CN108037658A - A kind of method for correcting error of the robot kinematic error based on navigation system - Google Patents
A kind of method for correcting error of the robot kinematic error based on navigation system Download PDFInfo
- Publication number
- CN108037658A CN108037658A CN201711129855.4A CN201711129855A CN108037658A CN 108037658 A CN108037658 A CN 108037658A CN 201711129855 A CN201711129855 A CN 201711129855A CN 108037658 A CN108037658 A CN 108037658A
- Authority
- CN
- China
- Prior art keywords
- posture
- agv robots
- agv
- reference system
- error
- 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.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0205—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system
- G05B13/024—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system in which a parameter or coefficient is automatically adjusted to optimise the performance
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The present invention relates to technical field of robot control, refer in particular to a kind of method for correcting error of the AGV robots kinematic error based on micro- inertia attitude heading reference system, the present invention uses MEMS inertial sensor, magnetometer and gyroscope are analyzed by self-adapted sensor data anastomosing algorithm, and the posture of AGV robots is calculated by attitude algorithm, by the posture of calculating compared with micro- inertia attitude heading reference system, so as to adjust the current posture of AGV robots, compared with traditional AGV robots, this method can be the more steady of AGV robots operation, it is not required guide rail also to complete to run, reduce cost.
Description
Technical field
The present invention relates to robotic technology field, refers in particular to a kind of method for correcting error of robot kinematic error.
Background technology
Robot is the product of the subject crossings such as electronic technology, computer technology, mechanical structure and control theory fusion, is transported
Production cost can be compressed with robot, improves production efficiency, people from lengthy and tedious uninteresting, easily tired out, with low content of technology work
In free.The robots such as spraying, spot welding, stacking have been widely used in industry, but the robot of this " fixation " formula is simultaneously
The demand of people cannot be fully met, then proposes the concept of mobile robot.Usually by the machine person with locomotive function
For mobile robot or automatic guided vehicle (AGV, Autonomous Guided Vehicle).
AGV controls speed and course angle according to path offset amount, so as to ensure that AGV accurately drives to the position of target point
And the process in course, referred to as " guide " (Guidance).Industrial AGV is mainly used in manufacturing industry and logistics at present, guides
Mode is traditional electromagnetism guiding and magnetic stripe guiding mostly.The relatively more accurate safety of this guide mode, control system is fairly simple,
But either guide rail limits the operation area of AGV and the road sign or guide rail of route and marking paths are pacified in fixed path
It is complicated to fill construction cost height, process.Compel to be essential so having AGV simplicity, non-fixed path guide mode in reality production
Ask.
The content of the invention
The present invention provides a kind of AGV robots operation based on micro- inertia attitude heading reference system for problem of the prior art
The method for correcting error of error.
In order to solve the above-mentioned technical problem, the present invention adopts the following technical scheme that：
A kind of method for correcting error of AGV robots kinematic error based on micro- inertia attitude heading reference system provided by the invention：
Step 1, DATA REASONING：AGV robots are measured respectively by MEMS inertial sensor, magnetometer and gyroscope to transport
Capable acceleration, magnetic field intensity and angular speed；
The data of MEMS inertial sensor, gyroscope and magnetometer measures are passed through self-adapted sensor data by step 2
Blending algorithm is analyzed；
Step 3, by step 2 analyze data handled by attitude heading reference system, complete acceleration, earth's magnetic field,
The real-time of angular velocity information gathers, and the posture of AGV robots is calculated according to attitude algorithm；
Step 4, the posture for the AGV robots that step 3 is calculated is compared with micro- inertia attitude heading reference system, really
Determine the current pose of AGV robots.
Step 5, movement instruction is sent by controller to AGV robots, instigates AGV robot motions or adjustment appearance
State；If adjustment attitude command, then step 3 is fed back to after adjusting posture.
Wherein, the Data Fusion of Sensor algorithm includes carrying out under gradient the data that accelerometer and magnetometer gather
The processing of drop method, further reduces interference of the sensor error to Attitude estimation result；Complementary filter is reused to speculate gyroscope
Posture merged with magnetometer, accelerometer data.
Preferably, the model of the MEMS inertial sensor, magnetometer and gyroscope is respectively ADXL345, ITG3200
And HMC3885L.
Wherein, the controller includes processor and communication module, and the processor is electrically connected with communication module.
Wherein, the attitude algorithm includes calculating the acceleration of gravity and field projection under current estimation posture, constructs magnetic
And acceleration of gravity error function and gradient, then calculate gradient difference, using angular speed error correction angular velocity measurement value,
Posture is updated using quaternion differential equation, quaternary number is corrected using gradient descent method, completes Attitude estimation.
Beneficial effects of the present invention：
A kind of method for correcting error of AGV robots kinematic error based on micro- inertia attitude heading reference system provided by the invention,
The present invention is analyzed using MEMS inertial sensor, magnetometer and gyroscope by self-adapted sensor data anastomosing algorithm,
And the posture of AGV robots is calculated by attitude algorithm, by the posture of calculating compared with micro- inertia attitude heading reference system,
So as to adjust the current posture of AGV robots, compared with traditional AGV robots, this method can be the operation of AGV robots more
Add steady, it is not necessary to which guide rail can also be completed to run, and reduce cost.
Brief description of the drawings
Fig. 1 is the flow chart of the present invention.
Embodiment
For the ease of the understanding of those skilled in the art, the present invention is made further with reference to embodiment and attached drawing
Bright, the content that embodiment refers to not is limitation of the invention.The present invention is described in detail below in conjunction with attached drawing.
A kind of method for correcting error of AGV robots kinematic error based on micro- inertia attitude heading reference system provided by the invention：
Step 1, DATA REASONING：AGV robots are measured respectively by MEMS inertial sensor, magnetometer and gyroscope to transport
Capable acceleration, magnetic field intensity and angular speed；
The data of MEMS inertial sensor, gyroscope and magnetometer measures are passed through self-adapted sensor data by step 2
Blending algorithm is analyzed；
Step 3, by step 2 analyze data handled by attitude heading reference system, complete acceleration, earth's magnetic field,
The real-time of angular velocity information gathers, and the posture of AGV robots is calculated according to attitude algorithm；
Step 4, the posture for the AGV robots that step 3 is calculated is compared with micro- inertia attitude heading reference system, really
Determine the current pose of AGV robots.
Step 5, movement instruction is sent by controller to AGV robots, instigates AGV robot motions or adjustment appearance
State, adjustment posture can carry out negative-feedback to micro- inertia attitude heading reference system afterwards；If attitude command is adjusted, then after adjustment posture
Feed back to step 3.
The present invention using MEMS inertial sensor, magnetometer and gyroscope by self-adapted sensor data anastomosing algorithm into
Row analysis, and calculate by attitude algorithm the posture of AGV robots, by the posture of calculating and micro- inertia attitude heading reference system into
Row compares, so as to adjust the current posture of AGV robots, compared with traditional AGV robots, this method can make AGV robots
Adjustment posture in time, runs more steady, the operation of robot is completed by adjusting posture, it is not necessary to which guide rail can also be completed to control
System, reduces cost.
Gradient descent algorithm is a kind of Unconstrained Optimization Algorithms, is exactly that the direction declined along the gradient of function is asked in simple terms
Solve minimum.Assuming that there is one-dimensional functions f (x), its gradient is ▽ f (x), then the iterative formula of gradient descent method is as follows：
▽ f (xn-1) are function in x in formulan-1The gradient at place, μtFor iteration step length.Iteration obtains each xnPlace makes f (x)
Decline most fast as a result, to obtain optimal iteration result xn+1。
Represent quaternary number multiplication,For acceleration of gravity in reference frame or earth's magnetic field
Projection components,Actual measured results are measured for sensor.
The gradient of error function can be obtained by the Jacobian Matrix Calculatings of f (Q).
Defining gradient is
Assuming that ideally magnetic dip angle D=0, and local gravity field is parallel to referential z-axis.In being used in reference coordinate
Component in system is Mn=[0 Fx 0 Fz]T, according to formula Fx=cos α, Fz=sin α, and gn=[0 0 0-1]TSubstituted J (Q)
In DnObtain Jacobian matrixes
According to definition, the gradient of error function is obtained：
Define the direction of gradient decline, i.e., | | f (Q) | | decline most fast direction.It can obtain changing for quaternary number
For formula：
Wherein step size mutIt is related with gyroscope measurement noise and sample frequency.By iteration result numerical integration, so just make
With gradient descent method.
Gyroscope transient measurement is more accurate, but understands accumulated error in long-term integral operation and then produce drift now
As.And do not accumulated in time without integral operation, error when accelerometer, magnetometer calculating posture, but measurement result
Easily shaken by car body, motor-field interference.In other words, accelerometer, magnetometer are more accurately, in short-term in long-time
Between have large error, gyroscope is then opposite.Therefore, it is possible to use complementary filter, acceleration, the earth magnetism of low-pass characteristic are merged
The angular velocity information of information and high pass characteristic, achievees the purpose that accurately to estimate posture.
Complementary filter can be regarded as a kind of data anastomosing algorithm based on Systems of First-Order Differential, and energy effective integration has
The metrical information of high frequency, low frequency characteristic.Consider Systems of First-Order DifferentialAnd typical measurement information：
L (s) is low-pass filter, magnetometer, accelerometer frequency response range in be 1, yaFor magnetometer, accelerate
Spend the low-frequency information of meter；μ represents sensor measurement noise, b0For gyroscope zero bias, ybTo be mingled with the gyroscope of noise and zero bias letter
Breath.Two groups of metrical informations can utilize following complementary filter to merge, and obtain the estimate to posture：
FL (s), FL (s) are low pass and high-pass filter, gyroscope information ybIntegrated to obtain posture
Predicted value, then the μ by high-pass filter removing low frequencyb+b0Part, obtains the predicted value of high frequency section.Do not declined in order to obtain
The attitude information subtracted, it should make the cutoff frequency of low pass and high-pass filter match [50], that is, meet following formula：
FL(s)+FH(s)=1
Obtain：
Design compensation link isWherein kpDetermineCutoff frequency
Rate, kpIt is related according to the frequency characteristic of accelerometer, magnetometer and gyroscope；Integral elementIt can suppress zero bias part.
The posture gone out using the Db and renewal of measurement calculates that data Dpre defines error using vector multiplication cross：
The result of vector multiplication cross is vector, and naturally describes the rotation relationship between two vector of multiplication cross.
In fact, there is interference in accelerometer and magnetometer data.Although do not having or disturbing less moment, algorithm
Can effective rectification error；Therefore in the case of movement fierceness, serious interference, algorithm can cause posture renewal error to become larger, even
Posture renewal effect to be carried out with gyroscope worse than only.
The algorithm carries out gradient descent method processing to accelerometer and magnetometer information, further reduces sensor error pair
The interference of Attitude estimation result；Complementary filter is reused to carry out the posture that gyroscope speculates and magnetometer, accelerometer data
Fusion.For gyroscope output by the parameter k in complementary filterpSelf-adaptive processing is done, to adapt to carrier in high speed, low speed
The characteristics of sensor is different during movement.
Magnetometer and accelerometer information obtain making error function after formula computing | | f | | decline most fast quaternary number and miss
DifferenceIt can useCalculate angular velocity error ωe：
ωe" direct current " partly can be understood as the zero bias of gyroscope, remainder can be understood as measurement error, use
Removed after C (s) computings.In view of real-time, the ki in C (s) links is remained unchanged, and kpFollowed down as adaptation coefficient
Formula：
Parameter is changed according to the height of gyroscope Output speed, to adapt to the error feelings in the case of carrier different motion
Condition：Gyroscope maximum range is ωmax, frequency response cut-off angular velocity omegac(ωc<ωmax), kpaFor low speed coefficient；kpbFor at a high speed
Coefficient.Pass through ωcGyroscope range is divided into 3 regions：In low-speed region, using fixed kpaParameter, makes complementary filter
Cutoff frequency stabilization as far as possible to accept and believe magnetometer and accelerometer information in a low value more；In intermediate speed region, using variable kp
Coefficient；It is more serious in high-speed region, carrier vigorous exercise, accelerometer, magnetometer interference, using fixed kpbMore accept and believe top
Spiral shell instrument information, reduces operand.It is appropriate to reduce gradient descent method step size mut, prevent unstable caused by overshoot.
The Data Fusion of Sensor algorithm includes carrying out gradient descent method to the data that accelerometer and magnetometer gather
Processing, further reduces interference of the sensor error to Attitude estimation result；Reuse the appearance that complementary filter speculates gyroscope
State is merged with magnetometer, accelerometer data.
In the present embodiment, the model of the MEMS inertial sensor, magnetometer and gyroscope be respectively ADXL345,
ITG3200 and HMC3885L.
In the present embodiment, the controller includes processor and communication module, and the processor electrically connects with communication module
Connect, the communication module is used to receive external device (such as remote controler) control data, so as to control AGV robot motions.
In the present embodiment, the attitude algorithm includes calculating the acceleration of gravity and field projection under current estimation posture,
The error function and gradient of magnetic field and acceleration of gravity are constructed, gradient difference is then calculated, uses angular speed error correction angular speed
Measured value, updates posture using quaternion differential equation, corrects quaternary number using gradient descent method, completes attitude algorithm.
The above, is only present pre-ferred embodiments, not makees limitation in any form to the present invention, although
The present invention is disclosed as above with preferred embodiment, but is not limited to the present invention, any person skilled in the art,
Do not depart from the range of technical solution of the present invention, when the technology contents using the disclosure above make a little change or are modified to equivalent change
The equivalent embodiment of change, as long as being without departing from technical solution of the present invention content, refers to above example according to the technology of the present invention
Any simple modification, equivalent change and modification made, belongs in the range of technical solution of the present invention.
Claims (5)
- A kind of 1. method for correcting error of the AGV robots kinematic error based on micro- inertia attitude heading reference system, it is characterised in that：Step 1, DATA REASONING：Measure what AGV robots were run respectively by MEMS inertial sensor, magnetometer and gyroscope Acceleration, magnetic field intensity and angular speed；The data of MEMS inertial sensor, gyroscope and magnetometer measures are passed through self-adapted sensor data fusion by step 2 Algorithm is analyzed；Step 3, the data that step 2 is analyzed are handled by attitude heading reference system, complete acceleration, earth's magnetic field, angle speed The real-time collection of information is spent, and the posture of AGV robots is calculated according to attitude algorithm；Step 4, the posture for the AGV robots that step 3 is calculated determine compared with micro- inertia attitude heading reference system The current pose of AGV robots.Step 5, movement instruction is sent by controller to AGV robots, instigates AGV robot motions or adjustment posture, whole After posture negative-feedback can be carried out to micro- inertia attitude heading reference system；If adjustment attitude command, then fed back to after adjusting posture Step 3.
- A kind of 2. AGV robots based on micro- inertia attitude heading reference system according to claim 1, it is characterised in that：Institute Stating Data Fusion of Sensor algorithm includes carrying out gradient descent method processing to the data that accelerometer and magnetometer gather, further Reduce interference of the sensor error to Attitude estimation result；Reuse posture that complementary filter speculates gyroscope and magnetometer, Accelerometer data is merged.
- A kind of 3. AGV robots based on micro- inertia attitude heading reference system according to claim 1, it is characterised in that：Institute The model for stating MEMS inertial sensor, magnetometer and gyroscope is respectively ADXL345, ITG3200 and HMC3885L.
- A kind of 4. AGV robots based on micro- inertia attitude heading reference system according to claim 1, it is characterised in that：Institute Stating controller includes processor and communication module, and the processor is electrically connected with communication module.
- A kind of 5. method for correcting error based on AGV robots kinematic error according to claim 1, it is characterised in that：It is described Attitude algorithm includes calculating the acceleration of gravity and field projection under current estimation posture, constructs the mistake of magnetic field and acceleration of gravity Difference function and gradient, then calculate gradient difference, using angular speed error correction angular velocity measurement value, utilize quaternion differential equation Posture is updated, quaternary number is corrected using gradient descent method, completes posture clearing.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711129855.4A CN108037658A (en) | 2017-11-15 | 2017-11-15 | A kind of method for correcting error of the robot kinematic error based on navigation system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711129855.4A CN108037658A (en) | 2017-11-15 | 2017-11-15 | A kind of method for correcting error of the robot kinematic error based on navigation system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108037658A true CN108037658A (en) | 2018-05-15 |
Family
ID=62092770
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711129855.4A Pending CN108037658A (en) | 2017-11-15 | 2017-11-15 | A kind of method for correcting error of the robot kinematic error based on navigation system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108037658A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109682377A (en) * | 2019-03-08 | 2019-04-26 | 兰州交通大学 | A kind of Attitude estimation method based on the decline of dynamic step length gradient |
| CN111121768A (en) * | 2019-12-23 | 2020-05-08 | 深圳市优必选科技股份有限公司 | Robot pose estimation method and device, readable storage medium and robot |
| CN113892942A (en) * | 2021-08-24 | 2022-01-07 | 重庆大学 | Wearing equipment for tracking motion of lower limbs of human body in real time |
| CN115268442A (en) * | 2022-07-27 | 2022-11-01 | 湖州丽天智能科技有限公司 | Automatic deviation rectifying method and system for photovoltaic cleaning robot and cleaning robot |
| WO2023226375A1 (en) * | 2022-05-22 | 2023-11-30 | 远也科技（苏州）有限公司 | Method and apparatus for determining motion parameter, and system |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101033973A (en) * | 2007-04-10 | 2007-09-12 | 南京航空航天大学 | Attitude determination method of mini-aircraft inertial integrated navigation system |
| CN103363992A (en) * | 2013-06-29 | 2013-10-23 | 天津大学 | Method for solving attitude and heading reference system of four-rotor unmanned aerial vehicle based on gradient descent |
| CN104197927A (en) * | 2014-08-20 | 2014-12-10 | 江苏科技大学 | Real-time navigation system and real-time navigation method for underwater structure detection robot |
| CN104596533A (en) * | 2015-01-07 | 2015-05-06 | 上海交通大学 | Automatic guided vehicle based on map matching and guide method of automatic guided vehicle |
| US20160070261A1 (en) * | 2014-09-10 | 2016-03-10 | Appareo Systems, Llc | Automated flight control system for unmanned aerial vehicles |
| CN105607760A (en) * | 2015-12-18 | 2016-05-25 | 上海开圣影视文化传媒股份有限公司 | Micro-inertial sensor based track recovery method and system |
| CN106767804A (en) * | 2016-12-28 | 2017-05-31 | 华中科技大学 | The multidimensional data measurement apparatus and method of a kind of moving object |
| CN107272008A (en) * | 2017-06-14 | 2017-10-20 | 上海大学 | A kind of AGV Laser navigation systems with inertia compensation |
-
2017
- 2017-11-15 CN CN201711129855.4A patent/CN108037658A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101033973A (en) * | 2007-04-10 | 2007-09-12 | 南京航空航天大学 | Attitude determination method of mini-aircraft inertial integrated navigation system |
| CN103363992A (en) * | 2013-06-29 | 2013-10-23 | 天津大学 | Method for solving attitude and heading reference system of four-rotor unmanned aerial vehicle based on gradient descent |
| CN104197927A (en) * | 2014-08-20 | 2014-12-10 | 江苏科技大学 | Real-time navigation system and real-time navigation method for underwater structure detection robot |
| US20160070261A1 (en) * | 2014-09-10 | 2016-03-10 | Appareo Systems, Llc | Automated flight control system for unmanned aerial vehicles |
| CN104596533A (en) * | 2015-01-07 | 2015-05-06 | 上海交通大学 | Automatic guided vehicle based on map matching and guide method of automatic guided vehicle |
| CN105607760A (en) * | 2015-12-18 | 2016-05-25 | 上海开圣影视文化传媒股份有限公司 | Micro-inertial sensor based track recovery method and system |
| CN106767804A (en) * | 2016-12-28 | 2017-05-31 | 华中科技大学 | The multidimensional data measurement apparatus and method of a kind of moving object |
| CN107272008A (en) * | 2017-06-14 | 2017-10-20 | 上海大学 | A kind of AGV Laser navigation systems with inertia compensation |
Non-Patent Citations (3)
| Title |
|---|
| 王树磊: "《先进无人机系统制导与控制》", 30 April 2017 * |
| 陈丽: "《大气动静飞行器飞行原理》", 31 December 2015 * |
| 陈亮: "基于梯度下降法和互补滤波的航向姿态参考系统", 《电子设计工程》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109682377A (en) * | 2019-03-08 | 2019-04-26 | 兰州交通大学 | A kind of Attitude estimation method based on the decline of dynamic step length gradient |
| CN111121768A (en) * | 2019-12-23 | 2020-05-08 | 深圳市优必选科技股份有限公司 | Robot pose estimation method and device, readable storage medium and robot |
| CN113892942A (en) * | 2021-08-24 | 2022-01-07 | 重庆大学 | Wearing equipment for tracking motion of lower limbs of human body in real time |
| CN113892942B (en) * | 2021-08-24 | 2023-09-19 | 重庆大学 | Wearing equipment for tracking human lower limb movement in real time |
| WO2023226375A1 (en) * | 2022-05-22 | 2023-11-30 | 远也科技（苏州）有限公司 | Method and apparatus for determining motion parameter, and system |
| CN115268442A (en) * | 2022-07-27 | 2022-11-01 | 湖州丽天智能科技有限公司 | Automatic deviation rectifying method and system for photovoltaic cleaning robot and cleaning robot |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108037658A (en) | A kind of method for correcting error of the robot kinematic error based on navigation system | |
| CN110125936B (en) | Ground experiment verification system of space robot | |
| KR100981698B1 (en) | Legged locomotion robot | |
| CN110146077A (en) | Pose of mobile robot angle calculation method | |
| Göhrle et al. | Road profile estimation and preview control for low-bandwidth active suspension systems | |
| CN103517789B (en) | motion prediction control device and method | |
| US9221170B2 (en) | Method and apparatus for controlling a robotic device via wearable sensors | |
| CN111185907B (en) | Pose stability control method for operation type flying robot after grabbing | |
| CN110427043B (en) | Pose controller design method based on gravity center offset of operation flying robot | |
| CN109848983A (en) | A kind of method of highly conforming properties people guided robot work compound | |
| CN103149937A (en) | Transverse lateral curve flight-path tracking method based on curvature compensation | |
| CN107422733B (en) | Motion control method based on two-wheeled differential robot | |
| CN104764451A (en) | Target posture tracking method based on inertia and geomagnetic sensor | |
| Spasojevic et al. | Perception-aware time optimal path parameterization for quadrotors | |
| CN106863308B (en) | A kind of mobile robot trace Correction and Control algorithm | |
| CN107883965A (en) | Based on optical information Interactive Multiple-Model strong tracking volume Kalman filtering air navigation aid | |
| CN109760047B (en) | Stage robot prediction control method based on vision sensor | |
| Xiong et al. | Path tracking of a four-wheel independently driven skid steer robotic vehicle through a cascaded NTSM-PID control method | |
| CN106774374B (en) | Automatic unmanned aerial vehicle inspection method and system | |
| CN106403952A (en) | Method for measuring combined attitudes of Satcom on the move with low cost | |
| US20220411234A1 (en) | Dynamic flex compensation, coordinated hoist control, and anti-sway control for load handling machines | |
| CN107807646A (en) | A kind of control device of Mecanum wheel omnirange operation | |
| CN114771551A (en) | Method and device for planning track of automatic driving vehicle and automatic driving vehicle | |
| CN115046545A (en) | Positioning method combining deep network and filtering | |
| Prkačin et al. | State and parameter estimation of suspended load using quadrotor onboard sensors |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180515 |
|
| RJ01 | Rejection of invention patent application after publication |