技术领域technical field
本发明涉及图像处理领域，具体涉及一种基于无人机感知的无人驾驶车辆路径规划技术，能够通过无人飞行器感知车辆周围道路环境，并快速规划该车辆行驶路径。The present invention relates to the field of image processing, in particular to an unmanned vehicle path planning technology based on unmanned aerial vehicle perception, which can sense the road environment around the vehicle through the unmanned aerial vehicle and quickly plan the driving path of the vehicle.
背景技术Background technique
智能车辆与交通技术兴起，无人驾驶车辆技术作为其关键技术之一被开发。无人驾驶车辆行驶过程需要多部分协同工作，保证对大量的内部数据监测，外部附近障碍感知以及行驶路线预判与规划等才能使其安全行使。With the rise of intelligent vehicles and transportation technologies, unmanned vehicle technology has been developed as one of its key technologies. The driving process of unmanned vehicles requires multiple parts to work together to ensure the monitoring of a large amount of internal data, the perception of external nearby obstacles, and the prediction and planning of driving routes in order to make them safe.
现行无人驾驶车辆的行驶路线预判与规划技术部分通过道路行驶学习，部分依赖于GPS导航。对于道路行驶学习，多次进行行驶路线训练后，无人驾驶车辆机器学习的成果显著，但是之后无人驾驶车辆只能应对单一路线，应用范围有限。GPS导航技术成熟，应用广泛，使用方便，与智能交通系统结合后，能够结合道路状况进行导航，制定合理路线。但是GPS定位对信号有要求，在城市环境中，信号受到干扰，容易产生定位不准的情况，影响行驶安全。同时，随着各种城建项目实施，城市电子地图更新缓慢，造成地图与城市实际情况不符合，同样威胁无人驾驶车辆的安全行使。此外，智能交通系统目前应用范围较小，反应交通状况存在延迟，无法及时修改实行路径，导致车辆停滞，辅助规划效果有限。The current route prediction and planning technology of unmanned vehicles partly learns through road driving and partly relies on GPS navigation. For road driving learning, after repeated driving route training, the results of machine learning for unmanned vehicles are remarkable, but after that, unmanned vehicles can only cope with a single route, and the scope of application is limited. GPS navigation technology is mature, widely used, and easy to use. After combining with intelligent transportation system, it can navigate according to road conditions and formulate reasonable routes. However, GPS positioning has requirements on the signal. In the urban environment, the signal is interfered, which is prone to inaccurate positioning and affects driving safety. At the same time, with the implementation of various urban construction projects, the urban electronic map is updated slowly, causing the map to be inconsistent with the actual situation of the city, which also threatens the safe use of unmanned vehicles. In addition, the intelligent transportation system is currently applied in a small range, and there is a delay in responding to traffic conditions, and it is impossible to modify the implementation route in time, resulting in vehicle stagnation, and the auxiliary planning effect is limited.
发明内容Contents of the invention
本发明针对现有车辆路径规划技术中的不足，提供了一种基于无人机感知的无人驾驶车辆路径规划方法。该方法依据通过无人飞行器高空平台得到的道路与交通信息进行目标车辆行驶路径的规划。Aiming at the deficiencies in the existing vehicle path planning technology, the present invention provides an unmanned vehicle path planning method based on UAV perception. The method plans the driving path of the target vehicle based on the road and traffic information obtained through the high-altitude platform of the unmanned aerial vehicle.
本发明提供的基于无人机感知的无人驾驶车辆路径规划方法，基于无人飞行器感知系统。The unmanned vehicle path planning method based on unmanned aerial vehicle perception provided by the present invention is based on the unmanned aerial vehicle perception system.
所述的无人飞行器感知系统包括无人飞行器和地面站。所述的无人飞行器通过系留绳由目标车辆供电，无人飞行器上搭载摄像装置、第一无线通信模块和第一图像传输模块；通过摄像装置对路面进行拍摄，获得道路的视频图像；通过第一图像传输模块将拍摄的视频图像传输至地面站；通过第一无线通信模块接收飞行控制信号。所述的地面站放置在目标车辆上，并由目标车辆供电；地面站装有第二无线通信模块、第二图像传输模块和计算机模块；通过第二无线通信模块传输飞行控制信号给无人飞行器；通过第二图像传输模块接收无人飞行器传来视频图像；通过计算机模块，对接收的视频图像进行处理，获得道路信息并进行路径规划，并计算飞行控制参数给无人飞行器，同时通过数据线与无人驾驶车辆进行信息交互。The unmanned aerial vehicle perception system includes an unmanned aerial vehicle and a ground station. The unmanned aerial vehicle is powered by the target vehicle through the mooring rope, and the unmanned aerial vehicle is equipped with a camera device, a first wireless communication module and a first image transmission module; the road surface is photographed by the camera device to obtain a video image of the road; The first image transmission module transmits the captured video images to the ground station; and receives flight control signals through the first wireless communication module. The ground station is placed on the target vehicle and powered by the target vehicle; the ground station is equipped with a second wireless communication module, a second image transmission module and a computer module; the flight control signal is transmitted to the unmanned aerial vehicle through the second wireless communication module ;Receive the video image from the unmanned aerial vehicle through the second image transmission module; process the received video image through the computer module, obtain road information and perform path planning, and calculate flight control parameters for the unmanned aerial vehicle, and at the same time pass the data line Information interaction with unmanned vehicles.
所述的无人驾驶车辆路径规划方法，实现步骤如下：The implementation steps of the unmanned vehicle path planning method are as follows:
步骤(1)启动无人飞行器感知系统，无人飞行器起飞，悬停后与地面站建立通信，拍摄目标车辆周围的道路视频图像，发送给地面站；Step (1) Start the unmanned aerial vehicle perception system, the unmanned aerial vehicle takes off, establishes communication with the ground station after hovering, takes the road video image around the target vehicle, and sends it to the ground station;
步骤(2)地面站接收视频图像并进行处理，获得车辆与道路信息，根据车辆位置进行路径规划，并将路线传递给无人驾驶车辆。所述的车辆与道路信息包括目标车辆与道路的位置关系、车辆之间的距离、以及道路区域。Step (2) The ground station receives and processes the video image, obtains vehicle and road information, performs path planning according to the vehicle position, and transmits the route to the unmanned vehicle. The vehicle and road information includes the positional relationship between the target vehicle and the road, the distance between the vehicles, and the road area.
步骤(3)计算机模块控制无人飞行器跟随车辆前进，实时修正路径。Step (3) The computer module controls the UAV to follow the vehicle and correct the path in real time.
进一步，所述的步骤(2)的工作过程包括：Further, the working process of described step (2) includes:
步骤2.1、无人飞行器拍摄时，增稳云台控制摄像装置的镜头垂直地面进行拍摄，图像为俯视投影，通过比例变换获得物体间的实际距离。Step 2.1. When the unmanned aerial vehicle is shooting, the lens of the stabilized gimbal controls the camera device to shoot vertically to the ground. The image is a top-view projection, and the actual distance between objects is obtained by scaling.
步骤2.2、地面站的计算机模块对视频图形进行图像处理，绘制道路区域，检索车辆，得到各条道路上车辆总面积与道路面积的比值，并对目标车辆进行路径规划。Step 2.2, the computer module of the ground station performs image processing on the video graphics, draws the road area, retrieves vehicles, obtains the ratio of the total area of vehicles on each road to the area of the road, and performs path planning for the target vehicle.
进一步，所述的步骤(3)中，计算机模块将视频图像的画面中心作为无人飞行器所处位置，在设定的区域范围内寻找用特殊颜色标记车顶的目标车辆，发现目标车辆位置后，计算无人飞行器与车辆间的实际偏移距离与方向。地面站只对无人飞行机的俯仰与偏航两个参数进行控制，采用双模参数自校正模糊PID计算飞行控制参数，在得到飞行控制参数后发送给无人飞行器。步骤(3)中，当无人飞行器丢失目标车辆后，通过系留绳提供对无人飞行器的引导。Further, in the described step (3), the computer module uses the frame center of the video image as the position of the unmanned aerial vehicle, searches for the target vehicle with the roof marked with a special color within the set area range, and finds the position of the target vehicle , to calculate the actual offset distance and direction between the UAV and the vehicle. The ground station only controls the pitch and yaw parameters of the UAV, and uses dual-mode parameter self-calibration fuzzy PID to calculate the flight control parameters, and sends them to the UAV after obtaining the flight control parameters. In step (3), when the unmanned aerial vehicle loses the target vehicle, the guidance of the unmanned aerial vehicle is provided through the mooring rope.
本发明技术方案，有如下创新点与优势：The technical solution of the present invention has the following innovations and advantages:
(1)本发明基于无人飞行器平台，工作状态稳定，从高空拍摄车辆周围道路环境，覆盖范围广，该位置下所观察到的道路交通信息清晰明确，通过图像处理后可以快速得到目标车辆周围道路分布状况与交通状况，该信息实时、准确，依据该数据能够获得合理的路径规划。基于无人飞行器感知平台相比于传统的GPS进行路线导航，路线更为精确，相比于智能交通网络提供的道路交通状况，路况分析更为及时。(1) The present invention is based on the unmanned aerial vehicle platform, and the working state is stable. The road environment around the vehicle is photographed from a high altitude, and the coverage is wide. Road distribution and traffic conditions, the information is real-time and accurate, and reasonable route planning can be obtained based on this data. Compared with the traditional GPS for route navigation based on the unmanned aerial vehicle perception platform, the route is more accurate. Compared with the road traffic conditions provided by the intelligent transportation network, the road condition analysis is more timely.
(2)本发明所采用的无人飞行器跟随无人驾驶车辆移动，可以实时采集到以车辆为中心周围的道路分布与交通状况，实现实时修正无人驾驶车辆的行驶路径，确保能够获得最佳路线。由于无人飞行器的速度有限，同时出于安全性考虑，无人驾驶车辆行驶速度需要控制在40km/h以下。(2) The unmanned aerial vehicle adopted in the present invention follows the movement of the unmanned vehicle, and can collect the road distribution and traffic conditions around the vehicle in real time, realize real-time correction of the driving path of the unmanned vehicle, and ensure that the best route. Due to the limited speed of unmanned aerial vehicles, and for safety reasons, the speed of unmanned vehicles needs to be controlled below 40km/h.
(3)本发明采用的无人飞行器系统通过车载装置供电。地面站直接将充电接口与信息交互接口集成，简化了地面站组成结构与系统搭建操作，同时维持地面站的稳定运行。道路与交通信息的感知则需要无人飞行器长时间滞空拍摄，由于现行无人飞行器电池续航时间有限，采用系留绳供电维持其飞行。此外，系留绳具备一定引导能力，能够在一定程度上阻止无人飞行器与车辆失散。(3) The unmanned aerial vehicle system adopted in the present invention is powered by the vehicle-mounted device. The ground station directly integrates the charging interface and the information interaction interface, which simplifies the composition structure and system construction operation of the ground station, while maintaining the stable operation of the ground station. The perception of road and traffic information requires the unmanned aerial vehicle to stay in the air for a long time to take pictures. Due to the limited battery life of the current unmanned aerial vehicle, a tethered rope is used to supply power to maintain its flight. In addition, the tethering rope has a certain guiding ability, which can prevent the unmanned aerial vehicle from being separated from the vehicle to a certain extent.
(4)本发明中，无人飞行器用于采集道路信息，而地面站承当数据处理工作。减少无人飞行器处理进程，使无人飞行器拥有更多资源来优化飞行控制过程以提升飞行精度与飞行安全。地面站的计算机模块计算能力较高，稳定性高，负责接收图像、处理图像，从而进行路径规划与无人机控制参数计算，并且完成信息交互。两部分装置互相协同，可快捷、流畅地实现无人飞行器感知系统的车辆路径规划。(4) In the present invention, the unmanned aerial vehicle is used to collect road information, and the ground station is responsible for data processing. Reduce the processing process of the unmanned aerial vehicle, so that the unmanned aerial vehicle has more resources to optimize the flight control process to improve flight accuracy and flight safety. The computer module of the ground station has high computing power and high stability. It is responsible for receiving and processing images, thereby performing path planning and calculation of UAV control parameters, and completing information interaction. The two parts of the device cooperate with each other to quickly and smoothly realize the vehicle path planning of the UAV perception system.
附图说明Description of drawings
图1是本发明采用的无人飞行器感知系统组成示意图；Fig. 1 is a schematic diagram of the composition of the unmanned aerial vehicle perception system adopted in the present invention;
图2是本发明的无人驾驶车辆路径规划方法的流程示意图；Fig. 2 is a schematic flow chart of the unmanned vehicle path planning method of the present invention;
图3是本发明的道路与车辆识别过程示意图；Fig. 3 is a schematic diagram of the road and vehicle identification process of the present invention;
图4是本发明实施例中对路径进行全局规划的示意图；FIG. 4 is a schematic diagram of global planning of paths in an embodiment of the present invention;
图5是本发明实施例中对车辆周围行驶路径进行局部规划的示意图。Fig. 5 is a schematic diagram of the local planning of the driving route around the vehicle in the embodiment of the present invention.
附图中：1、需要路径规划的目标车辆，2、地面站，3、系留绳，4、无人飞行器。In the attached drawings: 1. Target vehicle requiring path planning, 2. Ground station, 3. Tethering rope, 4. Unmanned aerial vehicle.
具体实施方式Detailed ways
下面结合附图对本发明作进一步说明：The present invention will be further described below in conjunction with accompanying drawing:
如图1所示，本发明采用的无人飞行器感知系统，其包括：需要路径规划的目标车辆1，地面站2，系留绳3，以及无人飞行器4。所述的地面站2放置在目标车辆1上，由车辆1供电，无人飞行器4通过系留绳3由车辆1供电。As shown in FIG. 1 , the UAV perception system adopted in the present invention includes: a target vehicle 1 requiring path planning, a ground station 2 , a mooring rope 3 , and an UAV 4 . The ground station 2 is placed on the target vehicle 1 and powered by the vehicle 1 , and the unmanned aerial vehicle 4 is powered by the vehicle 1 through the tethering rope 3 .
本发明实施例无人飞行器4为四旋翼无人飞行器。无人飞行器4上搭载有GPS(全球定位系统)模块、AHRS(航姿参考系统)模块和飞行控制装置，以保证无人飞行器的稳定安全飞行。无人飞行器4上搭载增稳云台和摄像装置，以实现无人飞行器对路面的稳定拍摄，获得道路与交通的视频图像。无人飞行器4上搭载第一无线通信模块和第一图像传输模块。通过第一无线通信模块接收飞行控制信号。通过第一图像传输模块将拍摄的视频图像传输至地面站2。The unmanned aerial vehicle 4 of the embodiment of the present invention is a quadrotor unmanned aerial vehicle. The UAV 4 is equipped with a GPS (Global Positioning System) module, an AHRS (Heading Attitude Reference System) module and a flight control device to ensure the stable and safe flight of the UAV. The unmanned aerial vehicle 4 is equipped with a stabilizing platform and a camera device to realize the stable shooting of the unmanned aerial vehicle on the road surface and obtain video images of roads and traffic. The UAV 4 is equipped with a first wireless communication module and a first image transmission module. A flight control signal is received through the first wireless communication module. The captured video images are transmitted to the ground station 2 through the first image transmission module.
地面站2装有第二无线通信模块、第二图像传输模块和计算机模块。通过第二无线通信模块传输飞行控制信号给无人飞行器4。通过第二图像传输模块接收无人飞行器4传来视频图像。通过计算机模块，对接收的视频图像进行处理，获得道路信息并进行路径规划，并通过数据线与无人驾驶车辆1进行信息交互。地面站2可以将充电接口与信息交互接口集成，以简化地面站结构，同时解决供电问题。The ground station 2 is equipped with a second wireless communication module, a second image transmission module and a computer module. The flight control signal is transmitted to the UAV 4 through the second wireless communication module. The video image transmitted by the unmanned aerial vehicle 4 is received by the second image transmission module. The computer module processes the received video images, obtains road information and performs path planning, and performs information interaction with the unmanned vehicle 1 through the data line. The ground station 2 can integrate the charging interface and the information interaction interface to simplify the structure of the ground station and solve the power supply problem at the same time.
如图2所示，本发明基于无人机感知的无人驾驶车辆路径规划方法的工作流程图，其步骤如下：As shown in Figure 2, the present invention is based on the work flowchart of the unmanned vehicle path planning method of UAV perception, and its steps are as follows:
步骤(1)本发明采用无人飞行器4拍摄目标车辆1周围的道路视频图像，并将视频图像发送给地面站2，以进行目标车辆1周围道路分布与路上车辆数量信息的收集。Step (1) The present invention adopts the unmanned aerial vehicle 4 to shoot the road video image around the target vehicle 1, and sends the video image to the ground station 2 to collect the road distribution around the target vehicle 1 and the number of vehicles on the road.
系统开始运行时，无人飞行器4起飞上升100-200米并悬停，同时与车辆1建立通信，开始拍摄目标车辆1周围的道路与交通信息的视频图像，发送给地面站2。When the system starts to run, the unmanned aerial vehicle 4 takes off and rises 100-200 meters and hovers. At the same time, it establishes communication with the vehicle 1, starts to take video images of roads and traffic information around the target vehicle 1, and sends them to the ground station 2.
当无人飞行器4飞行高度为120-150米时，镜头角度为60°-80°。位于该高度时，无人飞行器4的视野半径在70-125米，包含目标车辆1在内的大范围物体，可以保证能够获取足够多的道路信息进行路径规划。When the flying height of the unmanned aerial vehicle 4 is 120-150 meters, the lens angle is 60°-80°. At this height, the UAV 4 has a field of view radius of 70-125 meters, and large-scale objects including the target vehicle 1 can ensure that enough road information can be obtained for path planning.
无人飞行器4所搭载的摄像装置单目镜头需要进行标定，以修正在其拍摄过程中直线产生的弧度，还原道路与车辆变形，降低环境感知的干扰与误检率。The monocular lens of the camera device mounted on the unmanned aerial vehicle 4 needs to be calibrated to correct the arc generated by the straight line during the shooting process, restore the deformation of the road and the vehicle, and reduce the interference and false detection rate of environmental perception.
步骤(2)地面站2接收视频图像并进行处理，获得车辆与道路信息，根据车辆位置进行路径规划，然后地面站2通过数据线将路线传递给无人驾驶车辆。车辆与道路信息包括道路的长度和宽度，目标车辆与其他车辆之间的距离等。Step (2) The ground station 2 receives and processes the video images, obtains vehicle and road information, and performs path planning according to the vehicle position, and then the ground station 2 transmits the route to the unmanned vehicle through the data line. Vehicle and road information includes the length and width of the road, the distance between the target vehicle and other vehicles, etc.
无人飞行器4进行拍摄的视频通过第一图像传输模块将视频图像传回地面站2。地面站2的计算机模块将立即进行图像处理，以获得车辆、道路信息，并进行路径规划。步骤(2)的工作过程如下：The video shot by the unmanned aerial vehicle 4 transmits the video image back to the ground station 2 through the first image transmission module. The computer module of the ground station 2 will immediately process the image to obtain vehicle and road information, and perform path planning. The working process of step (2) is as follows:
步骤2.1、拍摄时，增稳云台控制摄像装置的镜头垂直地面进行拍摄，图像为俯视投影，其中物体的距离信息可以直接通过比例计算得到，避免透视运算与坐标变换。Step 2.1. When shooting, the stabilized gimbal controls the lens of the camera device to shoot vertically to the ground, and the image is a top-view projection, in which the distance information of the object can be directly obtained by proportional calculation, avoiding perspective calculation and coordinate transformation.
通过输入目标车辆长度a，宽度b，根据比例k：By inputting the target vehicle length a, width b, according to the ratio k:
s＝kSs=kS
其中，A、B为图像中位于目标车辆1的对角线的两点，坐标分别为(xA,yA)、(xB,yB)；S为图像中两物体的距离，s为两物体现实中的距离。Among them, A and B are two points located on the diagonal of the target vehicle 1 in the image, and the coordinates are (xA , yA ), (xB , yB ); S is the distance between two objects in the image, and s is The actual distance between two objects.
可以获得图像中的距离信息，包括道路长度、宽度等。The distance information in the image can be obtained, including road length, width, etc.
步骤2.2、地面站2的计算机模块对采集到的道路视频图像进行图像处理，通过图像配准、颜色阈值分割、形态学操作以及分类器识别等方法获得车辆与道路信息，从而进行路径规划。具体过程如下：Step 2.2. The computer module of the ground station 2 performs image processing on the collected road video images, and obtains vehicle and road information through image registration, color threshold segmentation, morphological operations, and classifier identification, thereby performing path planning. The specific process is as follows:
①、通过图像配准进行增稳，跟踪图像中的特征点获取图像背景的运动，从而通过仿射变换方法消除抖动。①. Stabilize through image registration, track the feature points in the image to obtain the motion of the image background, and eliminate the shake through the affine transformation method.
②、如图3所示，将拍摄的图像从RGB(红，绿，蓝)颜色空间转换到HSV(色调，饱和度，亮度)颜色空间。然后选取图像中饱和度值在0％-5％内的区域将其亮度调整成0(黑色)，其余区域的亮度设置为255(白色)，从而得到道路二值图像。2. As shown in FIG. 3 , convert the captured image from the RGB (red, green, blue) color space to the HSV (hue, saturation, brightness) color space. Then select the region whose saturation value is within 0%-5% in the image and adjust its brightness to 0 (black), and set the brightness of the rest of the region to 255 (white), so as to obtain the road binary image.
对二值图像进行形态学开闭运算，其中，采用圆为结构元素，其直径为道路上车辆宽度的1/2，从而消除道路上车辆造成的空洞，平滑道路边界。The morphological opening and closing operation is performed on the binary image, in which the circle is used as the structural element, and its diameter is 1/2 of the width of the vehicle on the road, so as to eliminate the holes caused by the vehicles on the road and smooth the road boundary.
然后进行边缘检测，由于二值图像中每个像素点的灰度值为0或255，其中，灰度突变处则为道路边界。但是通过形态学操作后得到的边缘可能为不规则曲线，需要通过霍夫变换绘制边缘直线，从而获得规则的道路区域，建立二维道路模型。Then edge detection is performed, because the gray value of each pixel in the binary image is 0 or 255, and the sudden change in gray value is the road boundary. However, the edge obtained through the morphological operation may be an irregular curve, and it is necessary to draw the edge straight line through the Hough transform, so as to obtain a regular road area and establish a two-dimensional road model.
霍夫变换过程：对于道路边缘线a上的若干个点A1(x1,y1,),A2(x2,y2,),…,An(xn,yn,)，近似满足直线：Xcosθ+Ysinθ＝r，其中θ、r是参数。以点A1(x1,y1,)为例，将x1、y1作为参数，则在r-θ参数平面上，可以得到曲线：r＝Xcosθ+Ysinθ。由此，可以将点集A转换成r-θ平面上的若干曲线，这些曲线过同一点(θ,r)。经过霍夫变换后，统计r-θ平面上的峰值点，获取该点坐标，即直线的参数θ、r，从而绘制出直线。Hough transform process: For several points A1 (x1 ,y1 ,),A2 (x2 ,y2 ,),…,An (xn ,yn ,) on the road edge line a, A straight line is approximately satisfied: Xcosθ+Ysinθ=r, where θ and r are parameters. Taking the point A1 (x1 ,y1 ,) as an example, and taking x1 and y1 as parameters, on the r-θ parameter plane, a curve can be obtained: r=Xcosθ+Ysinθ. Thus, the point set A can be converted into several curves on the r-θ plane, and these curves pass through the same point (θ, r). After the Hough transform, the peak point on the r-θ plane is counted, and the coordinates of the point are obtained, that is, the parameters θ and r of the straight line, so as to draw a straight line.
③、随后，通过采用基于Haar特征级联分类器进行车辆检测，获取图像中各车辆中心点的坐标以及车辆长、宽与大小。使用分类器检测时，寻找图形中的某一大型车辆，设定搜索窗口的长为该大型车辆长度的1.2倍，搜索窗口的宽为该大型车辆宽度的1.2倍，并保持检索窗口尺寸不变，以排除检测到其他非车辆物体，保证检测精度。检测结束后，计算各条道路上车辆总面积与道路面积的比值，该比值将作为路径规划的依据之一。③. Subsequently, by using the Haar feature-based cascade classifier for vehicle detection, the coordinates of the center point of each vehicle in the image and the length, width and size of the vehicle are obtained. When using a classifier to detect a certain large vehicle in the graph, set the length of the search window to be 1.2 times the length of the large vehicle, and the width of the search window to be 1.2 times the width of the large vehicle, and keep the size of the search window unchanged , to exclude the detection of other non-vehicle objects and ensure the detection accuracy. After the detection, the ratio of the total area of vehicles on each road to the area of the road is calculated, and this ratio will be used as one of the basis for path planning.
④、最后进行路径规划，其包含：全局规划和局部规划。④. Finally, path planning is carried out, which includes: global planning and local planning.
全局规划时，如图4所示，计算机模块根据无人飞行器4上的GPS信息，在当前地图位置中设置感兴趣区域，并在该区域内将上述步骤得到的道路模型进行模板匹配，得到目标车辆在电子地图中的位置。根据车辆位置与终点位置，结合C空间算法与Dijkstra算法，进行全局规划。利用C空间算法，在电子地图中，将不可通行区域拓展成多边形，将起始点、终点和所有多边形顶点间用直线连接，建立路径信息拓扑网络。然后采用Dijkstra算法搜索最短路径，该算法依次遍历所有顶点，并以道路长度与道路上车辆数量的加权值w＝k1d+k2h为各个顶点的权值，进行计算得到最优解，其中d为两顶点间距，h为车辆道路面积比值，k1、k2取值由所在城市决定，为预先设定的经验值。全局规划在无人飞行器4悬停建立与地面站2的通信后，执行一次即可，随后判断车辆1的行驶轨迹是否在路线上，如果偏离则重新规划。During global planning, as shown in Figure 4, the computer module sets an area of interest in the current map position according to the GPS information on the UAV 4, and performs template matching on the road model obtained in the above steps in this area to obtain the target The position of the vehicle on the electronic map. According to the position of the vehicle and the end position, combined with the C space algorithm and the Dijkstra algorithm, the global planning is carried out. Using the C space algorithm, in the electronic map, the impassable area is expanded into polygons, and the starting point, the end point, and all polygon vertices are connected with straight lines to establish a path information topology network. Then use the Dijkstra algorithm to search for the shortest path. This algorithm traverses all vertices in turn, and uses the weighted value w=k1 d+k2 h of the road length and the number of vehicles on the road as the weight of each vertex to calculate the optimal solution. Among them, d is the distance between two vertices, h is the ratio of vehicle road area, and the values of k1 and k2 are determined by the city where they are located, which are preset empirical values. After the UAV 4 hovers and establishes communication with the ground station 2, the global planning can be executed only once, and then it is judged whether the driving track of the vehicle 1 is on the route, and if it deviates, re-plan.
局部规划时，计算机模块图像处理时得到目标车辆2与其他车辆的位置距离关系，采用人工势场法，限定范围在当前可行驶道路(避免逆行)上，虚拟前方为力场方向，其他车辆为斥力，建立引力场函数进行路径寻找。必要时通过连续帧计算其他车辆运动速度，并将其导入函数进行计算以进行路径寻找。局部规划提供一种行驶轨迹参考，实际决策由车辆1电子控制单元结合各传感器数据综合给出。During local planning, the position and distance relationship between the target vehicle 2 and other vehicles is obtained during the image processing of the computer module, and the artificial potential field method is used to limit the range on the current drivable road (to avoid retrograde), the virtual front is the direction of the force field, and other vehicles are Repulsion, to establish a gravitational field function for path finding. If necessary, calculate the speed of other vehicles through consecutive frames, and import them into the function for calculation for path finding. The local planning provides a driving trajectory reference, and the actual decision is given by the electronic control unit of the vehicle 1 combined with the data of various sensors.
步骤(3)计算机模块控制无人飞行器4跟随目标车辆1前进，用于实时修正路径。Step (3) The computer module controls the UAV 4 to follow the target vehicle 1 for real-time path correction.
步骤(3)工作原理包括：The working principle of step (3) includes:
1、计算机模块通过图像处理得到无人飞行器4与目标车辆1的位置关系，根据偏移距离和方向与PID控制原理，计算无人飞行器4的飞行控制参数并发送至无人飞行器4，控制无人飞行器4跟随车辆前进，用于实时修正路径。1. The computer module obtains the positional relationship between the unmanned aerial vehicle 4 and the target vehicle 1 through image processing, calculates the flight control parameters of the unmanned aerial vehicle 4 and sends them to the unmanned aerial vehicle 4 according to the offset distance and direction and the PID control principle, and controls the unmanned aerial vehicle 4. The manned aerial vehicle 4 follows the vehicle and is used for real-time correction of the path.
2、为了简化控制过程，只对飞行过程中的无人飞行器4的俯仰与偏航两个参数进行控制。控制采用双模参数自校正模糊PID计算飞行控制参数。2. In order to simplify the control process, only the pitch and yaw parameters of the UAV 4 are controlled during flight. The control adopts dual-mode parameter self-correcting fuzzy PID to calculate the flight control parameters.
3、其中，计算位置关系时，在图像处理过程中以画面中心作为无人飞行器4所处位置，在画面中心附近设定一个感兴趣区域范围，寻找用特殊颜色标记车顶的目标车辆。在根据图像中两物体的位置关系计算现实中距离。然后获得无人飞行器与车辆间的实际偏移距离与方向。3. Wherein, when calculating the positional relationship, the center of the picture is used as the position of the unmanned aerial vehicle 4 in the image processing process, and an area of interest is set near the center of the picture to find the target vehicle whose roof is marked with a special color. Calculate the distance in reality based on the positional relationship between two objects in the image. Then the actual offset distance and direction between the UAV and the vehicle are obtained.
4、当无人飞行器丢失目标车辆后，系留绳能够在一定程度上提供对无人飞行器的引导。4. When the UAV loses the target vehicle, the tethering rope can provide guidance to the UAV to a certain extent.
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