CN106767777A - A kind of underwater robot redundancy inertial navigation device - Google Patents
A kind of underwater robot redundancy inertial navigation device Download PDFInfo
- Publication number
- CN106767777A CN106767777A CN201611017009.9A CN201611017009A CN106767777A CN 106767777 A CN106767777 A CN 106767777A CN 201611017009 A CN201611017009 A CN 201611017009A CN 106767777 A CN106767777 A CN 106767777A
- Authority
- CN
- China
- Prior art keywords
- data
- module
- sensor
- underwater robot
- navigation
- 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
- 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
Abstract
The present invention discloses a kind of underwater robot redundancy inertial navigation device, including inertial sensor module, photoelectric coupling module, data acquisition module, data processing module, data transmission blocks and power module, the inertial sensor module is by after the light-coupled isolation of photoelectric coupling module, transmitted to data processing module by the sampling channel of data acquisition module, externally sent by data transmission blocks by the navigation data after data processing module treatment.The present invention can not influence the work of whole system in certain sensor failure, while fault sensor can be detected, the design of multisensor redundancy effectively increases the precision of navigation data；Data are carried out using distribution map method delete choosing, data are merged with kalman filter method, improve the precision of data processing；The present invention is independent of external system and environment, entirely autonomous.
Description
Technical field
A kind of data underwater robot airmanship of the present invention, and in particular to underwater robot redundancy inertial navigation dress
Put.
Background technology
Rich in natural resources is contained in ocean, is the second space of human survival, rationally utilizes, develops and monitoring marine resources
Sustainable development for human society will play huge effect.The mankind that appear as of underwater robot carry out deep-sea resources
Research and development provide strong instrument.At present, the mankind are in deep-sea salvaging, resource investigation, biotic population investigation, deep-sea base
Because the breakthrough acquired by the aspects such as acquisition is closely related with underwater robot.The development of underwater robot technology for
Scientific research of seas serves huge impetus.At present, underwater robot is widely used in sea-going rescue with salvaging, deep-sea
All many-sides such as resource investigation, marine petroleum exploitation, underwater project construction, military affairs and national defense construction, oneself is huge through generating
Economic benefit and social benefit, with potential application prospect.
Accurate navigation data is the key that underwater robot performs task and track positioning, underwater robot work under water
It is long to make the time, and underwater environment is complicated, is easily influenceed by factors such as ocean current, noises, and the levels of precision of navigation data decides
The overall performance of underwater robot, so, the precision of underwater robot navigation data is improved to improving underwater robot performance, increasing
It is significant that strong underwater robot completes task ability.Inertial navigation system is a kind of navigation for growing up 20 beginnings of the century
Method, is a kind of passive system navigated by equipment of itself, and it relies primarily on newton principle of inertia, using inertia sensing
The line motion in device (gyroscope, accelerometer) measurement object relative inertness space and angular movement parameter, in given primary condition
Under, by the attitude parameter and navigational parameter that integrate output carrier.At present, strapdown inertial navigation technology quickly grows, and overcomes
Early stage inertial navigation system volume is big, high energy consumption the shortcomings of, with the continuous research and development of optical fibre gyro, fiber strapdown formula
Inertial navigation has become inertial navigation research emphasis.
The drift error of gyroscope and accelerometer is the principal element for influenceing inertial navigation system precision, inertial navigation
Data are obtained by the data quadratic integral to sensor, and inertial sensor can constantly drift about over time, and these are missed
Difference can be constantly run up in navigation system.The precision for improving inertial sensor is to improve the most direct method of inertial navigation system precision,
And according to the development level of current inertial sensor, bringing up to enough precision will necessarily increase substantial amounts of cost.
Chinese patent (Application No. 201010559044.X) discloses a kind of small-sized underwater robot combined navigation system
And air navigation aid, with reference to external GPS data, navigation information being derived according to magnetic declination database, the device navigation data precision is not
Height, and cannot realize completely independent.
Chinese patent (Application No. 201410413791.0) discloses a kind of submerged structure detection robot and implements navigation
System and method, dissection process is carried out to sensing data using many algorithms, but fundamentally solves sensor itself error
Problem, the device cannot realize precise guidance, and algorithm is complicated, relatively costly.
Chinese patent (Application No. 201310315126.3) discloses a kind of six redundancy-types fiber strapdown inertial navigation system
System, inertial sensor is distributed on hollow regular dodecahedron, with stronger impact resistance, but does not provide sensor number
According to processing method, simultaneously for many ocean currents of underwater robot, the complex work environment of noise, the system cannot be supported effectively
The navigation of underwater robot.
Chinese patent (Application No. 201510559788.4) discloses a kind of underwater robot inertial navigation method and is
System, the main negative function using inertial sensor reverse turn, the drift to sensor is compensated, and the method is realized
Difficulty is big, and can increase extra error in implementation process, although later stage algorithm can obtain certain effect, but algorithm is complicated, surely
It is qualitative not high.
The content of the invention
Goal of the invention：It is an object of the invention to solve the deficiencies in the prior art, there is provided a kind of underwater machine
Device people's redundancy inertial navigation device.
Technical scheme：A kind of underwater robot redundancy inertial navigation device of the invention, including inertial sensor mould
Block, photoelectric coupling module, data acquisition module, data processing module, data transmission blocks and power module, the inertia are passed
Sensor module by the sampling channel of data acquisition module by after the light-coupled isolation of photoelectric coupling module, being transmitted to data processing
Module, is externally sent by the navigation data after data processing module treatment by data transmission blocks；Wherein, the inertia sensing
Device module is included according to 6 three-axis gyroscopes and 6 three axis accelerometers of regular dodecahedron regular distribution, and identical sensing
Device is symmetrically placed；The photoelectric coupling module is isolated to signal；The data acquisition module is made up of operational amplifier
Data sampling circuit, complete the data acquisition of each sensor, the data processing module includes main process task chip, and it is right to complete
Sensing data delete choosing, fusion and calculate；The data transmission blocks include RS232 data collector chips, complete to leading
The timing of data of navigating sends；The power module includes insulating power supply and voltage-stabilized power supply.
Further, the gyroscope in the inertial sensor module is optical fibre gyro, and accelerometer adds for quartz flexible
Speedometer, in order to improve precision, light path part and circuit part is separated.
Further, photoelectric isolating circuit is constituted by photoelectrical coupler in the photoelectric coupling module, to the letter of sensor
Number isolated, it is ensured that the stability of system.
Further, the power module include voltage-stabilized power supply and insulating power supply, for system provide needed for 3.3V, ±
5V, ± 12V power supplys, each power input are connected in parallel with a capacitor and are filtered treatment, improve power quality, the ground of digital quantity and
Separated with zero ohm of resistance between the ground of analog quantity.
Further, the uniformity of each data of the data processing module to receiving detects, with distribution map method,
The interference of divorced value is excluded, data are merged using EKF method, using integration method to fusion institute total
According to being calculated, optimal navigation data is finally obtained.
Adopted the invention also discloses a kind of processing method of underwater robot redundancy inertial navigation device, including data
Collection, data processing and data transmission procedure, specifically include following steps：
Step one：Six gyroscopes and six data of accelerometer by after light-coupled isolation, by data acquisition module,
The main process task chip of data processing module is transferred to by 6 I/O mouthfuls and 6 AD sampling channels respectively；
Step 2：In main process task chip, the data to each received sensor carry out data consistency detection,
Choosing is deleted to carrying out data with profiling method, is deleted and is selected effective consistent sensing data；
Step 3：Transducer fault detection is carried out, if within the out-of-service time, the data of a certain sensor are always non-have
Effect data, and data position apart from valid interval farther out, then can determine whether out the sensor failure.Out-of-service time can basis
Actual conditions are adjusted；
Step 4：Data Fusion of Sensor is carried out to deleting the effective sensor data elected.It is directed to underwater robot
Local environment it is non-linear, with EKF method, effective sensor data are merged；
Step 5：Data to merging gyroscope and accelerometer out, carry out navigation data calculating, obtain machine under water
, finally be put into navigation data in data transmission blocks by the navigation datas such as attitude angle, speed, the position of device people, is gone here and there by RS232
Mouth circuit timing is sent out, and supply underwater robot navigation is used.
Beneficial effect：Compared with prior art, the present invention has advantages below：
(1) present invention uses multisensor redundancy, does not influence whole in the case of may be implemented in a certain sensor failure
The normal work of individual system；
(2) multisensor Redundancy Design in the present invention, the data for multiple sensors carry out convergence analysis, finally give
Navigation data, effectively increases the precision of navigation data；
(3) present invention carries out deleting choosing using distribution map method to data, and data are merged with kalman filter method,
Improve the precision of data processing；
(4) for fail sensor, the present invention carry out data delete select when, the data of lasting failure can be analyzed
Record, so as to detect fault sensor, to change；
(5) present invention can be independent of external system and environment, entirely autonomous.
Brief description of the drawings
Fig. 1 is data flow figure of the invention；
Fig. 2 is structural representation of the invention；
Fig. 3 is system flow chart of the invention.
Specific embodiment
Technical solution of the present invention is described in detail below, but protection scope of the present invention is not limited to the implementation
Example.As shown in figure 1, a kind of underwater robot redundancy inertial navigation device of the invention, including inertial sensor module 1, light
It is electrically coupled module 2, data acquisition module 3, data processing module 4, data transmission blocks 5 and power module 6.Inertial sensor
Module 1 includes 6 three-axis gyroscope sensors 11 and 6 3-axis acceleration flowmeter sensors 12.Data acquisition module 3 is main by transporting
The data sampling circuit that amplifier is constituted is calculated, the collection of each sensing data is completed.Data processing module 4 includes main process task core
Piece and peripheral circuits, complete to delete choosing, fusion and calculating to sensing data.Data transmission blocks 5 are by RS232 data transmit-receives
Device chip is constituted, and completes to send the timing of navigation data；Power module 6 is made up of insulating power supply and voltage-stabilized power supply.
As shown in Fig. 2 the whole guider in the present invention is positioned in shell (not shown), in order to maximum reduces power supply
Influence to sensor, power module 6 and the paired linea angulata of inertial sensor module 1 are placed.Inertial sensor module 1 and photoelectricity
Coupling module 2 is connected, and photoelectric coupling module 2 is connected with data acquisition module 3, and data acquisition module 3 connects with data processing module 4
Connect, data processing module 4 is connected with data transmission blocks 5, power module 6 is connected with modules respectively, be that modules are carried
Power supply source.
Inertial sensor module 1 is made up of 6 three-axis gyroscopes 11 and 6 three axis accelerometers 12, gyroscope with accelerate
, according to regular dodecahedron regular distribution, identical sensor is symmetrically placed, and gyroscope is optical fibre gyro for degree meter, in order to improve precision,
Light path part is separated with circuit part, and accelerometer is quartz flexible accelerometer.
The main photoelectric isolating circuit that is made up of photoelectrical coupler of photoelectric coupling module 2, the signal of sensor is carried out every
From, it is ensured that the stability of system
Data acquisition module 3 mainly includes the data sampling circuit being made up of operational amplifier, and sensing data passes through light
After being electrically coupled module 2, gather and be transferred in data processing module 4 via data acquisition module 3.
Data processing module 4 is made up of main process task chip and peripheral circuits, complete to sensing data delete choosing, fusion,
Calculate.
Data transmission blocks 5 are made up of RS232 data collectors chip and peripheral circuits, complete navigation data external
Send in real time.
Power module 6 is made up of voltage-stabilized power supply, insulating power supply and peripheral circuit, for system provide needed for 3.3V, ±
5V, ± 12V power supplys, each power input are connected in parallel with a capacitor and are filtered treatment, improve power quality, the ground of digital quantity and
Separated with zero ohm of resistance between the ground of analog quantity.
As shown in figure 3, underwater robot redundancy inertial navigation device of the invention is carrying out specific data acquisition, place
When reason and transmission, 6 three-axis gyroscopes 11 and 6 sensing datas of three axis accelerometer 12 are by photoelectric coupling circuit 2
Isolation after, be transferred to data acquisition module 3, data acquisition module 3 comes data acquisition, and data are by 6 I/O mouthfuls and 6
Individual AD sampling channels are transferred in the main process task chip of data processing module 4, and data initially enter data consistency detection module
41, choosing is deleted by what data deleted modeling block 412, the preprocessed data transmission obtained after choosing is deleted in line sensor fault detect of going forward side by side
To data fusion module 42, respectively by the treatment of gyro data Fusion Module 421 and accelerometer data Fusion Module 422
Afterwards, the final sensing data of gyroscope and accelerometer, this data transfer to navigation data computing module 43, difference are obtained
The navigation datas such as attitude angle, speed, position are calculated, after the navigation data being calculated is transferred to data transmission blocks 5, thus
Module timing is sent out.
Comprise the following steps that：
Step one：6 three-axis gyroscopes 11 and 6 data of three axis accelerometer 12, by photoelectric coupling module 2 every
From rear, by data acquisition module 3, main process task chip 4 is transferred to by 6 I/O mouthfuls and 6 AD sampling channels respectively, collection
Gyroscope 11 and accelerometer 12 are respectively ω in the angular speed in tri- directions of X, Y, Z and the data of accelerationi=[ωxi,ωyi,
ωzi] (i=1,2 ..., 6), ai=[axi,ayi,azi] (i=1,2 ..., 6).
Step 2：In main process task chip 4, the data to each received sensor carry out data consistency detection
41, carry out distribution map method data and delete to select 412, to delete and select effective consistent sensing data, detailed process is as follows：
With 6 data instances of gyroscope 11, the data that a certain moment is transferred to main process task chip 4 are ω1, ω2...,
ω6, it is 3-dimensional vector, corresponding norm is Yi, then
By YiY is arranged according to ascending order1, Y2..., Y6, then Y1, Y6It is high-low limit value, median M=(Y3
+Y4)/2, lower quartile F1=(Y1+ M)/2, upper quartile Fu=(M+Y6)/2, quartile dispersion is dF=F1+Fu,
Superseded point spacing up and down can then be obtained is：
β is constant in formula, can be debugged according to practical application.Then, if sensing data is in [ρ1,ρ2] interval interior,
As valid data.The acquisition methods of the valid data of accelerometer 12 are similar.
Step 3：Carry out Transducer fault detection 411.If within the out-of-service time, the data of a certain sensor are always
Non-effective data, and data position apart from valid interval farther out, then can determine whether out the sensor failure, and externally make and carrying
Show.Out-of-service time can adjust according to actual conditions.
Step 4：Data Fusion of Sensor 42 is carried out to deleting the effective sensor data elected.It is directed to underwater
People's local environment it is non-linear, with EKF method, effective sensor data are merged.First carry out top
Spiral shell instrument data fusion 421, detailed process is as follows：
Assuming that the state model of nonlinear time-varying stochastic system is
X (t+1)=f (t, x (t))+G (t, x (t)) u (t)+T (t) v (t)
Y (t+1)=h (t+1) x (t+1)+D (t+1) b (t+1)+e (t+1)
Wherein, X ∈ Rn, input u ∈ Rq, output y ∈ Rm, sensor bias b ∈ Rl, nonlinear function f：Rn→Rn, G ∈ Rn ×q, h:Rn→Rm, D ∈ Rm×l, T ∈ Rn×p, system noise v ∈ Rp, measurement noise e ∈ Rm, v, e are uncorrelated and only with X (0) statistics
It is vertical.
Data fusion is carried out to pretreated valid data, the error that measurement noise is caused, optimal filter are only considered herein
Wave equation is
Wherein, K is Kalman filtering gain,
Wherein, the primary condition of recursion equation is
Assuming that the effective data of gyroscope 11 are X1, X2..., X6Then, the state to be estimated is
Xi=x+ei(i=1,2 ..., 6)
Wherein, eiIt is varianceZero-mean gaussian stochastic variable.
Then, the result of final data fusion is
Wherein,
The process of 12 data fusion of accelerometer 422 is identical with gyroscope 11 data fusion, 421 processes, is not repeated.
Step 5：Final navigation data is carried out to the data of fusion gyroscope 11 out and accelerometer 12 to calculate
43, the navigation datas such as attitude angle, speed, the position of underwater robot are obtained by integral, integration method used is
It is frequently-used data computational methods, is not repeated herein.Finally, navigation data is put into data transmission blocks 5, by RS232
Serial port circuit timing is sent out, and supply underwater robot navigation is used.
Claims (6)
1. a kind of underwater robot redundancy inertial navigation device, it is characterised in that：Including inertial sensor module, light thermocouple
Matched moulds block, data acquisition module, data processing module, data transmission blocks and power module, the inertial sensor module
By after the light-coupled isolation of photoelectric coupling module, being transmitted to data processing module, warp by the sampling channel of data acquisition module
The navigation data crossed after data processing module treatment is externally sent by data transmission blocks；
Wherein, the inertial sensor module is included according to 6 three-axis gyroscopes and 6 three axles of regular dodecahedron regular distribution
Accelerometer, and identical sensor is symmetrically placed；The photoelectric coupling module is isolated to signal；The data acquisition module
Block is the data sampling circuit being made up of operational amplifier, completes the data acquisition of each sensor, the data processing module
Including main process task chip, complete to delete choosing, fusion and calculating to sensing data；The data transmission blocks include RS232 numbers
According to transponder chip, complete to send the timing of navigation data；The power module includes insulating power supply and voltage-stabilized power supply.
2. underwater robot redundancy inertial navigation device according to claim 1, it is characterised in that：The inertia is passed
Gyroscope in sensor module is optical fibre gyro, and light path part is separated with circuit part, and accelerometer is quartz flexible
Accelerometer.
3. underwater robot redundancy inertial navigation device according to claim 1, it is characterised in that：The smooth thermocouple
Photoelectric isolating circuit is constituted by photoelectrical coupler in matched moulds block, the signal to sensor is isolated.
4. underwater robot redundancy inertial navigation device according to claim 1, it is characterised in that：The power supply mould
Block includes voltage-stabilized power supply and insulating power supply, 3.3V, ± 5V, ± 12V power supplys for needed for system is provided, and each power input is simultaneously
One electric capacity of connection is filtered treatment, raising power quality, with zero ohm of resistance between the ground of digital quantity and the ground of analog quantity
Separate.
5. underwater robot redundancy inertial navigation device according to claim 1, it is characterised in that：Data processing mould
The uniformity of each data of the block to receiving is detected, with distribution map method, the interference of exclusion divorced value, using expansion card
Kalman Filtering method is merged to data, and fusion the data obtained is calculated using integration method, finally obtains optimal navigation
Data.
6. a kind for the treatment of of the underwater robot redundancy inertial navigation device based on described in claim 1 to 5 any one
Method, it is characterised in that：Including data acquisition, data processing and data transmission procedure, following steps are specifically included：
Step one：6 gyroscopes and 6 data of accelerometer, by after the isolation of photoelectric coupling module, by data acquisition
Module, is transferred to the main process task chip of data processing module by 6 I/O mouthfuls and 6 AD sampling channels respectively；
Step 2：In main process task chip, the data to each received sensor carry out data consistency detection, use
Profiling method deletes choosing to carrying out data, deletes and selects effective consistent sensing data；
Step 3：Transducer fault detection is carried out, if within the out-of-service time, the data of a certain sensor are always non-effective number
According to, and data position apart from valid interval farther out, then can determine whether out the sensor failure.Out-of-service time can be according to reality
Situation is adjusted；
Step 4：Data Fusion of Sensor is carried out to deleting the effective sensor data elected.It is directed to residing for underwater robot
Environment it is non-linear, with EKF method, effective sensor data are merged；
Step 5：Data to merging gyroscope and accelerometer out, carry out navigation data calculating, obtain underwater robot
The navigation data such as attitude angle, speed, position, finally navigation data is put into data transmission blocks, by RS232 serial port powers
Road timing is sent out, and supply underwater robot navigation is used.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611017009.9A CN106767777A (en) | 2016-11-16 | 2016-11-16 | A kind of underwater robot redundancy inertial navigation device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611017009.9A CN106767777A (en) | 2016-11-16 | 2016-11-16 | A kind of underwater robot redundancy inertial navigation device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106767777A true CN106767777A (en) | 2017-05-31 |
Family
ID=58969007
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611017009.9A Pending CN106767777A (en) | 2016-11-16 | 2016-11-16 | A kind of underwater robot redundancy inertial navigation device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106767777A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107588772A (en) * | 2017-09-01 | 2018-01-16 | 北京臻迪科技股份有限公司 | Robot pose of paddling monitoring method, device and monitoring system |
| CN108037677A (en) * | 2017-11-30 | 2018-05-15 | 中国科学院国家天文台南京天文光学技术研究所 | The fault self-recovery semi-physical emulation platform that hides for South Pole astronomical telescope |
| CN108225313A (en) * | 2017-12-29 | 2018-06-29 | 中国电子科技集团公司第十三研究所 | Navigation attitude instrument based on redundancy MEMS sensor |
| CN109144091A (en) * | 2018-11-06 | 2019-01-04 | 广州极飞科技有限公司 | A kind of flight controller and unmanned vehicle |
| CN109596119A (en) * | 2018-11-23 | 2019-04-09 | 中国船舶重工集团公司第七0七研究所 | Ship craft integrated PNT system and its monitoring method based on adaptive information fusion |
| CN110383186A (en) * | 2018-05-30 | 2019-10-25 | 深圳市大疆创新科技有限公司 | A kind of emulation mode and device of unmanned plane |
| CN112419402A (en) * | 2020-11-27 | 2021-02-26 | 广东电网有限责任公司肇庆供电局 | Positioning method and system based on multispectral image and laser point cloud |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN2066975U (en) * | 1989-12-06 | 1990-12-05 | 刘森磊 | Electric sensing speed odometer |
| CN101436074A (en) * | 2008-12-06 | 2009-05-20 | 中国海洋大学 | Autonomous type underwater robot by simultaneous positioning and map constructing method |
| CN103389089A (en) * | 2013-07-25 | 2013-11-13 | 北京航空航天大学 | Six-redundancy-type optical fiber strapdown inertial navigation system (SINS) |
| CN104023356A (en) * | 2014-06-26 | 2014-09-03 | 南京农业大学 | Facilitate environmental control-oriented wireless sensor network data transmission method |
| CN204228171U (en) * | 2014-11-19 | 2015-03-25 | 山东华盾科技股份有限公司 | A kind of underwater robot guider |
| CN105792110A (en) * | 2016-03-30 | 2016-07-20 | 上海申腾信息技术有限公司 | Data fusion and intelligent searching processing method for multiple data sources |
-
2016
- 2016-11-16 CN CN201611017009.9A patent/CN106767777A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN2066975U (en) * | 1989-12-06 | 1990-12-05 | 刘森磊 | Electric sensing speed odometer |
| CN101436074A (en) * | 2008-12-06 | 2009-05-20 | 中国海洋大学 | Autonomous type underwater robot by simultaneous positioning and map constructing method |
| CN103389089A (en) * | 2013-07-25 | 2013-11-13 | 北京航空航天大学 | Six-redundancy-type optical fiber strapdown inertial navigation system (SINS) |
| CN104023356A (en) * | 2014-06-26 | 2014-09-03 | 南京农业大学 | Facilitate environmental control-oriented wireless sensor network data transmission method |
| CN204228171U (en) * | 2014-11-19 | 2015-03-25 | 山东华盾科技股份有限公司 | A kind of underwater robot guider |
| CN105792110A (en) * | 2016-03-30 | 2016-07-20 | 上海申腾信息技术有限公司 | Data fusion and intelligent searching processing method for multiple data sources |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107588772A (en) * | 2017-09-01 | 2018-01-16 | 北京臻迪科技股份有限公司 | Robot pose of paddling monitoring method, device and monitoring system |
| CN107588772B (en) * | 2017-09-01 | 2020-02-21 | 北京臻迪科技股份有限公司 | Wading robot posture monitoring method, device and system |
| CN108037677A (en) * | 2017-11-30 | 2018-05-15 | 中国科学院国家天文台南京天文光学技术研究所 | The fault self-recovery semi-physical emulation platform that hides for South Pole astronomical telescope |
| CN108225313A (en) * | 2017-12-29 | 2018-06-29 | 中国电子科技集团公司第十三研究所 | Navigation attitude instrument based on redundancy MEMS sensor |
| CN110383186A (en) * | 2018-05-30 | 2019-10-25 | 深圳市大疆创新科技有限公司 | A kind of emulation mode and device of unmanned plane |
| CN109144091A (en) * | 2018-11-06 | 2019-01-04 | 广州极飞科技有限公司 | A kind of flight controller and unmanned vehicle |
| CN109596119A (en) * | 2018-11-23 | 2019-04-09 | 中国船舶重工集团公司第七0七研究所 | Ship craft integrated PNT system and its monitoring method based on adaptive information fusion |
| CN112419402A (en) * | 2020-11-27 | 2021-02-26 | 广东电网有限责任公司肇庆供电局 | Positioning method and system based on multispectral image and laser point cloud |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106767777A (en) | A kind of underwater robot redundancy inertial navigation device | |
| CN106017463B (en) | A kind of Aerial vehicle position method based on orientation sensing device | |
| CN105241445B (en) | A kind of indoor navigation data capture method and system based on intelligent mobile terminal | |
| CN103674021A (en) | Integrated navigation system and method based on SINS (Strapdown Inertial Navigation System) and star sensor | |
| CN107796391A (en) | A kind of strapdown inertial navigation system/visual odometry Combinated navigation method | |
| CN106052685B (en) | A kind of posture and course estimation method of two-stage separation fusion | |
| CN105698822B (en) | Initial Alignment Method between autonomous type inertial navigation based on reversed Attitude Tracking is advanced | |
| CN106595653A (en) | Wearable autonomous navigation system for pedestrian and navigation method thereof | |
| CN109556632A (en) | INS/GNSS/polarization/geomagnetic integrated navigation alignment method based on Kalman filtering | |
| CN107390246A (en) | A kind of GPS/INS Combinated navigation methods based on genetic neural network | |
| CN103744101B (en) | A kind of low cost AHRS assistant GPS determination integer ambiguity device and method | |
| CN104181573B (en) | Big Dipper inertial navigation deep integrated navigation micro-system | |
| CN108225308A (en) | A kind of attitude algorithm method of the expanded Kalman filtration algorithm based on quaternary number | |
| CN107014377A (en) | A kind of multifunction shoe pads based on inertial positioning | |
| CN106125769A (en) | A kind of wireless head movement design of follow-up system method | |
| CN109724602A (en) | A kind of attitude algorithm system and its calculation method based on hardware FPU | |
| CN110017837A (en) | A kind of Combinated navigation method of the diamagnetic interference of posture | |
| CN103196445A (en) | Geomagnetism-assisted inertial carrier attitude measurement method based on matching technology | |
| CN103630147B (en) | A kind of individual autonomy navigation system zero-speed detection method based on HMM | |
| CN103114846B (en) | A kind for the treatment of system afterwards of the deviational survey data based on optic fiber gyroscope inclinometer | |
| CN108519090A (en) | A kind of implementation method of the binary channels integrated attitude determination algorithm of the UKF algorithms based on optimization | |
| CN107702712A (en) | Indoor pedestrian's combined positioning method based on inertia measurement bilayer WLAN fingerprint bases | |
| CN104266648A (en) | Indoor location system based on Android platform MARG sensor | |
| CN206074000U (en) | A kind of underwater hiding-machine inertial navigation system aided in based on gravity | |
| CN105547300A (en) | All-source navigation system and method used for AUV (Autonomous Underwater Vehicle) |
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: 20170531 |
|
| RJ01 | Rejection of invention patent application after publication |