CN111796602A - Plant protection unmanned aerial vehicle barrier is surveyed and early warning system - Google Patents
Plant protection unmanned aerial vehicle barrier is surveyed and early warning system Download PDFInfo
- Publication number
- CN111796602A CN111796602A CN202010560491.0A CN202010560491A CN111796602A CN 111796602 A CN111796602 A CN 111796602A CN 202010560491 A CN202010560491 A CN 202010560491A CN 111796602 A CN111796602 A CN 111796602A
- Authority
- CN
- China
- Prior art keywords
- unmanned aerial
- aerial vehicle
- plant protection
- protection unmanned
- detection device
- 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
- 230000004888 barrier function Effects 0.000 title claims abstract description 42
- 238000001514 detection method Methods 0.000 claims abstract description 162
- RZVHIXYEVGDQDX-UHFFFAOYSA-N 9,10-anthraquinone Chemical compound C1=CC=C2C(=O)C3=CC=CC=C3C(=O)C2=C1 RZVHIXYEVGDQDX-UHFFFAOYSA-N 0.000 claims abstract description 37
- 230000003993 interaction Effects 0.000 claims abstract description 33
- 230000005540 biological transmission Effects 0.000 claims abstract description 24
- 238000012545 processing Methods 0.000 claims abstract description 19
- 238000004364 calculation method Methods 0.000 claims abstract description 12
- 238000012546 transfer Methods 0.000 claims abstract description 9
- 238000004458 analytical method Methods 0.000 claims abstract description 8
- 230000004927 fusion Effects 0.000 claims description 32
- 238000000034 method Methods 0.000 claims description 19
- 230000006870 function Effects 0.000 claims description 17
- 230000000007 visual effect Effects 0.000 claims description 16
- 238000007726 management method Methods 0.000 claims description 6
- 238000000605 extraction Methods 0.000 claims description 5
- 238000003384 imaging method Methods 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 238000004891 communication Methods 0.000 claims description 2
- 238000012544 monitoring process Methods 0.000 claims description 2
- 230000008569 process Effects 0.000 claims description 2
- 210000001015 abdomen Anatomy 0.000 claims 1
- 230000001681 protective effect Effects 0.000 claims 1
- 238000009434 installation Methods 0.000 abstract description 4
- 230000008901 benefit Effects 0.000 description 12
- 238000002790 cross-validation Methods 0.000 description 5
- 241001155433 Centrarchus macropterus Species 0.000 description 4
- 238000007405 data analysis Methods 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 238000005507 spraying Methods 0.000 description 3
- 230000001133 acceleration Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000010200 validation analysis Methods 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 230000006698 induction Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000011897 real-time detection Methods 0.000 description 1
- 238000006467 substitution reaction Methods 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 or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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 or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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
Abstract
The invention relates to a plant protection unmanned aerial vehicle, in particular to a barrier detection and early warning system for the plant protection unmanned aerial vehicle. The system comprises a data transmission radio station device, a laser radar detection device and a binocular vision detection device. Laser radar detection device installation is located unmanned aerial vehicle's ventral below, two mesh vision detection device installations are located unmanned aerial vehicle's top, and just to the direction of advance of fuselage, data transfer radio station device installs in the safety cover at plant protection unmanned aerial vehicle top, laser radar detection device and two mesh vision detection device will gather and the data transmission who surveys to flight control system, flight control system receives data and transmits to human-computer interaction terminal and ground basic station through data transfer radio station device after data calculation and analysis, the flyer passes through the human-computer interaction terminal and obtains the information and the timely processing about the barrier. The invention adopts a detection means combining vision and non-vision sensors to detect the obstacles in the farmland environment and perform obstacle early warning on the flyer, thereby ensuring the operation safety of the plant protection unmanned aerial vehicle.
Description
Technical Field
The invention relates to a plant protection unmanned aerial vehicle, in particular to a barrier detection and early warning system for the plant protection unmanned aerial vehicle.
Background
The plant protection unmanned aerial vehicle has the advantages of flexible taking-off and landing operation, high operation efficiency, good prevention and control effect and obvious economic benefit, is beneficial to resource saving and environment friendliness, and is widely applicable to agricultural areas where ground machinery is difficult to cultivate. Because farmland operation environment is complicated, can often meet all kinds of barriers that use branch, electric wire and the outstanding crops of growing trend etc. to represent during the operation, when the flight hand is far away from plant protection unmanned aerial vehicle, be difficult to judge its peripheral flight environment. Therefore, with the rapid development of science and technology, the realization of autonomous identification and effective avoidance of obstacles is one of the inevitable trends of the intelligent development of agricultural unmanned aerial vehicles. With the further development of the precision agricultural aviation technology, the detection system with the multi-sensor fusion becomes the mainstream trend of the real-time detection system of the plant protection unmanned aerial vehicle. The combination of vision and non-vision sensor also will improve the security of plant protection operation, for independently spraying, intelligent navigation's realization provides multiple possibility, in time discerns and surveys the barrier to in time carry out the early warning to the flight hand with the information of barrier, guaranteed the safety of plant protection unmanned aerial vehicle in field operation.
Chinese patent CN109976379A discloses an autonomous navigation and obstacle avoidance unmanned aerial vehicle with integration of laser radar and depth camera, which uses the depth camera, monocular camera and laser radar to control the unmanned aerial vehicle to complete the detection of obstacles and achieve the avoidance. This patent adopts 2D laser radar to realize that the barrier is modelled, fix a position and is drawn, and two-dimensional laser scanning can only acquire the depth information of the fixed angle in place ahead, can't acquire the three-dimensional depth information of whole scene, and the unmanned aerial vehicle that carries out the plant protection operation in the field need survey the three-dimensional information of barrier, can make effectual obstacle avoidance decision, just can effectively guarantee the safety of plant protection operation.
Chinese patent CN109839938A discloses an unmanned aerial vehicle range finding and avoiding system based on laser radar, which adopts the form of laser radar to detect obstacles. The invention only concerns the 90-degree sector of the horizontal plane right in front of the flight direction of the unmanned aerial vehicle and has a space area with a certain horizontal included angle, and the mode has an obstacle avoidance blind area and cannot adapt to complex farmland operation environments.
Chinese patent CN109032185A discloses a transmission line inspection unmanned aerial vehicle keeps away barrier controlling means, and this patent is that the judgement that receives obstacle induction signal and accomplish the obstacle signal through the treater is calculated, carries out control command's transmission, and automatic control unmanned aerial vehicle accomplishes the obstacle and avoids the operation for in the plant protection operation, owing to do not have early warning system, can not carry out the early warning to the obstacle, make the flier in time make judgement and processing.
Disclosure of Invention
The invention aims to solve the technical problems that the existing mode for detecting the obstacle of the plant protection unmanned aerial vehicle as a single signal source cannot perform early warning on the obstacle information of a flying hand in time. Due to the complex working environment of the farmland, various obstacles represented by branches, electric wires, crops with prominent growth vigor and the like are often encountered during the operation. A single sensor can only obtain local, one-sided environmental characteristic information. And because the sensor is also influenced by the quality and performance of the sensor, the acquired information is often incomplete with large uncertainty. Adopt the multisensor obstacle detection means that laser radar and binocular vision combined together, follow certain distance and survey branch, electric wire, wire pole and the outstanding crops of growing trend in the farmland to in time carry out the early warning to the flier with the information of obstacle, let the flier in time make judgement and processing, guaranteed plant protection unmanned aerial vehicle's operation safety. Plant protection unmanned aerial vehicle barrier is surveyed and early warning system assists the flight hand and uses plant protection unmanned aerial vehicle to carry out the plant protection operation.
The invention is realized by the following technical scheme:
the invention relates to a barrier detection and early warning system for a plant protection unmanned aerial vehicle, which comprises a data transmission radio station device, a laser radar detection device and a binocular vision detection device, wherein the data transmission radio station device, the laser radar detection device and the binocular vision detection device are installed on the plant protection unmanned aerial vehicle. Laser radar detection device installation is located plant protection unmanned aerial vehicle's ventral below, two mesh visual detection device installations are located plant protection unmanned aerial vehicle's top, and just to the direction of advance of fuselage, data transfer radio station device is installed in the safety cover at plant protection unmanned aerial vehicle top, laser radar detection device and two mesh visual detection device will gather and the data transmission who surveys to flight control system, flight control system received data and through data calculation and analysis after transmit to human-computer interaction terminal and ground basic station through data transfer radio station device, the flier passes through the human-computer interaction terminal and obtains the information about the barrier and in time handles.
The obstacle detection and early warning system for the plant protection unmanned aerial vehicle adopts a detection means combining vision and non-vision sensors, detects obstacles in a farmland environment from a certain distance, and performs obstacle early warning on a flyer, and the flyer timely makes judgment and processing when receiving early warning information, so that the operation safety of the plant protection unmanned aerial vehicle is ensured. The laser radar detection device has the advantages of long detection distance, high detection precision, no influence of weather conditions and the like, but the laser radar detection device does not have the function of visual identification and can only roughly judge the detected target according to the characteristics of the appearance and the like of the object detected by the laser radar detection device. The binocular vision detection device makes up the disadvantage of the laser radar detection device in the aspect of identifying the object, and can capture the details of the object, such as the characteristics of brightness, texture and the like. The obstacle detection and early warning system based on the laser radar detection device and the binocular vision detection device integrates the advantages of the two detection devices, can achieve the advantages of long detection distance, high precision and strong reliability, can accurately identify the obstacle target in the field, and can effectively cope with the complex operation environment in the field by combining the two detection devices. When the obstacle is in the effective range of the visual detection device, the binocular visual system acquires an image through the lens and transmits the image to the flight control system, and the flight control system performs data intelligent analysis on the image, so that whether the obstacle affects the operation safety of the plant protection unmanned aerial vehicle can be judged, and if the obstacle affects the operation safety of the plant protection unmanned aerial vehicle, the system informs a flying hand through the human-computer interaction terminal. When the laser radar detection device identifies the obstacle in the detection range, the laser radar detection device transmits information of the obstacle (such as parameters of the distance, the direction, the height, the speed, the attitude, the shape and the like of the obstacle) to the flight control system, the flight control system receives data and judges whether the obstacle affects the operation safety of the plant protection unmanned aerial vehicle through data intelligent analysis, the laser radar detection device detects the current distance between the obstacle and the plant protection unmanned aerial vehicle, then the flight control system intelligently sends out corresponding control instructions and carries out corresponding processing (such as intelligently controlling the speed of the plant protection unmanned aerial vehicle, planning an obstacle avoidance path and making a corresponding obstacle avoidance attitude), a flying hand on the ground receives the obstacle information through a human-computer interaction system and timely makes judgment and processing, and the operation safety of the unmanned aerial vehicle is ensured.
The invention relates to a plant protection unmanned aerial vehicle barrier detection and early warning system, which adopts a detection means combining vision and non-vision sensors, and has the difficulty that information fusion is realized by combining a laser radar detection device and a binocular vision detection device. The information fusion can be divided into pixel layer fusion, feature layer fusion and decision layer fusion from a low layer to a high layer according to different fusion levels. The pixel layer fusion is to fuse the original information of various sensors according to the acquired original information, the information of the original data layer is comprehensively analyzed before the original data layer is not processed, the original data is not processed, so the data calculation amount is large, the real-time performance is poor, the original data contains certain noise, the anti-interference capability is not high, but the pixel level fusion reflects the essential information of an object, and the field data information is kept as much as possible. Since the pixel level fusion is fused on the original information, it belongs to the lowest level fusion. The feature layer fusion is to perform optimization processing on the collected sensor original information, extract useful feature information easy to identify, and process the feature information from a plurality of sensors after processing improvement, such as weighting processing operation, and is intermediate-level fusion. The target state fusion and the target characteristic fusion both belong to a feature level fusion mode. The target state fusion is mainly used for target tracking, the target characteristic fusion is essentially to cascade characteristic information to identify a target, and before the characteristic fusion of multiple sensors, the selection of characteristics is particularly important, because the characteristics are raw materials for identification, the detection result is directly influenced by the quality of characteristic extraction, so that the characteristic information is analyzed and processed before the characteristic fusion, useless characteristic information is filtered, meaningful characteristics are reserved, the advantages of realizing characteristic information compression, reserving main characteristics and facilitating real-time processing are achieved. Decision-level fusion is different from pixel-level fusionAnd (4) merging and feature level fusion, namely performing judgment on various sensors respectively, and then fusing judgment results of the sensors to provide a final comprehensive decision scheme. The decision-level fusion belongs to high-level fusion, the decision-level fusion result directly influences the decision-level height, the preprocessing cost is high, but compared with pixel-level fusion and feature-level fusion, the method has the advantages of minimum calculated amount, strong anti-interference capability and low dependence on a sensor, and improves the real-time performance and certain fault-tolerant capability of the system. The method has the advantages of small decision-level fusion calculated amount and strong anti-interference capability, and combines detection results of the laser radar and the visual detection of the target barrier to finally give a corresponding decision to determine the final position of the target barrier. Two methods of laser radar and camera decision-level fusion are generally available, namely a visual laser radar cross validation method and a visual validation laser radar detection method. The vision and laser radar cross validation method is characterized in that a vision sensor and a laser radar sensor respectively adopt corresponding methods to simultaneously detect a target, then the three-dimensional coordinate position of the target detected under a laser radar coordinate system is calculated and projected to a corresponding point on an image according to the camera and laser radar standard parameters, and then the final position of the target is comprehensively judged and determined by combining the vision detection result. The detection results of the vision and the laser radar are synthesized through cross validation, so that the detection accuracy is high, but the laser radar is required to scan a video frame for target detection while detecting a target, so that the calculation amount is large. The method for detecting vision verification laser radar includes utilizing laser radar to detect target, projecting three-dimensional coordinate position of laser radar detected target onto image, drawing out certain size ROI (region of interest) area at projection point position, using vision detection method to detect target at ROI of projection point position, comparing with method for cross-verifying vision and laser radar, because vision detection does not scan whole video frame, only using ROI area drawn out by projection point position to scan calculation, calculation amount is small, but if calibration error of laser radar and camera is larger, ROI area drawn out by projection point position may be scannedThe method does not contain targets, has low detection accuracy, and is easy to miss detection of the targets when the targets are more and too concentrated. In consideration of the advantages and disadvantages of the cross validation method and the vision validation laser radar detection method, the invention adopts the information fusion mode of the vision and laser radar cross validation, the laser radar detection and the vision detection are not completed step by step, the laser radar detection and the vision detection are simultaneously carried out on the whole frame image, the laser radar projects the detected target to the corresponding position of the image, and the target which contains the obstacle is comprehensively judged according to the position of the projection point and the position of the vision detection target by combining the vision detection resultR(uR,vR) The position of the center point of the visual detection target is pc(uc,vc) The specific fusion of the two cases is divided into three cases: 1) if the laser radar and the binocular vision simultaneously detect the target and the projection position of the detection target of the laser radar, the position of the detection target of the binocular vision and the position of the saliency detection target are matched, directly determining the position of the target, namely the detection results of the laser radar and the saliency detection target; 2) if the detection results of the laser radar and the binocular vision saliency are not consistent, continuously detecting 10 frames by the laser radar, and if the projection position of the target detected by more than or equal to 5 frames in the 10 frames is matched with the binocular vision saliency detection, determining the target position, namely the target position determined by more than or equal to 5 frames in the 10 frames of the laser radar; 3) if the two conditions are not the first two conditions, the laser radar and the binocular vision continue to detect. Judging the central projection point p of the target detected by the laser radarR(uR,vR) And the target center point position p of the visual detectionc(uc,vc) Whether there is a match is determined by the following equation: c (P)R,PC)＝α|μR-μC|+β|νR-νCWherein the parameters alpha and beta are respectively the transverse weight and the longitudinal weight of the image, when the laser radar three-dimensional data is projected on the image, the transverse error is large, so that the alpha and the beta are endowed with different weights respectivelyα is 0.5, β is 1, and a threshold C is givenT(CTIs 50), if C (p)R,pC) Not more than CTThe lidar and the target of the visual detection are considered to be matched, otherwise, the lidar and the target of the visual detection are not matched.
The invention relates to a plant protection unmanned aerial vehicle obstacle detection and early warning system, which adopts a control method, wherein a Task management system (TCS) is designed based on a Python programming language, the TCS relies on ROS to carry out flight Task management on a plant protection unmanned aerial vehicle and comprises a plurality of Python files, wherein, Main is a master file which is responsible for ROS node establishment, pre-takeoff state detection of the plant protection unmanned aerial vehicle, flight mode selection, autonomous take-off and landing control of the plant protection unmanned aerial vehicle, autonomous detection of obstacles of the plant protection unmanned aerial vehicle and early warning of the plant protection unmanned aerial vehicle, on the other hand, all flight tasks are extracted by analyzing the Task _ List.txt file and corresponding files are sequentially called to carry out the plant protection unmanned aerial vehicle flight tasks and simultaneously monitor the execution progress of the flight tasks, the TCS _ Utility.py file is responsible for maintaining the connection between a ground base station and the flight control system and monitoring the execution progress of each flight Task, and each row of the content of the Task _ List.txt.flight control file represents one flight Task, the method comprises the steps that relative coordinates of a target point and a takeoff position and hovering time serve as parameters, a Task _ Local _ Goto. py is responsible for controlling the plant protection unmanned aerial vehicle to fly to the target position from the current position at a set speed in a straight line mode, namely the position close to an obstacle, according to current Task parameters, the Task _ Simple _ Hover. py is responsible for controlling the unmanned aerial vehicle to precisely hover in front of the obstacle and give out early warning to a ground flyer according to the current Task parameters, the flyer can expand functions of a Task management system according to the method, and a specific flight Task is formulated by changing Task list parameters, so that the method can cope with complex operation environments of farmlands.
According to the invention, the barrier can be detected in all directions through the binocular vision detection device for long-distance detection and then the laser radar detection device for short-distance detection, early warning is given to the flyer in time, and the operation safety of the plant protection unmanned aerial vehicle can be ensured.
The plant protection unmanned aerial vehicle system comprises a human-computer interaction terminal, a flight control system, a plant protection unmanned aerial vehicle signal acquisition and data operation processing system, a flight control system and a ground base station, wherein the human-computer interaction terminal can display barrier information encountered in operation of the plant protection unmanned aerial vehicle and receive early warning information, the flight control system has data operation capability and data acquisition and scheduling capability, the flight control system is communicated with other airborne systems (for example, an inertia measurement unit, an accelerometer, a gyroscope, a magnetic compass and a GPS device of the plant protection unmanned aerial vehicle) and can acquire plant protection unmanned aerial vehicle signals (for example, the three-dimensional position, the three-dimensional speed, the three-dimensional acceleration, the three-axis angle, the three-axis angular speed and the like of.
Laser radar detection device has laser radar three-dimensional imaging and 3D intelligent analysis's function, can obtain distance, position, height, speed, gesture, shape isoparametric of barrier, can realize the three-dimensional discernment to closely barrier, thereby can judge intelligently whether the place ahead barrier influences plant protection unmanned aerial vehicle's operation safety and let plant protection unmanned aerial vehicle's flying hand in time make judgement and processing. The binocular vision detection device can obtain depth information and a three-dimensional model of a scene in real time, has the functions of image acquisition, camera calibration, feature extraction, image matching and three-dimensional reconstruction, and can realize remote obstacle identification.
Data pass radio station device can receive the control command that the flying hand passed for plant protection unmanned aerial vehicle through man-machine interaction terminal and comes scheduling information and other data of ground basic station, and data pass radio station device upload unmanned aerial vehicle position data and farmland environmental information and give ground basic station, and the man-machine interaction terminal of flying hand can receive image data and laser radar data about the barrier that comes from plant protection unmanned aerial vehicle to survey.
The ground base station can know the condition of the plant protection unmanned aerial vehicle carrying out plant protection operation in the field and direct the operation of the plant protection unmanned aerial vehicle, and the ground base station can monitor the current flight route and flight state of the plant protection unmanned aerial vehicle in real time and carry out real-time scheduling on the plant protection unmanned aerial vehicle. The special air route planning tool plans the flight route of the plant protection unmanned aerial vehicle, sets the flight height, the flight speed, the flight place, the flight mission and the like, and compiles and transmits the mission data to the flight control system through a data transmission radio station device connected with a data port.
The working realization principles of the data transmission radio station device, the laser radar detection device, the binocular vision detection device and the ground base station are all the prior art.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the obstacle detection and early warning system for the plant protection unmanned aerial vehicle adopts a detection means combining vision and non-vision sensors to detect obstacles from a long distance to a short distance, and ensures the operation safety of the plant protection unmanned aerial vehicle.
2. The obstacle detection and early warning system for the plant protection unmanned aerial vehicle can realize the three-dimensional identification of short-distance and long-distance obstacles, can intelligently judge whether the obstacles affect the operation safety of the plant protection unmanned aerial vehicle, can early warn a flyer in time according to the information of the obstacles, and can adapt to the complex farmland operation environment.
3. The obstacle detection and early warning system for the plant protection unmanned aerial vehicle, disclosed by the invention, is based on the combination of vision and non-vision sensors, so that the safety of plant protection operation is also improved, and multiple possibilities are provided for the realization of autonomous spraying and intelligent navigation.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
fig. 1 is a front view of the plant protection unmanned aerial vehicle obstacle detection and early warning system of the present invention;
fig. 2 is a top view of the plant protection unmanned aerial vehicle obstacle detection and early warning system of the present invention;
FIG. 3 is a schematic diagram of the operation of the present invention;
fig. 4 is a schematic diagram of the working principle of the present invention.
Reference numbers and corresponding part names in the drawings:
1-plant protection unmanned aerial vehicle paddle, 2-binocular vision detection device, 3-laser radar detection device, 4-airborne other system, 5-data transmission radio station device, 6-flight control system, 7-human-computer interaction terminal, 8-ground base station
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Example of the implementation
As shown in fig. 1 and 2, the obstacle detection and early warning system for the plant protection unmanned aerial vehicle comprises a data transmission radio station device 5, a laser radar detection device 3 and a binocular vision detection device 2 which are installed on the plant protection unmanned aerial vehicle, wherein the data transmission radio station device 5, the laser radar detection device 3 and the binocular vision detection device 2 are respectively in signal connection with a flight control system 6 of the plant protection unmanned aerial vehicle.
Further, the other onboard systems 4 of the plant protection unmanned aerial vehicle are also in signal connection with the flight control system 6. The other airborne systems are an inertial measurement unit, an accelerometer, a gyroscope, a magnetic compass and a GPS device of the plant protection unmanned aerial vehicle.
Laser radar detection device 3, binocular vision detection device 2 send the data of gathering or surveying to plant protection unmanned aerial vehicle's flight control system 6, and flight control system 6 received data transmits to ground basic station and man-machine interaction terminal after intelligent analysis.
The obstacle detection and early warning system for the plant protection unmanned aerial vehicle adopts a mode of combining the vision sensor and the non-vision sensor to detect the plant protection unmanned aerial vehicle and field obstacles (such as branches, electric wires and crops with prominent growth vigor) which are operated from a long distance to a short distance in an all-around mode, can perform early warning on obstacles for a flyer under the condition that the obstacles appear, assists the flyer to make judgment and processing conditions in time, and guarantees the operation safety of the plant protection unmanned aerial vehicle. When the plant protection unmanned aerial vehicle works, a data transmission radio station device can receive a control instruction transmitted to the plant protection unmanned aerial vehicle by a flyer through a human-computer interaction terminal and data from a ground base station, the data transmission radio station device can upload position data of the plant protection unmanned aerial vehicle and information of obstacles to the human-computer interaction terminal and the ground base station, when a plurality of plant protection unmanned aerial vehicles work in the field, the data transmission radio station device on the plant protection unmanned aerial vehicle can be used for mutually communicating to know the distance and the speed of the two plant protection unmanned aerial vehicles and transmitting the data to a flight control system, the flight control system can inform the flyer through the human-computer interaction terminal after data calculation and data analysis, the ground base station can receive the data transmitted by the unmanned aerial vehicles and monitor and schedule the plant protection unmanned aerial vehicle which carries out plant protection work in the; when obstacles (such as telegraph poles, branches, electric wires and crops with prominent growth vigor) appear in the effective range of the binocular vision detection device, the binocular vision detection device performs image acquisition, camera calibration, feature extraction, image matching and three-dimensional reconstruction through a lens, transmits image data to a flight control system, the flight control system calculates the image data and performs data analysis, and determines whether the obstacles affect the operation safety of the plant protection unmanned aerial vehicle or not by combining with the interaction of information received by a laser radar detection device, if the obstacles affect the operation safety of the plant protection unmanned aerial vehicle, the plant protection unmanned aerial vehicle informs a flying hand through a human-computer interaction terminal and stores the image data; when the laser radar detection device discerned the barrier in detection range, through detection device with the 3D data transmission of barrier to flight control system, flight control system receives behind the data through data analysis and the mutual affirmation of combining two mesh vision detection device received information and judges whether this barrier influences plant protection unmanned aerial vehicle's operation safety, through the data that obtains from laser radar detection device, reachs this barrier and plant protection unmanned aerial vehicle's current distance, flight control system makes corresponding processing and tells ground flight hand and ground basic station with the information of barrier through man-machine interaction terminal.
According to the invention, the barrier encountered by field operation can be detected in all directions through the binocular vision detection device for long-distance detection and then the laser radar detection device for short-distance detection, the information of the barrier is used for early warning the ground flyer, the safety of plant protection operation can be improved through the combination of the vision sensor and the non-vision sensor, and multiple possibilities are provided for the realization of autonomous spraying and intelligent navigation.
Preferably, the human-computer interaction terminal can display obstacles encountered in the operation of the plant protection unmanned aerial vehicle and send out early warning, the flight control system has data operation capacity and data acquisition scheduling capacity, the flight control system can be communicated with other airborne systems (the airborne systems in the position refer to systems carried by the existing plant protection unmanned aerial vehicle), the acquisition of signals (such as three-dimensional position, three-dimensional speed, three-dimensional acceleration, three-axis angle, three-axis angular speed and the like) of the plant protection unmanned aerial vehicle can be realized, the flight control system can realize data acquisition and data operation processing of other airborne systems, data processing results are transmitted to the human-computer interaction terminal and the ground base station, the pilot sends a control instruction to the plant protection unmanned aerial vehicle through the human-computer interaction terminal, and the ground base station can control and schedule the plant protection unmanned aerial vehicle.
Preferably, laser radar detection device has laser radar three-dimensional imaging and 3D data analysis function, can realize closely the three-dimensional discernment of barrier, thereby can judge intelligently whether the place ahead barrier influences plant protection unmanned aerial vehicle's operation safety and carry out the early warning to plant protection unmanned aerial vehicle's flyer for the flyer makes judgement and processing more in time. The binocular vision detection device can obtain scene depth information and a three-dimensional model in real time, has the functions of image acquisition, camera calibration, feature extraction, image matching and three-dimensional reconstruction, and can realize remote obstacle identification.
Preferably, data transfer radio station device has WIFI and 3G 4G 5G wireless communication function, can transmit the plant protection unmanned aerial vehicle situation to the human-computer interaction terminal of ground basic station and flywheel, thereby the flywheel accessible human-computer interaction terminal sends control command control plant protection unmanned aerial vehicle and avoids the barrier, and ground basic station does also assign the order and inform the message to plant protection unmanned aerial vehicle through wireless network.
Preferably, the data transfer radio station device can receive the control command that the man-machine interaction terminal of flying hand passed to plant protection unmanned aerial vehicle and the data that comes from the basic station, uploads unmanned aerial vehicle position data and gives the basic station, and the man-machine interaction terminal of flying hand can receive image data and the laser radar data that come from unmanned aerial vehicle data transfer radio station device.
Preferably, the human-computer interaction terminal has the state that distance shows, speed shows, height display, manual setting unmanned aerial vehicle, barrier information display sets up, barrier early warning information sets up, can report whether the condition of the barrier that meets in the plant protection operation and this barrier can influence plant protection unmanned aerial vehicle's operation safety, and flyer's accessible human-computer interaction terminal sends control command to plant protection unmanned aerial vehicle.
Example of obstacle avoidance
The obstacle avoidance example is mainly explained in an algorithm, as shown in fig. 3, the distance between the unmanned aerial vehicle and the obstacle is measured by the plant protection unmanned aerial vehicle obstacle detection system, a specific implementation manner in the code is distance ═ pulseIn (echo pin, HIGH)/58, where the pulseIn measures the time that the laser passes from transmission to reception, and the distance information in each direction can be obtained through the I/O port. The method comprises the following steps of generating channel values corresponding to all channels of PIXHAWK flight control through an obstacle avoidance system, and then converting the channel values into PWM signals to realize the obstacle avoidance of the unmanned aerial vehicle: first, calling a map function in the Arduino rudder engine library Servo maps a PWM signal to be generated to a channel value in the range of [ 1000, 2000 ], and for the first channel Roll (Roll), when the channel value is equal to 1500, that is, Roll _ OUT is 1500, the drone is in a stationary state in the left-right direction. Likewise, for the second channel Pitch (Pitch), when Pit _ OUT is 1500, the drone is at rest in the fore-aft direction. When the channel value is larger than 1500, no person can fly to the right and the front, otherwise, the person can fly to the left and the rear. In addition, when the laser radar module detects that there is the barrier below, directly make Throttle (Throttle) channel value equal to 1800 (through many times of experimental verification), unmanned aerial vehicle keeps away the barrier effect obvious this moment. The switching of the flight mode is completed by means of the channel value change of the fifth channel, such as switching to the cruise mode when the channel value is less than 1500 and Aux _ OUT is less than 1500, and switching to the fixed point mode when the channel value is more than 1500. Set up safe distance in keeping away barrier system and be 500cm, when distance <500, unmanned aerial vehicle is outside safety range this moment, needs keep away the barrier. 1) When the plant protection unmanned aerial vehicle detection device detects that an obstacle (a telegraph pole in a field) exists in front of or behind the unmanned aerial vehicle, the Aux _ OUT is enabled to be more than 1500, the unmanned aerial vehicle is in a fixed-point mode at the moment, then the channel value Pit _ OUT of a pitch channel is enabled to be 1400 (1600) and the channel value Thr _ OUT of an accelerator channel is enabled to be 1800, then the channel values of the second channel and the third channel are converted into PWM signals and sent to a PPM encoder through calling functions pit.WriteMicrosconds (Pit _ OUT) and Thr.WriteMicrosconds (Thr _ OUT), the PPM signals are sent to a PIXHAWK signal receiving module after being encoded by the PPM encoder, and the PIAWK processor controls the unmanned aerial vehicle to fly backwards (forwards) and upwards until the obstacle is bypassed.
2) When the plant protection unmanned aerial vehicle detection device detects that an obstacle exists on the left (right) side of the unmanned aerial vehicle, Aux _ OUT is more than 1500, the unmanned aerial vehicle is in a fixed-point mode at the moment, then a channel value Rol _ OUT of a roll channel is 1600(1400) and a channel value Thr _ OUT of an accelerator channel is 1800, then channel values of a first channel and a third channel are converted into PWM signals through calling functions Rol.writemicseconds (Rol _ OUT) and Thr.writemicseconds (Thr _ OUT), the PWM signals are sent to a PPM encoder, the PPM signals are sent to a PIXHAWK signal receiving module after being encoded by the PPM encoder, and the PIXHAWK processor controls the unmanned aerial vehicle to fly backwards (forwards) and upwards after attitude calculation and position calculation until the obstacle is bypassed.
3) When the plant protection unmanned aerial vehicle detection device detects that an obstacle exists below the unmanned aerial vehicle, Aux _ OUT is more than 1500, the unmanned aerial vehicle is in a fixed-point mode at the moment, then a channel value Thr _ OUT of an accelerator channel is 1800, then a channel value of a third channel at the moment is converted into a PWM signal through a calling function Thr.write electromagnetic seconds (Thr _ OUT), the PWM signal is sent to a PPM encoder, the PPM signal is sent to a PIXHAWK signal receiving module after being encoded by the PPM encoder, and the PIAWK processor controls the unmanned aerial vehicle to automatically fly upwards after attitude calculation and position calculation to keep away from the obstacle. The attribute () function is called in the software design to achieve channel consistency, e.g., rol. attribute (8), i.e., PWM signal for controlling the left and right flight of the drone, is output through digital pin D8 of Arduino Mega 2560. The D8 is connected with the first channel of the PPM encoder, thereby ensuring the reliability of the signal output of the obstacle avoidance system.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (9)
1. A plant protection unmanned aerial vehicle barrier detection and early warning system is characterized by comprising a data transmission radio station device, a laser radar detection device and a binocular vision detection device, wherein the data transmission radio station device, the laser radar detection device and the binocular vision detection device are installed on a plant protection unmanned aerial vehicle; the device comprises a laser radar detection device, a binocular vision detection device, a data transmission radio station device, a laser radar detection device and a binocular vision detection device, wherein the laser radar detection device is arranged below the belly of the plant protection unmanned aerial vehicle, the binocular vision detection device is arranged at the top of the plant protection unmanned aerial vehicle and is just opposite to the advancing direction of a machine body, the data transmission radio station device is arranged in a protective cover at the top of the plant protection unmanned aerial vehicle, and the data transmission radio station device, the laser radar detection device and the binocular vision detection device are; the laser radar detection device and the binocular vision detection device send collected and detected data to the flight control system, the flight control system receives the data, the data are calculated and analyzed and then transmitted to the human-computer interaction terminal and the ground base station through the data transmission radio station device, and the flyer obtains information about obstacles through the human-computer interaction terminal and processes the information in time.
2. The plant protection unmanned aerial vehicle obstacle detection and early warning system of claim 1, wherein other systems onboard the plant protection unmanned aerial vehicle are also in signal connection with the flight control system; the other airborne systems are an inertial measurement unit, an accelerometer, a gyroscope, a magnetic compass and a GPS device of the plant protection unmanned aerial vehicle.
3. The plant protection unmanned aerial vehicle obstacle detection and early warning system of claim 1, wherein the lidar detection device has functions of lidar stereo imaging and 3D intelligent analysis, can obtain distance, orientation, height, speed, attitude and shape parameters of an obstacle, realizes stereo recognition of a close-range obstacle, and can intelligently judge whether the front obstacle affects the operation safety of the plant protection unmanned aerial vehicle so as to enable a flying hand of the plant protection unmanned aerial vehicle to make judgment and processing in time; the binocular vision detection device can obtain depth information and a three-dimensional model of a scene in real time, has the functions of image acquisition, camera calibration, feature extraction, image matching and three-dimensional reconstruction, and can realize remote obstacle identification.
4. The plant protection unmanned aerial vehicle obstacle detection and early warning system of claim 1, wherein the flight control system has a function of processing obstacle data collected by the laser radar detection device and the binocular vision detection device, and is capable of sending various instructions to the laser radar detection device, the binocular vision detection device and an unmanned aerial vehicle power system, an attitude control system and a communication system of the unmanned aerial vehicle, and receiving instructions from a ground base station or a human-computer interaction terminal.
5. The plant protection unmanned aerial vehicle obstacle detection and early warning system of claim 1, wherein the data transfer radio device has a function of communicating with the human-computer interaction terminal and the ground base station, and is capable of transmitting obstacle data to the human-computer interaction terminal and receiving a control command of the unmanned aerial vehicle from the human-computer interaction terminal; the data transfer station apparatus is capable of transmitting the obstacle data to the ground base station and receiving the scheduling data from the ground base station.
6. The plant protection unmanned aerial vehicle obstacle detection and early warning system of claim 1, wherein the ground base station can plan the flight line of the plant protection unmanned aerial vehicle, set the flight altitude, flight speed, flight place and flight mission, and compile and transmit mission data to the flight control system through the data transmission radio station device.
7. The plant protection unmanned aerial vehicle obstacle detection and early warning system of claim 1, wherein the data calculation and analysis steps are: assuming that the position of the target central point detected by the laser radar projected on the image is pR(uR,vR)，pR(uR,vR) Is a projection point of the radar target; the position of the center point of the visual detection target is pc(uc,vc) The specific fusion of the two cases is divided into three cases: 1) if the laser radar and the binocular vision simultaneously detect the target and the projection position of the detection target of the laser radar, the position of the detection target of the binocular vision and the position of the saliency detection target are matched, directly determining the position of the target, namely the detection results of the laser radar and the saliency detection target; 2) if the detection results of the laser radar and the binocular vision saliency are not consistent, continuously detecting 10 frames by the laser radar, and if the projection position of the target detected by more than or equal to 5 frames in the 10 frames is matched with the binocular vision saliency detection, determining the target position, namely the target position determined by more than or equal to 5 frames in the 10 frames of the laser radar; 3) if the two conditions are not the first two conditions, the laser radar and the binocular vision continue to detect.
8. The plant protection unmanned aerial vehicle obstacle detection and early warning system of claim 1, wherein it is determined that lidar detected target center projection point pR(uR,vR) And the target center point position p of the visual detectionc(uc,vc) Whether there is a match is determined by the following equation: c (P)R,PC)＝α|μR-μC|+β|νR-νCWhere the parameters α and β are the image horizontal weight and the vertical weight, respectively, and the horizontal error is large when the lidar three-dimensional data is projected onto the image, so α and β are given different weights, α is 0.5, β is 1, and a threshold C is givenT，CTIs 50, if C (p)R,pC) Not more than CTThe lidar and the target of the visual detection are considered to be matched, otherwise, the lidar and the target of the visual detection are not matched.
9. The plant protection unmanned aerial vehicle obstacle detection and early warning system of claim 1, wherein the following control method is adopted: a Task management system (TCS) is designed based on a Python programming language, the TCS relies on ROS to carry out flight Task management on a plant protection unmanned aerial vehicle and comprises a plurality of Python files, wherein main is a master file which is responsible for ROS node establishment, pre-takeoff state detection, flight mode selection, autonomous take-off and landing control of the plant protection unmanned aerial vehicle, autonomous detection of obstacles of the plant protection unmanned aerial vehicle and early warning of obstacles of the plant protection unmanned aerial vehicle on one hand, on the other hand, all flight tasks are extracted by analyzing a Task _ List.txt file and corresponding files are sequentially called to carry out the flight tasks of the plant protection unmanned aerial vehicle, the execution progress of the flight tasks is monitored, the TCS _ Utility.py file is responsible for maintaining connection between a ground base station and a flight control system and monitoring the execution progress of each flight Task, each line of the content of the Task _ List.txt file represents one flight Task, and the relative coordinates and the hovering time of a target point and a takeoff position are taken as parameters, the Task _ Local _ goto.py is responsible for controlling the plant protection unmanned aerial vehicle to fly to the position close to the obstacle from the current position at a set speed in a straight line mode according to the current Task parameters, the Task _ Simple _ lever.py is responsible for controlling the unmanned aerial vehicle to accurately hover in front of the obstacle and give an early warning to a ground flyer according to the current Task parameters, the flyer can also expand the functions of a Task management system according to the method, and set a specific flight Task by changing the parameters of a Task list so as to cope with the complex operation environment of the farmland.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010560491.0A CN111796602A (en) | 2020-06-18 | 2020-06-18 | Plant protection unmanned aerial vehicle barrier is surveyed and early warning system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010560491.0A CN111796602A (en) | 2020-06-18 | 2020-06-18 | Plant protection unmanned aerial vehicle barrier is surveyed and early warning system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111796602A true CN111796602A (en) | 2020-10-20 |
Family
ID=72803119
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010560491.0A Pending CN111796602A (en) | 2020-06-18 | 2020-06-18 | Plant protection unmanned aerial vehicle barrier is surveyed and early warning system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111796602A (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112298555A (en) * | 2020-10-31 | 2021-02-02 | 夏喜明 | A5G unmanned aerial vehicle for high accuracy survey and drawing |
| CN112987014A (en) * | 2021-02-20 | 2021-06-18 | 纳瓦电子（上海）有限公司 | Vehicle and detection system and detection method thereof |
| CN113093792A (en) * | 2021-03-25 | 2021-07-09 | 上海工程技术大学 | Underground space detection system based on unmanned aerial vehicle |
| CN113759947A (en) * | 2021-09-10 | 2021-12-07 | 中航空管系统装备有限公司 | Airborne flight obstacle avoidance auxiliary method, device and system based on laser radar |
| CN113917946A (en) * | 2021-11-18 | 2022-01-11 | 上海顺诠科技有限公司 | Unmanned aerial vehicle automatic spraying operation system and method based on dynamic adjustment early warning range |
| CN115616578A (en) * | 2022-12-05 | 2023-01-17 | 成都航空职业技术学院 | Radar detection method and device for unmanned aerial vehicle |
| CN116820132A (en) * | 2023-07-06 | 2023-09-29 | 杭州牧星科技有限公司 | Flight obstacle avoidance early warning prompting method and system based on remote vision sensor |
| CN117092641A (en) * | 2023-10-20 | 2023-11-21 | 江苏翰林正川工程技术有限公司 | Information fusion method based on radar ranging and image recognition |
| CN117472082A (en) * | 2023-12-26 | 2024-01-30 | 众芯汉创(江苏)科技有限公司 | Unmanned aerial vehicle route generation method and device based on AI vision assistance |
| CN113917946B (en) * | 2021-11-18 | 2024-05-10 | 上海顺诠科技有限公司 | Unmanned aerial vehicle automatic spraying operation system and method based on dynamic adjustment early warning range |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107608371A (en) * | 2016-07-12 | 2018-01-19 | 何守印 | Four rotor automatic obstacle avoiding unmanned plane under the environment of community in urban areas |
| CN110007313A (en) * | 2019-03-08 | 2019-07-12 | 中国科学院深圳先进技术研究院 | Obstacle detection method and device based on unmanned plane |
-
2020
- 2020-06-18 CN CN202010560491.0A patent/CN111796602A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107608371A (en) * | 2016-07-12 | 2018-01-19 | 何守印 | Four rotor automatic obstacle avoiding unmanned plane under the environment of community in urban areas |
| CN110007313A (en) * | 2019-03-08 | 2019-07-12 | 中国科学院深圳先进技术研究院 | Obstacle detection method and device based on unmanned plane |
Non-Patent Citations (3)
| Title |
|---|
| 张香利: ""基于激光雷达和视觉信息融合的水面船只检测与识别研究"", 《上海交通大学工学硕士学位论文》 * |
| 樊宝安 等: ""四旋翼无人机巡航飞行模式下的自主避障方案设计"", 《电子设计工程》 * |
| 王大帅 等: ""基于多传感器融合的无人机精准自主飞行控制方法"", 《农业机械学报》 * |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112298555A (en) * | 2020-10-31 | 2021-02-02 | 夏喜明 | A5G unmanned aerial vehicle for high accuracy survey and drawing |
| CN112987014A (en) * | 2021-02-20 | 2021-06-18 | 纳瓦电子（上海）有限公司 | Vehicle and detection system and detection method thereof |
| CN113093792A (en) * | 2021-03-25 | 2021-07-09 | 上海工程技术大学 | Underground space detection system based on unmanned aerial vehicle |
| CN113759947B (en) * | 2021-09-10 | 2023-08-08 | 中航空管系统装备有限公司 | Airborne flight obstacle avoidance assisting method, device and system based on laser radar |
| CN113759947A (en) * | 2021-09-10 | 2021-12-07 | 中航空管系统装备有限公司 | Airborne flight obstacle avoidance auxiliary method, device and system based on laser radar |
| CN113917946A (en) * | 2021-11-18 | 2022-01-11 | 上海顺诠科技有限公司 | Unmanned aerial vehicle automatic spraying operation system and method based on dynamic adjustment early warning range |
| CN113917946B (en) * | 2021-11-18 | 2024-05-10 | 上海顺诠科技有限公司 | Unmanned aerial vehicle automatic spraying operation system and method based on dynamic adjustment early warning range |
| CN115616578A (en) * | 2022-12-05 | 2023-01-17 | 成都航空职业技术学院 | Radar detection method and device for unmanned aerial vehicle |
| CN116820132A (en) * | 2023-07-06 | 2023-09-29 | 杭州牧星科技有限公司 | Flight obstacle avoidance early warning prompting method and system based on remote vision sensor |
| CN116820132B (en) * | 2023-07-06 | 2024-01-09 | 杭州牧星科技有限公司 | Flight obstacle avoidance early warning prompting method and system based on remote vision sensor |
| CN117092641A (en) * | 2023-10-20 | 2023-11-21 | 江苏翰林正川工程技术有限公司 | Information fusion method based on radar ranging and image recognition |
| CN117092641B (en) * | 2023-10-20 | 2023-12-15 | 江苏翰林正川工程技术有限公司 | Information fusion method based on radar ranging and image recognition |
| CN117472082A (en) * | 2023-12-26 | 2024-01-30 | 众芯汉创(江苏)科技有限公司 | Unmanned aerial vehicle route generation method and device based on AI vision assistance |
| CN117472082B (en) * | 2023-12-26 | 2024-03-22 | 众芯汉创(江苏)科技有限公司 | Unmanned aerial vehicle route generation method and device based on AI vision assistance |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111796602A (en) | Plant protection unmanned aerial vehicle barrier is surveyed and early warning system | |
| JP7274674B1 (en) | Performing 3D reconstruction with unmanned aerial vehicle | |
| CN109029422B (en) | Method and device for building three-dimensional survey map through cooperation of multiple unmanned aerial vehicles | |
| CN206691107U (en) | Pilotless automobile system and automobile | |
| CN108496129B (en) | Aircraft-based facility detection method and control equipment | |
| CN110765894B (en) | Target detection method, device, equipment and computer readable storage medium | |
| CA2950791C (en) | Binocular visual navigation system and method based on power robot | |
| EP2413096B1 (en) | Ground-based videometrics guiding method for aircraft landing or unmanned aerial vehicles recovery | |
| CN112506222A (en) | Unmanned aerial vehicle intelligent obstacle avoidance method and device | |
| CN107161141A (en) | Pilotless automobile system and automobile | |
| CN108508916B (en) | Control method, device and equipment for unmanned aerial vehicle formation and storage medium | |
| CN212135234U (en) | A flight auxiliary device for transmission line patrols and examines unmanned aerial vehicle | |
| CN108469817B (en) | Unmanned ship obstacle avoidance control system based on FPGA and information fusion | |
| CN111045425A (en) | Auxiliary teleoperation driving method for ground unmanned vehicle | |
| CN112558608A (en) | Vehicle-mounted machine cooperative control and path optimization method based on unmanned aerial vehicle assistance | |
| CN115933754A (en) | Electric power inspection unmanned aerial vehicle obstacle avoidance method based on millimeter wave radar and binocular vision | |
| CN107783545A (en) | Post disaster relief rotor wing unmanned aerial vehicle obstacle avoidance system based on OODA ring multi-sensor information fusions | |
| CN116540784B (en) | Unmanned system air-ground collaborative navigation and obstacle avoidance method based on vision | |
| CN114564042A (en) | Unmanned aerial vehicle landing method based on multi-sensor fusion | |
| CN114578839B (en) | Unmanned aerial vehicle path calculation system and method based on big data | |
| CN113759947A (en) | Airborne flight obstacle avoidance auxiliary method, device and system based on laser radar | |
| CN109708659B (en) | Distributed intelligent photoelectric low-altitude protection system | |
| CN106598065A (en) | Binocular-supersonic fusion obstacle avoidance control method for unmanned aerial vehicles | |
| CN111476134A (en) | Geological survey data processing system and method based on augmented reality | |
| CN117472081B (en) | Unmanned aerial vehicle obstacle avoidance method based on perception constraint |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201020 |