vector, θ is a rotation of a certain rotation by an angle θ around the unit vector, Q (t 0 ) Is t 0 The gesture quaternion of the moment in time,
will be
And->
Finite term calculation is performed according to the series expansion, the finite term is intercepted, and each-order approximation algorithm of the quaternion is obtained:
first order algorithm:
second order algorithm:
third-order algorithm:
fourth-order algorithm:
/>
Claims (5)
1. the offline teaching method of the robot offline teaching platform based on the inertial navigation positioning technology is characterized in that the robot offline teaching platform comprises a sensor, a main control chip, a WiFi module, a microSD card slot and a power supply system; the sensor acquires pose data; the main control chip receives data; the WiFi module sends data to the computer in a wireless transmission mode; the microSD card is arranged in the microSD card slot and used for storing and recording; the power supply system supplies power to the sensor, the main control chip, the WiFi module and the microSD card;
the robot offline teaching method comprises the following steps:
step 1: acquiring pose data of the robot by using four nine-axis sensors;
step 2: the pose data in the step 1 are transmitted to a main control chip, and the main control chip calculates angle data and displacement data;
step 3: the angle data and the displacement data calculated in the step 2 are transmitted to a computer in a wireless transmission mode, and pose tracks are displayed;
step 4: the robot receives the pose track of the step 3 and executes the command;
the step 2 is that the initial data collected are all based on a carrier coordinate system, and the carrier coordinate system is converted into data of a geographic coordinate system on a main control chip, namely ox is used g y g z g The origin is the center of gravity of the carrier, x g The axis points to the east, i.e., E; y is g The axis is directed north, N; z g The axis points to the sky, namely U;
the data are resolved by complementary filtering to obtain a gravity estimated value g under a carrier coordinate system b Then, calculating a pitch angle and a roll angle of the carrier, wherein g is the local gravity acceleration:
processing data by using a quaternion method, wherein Q (t) is an attitude quaternion;
wherein I is a unit vector, Δθ is a rotation of a certain rotation by an angle θ around the unit vector, and Q (t 0 ) Is t 0 The gesture quaternion of the moment in time,
will be
And->
Finite term calculation is performed according to the series expansion, the finite term is intercepted, and each-order approximation algorithm of the quaternion is obtained: />
First order algorithm:
second order algorithm:
third-order algorithm:
fourth-order algorithm:
2. the offline teaching method of the robot offline teaching platform based on the inertial navigation positioning technology according to claim 1, wherein the sensors comprise four nine-axis sensors, the nine-axis sensors are a three-axis acceleration sensor, a three-axis gyroscope sensor and a three-axis angle sensor, and the four nine-axis sensors are directly connected with a main control chip through SPI ports.
3. The offline teaching method of the robot offline teaching platform based on the inertial navigation positioning technology according to claim 2, wherein the four nine-axis sensors are independently powered, and the WiFi module is independently powered;
the power supply system comprises a 5V direct current power supply and a 3.3V direct current power supply, and the 5V direct current power supply is converted into the 3.3V direct current power supply.
4. The offline teaching method of the robot offline teaching platform based on the inertial navigation positioning technology according to claim 1, wherein the robot offline teaching platform is pen-shaped in appearance, an internal circuit board of the robot offline teaching platform is arranged in a long strip shape, four nine-axis modules are distributed on two sides of a central axis, a WiFi module is arranged on the leftmost side of the circuit board, a main control chip is arranged in the middle of the circuit board, a micro USB interface is arranged on the rightmost side of the circuit board, a power management module is closely attached to the micro USB interface, and a Tf clamping groove is arranged on the left side of the main control chip.
5. The offline teaching method of the robot offline teaching platform based on the inertial navigation positioning technology according to claim 1, wherein the step 1 specifically comprises the steps of establishing a carrier coordinate system and using ox b y b z b The origin is the center of gravity of the carrier, x b The axis is rightward along the transverse axis of the carrier, y b The axis being directed forward along the longitudinal axis of the carrier, z b The shaft is directed upwards along the vertical axis of the carrier.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111144102.7A CN113954065B (en) | 2021-09-28 | 2021-09-28 | Robot offline teaching platform based on inertial navigation positioning technology and offline teaching method thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111144102.7A CN113954065B (en) | 2021-09-28 | 2021-09-28 | Robot offline teaching platform based on inertial navigation positioning technology and offline teaching method thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113954065A CN113954065A (en) | 2022-01-21 |
| CN113954065B true CN113954065B (en) | 2023-05-12 |
Family
ID=79462489
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111144102.7A Active CN113954065B (en) | 2021-09-28 | 2021-09-28 | Robot offline teaching platform based on inertial navigation positioning technology and offline teaching method thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113954065B (en) |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05185387A (en) * | 1992-01-14 | 1993-07-27 | Mitsubishi Electric Corp | Robot control device |
| CN107560613B (en) * | 2017-08-15 | 2020-03-31 | 江苏科技大学 | Robot indoor track tracking system and method based on nine-axis inertial sensor |
| CN110146077A (en) * | 2019-06-21 | 2019-08-20 | 台州知通科技有限公司 | Pose of mobile robot angle calculation method |
| CN110900609A (en) * | 2019-12-11 | 2020-03-24 | 浙江钱江机器人有限公司 | Robot teaching device and method thereof |
| CN111551168A (en) * | 2020-04-30 | 2020-08-18 | 江苏帝一集团有限公司 | Underwater robot position and attitude data acquisition system and data fusion method thereof |
-
2021
- 2021-09-28 CN CN202111144102.7A patent/CN113954065B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN113954065A (en) | 2022-01-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Liu et al. | Accelerometer for mobile robot positioning | |
| Horaud et al. | Visually guided object grasping | |
| CN113311411B (en) | Laser radar point cloud motion distortion correction method for mobile robot | |
| CN108519090B (en) | Method for realizing double-channel combined attitude determination algorithm based on optimized UKF algorithm | |
| CN201594225U (en) | Joystick with gesture recognition function | |
| CN110076780B (en) | Robot assembly method and system based on vision and force feedback pose adjustment | |
| CN105607764B (en) | Trace input device and track input method | |
| CN112733409B (en) | Multi-source sensing comprehensive integrated composite navigation micro-system collaborative design platform | |
| CN108549319B (en) | Universal post-processing method for double-turntable five-axis numerical control machine tool | |
| CN104101293A (en) | Measurement machine station coordinate system unification system and method | |
| CN113954065B (en) | Robot offline teaching platform based on inertial navigation positioning technology and offline teaching method thereof | |
| CN204487287U (en) | A kind of Intelligent Machining System of oil-free vortex air compressor | |
| US11474621B2 (en) | Low-power tilt-compensated pointing method and corresponding pointing electronic device | |
| CN103134451A (en) | Three-coordinate measuring machine three-axis perpendicularity error compensation system and method | |
| US9523702B2 (en) | Sensor unit, electronic device, and moving body | |
| CN110967017B (en) | Cooperative positioning method for rigid body cooperative transportation of double mobile robots | |
| Fife et al. | Reconfigurable on-board vision processing for small autonomous vehicles | |
| Hoang et al. | Pre-processing technique for compass-less madgwick in heading estimation for industry 4.0 | |
| CN116644264A (en) | Nonlinear bias-invariant Kalman filtering method | |
| CN101674358A (en) | Three-dimensional space locating mobile communication device | |
| CN114131617A (en) | Intelligent compliance control method and device for industrial robot | |
| CN101655737B (en) | Inertial sensor device | |
| CN110779554B (en) | Mechanical arm, initial pose calibration system and method based on IMU | |
| Jung et al. | The miniature omni-directional mobile robot OmniKity-I (OK-I) | |
| CN104424601A (en) | Centering assembling method and apparatus of irregularly-shaped assembly part |
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 |