CN112558608A - Vehicle-mounted machine cooperative control and path optimization method based on unmanned aerial vehicle assistance - Google Patents
Vehicle-mounted machine cooperative control and path optimization method based on unmanned aerial vehicle assistance Download PDFInfo
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Abstract
The invention relates to a vehicle-machine cooperative control and path optimization method based on unmanned aerial vehicle assistance, and belongs to the field of intelligent transportation and vehicle networking. The vehicle-mounted machine cooperative control comprises an unmanned aerial vehicle and a vehicle; the unmanned aerial vehicle comprises a wireless communication module, a visual sensor module, a GPS positioning module, an air pressure sensor and an Internet of vehicles V2X communication module; the vehicle comprises a wireless communication module, a vision sensor module, a GPS positioning module, a radar, a vehicle-mounted image server and a vehicle networking V2X communication module. The invention combines the close range of the vehicle and the remote traffic state perception of the unmanned aerial vehicle, plans a more reasonable path for the vehicle driving, and improves the driving efficiency and the safety of the vehicle.
Description
Technical Field
The invention belongs to the field of unmanned aerial vehicle control, intelligent transportation and vehicle networking, and relates to a vehicle-machine cooperative control and path optimization method based on unmanned aerial vehicle assistance.
Background
At present, the quantity of vehicles kept continuously increases, and traffic jam becomes an urgent problem to be solved. In the process of solving increasingly severe traffic problems, the development of electronic information technology provides new possibility for solving the problems in traffic transportation. In the prior art, the internet is used for providing traffic environment information for users, but the provided traffic environment information has certain hysteresis and is not timely enough to reflect sudden events.
Meanwhile, the unmanned aerial vehicle technology has been developed greatly in recent years, and a vision sensor carried by the unmanned aerial vehicle can acquire wider traffic environment information in real time. Therefore, if the unmanned aerial vehicle acquires wider traffic environment information with higher real-time property under the control of the vehicle and transmits the real-time traffic information to the vehicle through the wireless network, the vehicle plans the path of the vehicle together according to the acquired traffic environment information and the traffic environment information acquired by the unmanned aerial vehicle.
Disclosure of Invention
In view of this, the present invention provides a vehicle-mounted device cooperative control and path optimization method based on unmanned aerial vehicle assistance.
In order to achieve the purpose, the invention provides the following technical scheme:
a vehicle-machine cooperative control and path optimization method based on unmanned aerial vehicle assistance comprises an unmanned aerial vehicle and a vehicle;
the unmanned aerial vehicle comprises a wireless communication module, a visual sensor module, a GPS positioning module, an air pressure sensor and an Internet of vehicles V2X communication module;
the vehicle comprises a wireless communication module, a vision sensor module, a GPS positioning module, a radar, a vehicle-mounted image server and a vehicle networking V2X communication module;
the vehicle-mounted image server is used for processing images acquired by the unmanned aerial vehicle and the vehicle, wherein the unmanned aerial vehicle acquires a top view of the state of a front traffic road, the vehicle acquires a plan view, and structural information of the road and contour information of obstacles are obtained through fusion of the two views;
the unmanned aerial vehicle is provided with a wireless communication and V2X communication module and establishes communication connection with the vehicle through two modes, namely a wireless network and an LTE-V/DSRC;
the first mode is used for sending a control instruction to the unmanned aerial vehicle by the vehicle and feeding back remote road state image information to the vehicle by the unmanned aerial vehicle;
the second mode is used for sharing state information of the unmanned aerial vehicle and the vehicle; the unmanned aerial vehicle vision camera is positioned in front of the unmanned aerial vehicle, the angle of the camera is adjustable, and the camera has two degrees of freedom, namely horizontal degree and vertical degree of freedom; the visual image acquired by the unmanned aerial vehicle camera is sent to a vehicle-mounted image server through a UDP (user Datagram protocol) datagram; the unmanned aerial vehicle is provided with a GPS communication module for estimating the current position of the unmanned aerial vehicle; the unmanned aerial vehicle is carried with IMU, barometer sensor and is used for estimating unmanned aerial vehicle's gesture.
Optionally, the information obtained by the unmanned aerial vehicle through the vision sensor is sent to the vehicle in the communication range in real time, and the traffic environment information at a far position is collected and sent to the vehicle under the control of the vehicle;
the vehicle control unmanned aerial vehicle's removal to the traffic information in a certain region of pertinence acquisition, acquires traffic environment information through radar and vision sensor, and can handle traffic environment information, through handling the traffic environment information that self gathered with control vehicle safety go forward and through handling the traffic environment information that is gathered by unmanned aerial vehicle in order to carry out route planning and optimization.
Optionally, the path optimization method includes the following steps:
s1: the method comprises the steps that a vehicle sends a take-off instruction to an unmanned aerial vehicle, the unmanned aerial vehicle acquires remote road state information through the visual angle of a visual sensor after taking off and feeds the remote road state information back to the vehicle, and the vehicle starts to execute a track planning algorithm of the vehicle after receiving the road state information sensed by the unmanned aerial vehicle;
s2: the unmanned aerial vehicle acquires GPS position, speed and direction angle information of the vehicle through V2X communication, keeps flying in front of the vehicle, collects road state information and continuously provides a remote road state image for the vehicle;
s3: the vehicle-mounted image server acquires the visual information of the unmanned aerial vehicle and the vehicle-mounted visual information through a wireless network and a vehicle-mounted local area network respectively, and obtains lane structure information, barrier outline and position information through feature extraction and template matching;
s4: the vehicle-mounted control platform acquires road state information sensed by the vehicle-mounted laser radar through a vehicle-mounted local area network, performs coordinate axis conversion and converts data under a laser radar spherical coordinate system into a rectangular coordinate system;
s5: based on the visual images and the laser radar data in the steps S3 and S4, time and space reference alignment is carried out, multi-source data fusion is carried out based on D-S theory, and structural information of a road and position and contour information of a road obstacle are obtained;
s6: predicting and planning the driving path of the vehicle by adopting an LSTM and a fast random search tree algorithm, and obtaining the error of the driving track by comparing the predicted driving path with the expected driving path;
s7: optimizing the vehicle travel path on the basis of the trajectory error of step S6;
s8: the vehicle sends the instruction of returning a journey to unmanned aerial vehicle, and unmanned aerial vehicle descends platform sign according to vehicle GPS position and roof, through visual image locking descending point position to adjust unmanned aerial vehicle and the relative position and the flying height of car through PID control, finally realize vehicle trajectory planning and optimization algorithm.
Optionally, step S2 specifically includes:
the vehicle is provided with a GPS module and a V2X communication module, and the vehicle shares position, speed and direction angle information with the unmanned aerial vehicle through V2X communication; after the unmanned aerial vehicle acquires the information, the unmanned aerial vehicle obtains a control error by combining position, speed and direction angle information of the unmanned aerial vehicle, and the control error is used as the input of the tracking controller, so that the unmanned aerial vehicle moves along with the vehicle, and the remote road state image information is continuously provided for the vehicle.
Optionally, step S4 specifically includes:
obtaining distance information of obstacles in front of a traffic road through a laser radar; the vehicle-mounted control platform obtains the front obstacle information in real time through the online analysis of radar point cloud data, wherein the conversion relation from the laser radar measuring point coordinate to the rectangular coordinate system is as follows:
wherein the front of the radar is in the x direction, the left is in the y direction, the upper is in the z direction, d is the distance measured by the laser radar, alpha is the included angle between each scanning surface and the horizontal plane, and theta is the rotating angle of the laser radar.
Optionally, step S5 specifically includes:
transmitting far/near road structure and barrier information obtained after processing by the vehicle-mounted image server to a vehicle-mounted control platform through a vehicle-mounted local area network; and the control platform is combined with the laser radar data to further match the radar detection points with the visual images in the rectangular coordinate system, so that the real position of the midpoint in the three-dimensional space is restored, and the object reconstruction in the three-dimensional space is completed.
Optionally, step S6 specifically includes:
generating a smooth track meeting the vehicle running constraint, simultaneously adopting two methods of path prediction and planning to predict the path of the vehicle and plan the path of the vehicle, obtaining the error of the running track according to the difference value of the two methods, and taking the error as one of the bases for optimizing the vehicle running path.
Optionally, step S7 specifically includes:
the path optimization algorithm uses the state information of the vehicle, and comprises the following steps: the position x, the speed v and the acceleration a, the curvature gamma of the running track, the running time t and the energy consumption w are performance constraint indexes, and the following optimal control model is established:
wherein JiIs an objective function, and according to the optimal control model, the driving path planned in the step S6 is optimized, and the vehicle drives along the optimal path under the action of the controller.
Optionally, step S8 specifically includes:
the unmanned aerial vehicle positions the vehicle through the vehicle GPS position information; then, the unmanned aerial vehicle hovers over the vehicle, and the image information of the roof landing point is acquired through the onboard camera, so that the position of the roof landing point is accurately positioned; the image acquired by the unmanned aerial vehicle is transmitted to a vehicle-mounted image server through a high-bandwidth wireless network, and the vehicle-mounted image server feeds back the result to the unmanned aerial vehicle after processing the image; the unmanned aerial vehicle adjusts the position of the unmanned aerial vehicle based on the result and lands on the roof; the vehicle provides the electric quantity for unmanned aerial vehicle and supplements.
The invention has the beneficial effects that: 1. under the complicated traffic environment, the characteristic that the unmanned aerial vehicle has a wide field of vision is utilized, and the road state of a target area far away from a vehicle can be detected. Meanwhile, the vehicle-mounted vision sensor and the laser radar can sense the state of a short distance road. Therefore, the perception range of the vehicle can be expanded by combining the long-distance road state visual information perceived by the unmanned aerial vehicle and the short-distance road state information perceived by the vehicle through the visual sensor and the laser radar, and the pre-judging capability and safety of vehicle running are enhanced.
2. Through the combination of unmanned aerial vehicle and vehicle, for unmanned aerial vehicle provides reliable removal electric quantity supply platform, because unmanned aerial vehicle's duration is short, can not carry out the large-scale task that requires high to the time length, and the vehicle has the ability of removing the power supply, can provide long-time duration support for unmanned aerial vehicle, improves unmanned aerial vehicle's operation scope, and unmanned aerial vehicle and vehicle have formed the complementary relation of an advantage.
3. The unmanned aerial vehicle and the vehicle are combined, so that the vehicle can acquire remote road state information in real time, a data basis of path planning is provided for the vehicle through the vision of the unmanned aerial vehicle vision sensor and the vehicle and the perception data fusion of the laser radar sensor, optimal control and track prediction are combined, the generated road track is optimized, and the efficiency and the safety of vehicle running can be improved.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
Drawings
For purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a general architecture diagram of a vehicle-mounted machine cooperative control and path optimization system based on unmanned aerial vehicle assistance according to a preferred embodiment of the present invention;
FIG. 2 is a data flow diagram of a vehicle travel path planning and optimization method based on unmanned aerial vehicle assistance;
FIG. 3 is a flowchart of the steps executed in the method for planning and optimizing a vehicle driving path based on unmanned aerial vehicle assistance;
FIG. 4 is a schematic diagram of vehicle lidar angle data;
fig. 5 is a schematic diagram of coordinates of an unmanned aerial vehicle body;
FIG. 6 is a flow chart of the relationship between threads of the UAV and the execution of control instructions;
fig. 7 is a flowchart of the unmanned aerial vehicle fixed point landing algorithm.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not an indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes, and are not to be construed as limiting the present invention, and the specific meaning of the terms may be understood by those skilled in the art according to specific situations.
Please refer to fig. 1 to 7, which illustrate a vehicle-mounted device cooperative control and path optimization method based on the assistance of an unmanned aerial vehicle.
A vehicle-machine cooperative control and path optimization method based on unmanned aerial vehicle assistance is shown in fig. 1 as a system overall architecture diagram of an example of the method. The system is composed of a vehicle and an unmanned aerial vehicle, wherein the unmanned aerial vehicle and the vehicle are communicated through a wireless communication module and a DSRC module and cooperate with each other, so that the whole system can stably operate. The vehicle is equipped with wireless communication module, DSRC communication module, radar and vision sensor, and wherein wireless communication module is used for receiving the traffic information that is sent by unmanned aerial vehicle, and DSRC communication module is used for sending unmanned aerial vehicle control command, and radar and vision sensor obtain near the traffic information of current position jointly to keep away the barrier when the vehicle goes. The vehicle can realize storing national electronic maps, and the electronic maps contain longitude and latitude information of roads and non-roads for the navigation of the vehicle and the unmanned aerial vehicle. The unmanned aerial vehicle is equipped with a wireless communication module, a DSRC communication module and a vision sensor, wherein the wireless communication module is used for sending collected traffic conditions, the DSRC communication module is used for receiving control instructions sent by vehicles, and the vision sensor can collect video traffic conditions of a target area from the air.
As shown in fig. 2, a data flow diagram of a vehicle driving path planning and optimizing method based on unmanned aerial vehicle assistance is that, first, remote road traffic state information acquired by an unmanned aerial vehicle vision sensor is overlook information under a rectangular coordinate system, specifically includes road structure information and obstacle information on a road, and is transmitted to a vehicle-mounted image server through a wireless communication module; meanwhile, the vehicle also acquires the nearby road structure and the barrier information on the road through a vehicle-mounted laser radar and a vehicle-mounted vision sensor, and aligns with the space-time reference through coordinate system conversion; then, carrying out multisource data fusion of unmanned aerial vehicle and vehicle perception to obtain numerically quantized road structure information (width and curvature) and distance and contour information of obstacles on the road; further obtaining errors of the expected running path and the actual possible running path of the vehicle through a path planning and vehicle running track prediction algorithm; and finally, inputting the track error, the performance constraint indexes (time and path curvature) and the running path error into a path optimization algorithm to obtain the optimal running path of the vehicle.
The vehicle driving path planning and optimizing method based on unmanned aerial vehicle assistance executes steps, referring to an execution step flow chart of fig. 3, and the specific example is as follows:
step 1: in the vehicle driving process, the vehicle judges that the front road state is abnormal through the vehicle-mounted sensor, but the specific situation of the far traffic state cannot be known due to the limited sensing distance of the vehicle-mounted sensor, at the moment, the vehicle sends a flying instruction to the unmanned aerial vehicle, the unmanned aerial vehicle flies to the position area appointed by the vehicle after flying, the far road state information is obtained through the wide visual angle of the high-altitude visual sensor and is fed back to the vehicle through the wireless transmission module, and the vehicle starts to execute the track planning algorithm of the vehicle after receiving the road state information sensed by the unmanned aerial vehicle.
Step 2: in the process that the unmanned aerial vehicle explores traffic conditions in the front, the vehicle may move, at this moment, the unmanned aerial vehicle acquires the GPS position, speed and direction angle information of the vehicle through V2X communication, so that the unmanned aerial vehicle can move along the road along with the movement of the vehicle in a communication range, and thus the unmanned aerial vehicle can continuously provide road traffic information at a distance for the vehicle, the information is original traffic image information, and the vehicle can be warned after being processed by the vehicle-mounted image server.
And step 3: after the vehicle-mounted image server acquires the visual information of the unmanned aerial vehicle and the vehicle-mounted visual information through a wireless network and a vehicle-mounted local area network respectively, feature point extraction is carried out by adopting an HOG method, the image is grayed and subjected to gradient calculation, and further feature vector normalization is carried out to obtain a final feature vector. And comparing the feature vector with template vectors in a feature library, calculating the Euclidean distance of the feature vector, judging the best matching when the distance is the minimum, judging the type of the obstacle on the lane, and obtaining the size of the obstacle through a contour extraction algorithm. Through the judgment of the type of the obstacle, the vehicle can obtain the warning information in advance.
And 4, step 4: the vehicle-mounted control platform acquires road state information sensed by the vehicle-mounted laser radar through a vehicle-mounted local area network, performs coordinate axis conversion, converts data under a laser radar spherical coordinate system into a rectangular coordinate system, and defines laser radar angles as shown in FIG. 4, wherein a specific conversion formula is as follows:
wherein the front of the radar is in the x direction, the left is in the y direction, the upper is in the z direction, d is the distance measured by the laser radar, alpha is the included angle between the scanning surface and the horizontal plane, and theta is the rotation angle of the laser radar.
And 5: based on the visual image and the laser radar data in the steps 3 and 4, because the data acquisition frequencies of different sensors are different, the radar point cloud and the road image information acquired by the unmanned aerial vehicle and the vehicle are aligned in a space-time reference manner, firstly, the 3D radar point cloud data is mapped into a 2D radar point map through a formula (2),
where p ═ (x, y, z)TFor a point in three-dimensional space, (r, c) is its projection in 2D space, theta and alpha denote horizontal and vertical angles, as shown in fig. 4, and delta theta and delta alpha are the horizontal and vertical accuracies of the laser beam splitter. Further, multi-source data fusion is carried out by adopting D-S theory, and a 2D projection diagram of laser radar point cloud are combinedAnd assigning the color of the image to a radar point cloud picture according to the depth information acquired by the laser radar so as to obtain the structural information of the road and the position and contour information of the road surface obstacle.
Step 6: considering factors such as road structure, road surface obstacles, vehicle motion behavior constraints and vehicle current state information, predicting and planning a vehicle running path by adopting an LSTM algorithm and a fast random search tree algorithm respectively, and for predicting a vehicle running track, firstly processing time sequence data of vehicle running:
wherein T is processed data, s is original state data (position, speed and acceleration) of the vehicle, n is a data smoothing parameter which can be adjusted according to requirements, then processed time series data is used as an LSTM model to be input, model parameters are trained to obtain a prediction model of vehicle driving behaviors, and the model is adopted to predict vehicle driving tracks in the vehicle driving process; for planning the vehicle running path, a fast random search tree algorithm is adopted, random search is carried out according to the current position of the vehicle, the road structure, the position of an obstacle and other information to obtain a better running path, and smooth processing is carried out on the track to ensure that the running path meets the motion rule of vehicle running; and obtaining the error of the running track by comparing the predicted running path with the expected running path.
And 7: on the basis of the step 6, optimizing the vehicle running path by adopting an optimization method (4) according to the error of the running track,
wherein JiIs an objective function that is related to the state of vehicle travel, time constraints and energy consumption constraints, x is the vehicle position, v is the vehicle speed and a is the vehicle acceleration, and γ is the vehicleThe curvature of the running track of the vehicle, t is the running time of the vehicle, and w is the energy consumption of the vehicle. And taking the optimized vehicle running path as the input of the vehicle-mounted control platform, and controlling the vehicle to run according to the output of the vehicle control algorithm.
And 8: the coordinate of the unmanned aerial vehicle is defined as shown in fig. 5, that is, the front of the unmanned aerial vehicle is the positive direction of the y axis, the right is the positive direction of the x axis, and the lower is the positive direction of the z axis. Unmanned aerial vehicle's camera is located under unmanned aerial vehicle, and has two degrees of freedom of level and vertical motion, when carrying out the roof descending algorithm, unmanned aerial vehicle camera orientation is under, and unmanned aerial vehicle's spatial position coordinate and the spatial position coordinate of camera can be approximately regarded as the coincidence promptly. The edge distortion of the camera is not considered, the distance between the number of the image pixels and the measured object is approximately in a linear relation according to experimental data, namely the ratio of the number of the pixel points reduced to the number of the distance increased is a constant, and the constant is related to the physical structure of the camera and can be obtained through experimental calibration.
The relationship between the threads of the unmanned aerial vehicle control program and the execution flow of the control instruction are shown in fig. 6, wherein a specific roof landing algorithm flow chart is shown in fig. 7, the vehicle sends a return flight instruction to the unmanned aerial vehicle, the unmanned aerial vehicle locks the landing point position through a visual image according to the vehicle GPS position and the roof landing platform identification received by the V2X communication, and the roof landing algorithm is started to be executed. The unmanned aerial vehicle transmits the image detected by the camera to the vehicle-mounted server, the server obtains longitudinal deviation delta y and transverse deviation delta x of a landing point and an image center through a target detection algorithm, the longitudinal deviation delta y and the transverse deviation delta x are transmitted to the unmanned aerial vehicle through the wireless transmission module in the form of the number of pixel points, the unmanned aerial vehicle converts the linear relation between the pixel points and the length into a distance error, the relative position and the flying height of the unmanned aerial vehicle and a vehicle are adjusted through PID control, the vehicle finally lands on the roof, and the vehicle track planning and optimization algorithm is terminated.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all that should be covered by the claims of the present invention.
Claims (9)
1. A vehicle-machine cooperative control and path optimization method based on unmanned aerial vehicle assistance is characterized in that: the vehicle machine cooperative control system comprises an unmanned aerial vehicle and a vehicle:
the unmanned aerial vehicle comprises a wireless communication module, a visual sensor module, a GPS positioning module, an air pressure sensor and an Internet of vehicles V2X communication module;
the vehicle comprises a wireless communication module, a vision sensor module, a GPS positioning module, a radar, a vehicle-mounted image server and a vehicle networking V2X communication module;
the vehicle-mounted image server is used for processing images acquired by the unmanned aerial vehicle and the vehicle, wherein the unmanned aerial vehicle acquires a top view of a front traffic road state, the vehicle acquires a plan view, and structural information of the road and contour information of obstacles are obtained through fusion of the two views;
the unmanned aerial vehicle is provided with a wireless communication and V2X communication module and establishes communication connection with the vehicle through two modes, namely a wireless network and an LTE-V/DSRC;
the first mode is used for sending a control instruction to the unmanned aerial vehicle by the vehicle and feeding back remote road state image information to the vehicle by the unmanned aerial vehicle;
the second mode is used for sharing state information of the unmanned aerial vehicle and the vehicle; the unmanned aerial vehicle vision camera is positioned in front of the unmanned aerial vehicle, the angle of the camera is adjustable, and the camera has two degrees of freedom, namely horizontal degree and vertical degree of freedom; the visual image acquired by the unmanned aerial vehicle camera is sent to a vehicle-mounted image server through a UDP datagram; the unmanned aerial vehicle is provided with a GPS communication module for estimating the current position of the unmanned aerial vehicle; the unmanned aerial vehicle is provided with an IMU and a barometer sensor for estimating the attitude of the unmanned aerial vehicle.
2. The vehicle-mounted machine cooperative control and path optimization method based on unmanned aerial vehicle assistance as claimed in claim 1, wherein:
the unmanned aerial vehicle sends information obtained by the visual sensor to vehicles in a communication range in real time, collects traffic environment information at a far position under the control of the vehicles and sends the traffic environment information to the vehicles;
the vehicle controls the unmanned aerial vehicle to move so as to pertinently acquire traffic information of a certain area, acquire traffic environment information through the radar and the vision sensor, process the traffic environment information, control the vehicle to safely drive forwards by processing the traffic environment information acquired by the vehicle and process the traffic environment information acquired by the unmanned aerial vehicle so as to plan and optimize a path.
3. The vehicle-machine cooperative control and path optimization method based on unmanned aerial vehicle assistance of the vehicle-machine cooperative control system according to claim 1 or 2, characterized in that: the method comprises the following steps:
s1: the method comprises the steps that a vehicle sends a take-off instruction to an unmanned aerial vehicle, the unmanned aerial vehicle acquires remote road state information through the visual angle of a visual sensor after taking off and feeds the remote road state information back to the vehicle, and the vehicle starts to execute a track planning algorithm of the vehicle after receiving the road state information sensed by the unmanned aerial vehicle;
s2: the unmanned aerial vehicle acquires GPS position, speed and direction angle information of the vehicle through V2X communication, keeps flying in front of the vehicle, collects road state information and continuously provides a remote road state image for the vehicle;
s3: the vehicle-mounted image server acquires the visual information of the unmanned aerial vehicle and the vehicle-mounted visual information through a wireless network and a vehicle-mounted local area network respectively, and obtains lane structure information, barrier outline and position information through feature extraction and template matching;
s4: the vehicle-mounted control platform acquires road state information sensed by the vehicle-mounted laser radar through a vehicle-mounted local area network, performs coordinate axis conversion, and converts data under a laser radar spherical coordinate system into a rectangular coordinate system;
s5: based on the visual images and the laser radar data in the steps S3 and S4, time and space reference alignment is carried out, multi-source data fusion is carried out based on D-S theory, and structural information of a road and position and contour information of a road obstacle are obtained;
s6: predicting and planning the driving path of the vehicle by adopting an LSTM and a fast random search tree algorithm, and obtaining the error of the driving track by comparing the predicted driving path with the expected driving path;
s7: optimizing the vehicle travel path on the basis of the trajectory error of step S6;
s8: the vehicle sends the instruction of returning a journey to unmanned aerial vehicle, and unmanned aerial vehicle descends platform sign according to vehicle GPS position and roof, through visual image locking descending point position to adjust unmanned aerial vehicle and the relative position and the flying height of car through PID control, finally realize vehicle track planning and optimization algorithm.
4. The vehicle-mounted machine cooperative control and path optimization method based on unmanned aerial vehicle assistance as claimed in claim 1, wherein: the step S2 specifically includes:
the vehicle is provided with a GPS module and a V2X communication module, and the vehicle shares position, speed and direction angle information with the unmanned aerial vehicle through V2X communication; after the unmanned aerial vehicle acquires the information, the unmanned aerial vehicle obtains a control error by combining position, speed and direction angle information of the unmanned aerial vehicle, and the control error is used as the input of the tracking controller, so that the unmanned aerial vehicle moves along with the vehicle, and the remote road state image information is continuously provided for the vehicle.
5. The vehicle-mounted machine cooperative control and path optimization method based on unmanned aerial vehicle assistance as claimed in claim 1, wherein: the step S4 specifically includes:
obtaining distance information of obstacles in front of a traffic road through a laser radar; the vehicle-mounted control platform obtains the front obstacle information in real time through the online analysis of the radar point cloud data, wherein the conversion relation from the laser radar measuring point coordinate to the rectangular coordinate system is as follows:
wherein the front of the radar is in the x direction, the left is in the y direction, the upper is in the z direction, d is the distance measured by the laser radar, alpha is the included angle between each scanning surface and the horizontal plane, and theta is the rotating angle of the laser radar.
6. The vehicle-mounted machine cooperative control and path optimization method based on unmanned aerial vehicle assistance as claimed in claim 1, wherein: the step S5 specifically includes:
transmitting far/near road structure and barrier information obtained after processing by the vehicle-mounted image server to a vehicle-mounted control platform through a vehicle-mounted local area network; and the control platform is combined with the laser radar data to further match the radar detection points with the visual images in the rectangular coordinate system, so that the real position of the midpoint in the three-dimensional space is restored, and the object reconstruction in the three-dimensional space is completed.
7. The vehicle-mounted machine cooperative control and path optimization method based on unmanned aerial vehicle assistance as claimed in claim 1, wherein: the step S6 specifically includes:
generating a smooth track meeting the vehicle running constraint, simultaneously adopting two methods of path prediction and planning to predict the path of the vehicle and plan the path of the vehicle, obtaining the error of the running track according to the difference value of the two methods, and taking the error as one of the bases for optimizing the vehicle running path.
8. The vehicle-mounted machine cooperative control and path optimization method based on unmanned aerial vehicle assistance as claimed in claim 1, wherein: the step S7 specifically includes:
the path optimization algorithm uses the state information of the vehicle, and comprises the following steps: the position x, the speed v and the acceleration a, the curvature gamma of the running track, the running time t and the energy consumption w are performance constraint indexes, and the following optimal control model is established:
minJi=f(xi，vi，ai，γi，wi)
wherein JiIs an objective function, and according to the optimal control model, the driving path planned in the step S6 is optimized, and the vehicle drives along the optimal path under the action of the controller.
9. The vehicle-mounted machine cooperative control and path optimization method based on unmanned aerial vehicle assistance as claimed in claim 1, wherein: the step S8 specifically includes:
the unmanned aerial vehicle positions the vehicle through the vehicle GPS position information; then, the unmanned aerial vehicle hovers over the vehicle, and the image information of the roof landing point is acquired through the airborne camera, so that the position of the roof landing point is accurately positioned; the images acquired by the unmanned aerial vehicle are transmitted to the vehicle-mounted image server through a high-bandwidth wireless network, and the vehicle-mounted image server feeds back the results to the unmanned aerial vehicle after processing the images; the unmanned aerial vehicle adjusts the position of the unmanned aerial vehicle based on the result and lands on the roof; the vehicle provides the electric quantity for unmanned aerial vehicle and supplements.
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