CN203941451U - Based on the automatic obstacle avoidance trolley of gesture identification - Google Patents
Based on the automatic obstacle avoidance trolley of gesture identification Download PDFInfo
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- CN203941451U CN203941451U CN201420180921.6U CN201420180921U CN203941451U CN 203941451 U CN203941451 U CN 203941451U CN 201420180921 U CN201420180921 U CN 201420180921U CN 203941451 U CN203941451 U CN 203941451U
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Abstract
Based on the automatic obstacle avoidance trolley of gesture identification, relate to human-computer interaction intelligent Image Information Processing, belong to based on field, depth camera non-contact 3-D Virtual Space and binocular stereo vision field, two cameras that its Vehicular body front setting separates, the inner slave computer that comprises single-chip microcomputer that arranges of car body, car body is settled two wireless wifi modules, and a wireless wifi module connects a camera, car body is also settled a bluetooth module, and two dolly trailing wheels are connecting respectively a precision DC motor; Its degree of depth body sense camera connects host computer, and host computer arranges serial ports bluetooth and a wireless wifi module.The utility model is by gesture control dolly and automatically realizing dolly automatic obstacle-avoiding under operational mode, easy to operate, is adapted at checking that whether surrounding environment is dangerous and carry out carrying work under the many landform of barrier under hazardous environment.
Description
Technical field
The utility model relates to a kind of dolly manipulating, and is specifically related to a kind of automatic obstacle avoidance trolley based on gesture identification.
Background technology
In the present general intelligentized epoch of computing machine, human-computer interaction intelligent Image Information Processing has become the important application of computer vision field.Since the nineties in last century, PC occurred, this cross-synthesis subject of man-machine interaction, has become the mainstream research technology that user experiences, and the appearance of body sense interactive mode allows human-computer interaction technology enter a New Times.Along with the rigid demand of user to the intelligent movable equipment emerging in an endless stream, intelligent image is processed the data processing and the multifunctional application development that more tend to bulky complex, thus for man-machine interactive platform has brought larger challenge and opportunity.Now, the transinformation content of computing machine processing is more and more huger, impels day by day efficient, stable, real-time novel interaction technique develop rapidly.Modern interaction technique is mainly divided into speech recognition and body language recognition technology, and wherein gesture identification is the important branch technology of body language recognition technology.
Along with scientific and technical development, mobile robot's application is more and more extensive, human being's production and life has been brought to significant impact simultaneously.Vision is obtained the important means of ambient condition information as robot, to contain much information as feature, significant to mobile robot's self-navigation and automatic obstacle-avoiding.Keeping away barrier dolly has and realizes hardware by similar infrared sensor and keep away barrier, but this barrier mode of keeping away can only closely kept away barrier, and judges inaccurate.And binocular vision is obtained the three-dimensional point cloud of surrounding environment by parallax, can accurately obtain the distance of barrier from dolly, the left and right margins of barrier, can provide good feasible scheme for self-navigation and automatic obstacle-avoiding.
Utility model content
Technical problem to be solved in the utility model is to provide a kind of gesture control dolly based on depth camera, can automatically under operational mode, realize dolly automatic obstacle-avoiding, can be effectively with multiple mode of operation control dolly, and automatic avoiding obstacles, be adapted at checking under hazardous environment carrying work under the landform that whether surrounding environment is dangerous and barrier is many.
Based on above-mentioned, the purpose of this utility model is to provide a kind of automatic obstacle avoidance trolley based on gesture identification.
Automatic obstacle avoidance trolley based on gesture identification of the present utility model, comprise dolly and tele-control system, it is characterized in that: two cameras that the Vehicular body front setting of described dolly separates, the inner slave computer that comprises single-chip microcomputer that arranges of car body, car body is settled two wireless wifi modules, a wireless wifi module connects a camera, and car body is also settled a bluetooth module, and two trailing wheels of dolly are respectively connecting a precision DC motor; Described tele-control system comprises degree of depth body sense camera and host computer, and degree of depth body sense camera connects host computer, serial ports bluetooth and the wireless wifi module that can be connected with the wireless wifi of car body of host computer setting and car body Bluetooth pairing.
The vision signal that wireless wifi module on dolly is obtained camera, sends to upper end, and host computer is linked to wifi module receiving video signals by wifi focus.Owing to there being two wifi modules, there are two focuses, so by network interface line, two wifi modules are linked up, host computer only need connect a focus like this, just can obtain two-path video signal.This scheme is conducive to simplified structure.
Bluetooth module on dolly, is responsible for the transmission of moving of car control signal.
Two cameras separate, and they obtain video image separately separately, by the method for image processing, be exactly specifically the method for binocular vision, obtain depth map, this depth map is mobile in real time, because dolly is moving, the depth map obtaining is in order to do automatic obstacle-avoiding.
In the utility model, the preferred Atmega128 AVR of single-chip microcomputer single-chip microcomputer.
As preferably, degree of depth body sense camera is selected Kinect body sense camera.
By the bluetooth of host computer and car body Bluetooth pairing, connect the wireless wifi of host computer and the wireless wifi of car body, afterwards, obtain the gesture of sending by kinect.
Gesture described in the utility model comprises:
Gesture 5: be parking gesture, represent that dolly stops immediately, wait for new gesture;
Gesture 0: for entering manual control model gesture, mobile this gesture is to suitable position, and dolly does corresponding movement; Also be the gesture that enters gesture state of a control simultaneously, only detect after gesture 0, gesture 5 or gesture 2 just can enter in algorithm and judge;
Gesture 2: for entering automatic control mode gesture, dolly is according to program automatic moving.
Gesture is obtained by kinect, in host computer, get image gesture identification complete after, only need to send bluetooth corresponding to gesture and output signal to slave computer control.
When gesture 0 moves, and while exceeding mobile minor increment 3cm, judge moving direction, according to moving direction, bluetooth is exported corresponding control signal.Moving direction has following four kinds: advance, retreat, turn left, turn right.In the time that gesture departs from these just to direction, calculate manyly to which side trend, and export corresponding control signal.In the time gesture 5 being detected, dolly stops immediately.Also can move hand to the position that is applicable to kinect detection by gesture 5.In the time gesture 2 being detected, dolly enters automatic control mode, and dolly, by automatic obstacle-avoiding, moves.In the time again gesture 5 being detected, stop automatic control mode, go forward side by side into manual control model.
Host computer reads after the computer video stream that slave computer transmits, and does binocular parallax processing, obtains image parallactic, obtains spatial point cloud with disparity map, and the some cloud lower than 15cm in spatial point cloud is made as to ground, higher than 15cm lower than the object of 100cm as barrier;
Detailed process is: by two precision DC motors of little back wheels of vehicle, can accurately control the distance that dolly is walked, measure the diameter (dolly wheel diameter is that the technical parameter that producer provides when buying obtains) of dolly wheel, thus can accurate Calculation dolly from row distance and angle of turn.
From DC motor technology and dolly trailing wheel parameter, little back wheels of vehicle turns around, and direct current chance produces 650 pulses, thus umber of pulse=θ * 360/650 corresponding to the angle that will make dolly turn, the distance d=umber of pulse * 2 п * R/650 that dolly is kept straight on.The AVR single-chip microcomputer of slave computer by external interrupt, obtains dolly pulse, and judges by Interruption whether dolly umber of pulse arrives specific pulse, and the angle turning calculating and from row distance sends to host computer.
In the utility model, the degree of depth body sense camera in control system obtains the gesture that people sends, host computer identification gesture, and by Bluetooth transmission, to slave computer, slave computer control dolly moves.Slave computer reaches host computer by dolly coordinate by bluetooth in real time simultaneously, host computer real-time update map, the camera collection of dolly to video flowing be transferred to host computer by wifi, host computer carries out the processing of binocular parallax image, obtain binocular image parallax, obtain spatial point cloud with disparity map, point cloud lower than 15cm in a cloud is made as to ground, using higher than 15cm lower than the object of 100cm as barrier, outermost point among point set lower than 15cm is drawn on map, is the extraneous landform of dolly process.The slave computer of dolly is differentiated y axle higher than 15cm, lower than the barrier of 100cm, extracts barrier point set, judge z wheelbase from x axle the rightest the most left distance, control dolly carries out automatic obstacle-avoiding under automatic control mode.
Brief description of the drawings
Fig. 1 is the structured flowchart of the automatic obstacle avoidance trolley based on gesture identification of the present utility model;
Fig. 2 is the gesture figure of gesture 5;
Fig. 3 is the gesture figure of gesture 0;
Fig. 4 is the gesture figure of gesture 2.
Embodiment
See Fig. 1.
Based on the automatic obstacle avoidance trolley of gesture identification, comprise dolly 1 and tele-control system 14, anterior two cameras 8 separately that arrange of car body 2 of described dolly 1, the inner slave computer 3 that comprises single-chip microcomputer 6 that arranges of car body 2, car body 2 is settled two wireless wifi modules 9, a wireless wifi module connects a camera, and car body 2 is also settled a bluetooth module 7, and two trailing wheels 5 of dolly are respectively connecting a precision DC motor 4; Described tele-control system 14 comprises degree of depth body sense camera 10 and host computer 11, degree of depth body sense camera 10 connects host computer 11, and host computer 11 arranges the serial ports bluetooth 13 of matching with car body bluetooth 7 and one can be connected with the wireless wifi of car body 9 wireless wifi module 12.
Two wifi modules on dolly 1 connect by network interface line.
Single-chip microcomputer 6 is Atmega128 AVR single-chip microcomputer.
Degree of depth body sense camera 10 is selected Kinect body sense camera.
See Fig. 2 ~ Fig. 4.Gesture 5: be parking gesture, represent that dolly stops immediately, wait for new gesture;
Gesture 0: for entering manual control model gesture, mobile this gesture is to suitable position, and dolly does corresponding movement; Also be the gesture that enters gesture state of a control simultaneously, only detect after gesture 0, gesture 5 or gesture 2 just can enter in algorithm and judge;
Gesture 2: for entering automatic control mode gesture, dolly is according to program automatic moving.
Claims (4)
1. the automatic obstacle avoidance trolley based on gesture identification, comprise dolly and tele-control system, it is characterized in that: two cameras that the Vehicular body front setting of described dolly separates, the inner slave computer that comprises single-chip microcomputer that arranges of car body, car body is settled two wireless wifi modules, a wireless wifi module connects a camera, and car body is also settled a bluetooth module, and two trailing wheels of dolly are respectively connecting a precision DC motor; Described tele-control system comprises degree of depth body sense camera and host computer, and degree of depth body sense camera connects host computer, serial ports bluetooth and the wireless wifi module that can be connected with the wireless wifi of car body of host computer setting and car body Bluetooth pairing.
2. automatic obstacle avoidance trolley according to claim 1, is characterized in that: single-chip microcomputer is selected Atmega128 AVR single-chip microcomputer.
3. automatic obstacle avoidance trolley according to claim 1, is characterized in that: two wireless wifi modules connect by network interface line.
4. automatic obstacle avoidance trolley according to claim 1, is characterized in that: degree of depth body sense camera is selected Kinect body sense camera.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201420180921.6U CN203941451U (en) | 2014-04-15 | 2014-04-15 | Based on the automatic obstacle avoidance trolley of gesture identification |
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| CN201420180921.6U CN203941451U (en) | 2014-04-15 | 2014-04-15 | Based on the automatic obstacle avoidance trolley of gesture identification |
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| CN203941451U true CN203941451U (en) | 2014-11-12 |
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104932502A (en) * | 2015-06-04 | 2015-09-23 | 福建天晴数码有限公司 | Short-distance obstacle avoiding method and short-distance obstacle avoiding system based on three-dimensional depth camera |
| CN106648115A (en) * | 2017-01-13 | 2017-05-10 | 广州大学 | Trolley Kinect fighting device and control method based on AR virtual control |
| CN106814738A (en) * | 2017-03-30 | 2017-06-09 | 南通大学 | A kind of wheeled robot and its control method based on motion sensing control technology |
| CN107053214A (en) * | 2017-01-13 | 2017-08-18 | 广州大学 | A kind of robot battle device and control method based on motion sensing control |
| CN107105956A (en) * | 2014-12-25 | 2017-08-29 | 东芝生活电器株式会社 | Electric dust collector |
| WO2017166723A1 (en) * | 2016-03-30 | 2017-10-05 | 乐视控股（北京）有限公司 | Unmanned aerial vehicle system and flight control method thereof |
| CN108303972A (en) * | 2017-10-31 | 2018-07-20 | 腾讯科技（深圳）有限公司 | The exchange method and device of mobile robot |
| CN110659543A (en) * | 2018-06-29 | 2020-01-07 | 比亚迪股份有限公司 | Vehicle control method and system based on gesture recognition and vehicle |
-
2014
- 2014-04-15 CN CN201420180921.6U patent/CN203941451U/en not_active Expired - Fee Related
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107105956A (en) * | 2014-12-25 | 2017-08-29 | 东芝生活电器株式会社 | Electric dust collector |
| CN107105956B (en) * | 2014-12-25 | 2019-08-23 | 东芝生活电器株式会社 | Electric dust collector |
| CN104932502A (en) * | 2015-06-04 | 2015-09-23 | 福建天晴数码有限公司 | Short-distance obstacle avoiding method and short-distance obstacle avoiding system based on three-dimensional depth camera |
| CN104932502B (en) * | 2015-06-04 | 2018-08-10 | 福建天晴数码有限公司 | Short distance barrier-avoiding method based on three dimensional depth video camera and short distance obstacle avoidance system |
| WO2017166723A1 (en) * | 2016-03-30 | 2017-10-05 | 乐视控股（北京）有限公司 | Unmanned aerial vehicle system and flight control method thereof |
| CN107053214B (en) * | 2017-01-13 | 2023-09-05 | 广州大学 | Robot fight device based on somatosensory control and control method |
| CN106648115A (en) * | 2017-01-13 | 2017-05-10 | 广州大学 | Trolley Kinect fighting device and control method based on AR virtual control |
| CN107053214A (en) * | 2017-01-13 | 2017-08-18 | 广州大学 | A kind of robot battle device and control method based on motion sensing control |
| CN106814738A (en) * | 2017-03-30 | 2017-06-09 | 南通大学 | A kind of wheeled robot and its control method based on motion sensing control technology |
| CN108303972A (en) * | 2017-10-31 | 2018-07-20 | 腾讯科技（深圳）有限公司 | The exchange method and device of mobile robot |
| US11142121B2 (en) | 2017-10-31 | 2021-10-12 | Tencent Technology (Shenzhen) Company Limited | Interaction method and apparatus of mobile robot, mobile robot, and storage medium |
| CN110659543B (en) * | 2018-06-29 | 2023-07-14 | 比亚迪股份有限公司 | Gesture recognition-based vehicle control method and system and vehicle |
| CN110659543A (en) * | 2018-06-29 | 2020-01-07 | 比亚迪股份有限公司 | Vehicle control method and system based on gesture recognition and vehicle |
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Legal Events
| Date | Code | Title | Description |
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| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20141112 Termination date: 20150415 |
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| EXPY | Termination of patent right or utility model |