CN110716578A - Aircraft control system based on hybrid brain-computer interface and control method thereof - Google Patents
Aircraft control system based on hybrid brain-computer interface and control method thereof Download PDFInfo
- Publication number
- CN110716578A CN110716578A CN201911132852.5A CN201911132852A CN110716578A CN 110716578 A CN110716578 A CN 110716578A CN 201911132852 A CN201911132852 A CN 201911132852A CN 110716578 A CN110716578 A CN 110716578A
- Authority
- CN
- China
- Prior art keywords
- aircraft
- module
- computer interface
- brain
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 18
- 238000004891 communication Methods 0.000 claims abstract description 26
- 238000012549 training Methods 0.000 claims description 14
- 230000004397 blinking Effects 0.000 claims description 13
- 230000033001 locomotion Effects 0.000 claims description 9
- 238000012545 processing Methods 0.000 claims description 8
- 238000005259 measurement Methods 0.000 claims description 6
- 230000001133 acceleration Effects 0.000 claims description 4
- 230000004927 fusion Effects 0.000 claims description 3
- 210000004556 brain Anatomy 0.000 abstract description 9
- 230000008901 benefit Effects 0.000 abstract description 5
- 208000012661 Dyskinesia Diseases 0.000 abstract description 2
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 230000035945 sensitivity Effects 0.000 abstract description 2
- 239000013598 vector Substances 0.000 description 7
- 230000001360 synchronised effect Effects 0.000 description 5
- 230000009471 action Effects 0.000 description 4
- 238000001514 detection method Methods 0.000 description 4
- 230000000763 evoking effect Effects 0.000 description 4
- 230000000007 visual effect Effects 0.000 description 4
- 238000001914 filtration Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000004044 response Effects 0.000 description 3
- 230000035484 reaction time Effects 0.000 description 2
- RZVHIXYEVGDQDX-UHFFFAOYSA-N 9,10-anthraquinone Chemical compound C1=CC=C2C(=O)C3=CC=CC=C3C(=O)C2=C1 RZVHIXYEVGDQDX-UHFFFAOYSA-N 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000003321 amplification Effects 0.000 description 1
- 230000001174 ascending effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 206010015037 epilepsy Diseases 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 230000002045 lasting effect Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 230000004936 stimulating effect Effects 0.000 description 1
- 230000000638 stimulation Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/08—Control of attitude, i.e. control of roll, pitch, or yaw
- G05D1/0808—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
- User Interface Of Digital Computer (AREA)
- Rehabilitation Tools (AREA)
Abstract
The invention discloses an aircraft control system based on a hybrid brain-computer interface, which comprises a main control module, a wireless communication module and an aircraft module; the invention also discloses an aircraft control method based on the hybrid brain-computer interface; the invention has the beneficial effects that: the aircraft remote control system can realize the remote control of the aircraft through the mixed type brain-computer interface, and compared with a single brain-computer interface, the mixed type brain-computer interface has the advantages of high accuracy, high variability and high control efficiency, and greatly enriches the types and the number of control instructions while reducing the false alarm rate; the invention controls the aircraft through the brain-computer interface, gets rid of the traditional manual control mode, solves the problem that the hands cannot execute a plurality of instructions simultaneously through the manual control and brain control modes, improves the control sensitivity, benefits people with dyskinesia of hands by simply adopting the brain control mode, and has wide application prospect in the fields of aircraft control and medical rehabilitation.
Description
Technical Field
The invention belongs to the technical field of flight control, and particularly relates to an aircraft control system based on a hybrid brain-computer interface and an aircraft control method based on the hybrid brain-computer interface.
Background
The invention patent with the application number of 201810580721.2 designs a portable brain-controlled unmanned aerial vehicle system based on motor imagery, which controls the flight of the unmanned aerial vehicle by collecting EEG (electroencephalogram) signals generated by imagining the body movement of a user, but the system extracts four types of motor imagery electroencephalogram characteristics (left hand, right hand, left leg and right leg) to control the unmanned aerial vehicle, and compared with two types of motor imagery electroencephalogram characteristic (left hand and right hand) control schemes used in the system, the portable brain-controlled unmanned aerial vehicle system based on motor imagery has the advantages of low recognition accuracy and high misoperation rate.
The invention patent with the application number of 201710298938.X discloses an asynchronous brain-controlled unmanned aerial vehicle system based on a wearable display, wherein steady-state visual evoked stimulus units with different frequencies are used for stimulating a brain to generate SSVEP (steady-state visual evoked potential), an unmanned aerial vehicle control instruction which a user wants to execute is judged according to a detected electric signal, but the high-frequency flickering interface is watched by human eyes for a long time to cause great discomfort, epilepsy can be induced under extreme conditions, and meanwhile, the response speed of the control system based on the SSVEP is very limited, and quick control cannot be realized.
The utility model patent application number 201720405541.1 designs an aircraft control system based on BCI, uses open source brain wave equipment OpenBCI to gather EEG, does not specifically describe which kind of EEG that uses and controls, simultaneously, does not carry on image acquisition equipment on the aircraft, just can't carry out effectual control after aircraft departure people's field of vision scope.
The EEG is only adopted for aircraft control, and the methods based on motor imagery, SSVEP (steady state visual evoked potential) or combination of the two methods have certain limitations (SSVEP easily causes discomfort of eyes of a user; motor imagery cannot provide more control instructions, and the accuracy rate is reduced along with the increase of motor imagery categories).
Disclosure of Invention
The invention aims to provide an aircraft control system based on a hybrid brain-computer interface and a control method thereof, which aim to solve the problems that the aircraft control is carried out by only adopting EEG (electroencephalogram) in the background technology, and certain limitations exist no matter the mode is based on motor imagery, SSVEP (steady state visual evoked potential) or the combination of the two modes (SSVEP easily causes discomfort of eyes of a user, motor imagery cannot provide more control instructions, and the accuracy is reduced along with the increase of motor imagery categories).
In order to achieve the purpose, the invention provides the following technical scheme: the utility model provides an aircraft control system based on mixed brain-computer interface, includes host system, wireless communication module and aircraft module, wherein:
the main control module comprises a signal acquisition sub-module, a data processing sub-module and an image display sub-module; wherein:
the signal acquisition sub-module is used for acquiring an EEG signal generated during motor imagery of a user and an EOG signal generated by active blinking;
the data processing submodule is a PC with certain computing power;
the image display sub-module is a display part of the PC and acquires real-time pictures shot by the aircraft through the wireless communication module;
the wireless communication module is connected with the display equipment through a serial port;
and an image acquisition submodule is carried on the aircraft module.
As a preferred technical scheme of the invention, the operation mode of the system is divided into a training mode and a normal use mode, and before the system is used, a user needs to train firstly.
As a preferable technical scheme of the invention, a communication mode can be used between the wireless communication module and the aircraft according to specific application occasions, and the communication mode is one or more of WiFi, Bluetooth and GPRS.
As a preferred technical solution of the present invention, the aircraft module is a quad-rotor aircraft carrying a camera (image capturing device), and can perform vertical flight, roll, pitch, and yaw control.
As a preferred technical scheme of the invention, the flight attitude is obtained by combining an attitude measurement sensor and an attitude fusion algorithm, and the commonly used attitude measurement sensor comprises an acceleration sensor, an angular velocity sensor, a magnetic sensor and an air pressure sensor.
As a preferable technical scheme of the invention, the aircraft is provided with a communication module, and the communication module is one or more of Bluetooth, Wi-Fi and GPRS.
As a preferred technical solution of the present invention, the data processing apparatus further includes a storage module, which is used for storing data.
The invention also discloses an aircraft control method based on the hybrid brain-computer interface, which comprises the following steps:
the method comprises the following steps: a user wears an electrode cap to collect EEG and EOG signals, and the electrode distribution on the electrode cap meets the international 10-20 standard;
step two: controlling the aircraft by responding to blinking stimuli on the display device and imagining the movement of the hands;
step three: the image acquisition equipment carried on the aircraft can transmit the real-time environment back to the display equipment so as to meet the requirement of remote control.
Compared with the prior art, the invention has the beneficial effects that:
(1) the aircraft remote control system can realize the remote control of the aircraft through the mixed type brain-computer interface, and compared with a single brain-computer interface, the mixed type brain-computer interface has the advantages of high accuracy, high variability and high control efficiency, and greatly enriches the types and the number of control instructions while reducing the false alarm rate;
(2) the invention controls the aircraft through the brain-computer interface, gets rid of the traditional manual control mode, solves the problem that the hands cannot execute a plurality of instructions simultaneously through the manual control and brain control modes, improves the control sensitivity, benefits people with dyskinesia of hands by simply adopting the brain control mode, and has wide application prospect in the fields of aircraft control and medical rehabilitation.
Drawings
FIG. 1 is a system architecture diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention provides a technical solution: a flight vehicle control system based on a mixed brain-computer interface, wherein a user wears an electrode cap to collect EEG and EOG signals, the electrode distribution on the electrode cap conforms to the international 10-20 standard, and the flight vehicle is controlled by responding to flickering stimulation on display equipment and imagining the movement of two hands, so that image collection equipment carried on the flight vehicle can transmit a real-time environment back to the display equipment to meet the requirement of remote control; this system includes host system, wireless communication module and aircraft module, wherein:
the main control module comprises a signal acquisition sub-module, a data processing sub-module and an image display sub-module; wherein:
the signal acquisition sub-module is an electrode cap and corresponding signal amplification equipment which meet the international 10-20 standard and is used for acquiring an EEG signal generated during motor imagery of a user and an EOG signal generated by active blinking;
the data processing submodule, usually a PC with certain computing power, before the system is used normally, a user needs to train first, and the training mode and the normal use mode both depend on the computing power of the data processing submodule;
the image display submodule, generally a display part of a PC (personal computer), acquires a real-time picture shot by an aircraft through a wireless communication module, and during normal use, a plurality of control keys are also displayed on the real-time picture, each key corresponds to a control instruction of the aircraft (two state bars correspond to left-turn and right-turn instructions of the unmanned aerial vehicle), the aircraft is controlled to fly forwards at a preset speed in an advancing way, the aircraft is controlled to ascend and descend vertically at the preset speed, the aircraft is stopped to be controlled to suspend at the current position, the left-turn and right-turn indicate that the aircraft rotates for a certain angle left and right, and the acceleration and deceleration are used for adjusting the flight speed of the aircraft according to conditions;
the wireless communication module is connected with the display equipment through a serial port, when a user selects a corresponding instruction, the display equipment sends a corresponding data packet to the wireless communication module through the serial port, the wireless communication module decodes the corresponding data packet and then packs the data packet to be forwarded to the aircraft, the aircraft can execute the corresponding instruction after receiving the data packet, and WiFi, Bluetooth and GPRS communication modes can be used between the wireless communication module and the aircraft according to specific application occasions;
the aircraft module is for carrying on the four rotor crafts of camera (image acquisition equipment), generally be by the flight motion device that 4 outer rotor direct current brushless motor driven propellers that can independent control rotational speed provide whole power, can carry out vertical flight, roll, every single move and yaw control, flight attitude is obtained by attitude measurement sensor combination gesture fusion algorithm, commonly used attitude measurement sensor has acceleration sensor, angular velocity transducer, magnetic force sensor and baroceptor, it is still generally carried on the aircraft and has bluetooth, Wi-Fi, GPRS communication module to realize remote control and data transmission.
The operation mode of the system is divided into a training mode and a normal use mode, and each user needs to train for a certain time before the system is used normally so as to ensure the accuracy and the controllability of the system.
In this embodiment, it is preferable that the apparatus further includes a storage module, and the storage module is configured to store data.
An aircraft control method based on a hybrid brain-computer interface comprises the following steps:
the method comprises the following steps: after a user wears the electrode cap, the EOG training mode is firstly entered according to the interface prompt, a random key is changed into red (lasting for 500ms) on the interface, the key is indicated to be a calibration key by the user, then the key flickers at a flickering interval of 500ms and for a flickering duration of 100ms, the key is changed into blue during flickering, and the user needs to execute synchronous blinking along with the flickering of the key;
aiming at the collected EOG signals, extracting corresponding key flickerEOG feature vector consisting of f (t)p)、dnAnd tpComposition, here f (t)p) Is the peak of the original EOG waveform, tpIndicating blink response time (the time interval from the start of a key blink to the peak of the original EOG waveform), dnIndicating the duration of blinking, and calculating the blink response time t corresponding to 5 key blinks from the current timepS (time unit is ms), if f (t) corresponds to 5 eigenvectorsp) If the values are all larger than 5 microvolts, the calibration value c is calculated by the following formula:
c＝(80-s)/80
if the condition calibration value c is not met, the calibration value c is set to be 0, the purpose of doing so is to eliminate the interference of noise, in the whole calibration process, the calibration value is updated once after each key flashing, to obtain a relatively high calibration value, a user needs to follow the key flashing for at least 5 times continuously to execute synchronous blinking actions at a stable frequency, when the calibration value is greater than 0.5, the EOG waveform characteristic of the user is relatively stable, at this time, the training of an EOG signal is automatically stopped, and the EEG signal training is performed;
meanwhile, 5 feature vectors corresponding to the maximum calibration value obtained by the user in the EOG signal training process are used for calculating four threshold values Amin(blink amplitude threshold), Dmin(minimum threshold for blink duration), Dmax(blink duration max threshold) and Tp(average blink reaction time), the mean of these 5 eigenvectors is first calculated
And
then the four thresholds are respectively set to
And
step two: in the training of the EEG signal, any state bar turns red for 500ms, which indicates that the user performs motor imagery training in a corresponding direction, the user needs to imagine that the left hand or the right hand of the user performs simple movement, such as repeatedly making a fist and waving the hand, and actually the hand of the user does not need to perform corresponding movement, the process is repeated for 30 times, the collected motor imagery EEG signal is firstly subjected to spatial filtering by a CAR filter and then subjected to band-pass filtering at 8-13Hz, a common joint diagonalization method is adopted to construct a CSP transformation matrix for the EEG data after the filtering, and three components at two ends are selected to perform spatial projection on the EEG data, so that 30 6-dimensional feature vectors can be obtained for each category from a training data set, wherein the label corresponding to imagined left-hand movement is-1, the label corresponding to imagined right-hand movement is +1, and the labeled feature vectors are utilized, training a linear SVM classifier for classifying the motor imagery electroencephalogram signals, and after the EEG training is finished, switching the system into a normal use mode;
step three: in a normal use mode, the forward, the ascending and the stop keys sequentially flash from left to right and from top to bottom (the flash duration is 100ms, the adjacent flash interval is 500ms), the keys are changed into blue when flashing, the key flashing is used for providing synchronous information for a user, if the user wants to select a certain key, the user only needs to execute synchronous blinking along with the flashing of the key, once the user selects the certain key, a corresponding instruction is sent to an aircraft through the wireless communication module, meanwhile, the background color of the key turns red and lasts for 500ms, and other keys also stop flashing for the same time;
extracting the same characteristic vector as that in EOG training for the EOG waveform after each key flashing, and regarding each extracted characteristic vector, if the two formula conditions are met, determining that the blinking action exists, otherwise, determining that the blinking action does not exist;
f(tp)＞Amin
Dmin＜dn＜Dmax
after each turn of key flashing, in all the feature vectors with the blinking action detected, firstly, the blinking reaction time t is passedpCalculating the error value e ═ t of blink synchronous timep-TpIf the same key exists in three continuous detections (including two continuous detections), the key is selected as a target key of the current decision, and if none of the keys meets the condition, the current detection is not output;
two state bars are arranged on two sides of the interface, the position of a red vertical bar on each state bar is a threshold value, the value range of the normalized motor imagery detection result s is [ -1,1], if s is positive, the state bar on the right side fills the state bars into blue according to the proportion from left to right according to the absolute value of s, the state bar on the right side is changed into blue corresponding to the whole state bar when the absolute value of s is 1, when the absolute value of s is larger than the threshold value, the aircraft executes a right turn instruction, and similarly, when the value of s is negative and exceeds the threshold value, the aircraft left turn control instruction is corresponding to the aircraft.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. The utility model provides an aircraft control system based on mixed brain-computer interface which characterized in that, includes host system, wireless communication module and aircraft module, wherein:
the main control module comprises a signal acquisition sub-module, a data processing sub-module and an image display sub-module; wherein:
the signal acquisition sub-module is used for acquiring an EEG signal generated during motor imagery of a user and an EOG signal generated by active blinking;
the data processing submodule is a PC with certain computing power;
the image display sub-module is a display part of the PC and acquires real-time pictures shot by the aircraft through the wireless communication module;
the wireless communication module is connected with the display equipment through a serial port;
and an image acquisition submodule is carried on the aircraft module.
2. The hybrid brain-computer interface based aircraft control system according to claim 1, wherein: the operation mode of the system is divided into a training mode and a normal use mode, and before the system is used, a user needs to train firstly.
3. The hybrid brain-computer interface based aircraft control system according to claim 1, wherein: the communication mode can be used between the wireless communication module and the aircraft according to specific application occasions, and the communication mode is one or more of WiFi, Bluetooth and GPRS.
4. The hybrid brain-computer interface based aircraft control system according to claim 1, wherein: the aircraft module is a four-rotor aircraft carrying a camera (image acquisition equipment), and can carry out vertical flight, roll, pitching and yawing control.
5. The hybrid brain-computer interface based aircraft control system according to claim 4, wherein: the flight attitude is obtained by combining an attitude measurement sensor and an attitude fusion algorithm, and the commonly used attitude measurement sensors comprise an acceleration sensor, an angular velocity sensor, a magnetic sensor and an air pressure sensor.
6. The hybrid brain-computer interface based aircraft control system according to claim 1, wherein: the aircraft is provided with a communication module, and the communication module is one or more of Bluetooth, Wi-Fi and GPRS.
7. The hybrid brain-computer interface based aircraft control system according to claim 1, wherein: the device also comprises a storage module which is used for storing the data.
8. The hybrid brain-computer interface based aircraft control method according to any one of claims 1 to 7, wherein: the control method comprises the following steps:
the method comprises the following steps: a user wears an electrode cap to collect EEG and EOG signals, and the electrode distribution on the electrode cap meets the international 10-20 standard;
step two: controlling the aircraft by responding to blinking stimuli on the display device and imagining the movement of the hands;
step three: the image acquisition equipment carried on the aircraft can transmit the real-time environment back to the display equipment so as to meet the requirement of remote control.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911132852.5A CN110716578A (en) | 2019-11-19 | 2019-11-19 | Aircraft control system based on hybrid brain-computer interface and control method thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911132852.5A CN110716578A (en) | 2019-11-19 | 2019-11-19 | Aircraft control system based on hybrid brain-computer interface and control method thereof |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110716578A true CN110716578A (en) | 2020-01-21 |
Family
ID=69216162
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911132852.5A Pending CN110716578A (en) | 2019-11-19 | 2019-11-19 | Aircraft control system based on hybrid brain-computer interface and control method thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110716578A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112327915A (en) * | 2020-11-10 | 2021-02-05 | 大连海事大学 | Idea control method of unmanned aerial vehicle |
| CN112904893A (en) * | 2021-01-19 | 2021-06-04 | 华南理工大学 | Unmanned aerial vehicle control system based on portable eye electricity collection system |
| CN113253750A (en) * | 2021-06-28 | 2021-08-13 | 北京科技大学 | Multi-mode control system for flapping wing aircraft |
| CN113867378A (en) * | 2021-09-30 | 2021-12-31 | 江苏集萃脑机融合智能技术研究所有限公司 | Aircraft control method and system based on electro-oculogram and flight device |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130138248A1 (en) * | 2011-11-30 | 2013-05-30 | Honeywell International Inc. | Thought enabled hands-free control of multiple degree-of-freedom systems |
| CN106292705A (en) * | 2016-09-14 | 2017-01-04 | 东南大学 | Many rotor wing unmanned aerial vehicles idea remote control system based on Bluetooth brain wave earphone and operational approach |
| CN108415565A (en) * | 2018-02-25 | 2018-08-17 | 西北工业大学 | The machine integrated intelligent control method of unmanned plane brain and technology |
| CN108733201A (en) * | 2017-04-19 | 2018-11-02 | 辽宁科技大学 | Aircraft three-dimensional extraterrestrial target search system and method based on brain electricity Yu eye electricity |
-
2019
- 2019-11-19 CN CN201911132852.5A patent/CN110716578A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130138248A1 (en) * | 2011-11-30 | 2013-05-30 | Honeywell International Inc. | Thought enabled hands-free control of multiple degree-of-freedom systems |
| CN106292705A (en) * | 2016-09-14 | 2017-01-04 | 东南大学 | Many rotor wing unmanned aerial vehicles idea remote control system based on Bluetooth brain wave earphone and operational approach |
| CN108733201A (en) * | 2017-04-19 | 2018-11-02 | 辽宁科技大学 | Aircraft three-dimensional extraterrestrial target search system and method based on brain electricity Yu eye electricity |
| CN108415565A (en) * | 2018-02-25 | 2018-08-17 | 西北工业大学 | The machine integrated intelligent control method of unmanned plane brain and technology |
Non-Patent Citations (1)
| Title |
|---|
| 史添玮等: "基于混合计算机接口的多旋翼飞行器3维空间目标搜索", 《机器人》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112327915A (en) * | 2020-11-10 | 2021-02-05 | 大连海事大学 | Idea control method of unmanned aerial vehicle |
| CN112904893A (en) * | 2021-01-19 | 2021-06-04 | 华南理工大学 | Unmanned aerial vehicle control system based on portable eye electricity collection system |
| CN113253750A (en) * | 2021-06-28 | 2021-08-13 | 北京科技大学 | Multi-mode control system for flapping wing aircraft |
| CN113867378A (en) * | 2021-09-30 | 2021-12-31 | 江苏集萃脑机融合智能技术研究所有限公司 | Aircraft control method and system based on electro-oculogram and flight device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110716578A (en) | Aircraft control system based on hybrid brain-computer interface and control method thereof | |
| Wang et al. | A wearable SSVEP-based BCI system for quadcopter control using head-mounted device | |
| CN104083258B (en) | A kind of method for controlling intelligent wheelchair based on brain-computer interface and automatic Pilot technology | |
| Wang et al. | An asynchronous wheelchair control by hybrid EEG–EOG brain–computer interface | |
| CN104799984B (en) | Assistance system for disabled people based on brain control mobile eye and control method for assistance system | |
| Kim et al. | Quadcopter flight control using a low-cost hybrid interface with EEG-based classification and eye tracking | |
| CN112114670B (en) | Man-machine co-driving system based on hybrid brain-computer interface and control method thereof | |
| Deng et al. | A bayesian shared control approach for wheelchair robot with brain machine interface | |
| CN108415565A (en) | The machine integrated intelligent control method of unmanned plane brain and technology | |
| Mao et al. | A brain–robot interaction system by fusing human and machine intelligence | |
| CN106371451A (en) | Unmanned aerial vehicle manipulation method and device based on steady state visual evoked potential | |
| CN101711709A (en) | Method for controlling electrically powered artificial hands by utilizing electro-coulogram and electroencephalogram information | |
| CN112051780B (en) | Brain-computer interface-based mobile robot formation control system and method | |
| CN108681403A (en) | A kind of trolley control method using eye tracking | |
| CN111487988B (en) | Brain-controlled unmanned aerial vehicle method based on steady-state visual evoked potential brain-computer interface | |
| CN109009887A (en) | A kind of man-machine interactive navigation system and method based on brain-computer interface | |
| CN106904169A (en) | Traffic safety method for early warning and device | |
| CN107961120A (en) | A kind of intelligent wheelchair control system based on brain electric control | |
| CN113253750A (en) | Multi-mode control system for flapping wing aircraft | |
| Deng et al. | A VR-based BCI interactive system for UAV swarm control | |
| CN205750354U (en) | A kind of expression robot | |
| Shi et al. | Indoor space target searching based on EEG and EOG for UAV | |
| CN110353899A (en) | A kind of intelligent wheel chair | |
| CN113625769A (en) | Unmanned aerial vehicle formation multi-mode control system based on electroencephalogram signals | |
| CN206249101U (en) | Unmanned plane actuation means based on Steady State Visual Evoked Potential |
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: 20200121 |
|
| RJ01 | Rejection of invention patent application after publication |