技术领域technical field
本发明涉及无人驾驶的技术领域，特别是一种场地无人车轨迹跟踪方法及系统。The invention relates to the technical field of unmanned driving, in particular to a track tracking method and system for an unmanned vehicle on a site.
背景技术Background technique
随着科技和生产力的发展，无人驾驶技术受到了人们的广泛关注。With the development of technology and productivity, unmanned driving technology has received widespread attention.
轨迹跟踪是实现无人驾驶最基本的技术之一。近几年来，国内外许多高校和研究所都进行了广泛和深入的研究，开发出了许多轨迹跟踪算法。目前无人车上的轨迹跟踪方法常采用单点预瞄方法，无人车侧向速度是直接根据GPS 的速度数据计算而来的，经常导致跳变严重；纯追踪模型方法是利用一种向预瞄点画圆弧的方式去到达预瞄点，该方法即使在直道上也会出现画龙现象。总而言之，目前无人车的轨迹跟踪方法在速度较高时或者在道路曲率变化较大时，算法参数不适用，经常导致画龙现象严重。Trajectory tracking is one of the most basic technologies to realize unmanned driving. In recent years, many universities and research institutes at home and abroad have conducted extensive and in-depth research and developed many trajectory tracking algorithms. At present, the trajectory tracking method on the unmanned vehicle often adopts the single-point preview method. The lateral velocity of the unmanned vehicle is directly calculated according to the speed data of the GPS, which often leads to serious jumps; the pure tracking model method uses a direction The way to reach the preview point is to draw an arc at the preview point. Even on the straight road, the phenomenon of drawing a dragon will appear in this method. All in all, the current trajectory tracking method for unmanned vehicles does not apply algorithm parameters when the speed is high or when the road curvature changes greatly, which often leads to serious dragon drawing.
目前无人驾驶车上路还没有明确的道路交通法规，而场地无人车不受道路交通法限制。据统计，国内共有2700多个景区、230个产业园区、650个机场、以及1.2万个省级及以下工业园。如果能在场地环境下率先实现无人驾驶技术一定程度的应用，必将大大促进无人驾驶技术的落地和产业化。At present, there are no clear road traffic regulations for unmanned vehicles on the road, and unmanned vehicles in the field are not restricted by road traffic laws. According to statistics, there are more than 2,700 scenic spots, 230 industrial parks, 650 airports, and 12,000 industrial parks at or below the provincial level in China. If we can take the lead in realizing a certain degree of application of unmanned driving technology in the field environment, it will greatly promote the landing and industrialization of unmanned driving technology.
场地无人车由于产品定位和运行环境的差异，其安装的传感器成本往往只有高速无人驾驶乘用车、商用车的1/20左右或更少。低成本的传感器的精度、分辨率等受到限制，必定需要特殊的解决方案。场地无人车运行在景区、园区等非结构化道路行驶，通常存在树木茂密遮挡或楼宇建筑物对空遮挡的情况，车辆GPS接收情况不容乐观。另外，缺少地面车道线和地面标识牌，道路不规则，还可能出现较大曲率转弯、坡道、岔道等情况。因此，场地无人车轨迹跟踪比结构化道路或半结构化道路下乘用车的轨迹跟踪难度更大。Due to the differences in product positioning and operating environment, the cost of sensors installed on field unmanned vehicles is often only about 1/20 or less of that of high-speed unmanned passenger vehicles and commercial vehicles. Low-cost sensors are limited in their accuracy, resolution, etc. and must require special solutions. On-site unmanned vehicles operate on unstructured roads such as scenic spots and parks. Usually, there are situations where dense trees block or buildings block the air. The GPS reception of the vehicle is not optimistic. In addition, there is a lack of ground lane lines and ground signs, the road is irregular, and there may be large curvature turns, ramps, forks, etc. Therefore, it is more difficult to track the trajectory of unmanned vehicles on the site than the trajectory tracking of passenger vehicles on structured roads or semi-structured roads.
公开号为CN107272692A的发明专利公开了一种基于微分平坦的自抗扰的无人车路径规划与跟踪控制方法，包括以下步骤：步骤一：建立三自由度四轮转向汽车单轨控制模型；步骤二：根据步骤一建立的控制模型，根据微分平坦理论，将欠驱动被空模型变换为带有扰动的没有零动态子系统的输入输出耦合模型；步骤三：在跟踪控制层上建立路径规划层；步骤四：根据步骤二建立的输入输出耦合模型，设计基于广义比例积分观测器的自抗扰控制器，实现对步骤三规划出的轨迹进行跟踪。该方法结合了四轮转向动力学原理进行无人车路径规划和跟踪，但只在MATLAB环境下进行了仿真实验，方法的有效性还有待实车验证。The invention patent with the publication number CN107272692A discloses a path planning and tracking control method for unmanned vehicles based on differential flat ADRR, including the following steps: Step 1: Establish a three-degree-of-freedom four-wheel steering vehicle monorail control model; Step 2 : According to the control model established in Step 1, according to the differential flatness theory, the underactuated null model is transformed into an input-output coupling model with disturbance and no zero dynamic subsystem; Step 3: Establish a path planning layer on the tracking control layer; Step 4: Based on the input-output coupling model established in Step 2, design an ADRC controller based on a generalized proportional integral observer to track the trajectory planned in Step 3. This method combines the principle of four-wheel steering dynamics for path planning and tracking of unmanned vehicles, but only the simulation experiments are carried out in the MATLAB environment, and the effectiveness of the method has yet to be verified by real vehicles.
公开号为CN106926840A的发明专利公开了一种无人车辆极限动力学轨迹跟踪控制系统，该控制系统包括：传感器模块、速度文件求解模块即计算控制模块；速度文件求解模块接收传感器模块采集的位置参量，映射到期望轨迹上得到理想位置，求解得到所述理想位置的期望纵向车速，将传感器模块采集的运动参数联合期望纵向车速输入计算控制模块中处理得到整车所需要的制动舵机转角、驱动电机所需的驱动力和转向舵机转角，从而控制无人车辆的运动。该方法所使用的传感器包括惯性导航系统、避震位移传感器以及转角传感器，不适用于利用激光雷达SLAM或图像SLAM进行导航的轨迹跟踪。The invention patent with the publication number CN106926840A discloses an unmanned vehicle limit dynamic trajectory tracking control system, the control system includes: a sensor module, a speed file solving module, namely a calculation control module; the speed file solving module receives the position parameters collected by the sensor module , is mapped to the desired trajectory to obtain the ideal position, and is solved to obtain the desired longitudinal vehicle speed of the ideal position, and the motion parameters collected by the sensor module combined with the expected longitudinal vehicle speed are input into the calculation and control module to obtain the brake servo angle required by the whole vehicle, The driving force required to drive the motor and the steering angle of the steering gear are used to control the movement of the unmanned vehicle. The sensors used in this method include an inertial navigation system, a shock absorber displacement sensor, and a rotation angle sensor, which are not suitable for trajectory tracking using lidar SLAM or image SLAM for navigation.
发明内容Contents of the invention
为了解决上述的技术问题，本发明提出一种场地无人车轨迹跟踪方法及系统，采用的技术方案为基于贝塞尔曲线拟合的场地无人车轨迹跟踪方法，可以实现场地无人车的精确轨迹跟踪，能够解决场地无人车对于惯性导航设备的依赖问题，且同时适用于基于GPS定位、基于激光雷达SLAM、基于图像SLAM 的无人车轨迹跟踪，并能提高场地无人车轨迹跟踪的精度和可靠性，适合于场地无人车在不同速度、直道、弯道、U型掉头等不同情况下的轨迹跟踪实现。In order to solve the above-mentioned technical problems, the present invention proposes a track tracking method and system for unmanned vehicles on the site. Accurate trajectory tracking can solve the problem of unmanned vehicles on the site relying on inertial navigation equipment, and is also applicable to trajectory tracking of unmanned vehicles based on GPS positioning, lidar SLAM, and image SLAM, and can improve the trajectory tracking of unmanned vehicles on the site High accuracy and reliability, suitable for track tracking of unmanned vehicles in different situations such as different speeds, straight roads, curves, U-turns, etc.
本发明的第一目的是提供一种场地无人车轨迹跟踪方法，包括以下步骤：步骤1：通过场地无人车上安装的传感器获取路点序列Pi空间位置信息，并对所述路点序列Pi进行高阶贝塞尔曲线拟合得到轨迹线；The first purpose of the present invention is to provide a track tracking method for unmanned vehicles on the site, comprising the following steps: Step 1: obtain waypoint sequence Pi spatial position information through the sensor installed on the unmanned vehicle on the site, and track the waypoints The sequence Pi performs high-order Bezier curve fitting to obtain the trajectory line;
步骤2：设定延时时间，将无人车当前位置沿速度方向延长，延长距离为当前时刻速度与延时时间乘积，得到一个延长线端点O(xO，yO)，进而找到路点序列上与所述延长线端点O处最近的点M(xM，yM)；Step 2: Set the delay time, extend the current position of the unmanned vehicle along the speed direction, the extension distance is the product of the current speed and the delay time, and obtain an extension line endpoint O(xO , yO ), and then find the waypoint The point M(xM , yM ) closest to the end point O of the extension line on the sequence;
步骤3：计算当前所述场地无人车与所述M点的横向偏差ex(t)和切线夹角e(t)；步骤4：计算当前车辆转角控制量δ(t)，并应用于所述场地无人车的横向控制策略中。Step 3: Calculate the lateral deviation ex(t) and tangent angle e(t) between the unmanned vehicle and the M point at the current site; Step 4: Calculate the current vehicle corner control amount δ(t), and apply it to the In the lateral control strategy of the unmanned vehicle in the above-mentioned field.
优选的是，所述传感器包括定位定向接收机、车载相机、激光雷达中至少一种。Preferably, the sensor includes at least one of a positioning and orientation receiver, a vehicle-mounted camera, and a laser radar.
在上述任一方案中优选的是，所述步骤1为对所述路点序列Pi进行高阶贝塞尔曲线拟合得到轨迹线，计算公式为其中，B(t)为经过高阶贝塞尔曲线拟合后的路径，n为贝塞尔曲线阶数(阶数取为待拟合点数-1)。Preferably in any of the above schemes, the step 1 is to perform high-order Bezier curve fitting on the waypoint sequence Pi to obtain a trajectory line, and the calculation formula is Among them, B(t) is the path after high-order Bezier curve fitting, and n is the order of the Bezier curve (the order is taken as the number of points to be fitted - 1).
在上述任一方案中优选的是，所述延长线端点O处与M点的横向偏差 ex(t)的计算公式为In any of the above-mentioned schemes, preferably, the calculation formula of the lateral deviation ex(t) between the end point O of the extension line and point M is:
在上述任一方案中优选的是，所述切线夹角e(t)的计算公式为e(t)＝θM-θP，其中θM表示经过高阶贝塞尔曲线拟合后的路径轨迹线M点切线正方向与横轴正方向的夹角，θP表示无人车实时行驶方向所对应的航向角。In any of the above schemes, preferably, the calculation formula of the tangent angle e(t) is e(t)=θM -θP , wherein θM represents the path after high-order Bezier curve fitting The angle between the positive direction of the tangent line at point M on the trajectory line and the positive direction of the horizontal axis, θP represents the heading angle corresponding to the real-time driving direction of the unmanned vehicle.
在上述任一方案中优选的是，所述当前车辆转角控制量δ(t)的计算公式为其中，Vx(t)为当前所述场地无人车的纵向速度， k为增益参数。In any of the above schemes, preferably, the calculation formula of the current vehicle steering angle control amount δ(t) is: Wherein, Vx(t) is the longitudinal velocity of the unmanned vehicle at the current site, and k is the gain parameter.
在上述任一方案中优选的是，所述横向控制策略包括将转角控制量δ(t)乘以一个传动比弱化系数，可以将车轮转角量转化为方向盘转角量，直接输出给控制模块，进行横向控制。In any of the above schemes, it is preferred that the lateral control strategy includes multiplying the steering angle control amount δ(t) by a transmission ratio weakening coefficient, which can convert the wheel rotation angle into a steering wheel rotation angle, and directly output it to the control module. lateral control.
本发明的第二目的是提供一种场地无人车轨迹跟踪系统，包括以下模块：拟合模块：用于通过场地无人车上安装的传感器获取路点序列Pi空间位置信息，并对所述路点序列Pi进行高阶贝塞尔曲线拟合得到轨迹线；The second object of the present invention is to provide a track tracking system for unmanned vehicles on the site, including the following modules: Fitting module: used to obtain the spatial position information of the waypoint sequence Pi through the sensors installed on the unmanned vehicles on the site, and Perform high-order Bezier curve fitting on the waypoint sequence Pi to obtain the trajectory line;
寻点模块：用于设定延时时间，将无人车当前位置沿速度方向延长，延长距离为当前时刻速度与延时时间乘积，得到一个延长线端点O(xo，yO)，进而找到路点序列上与所述延长线端点O处最近的点M(xM，yM)；Point finding module: used to set the delay time, extend the current position of the unmanned vehicle along the speed direction, the extension distance is the product of the current speed and the delay time, and obtain an extension line endpoint O(xo , yO ), and then Find the nearest point M(xM , yM ) at the end point O of the extension line on the waypoint sequence;
计算模块：用于计计算当前所述场地无人车与所述M点的横向偏差ex(t)和切线夹角e(t)；Calculation module: used to calculate the lateral deviation ex (t) and tangent angle e (t) between the unmanned vehicle and the M point in the current site;
应用模块：用于计算当前车辆转角控制量δ(t)，并应用于所述场地无人车的横向控制策略中。Application module: used to calculate the current vehicle steering angle control amount δ(t), and apply it to the lateral control strategy of the field unmanned vehicle.
优选的是，所述传感器包括定位定向接收机、车载相机、激光雷达中至少一种。Preferably, the sensor includes at least one of a positioning and orientation receiver, a vehicle-mounted camera, and a laser radar.
在上述任一方案中优选的是，所述获取模块用于所述路点序列Pi进行高阶贝塞尔曲线拟合得到轨迹线，计算公式为其中，B(t)为经过高阶贝塞尔曲线拟合后的路径，n为贝塞尔曲线阶数(阶数取为待拟合点数-1)。In any of the above schemes, preferably, the acquisition module is used to perform high-order Bezier curve fitting on the waypoint sequence Pi to obtain a trajectory line, and the calculation formula is Among them, B(t) is the path after high-order Bezier curve fitting, and n is the order of the Bezier curve (the order is taken as the number of points to be fitted - 1).
在上述任一方案中优选的是，所述延长线端点O处与M点的横向偏差 ex(t)的计算公式为In any of the above-mentioned schemes, preferably, the calculation formula of the lateral deviation ex(t) between the end point O of the extension line and point M is:
在上述任一方案中优选的是，所述切线夹角e(t)的计算公式为e(t)＝θM-θP，其中θM表示经过高阶贝塞尔曲线拟合后的路径轨迹线M点切线正方向与横轴正方向的夹角，θP表示无人车实时行驶方向所对应的航向。In any of the above schemes, preferably, the calculation formula of the tangent angle e(t) is e(t)=θM -θP , wherein θM represents the path after high-order Bezier curve fitting The angle between the positive direction of the tangent line at point M on the trajectory line and the positive direction of the horizontal axis, θP represents the heading corresponding to the real-time driving direction of the unmanned vehicle.
在上述任一方案中优选的是，所述当前车辆转角控制量δ(t)的计算公式为其中，Vx(t)为当前所述场地无人车的纵向速度， k为增益参数。In any of the above schemes, preferably, the calculation formula of the current vehicle steering angle control amount δ(t) is: Wherein, Vx(t) is the longitudinal velocity of the unmanned vehicle at the current site, and k is the gain parameter.
在上述任一方案中优选的是，所述横向控制策略包括将转角控制量δ(t)乘以一个传动比弱化系数，可以将车轮转角量转化为方向盘转角量，直接输出给控制模块，进行横向控制。In any of the above schemes, it is preferred that the lateral control strategy includes multiplying the steering angle control amount δ(t) by a transmission ratio weakening coefficient, which can convert the wheel rotation angle into a steering wheel rotation angle, and directly output it to the control module. lateral control.
本发明提出了一种场地无人车轨迹跟踪方法和系统，统一了GPS循线、激光雷达轨迹循线、图像轨迹循线算法，即同时适用于三种传感器(GPS设备、激光雷达、车载相机)定位的轨迹跟踪，大大降低场地无人车的设备成本，解决了景区、园区GPS信号被遮挡导致GPS导航信号不可用的问题；同时提升场地无人车的轨迹跟踪的鲁棒性及适应性，进而促进场地无人车技术落地，推动技术福利转化为社会福利。The present invention proposes a track tracking method and system for an unmanned vehicle on site, which unifies the algorithms of GPS tracking, laser radar tracking, and image tracking, and is applicable to three sensors (GPS equipment, laser radar, and vehicle-mounted camera) at the same time. ) positioning trajectory tracking, which greatly reduces the equipment cost of unmanned vehicles on the site, and solves the problem that the GPS navigation signal is unavailable due to the blocking of GPS signals in scenic spots and parks; at the same time, it improves the robustness and adaptability of the track tracking of unmanned vehicles on the site , and then promote the implementation of site unmanned vehicle technology, and promote the transformation of technical benefits into social benefits.
附图说明Description of drawings
图1为按照本发明的场地无人车轨迹跟踪方法的一优选实施例的流程图。Fig. 1 is a flow chart of a preferred embodiment of a track tracking method for an unmanned vehicle at a site according to the present invention.
图2为按照本发明的场地无人车轨迹跟踪系统的一优选实施例的模块图。Fig. 2 is a block diagram of a preferred embodiment of the site unmanned vehicle trajectory tracking system according to the present invention.
图3为按照本发明的场地无人车轨迹跟踪方法的如图1所示实施例的路点信息示意图。FIG. 3 is a schematic diagram of waypoint information of the embodiment shown in FIG. 1 of the track tracking method for field unmanned vehicles according to the present invention.
图4为按照本发明的场地无人车轨迹跟踪方法的如图1所示实施例的横向偏差示意图。FIG. 4 is a schematic diagram of the lateral deviation of the embodiment shown in FIG. 1 of the track tracking method for an unmanned vehicle on site according to the present invention.
图5为按照本发明的场地无人车轨迹跟踪方法的如图1所示实施例的切线夹角示意图。Fig. 5 is a schematic diagram of tangent angles in the embodiment shown in Fig. 1 of the track tracking method for field unmanned vehicles according to the present invention.
图6为按照本发明的场地无人车轨迹跟踪方法的如图1所示实施例的转角控制量示意图。Fig. 6 is a schematic diagram of the corner control amount of the embodiment shown in Fig. 1 of the track tracking method for an unmanned vehicle on site according to the present invention.
图7为按照本发明的场地无人车轨迹跟踪系统的一优选实施例的车载传感器设备位置示意图，Fig. 7 is a schematic diagram of the position of the on-board sensor equipment according to a preferred embodiment of the site unmanned vehicle trajectory tracking system of the present invention,
具体实施方式Detailed ways
下面结合附图和具体的实施例对本发明做进一步的阐述。The present invention will be further elaborated below in conjunction with the accompanying drawings and specific embodiments.
实施例一Embodiment one
如图1、2所示，执行步骤100，使用获取模块200获取传感器信息。通过安装在所述场地无人车上的所述传感器获取包含空间位置信息的引导无人车前进路线的路点序列Pi，路点信息如图3所示，场地无人车上配置有双天线定位定向接收机。执行步骤110，进行全局路点坐标转换，包括：先进行地图文件解析，然后将GPS的WGS-84坐标(World GeodeticSystem-1984 Coordinate System)转换为UTM坐标(Universal Transverse Mercator)描述的平面坐标，再将全局坐标转换为以无人车质点为坐标原点的局部坐标。执行步骤120，执行步骤120，使用拟合模块210对得到的路点序列进行高阶贝塞尔曲线拟合得到轨迹线，计算公式为其中，B(t)为经过高阶贝塞尔曲线拟合后的路径，n为贝塞尔曲线阶数(阶数取为待拟合点数-1)。执行步骤130，使用寻点模块220寻找最近点：设定一个延时时间，将车辆位置沿速度方向延长，延长距离为当前时刻速度与延时时间乘积，得到一个延长线端点。进而找到路点序列上与延长线端点O处最近的点M，坐标M(xM，yM)。执行步骤 140，使用计算模块230计算当前所述场地无人车与所述M点的横向偏差ex(t) 和切线夹角e(t)。忽略无人车的动力学特征，将无人车当成一个质点。在以无人车的质心为中心的局部栅格坐标系中，横向偏差ex(t)就是延长线端点处车辆横向偏差，如图4所示，同时计算可得延长线端点处无人车中轴线和M点处的切线夹角θe(t)，e(t)＝θM-θP，其中θM表示经过高阶贝塞尔曲线拟合后的路径轨迹线M点切线正方向与横轴正方向的夹角，θP表示无人车实时行驶方向所对应的航向角。延长线端点O处无人车中轴线和M点处的切线夹角示意图如图5所示。执行步骤150，使用应用模块240计算转角控制量δ(t)，计算公式为其中，Vx(t)为当前所述场地无人车的纵向速度， k为增益参数，转角控制量示意图如图6所示。执行步骤160，将得到的转角控制量δ(t)乘以一个传动比弱化系数，可以将车轮转角量转化为方向盘转角量，直接输出给控制模块，进行横向控制，场地无人车便可以进行精确的轨迹跟踪。执行步骤170，判断是否到达终点。执行步骤180，轨迹跟踪结束。As shown in FIG. 1 and FIG. 2 , step 100 is executed to acquire sensor information by using the acquisition module 200 . The waypoint sequence Pi that guides the forward route of the unmanned vehicle containing spatial position information is obtained by the sensor installed on the unmanned vehicle at the site. The waypoint information is shown in Figure 3. The unmanned vehicle is equipped with dual Antenna positioning directional receiver. Execute step 110, carry out global waypoint coordinate transformation, comprise: first carry out map file parsing, then WGS-84 coordinate (World GeodeticSystem-1984 Coordinate System) of GPS is converted into the plane coordinate described by UTM coordinate (Universal Transverse Mercator), then Transform the global coordinates into local coordinates with the particle of the unmanned vehicle as the coordinate origin. Execute step 120, execute step 120, use the fitting module 210 to carry out high-order Bezier curve fitting to the obtained waypoint sequence to obtain the trajectory line, the calculation formula is Among them, B(t) is the path after high-order Bezier curve fitting, and n is the order of the Bezier curve (the order is taken as the number of points to be fitted - 1). Execute step 130, use the point finding module 220 to find the closest point: set a delay time, extend the vehicle position along the speed direction, the extension distance is the product of the current speed and the delay time, and obtain an extension line endpoint. Then find the point M closest to the end point O of the extension line on the waypoint sequence, and the coordinates M(xM , yM ). Execute step 140, using the calculation module 230 to calculate the current lateral deviation ex(t) and tangent angle e(t) between the unmanned vehicle on the site and the M point. Ignoring the dynamic characteristics of the unmanned vehicle, the unmanned vehicle is regarded as a particle. In the local grid coordinate system centered on the center of mass of the unmanned vehicle, the lateral deviation ex(t) is the lateral deviation of the vehicle at the endpoint of the extension line, As shown in Figure 4, the tangent angle θe(t) between the central axis of the unmanned vehicle at the end point of the extension line and the pointM can be calculated at the same time, e(t)= θM-θP , where θM represents The angle between the positive direction of the tangent line of point M of the path trajectory after Bezier curve fitting and the positive direction of the horizontal axis, θP represents the heading angle corresponding to the real-time driving direction of the unmanned vehicle. The schematic diagram of the tangent angle between the central axis of the unmanned vehicle at the end point O of the extension line and the point M is shown in Figure 5. Execute step 150, use the application module 240 to calculate the control angle δ(t), the calculation formula is Among them, Vx(t) is the longitudinal speed of the unmanned vehicle at the current site, k is the gain parameter, and the schematic diagram of the control amount of the corner is shown in Figure 6. Execute step 160, multiply the obtained steering angle control amount δ(t) by a transmission ratio weakening coefficient, the steering wheel angle can be converted into the steering wheel angle, and directly output to the control module for lateral control, and the field unmanned vehicle can carry out Accurate trajectory tracking. Step 170 is executed to determine whether the end point is reached. Step 180 is executed, and the trajectory tracking ends.
实施例二Embodiment two
如图1、2所示，执行步骤100，使用获取模块200获取传感器信息。场地无人车车头中央位置上配置有Velodyne16线激光雷达，通过安装在所述场地无人车上的所述传感器获取包含空间位置信息的引导无人车前进路线的路点序列Pi，路点信息如图3所示。执行步骤110，进行全局路点坐标转换，包括：先进行激光雷达SLAM地图文件解析，然后将全局坐标转换为以无人车质点为坐标原点的局部坐标。执行步骤120，使用拟合模块210对得到的路点序列进行高阶贝塞尔曲线拟合得到轨迹线，计算公式为其中，B(t)为经过高阶贝塞尔曲线拟合后的路径，n为贝塞尔曲线阶数(阶数取为待拟合点数-1)。执行步骤130，使用寻点模块220寻找最近点：设定一个延时时间，将无人车位置沿速度方向延长，延长距离为当前时刻速度与延时时间乘积，得到一个延长线端点，进而找到路点序列上与延长线端点O处最近的点M，坐标M(xM,yM)。执行步骤140，使用计算模块230计算当前所述场地无人车与所述M点的横向偏差ex(t)和切线夹角θe(t)。忽略无人车的动力学特征，将无人车当成一个质点。在以无人车的质心为中心的局部栅格坐标系中，横向偏差ex(t)就是延长线端点处车辆横向偏差，如图4所示，同时计算可得延长线端点处无人车中轴线和M点处的切线夹角θe(t)，θe(t)＝θM-θP，延长线端点处无人车中轴线和M点处的切线夹角示意图，如图5所示。执行步骤150，使用应用模块240计算转角控制量δ(t)，计算公式为其中，Vx(t)为当前所述场地无人车的纵向速度，k为增益参数，转角控制量示意图如图6所示。执行步骤160，将得到的转角控制量δ(t)乘以一个传动比弱化系数，可以将车轮转角量转化为方向盘转角量，直接输出给控制模块，进行横向控制，场地无人车便可以进行精确的轨迹跟踪。执行步骤170，判断是否到达终点。执行步骤180，轨迹跟踪结束。As shown in FIG. 1 and FIG. 2 , step 100 is executed to acquire sensor information by using the acquisition module 200 . A Velodyne 16-line laser radar is installed on the central position of the front of the unmanned vehicle on the site, and the waypoint sequence Pi that guides the forward route of the unmanned vehicle containing spatial position information is obtained through the sensor installed on the unmanned vehicle on the site, and the waypoint The information is shown in Figure 3. Step 110 is executed to perform global waypoint coordinate conversion, including: first analyzing the lidar SLAM map file, and then converting the global coordinates into local coordinates with the mass point of the unmanned vehicle as the coordinate origin. Execute step 120, use the fitting module 210 to perform high-order Bezier curve fitting on the obtained waypoint sequence to obtain the trajectory line, and the calculation formula is Among them, B(t) is the path after high-order Bezier curve fitting, and n is the order of the Bezier curve (the order is taken as the number of points to be fitted - 1). Execute step 130, use the point-seeking module 220 to find the closest point: set a delay time, extend the position of the unmanned vehicle along the speed direction, the extension distance is the product of the current speed and the delay time, obtain an extension line endpoint, and then find The nearest point M on the waypoint sequence to the end point O of the extension line, coordinates M(xM ,yM ). Execute step 140, using the calculation module 230 to calculate the current lateral deviation ex(t) and tangent angle θe(t) between the unmanned vehicle on the site and the M point. Ignoring the dynamic characteristics of the unmanned vehicle, the unmanned vehicle is regarded as a particle. In the local grid coordinate system centered on the center of mass of the unmanned vehicle, the lateral deviation ex(t) is the lateral deviation of the vehicle at the endpoint of the extension line, As shown in Figure 4, the tangent angle θe(t) between the central axis of the unmanned vehicle at the end of the extension line and the point M can be calculated at the same time, θe(t)=θM -θP , the unmanned vehicle at the end of the extension line The schematic diagram of the tangent angle between the central axis and point M is shown in Figure 5. Execute step 150, use the application module 240 to calculate the steering angle control amount δ(t), the calculation formula is Among them, Vx(t) is the longitudinal speed of the unmanned vehicle at the current site, k is the gain parameter, and the schematic diagram of the control amount of the corner is shown in Figure 6. Execute step 160, multiply the obtained steering angle control amount δ(t) by a transmission ratio weakening coefficient, convert the wheel angle into steering wheel angle, and directly output it to the control module for lateral control. Accurate trajectory tracking. Step 170 is executed to determine whether the end point is reached. Step 180 is executed, and the trajectory tracking ends.
实施例三Embodiment three
如图1、2所示，执行步骤100，使用获取模块200获取传感器信息。场地无人车车头中央位置上配置有车载单目相机，通过安装在所述场地无人车上的所述传感器获取包含空间位置信息的引导无人车前进路线的路点序列Pi，路点信息如图3所示。执行步骤110，进行全局路点坐标转换，包括：先进行图像SLAM地图文件解析，然后将全局坐标转换为以无人车质点为坐标原点的局部坐标。执行步骤120，使用拟合模块210对得到的路点序列进行高阶贝塞尔曲线拟合得到轨迹线，计算公式为其中，B(t)为经过高阶贝塞尔曲线拟合后的路径，n为贝塞尔曲线阶数(阶数取为待拟合点数-1)。执行步骤130，使用寻点模块220寻找最近点：设定一个延时时间，将无人车位置沿速度方向延长，延长距离为当前时刻速度与延时时间乘积，得到一个延长线端点，进而找到路点序列上与延长线端点O处最近的点 M，坐标M(xM,yM)。执行步骤140，使用计算模块230计算当前所述场地无人车与所述M点的横向偏差ex(t)和切线夹角θe(t)。忽略无人车的动力学特征，将无人车当成一个质点。在以无人车的质心为中心的局部栅格坐标系中，横向偏差ex(t)就是延长线端点处车辆横向偏差，如图4所示，同时计算可得延长线端点处无人车中轴线和M点处的切线夹角θe(t)，θe(t)＝θM-θP，延长线端点处无人车中轴线和M点处的切线夹角示意图，如图5所示。执行步骤150，使用应用模块240计算转角控制量δ(t)，计算公式为其中，Vx(t)为当前所述场地无人车的纵向速度，k为增益参数，转角控制量示意图如图6所示。执行步骤160，将得到的转角控制量δ(t)乘以一个传动比弱化系数，可以将车轮转角量转化为方向盘转角量，直接输出给控制模块，进行横向控制，场地无人车便可以进行精确的轨迹跟踪。执行步骤170，判断是否到达终点。执行步骤180，轨迹跟踪结束。As shown in FIG. 1 and FIG. 2 , step 100 is executed to acquire sensor information by using the acquisition module 200 . A vehicle-mounted monocular camera is arranged on the central position of the head of the unmanned vehicle on the site, and the waypoint sequence Pi that guides the unmanned vehicle's forward route containing spatial position information is obtained through the sensor installed on the unmanned vehicle on the site, and the waypoint The information is shown in Figure 3. Execute step 110 to perform global waypoint coordinate conversion, including: first analyze the image SLAM map file, and then convert the global coordinates into local coordinates with the mass point of the unmanned vehicle as the coordinate origin. Execute step 120, use the fitting module 210 to perform high-order Bezier curve fitting on the obtained waypoint sequence to obtain the trajectory line, and the calculation formula is Among them, B(t) is the path after high-order Bezier curve fitting, and n is the order of the Bezier curve (the order is taken as the number of points to be fitted - 1). Execute step 130, use the point-seeking module 220 to find the closest point: set a delay time, extend the position of the unmanned vehicle along the speed direction, the extension distance is the product of the current speed and the delay time, obtain an extension line endpoint, and then find The nearest point M on the waypoint sequence to the end point O of the extension line, coordinates M(xM ,yM ). Execute step 140, using the calculation module 230 to calculate the current lateral deviation ex(t) and tangent angle θe(t) between the unmanned vehicle on the site and the M point. Ignoring the dynamic characteristics of the unmanned vehicle, the unmanned vehicle is regarded as a particle. In the local grid coordinate system centered on the center of mass of the unmanned vehicle, the lateral deviation ex(t) is the lateral deviation of the vehicle at the endpoint of the extension line, As shown in Figure 4, the tangent angle θe(t) between the central axis of the unmanned vehicle at the end of the extension line and the point M can be calculated at the same time, θe(t)=θM -θP , the unmanned vehicle at the end of the extension line The schematic diagram of the tangent angle between the central axis and point M is shown in Figure 5. Execute step 150, use the application module 240 to calculate the steering angle control amount δ(t), the calculation formula is Among them, Vx(t) is the longitudinal speed of the unmanned vehicle at the current site, k is the gain parameter, and the schematic diagram of the control amount of the corner is shown in Figure 6. Execute step 160, multiply the obtained steering angle control amount δ(t) by a transmission ratio weakening coefficient, convert the wheel angle into steering wheel angle, and directly output it to the control module for lateral control. Accurate trajectory tracking. Step 170 is executed to determine whether the end point is reached. Step 180 is executed, and the trajectory tracking ends.
实施例四Embodiment Four
1、本方法采用了高阶贝塞尔曲线进行无人车轨迹点的曲线拟合，进而计算出无人车中轴线和轨迹最近点处切线的夹角，然后计算无人车转角控制量，从而进行无人车转向的偏差矫正。本方法同时适用于基于GPS定位、基于激光雷达SLAM、基于图像SLAM的无人车轨迹跟踪，也可用于在传感器信息融合的基础上进行智能决策。本方法能解决场地无人车在景区、园区GPS信号被遮挡导致GPS导航信号不可用的问题：当GPS导航信号不可用，可切换为基于激光雷达SLAM或基于图像SLAM的无人车轨迹跟踪。1. This method uses high-order Bezier curves for curve fitting of unmanned vehicle trajectory points, and then calculates the angle between the central axis of the unmanned vehicle and the tangent line at the nearest point of the trajectory, and then calculates the control amount of the unmanned vehicle's rotation angle. In this way, the deviation correction of the steering of the unmanned vehicle is performed. This method is also applicable to unmanned vehicle trajectory tracking based on GPS positioning, lidar SLAM and image SLAM, and can also be used for intelligent decision-making based on sensor information fusion. This method can solve the problem that the unmanned vehicle on the site is blocked by the GPS signal in the scenic area and the park, resulting in the unavailable GPS navigation signal: when the GPS navigation signal is unavailable, it can switch to unmanned vehicle trajectory tracking based on lidar SLAM or image SLAM.
2、使用本方法，无人车无需配备价格昂贵的惯性导航设备，就可以进行高精度的轨迹跟踪，无人车导航设备只需配备定位接收机，大大降低了场地无人车的设备成本，对场地无人车的轨迹跟踪提供了一种可行的低成本解决方案。2. Using this method, the unmanned vehicle can perform high-precision trajectory tracking without the need for expensive inertial navigation equipment. The unmanned vehicle navigation equipment only needs to be equipped with a positioning receiver, which greatly reduces the equipment cost of the site unmanned vehicle. Trajectory tracking of unmanned vehicles on the field provides a feasible and low-cost solution.
3、本方法适合于场地无人车在不同速度、直道、弯道、U型掉头等不同情况下实现稳定、精确的轨迹跟踪，具有较好的动态跟踪性能和较高的横向偏差矫正性能，避免了速度较高时或者在道路曲率变化时的画龙现象。3. This method is suitable for unmanned vehicles on the field to achieve stable and accurate trajectory tracking under different conditions such as different speeds, straight roads, curves, and U-turns. It has better dynamic tracking performance and higher lateral deviation correction performance. It avoids the phenomenon of drawing dragons when the speed is high or when the curvature of the road changes.
实施例五Embodiment five
结合实例进行详细阐述。Elaborate in detail with examples.
1、场地无人车平台1. Site unmanned vehicle platform
本实例采用的是北京联合大学机器人学院自主研发的“小旋风”系列无人巡逻车，如图7所示，无人车具体配置如下：This example uses the "Little Cyclone" series of unmanned patrol vehicles independently developed by the School of Robotics of Beijing Union University, as shown in Figure 7. The specific configuration of the unmanned vehicles is as follows:
车体长2.78米，宽1.2米，高1.8米，额定乘员两人，续航里程100km，最大爬坡度30％，最大行驶速度24km/h，支持人工/无人驾驶功能切换。车辆配备联适R60S双天线定位定向接收机700、元橡科技车载单目相机710、 Velodyne16线激光雷达720和围绕车身12个超声波雷达等传感设备，并配置 i7四核高性能工业控制计算机。The car body is 2.78 meters long, 1.2 meters wide, and 1.8 meters high, with a rated occupant of two, a cruising range of 100km, a maximum gradient of 30%, and a maximum speed of 24km/h. It supports manual/unmanned driving function switching. The vehicle is equipped with Lianshi R60S dual-antenna positioning and directional receiver 700, Yuan Oak Technology vehicle-mounted monocular camera 710, Velodyne 16-line lidar 720, and 12 ultrasonic radars around the vehicle body, and is equipped with i7 quad-core high-performance industrial control computer.
本实例在Linux系统下的ROS操作平台上运行无人车轨迹跟踪算法实现。This example is realized by running the unmanned vehicle trajectory tracking algorithm on the ROS operating platform under the Linux system.
2、轨迹跟踪算法实现2. Realization of trajectory tracking algorithm
首先通过场地无人车上安装的定位定向接收机获得包含空间位置信息的引导车辆前进路线的路点序列Pi。First, through the positioning and orientation receiver installed on the unmanned vehicle at the site, the waypoint sequence Pi of the guiding vehicle's advancing route containing spatial position information is obtained.
对得到的路点序列进行高阶贝塞尔曲线拟合得到轨迹线。高阶贝塞尔曲线段为A high-order Bezier curve is fitted to the obtained waypoint sequence to obtain a trajectory line. Higher-order Bezier curve segments are
其中Pi,(i∈n)为先验地图中的路点序列。where Pi,(i∈n) is the waypoint sequence in the prior map.
根据高阶贝塞尔曲线拟合的轨迹线，寻找无人车当前位置的延长线端点处与拟合轨迹线的最近点M(xM,yM)。计算当前无人车与M点的横向偏差ex(t)，如图4所示；同时计算当前无人车中轴线和M点处的切线夹角θe(t)，θe(t)＝θM-θP，如图5所示。According to the trajectory line fitted by the high-order Bezier curve, find the closest point M(xM ,yM ) between the end point of the extension line of the current position of the unmanned vehicle and the fitted trajectory line. Calculate the lateral deviation ex(t) between the current unmanned vehicle and point M, As shown in Figure 4; at the same time, calculate the tangent angle θe(t) between the central axis of the current unmanned vehicle and pointM , θe(t)=θM-θP , as shown in Figure 5.
进而计算当前车辆转角控制量δ(t)。Then calculate the current vehicle steering angle control amount δ(t).
其中：in:
θe(t)为延长线端点处无人车中轴线和M点处的切线夹角；θe(t) is the tangent angle between the central axis of the unmanned vehicle at the endpoint of the extension line and the point M;
ex(t)为延长线端点处无人车与最近点M的偏差；ex(t) is the deviation between the unmanned vehicle at the endpoint of the extension line and the nearest point M;
Vx(t)为当前无人车的纵向速度；Vx(t) is the longitudinal velocity of the current unmanned vehicle;
k为增益参数。k is a gain parameter.
将得到的δ(t)应用于场地无人车的横向控制策略当中：将δ(t)乘以一个传动比弱化系数，可以将车轮转角量转化为方向盘转角量，直接输出给控制模块，进行横向控制，场地无人车可以进行精确的轨迹跟踪。Apply the obtained δ(t) to the lateral control strategy of the field unmanned vehicle: multiply δ(t) by a transmission ratio weakening coefficient, the wheel angle can be converted into the steering wheel angle, and directly output to the control module to carry out With lateral control, the field unmanned vehicle can perform precise trajectory tracking.
为了更好地理解本发明，以上结合本发明的具体实施例做了详细描述，但并非是对本发明的限制。凡是依据本发明的技术实质对以上实施例所做的任何简单修改，均仍属于本发明技术方案的范围。本说明书中每个实施例重点说明的都是与其它实施例的不同之处，各个实施例之间相同或相似的部分相互参见即可。对于系统实施例而言，由于其与方法实施例基本对应，所以描述的比较简单，相关之处参见方法实施例的部分说明即可。In order to better understand the present invention, the above has been described in detail in conjunction with specific embodiments of the present invention, but it is not intended to limit the present invention. Any simple modification made to the above embodiments according to the technical essence of the present invention still belongs to the scope of the technical solution of the present invention. What each embodiment in this specification focuses on is the difference from other embodiments, and the same or similar parts between the various embodiments can be referred to each other. As for the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and for the related parts, please refer to the part of the description of the method embodiment.
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810570937.0ACN108646748A (en) | 2018-06-05 | 2018-06-05 | A kind of place unmanned vehicle trace tracking method and system |
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810570937.0ACN108646748A (en) | 2018-06-05 | 2018-06-05 | A kind of place unmanned vehicle trace tracking method and system |
| Publication Number | Publication Date |
|---|---|
| CN108646748Atrue CN108646748A (en) | 2018-10-12 |
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810570937.0APendingCN108646748A (en) | 2018-06-05 | 2018-06-05 | A kind of place unmanned vehicle trace tracking method and system |
| Country | Link |
|---|---|
| CN (1) | CN108646748A (en) |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109407674A (en)* | 2018-12-19 | 2019-03-01 | 中山大学 | The path following method of Pure Pursuit combination PI based on genetic algorithm setting parameter |
| CN109683616A (en)* | 2018-12-26 | 2019-04-26 | 芜湖哈特机器人产业技术研究院有限公司 | A kind of straight line path bootstrap technique of list steering wheel postposition driving mobile platform |
| CN109726489A (en)* | 2019-01-02 | 2019-05-07 | 腾讯科技（深圳）有限公司 | A kind of method and system for establishing auxiliary driving data library |
| CN109901586A (en)* | 2019-03-27 | 2019-06-18 | 厦门金龙旅行车有限公司 | A kind of unmanned vehicle tracking control method, device, equipment and storage medium |
| CN110187372A (en)* | 2019-06-20 | 2019-08-30 | 北京联合大学 | A low-speed unmanned vehicle integrated navigation method and system in the park |
| CN110264586A (en)* | 2019-05-28 | 2019-09-20 | 浙江零跑科技有限公司 | L3 grades of automated driving system driving path data acquisitions, analysis and method for uploading |
| CN110789526A (en)* | 2019-10-18 | 2020-02-14 | 清华大学 | A method to overcome the large pure lag in lateral control of driverless vehicles |
| CN111142527A (en)* | 2019-12-31 | 2020-05-12 | 陕西欧卡电子智能科技有限公司 | Tracking control method for arbitrary path of unmanned ship |
| CN111158379A (en)* | 2020-01-16 | 2020-05-15 | 合肥中科智驰科技有限公司 | Steering wheel zero-bias self-learning unmanned vehicle track tracking method |
| CN111176298A (en)* | 2020-01-21 | 2020-05-19 | 广州赛特智能科技有限公司 | Unmanned vehicle track recording and tracking method |
| CN111193987A (en)* | 2019-12-27 | 2020-05-22 | 新石器慧通（北京）科技有限公司 | Method and device for directionally playing sound by vehicle and unmanned vehicle |
| CN111203870A (en)* | 2018-11-22 | 2020-05-29 | 深圳市优必选科技有限公司 | Steering gear motion control method, device and terminal equipment |
| CN111547066A (en)* | 2020-04-27 | 2020-08-18 | 中汽研(天津)汽车信息咨询有限公司 | Vehicle trajectory tracking method, device, equipment and storage medium |
| CN111949036A (en)* | 2020-08-25 | 2020-11-17 | 重庆邮电大学 | A trajectory tracking control method, system and two-wheel differential mobile robot |
| CN112578792A (en)* | 2020-11-12 | 2021-03-30 | 东风汽车集团有限公司 | Crossroad auxiliary control method and storage medium |
| CN112849222A (en)* | 2019-11-28 | 2021-05-28 | 中车株洲电力机车研究所有限公司 | Steering control method and device for following shaft |
| CN112945586A (en)* | 2021-01-29 | 2021-06-11 | 深圳一清创新科技有限公司 | Chassis deviation calibration method and device and unmanned automobile |
| CN113701756A (en)* | 2021-08-04 | 2021-11-26 | 东南大学 | Novel self-adaptive method for planning and tracking parking path of unmanned vehicle |
| CN114089730A (en)* | 2020-07-30 | 2022-02-25 | 上海快仓智能科技有限公司 | Robot motion planning method and automatic guided vehicle |
| CN115542925A (en)* | 2022-11-28 | 2022-12-30 | 安徽中科星驰自动驾驶技术有限责任公司 | Accurate deviation estimation method for transverse control of unmanned vehicle |
| CN116520857A (en)* | 2023-07-05 | 2023-08-01 | 华东交通大学 | Vehicle track tracking method |
| WO2025060782A1 (en)* | 2023-09-18 | 2025-03-27 | 上海联适导航技术股份有限公司 | Yaw rate-based trajectory tracking method for tracked vehicle |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000137833A (en)* | 1998-10-29 | 2000-05-16 | Mitsubishi Materials Corp | Device and method for track generation and recording medium thereof |
| CN101592731A (en)* | 2009-07-09 | 2009-12-02 | 浙江大学 | A method for processing side-scan sonar towfish track based on track file |
| CN104035446A (en)* | 2014-05-30 | 2014-09-10 | 深圳市大疆创新科技有限公司 | Unmanned aerial vehicle course generation method and system |
| CN106004996A (en)* | 2016-06-23 | 2016-10-12 | 北京智行者科技有限公司 | Intelligent vehicle steering control method and system |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000137833A (en)* | 1998-10-29 | 2000-05-16 | Mitsubishi Materials Corp | Device and method for track generation and recording medium thereof |
| CN101592731A (en)* | 2009-07-09 | 2009-12-02 | 浙江大学 | A method for processing side-scan sonar towfish track based on track file |
| CN104035446A (en)* | 2014-05-30 | 2014-09-10 | 深圳市大疆创新科技有限公司 | Unmanned aerial vehicle course generation method and system |
| CN106004996A (en)* | 2016-06-23 | 2016-10-12 | 北京智行者科技有限公司 | Intelligent vehicle steering control method and system |
| Title |
|---|
| LU SONG 等: "A Bezier curve based on path tracking in Computer Generated Force", 《2015 12TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD)》* |
| 余伶俐 等: "基于贝塞尔曲线的机器人非时间轨迹跟踪方法", 《仪器仪表学报》* |
| 张琨: "智能汽车自主循迹控制策略研究", 《中国博士学位论文全文数据库工程科技Ⅱ辑》* |
| 王聪: "基于预瞄的车辆路径跟踪控制研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》* |
| 靳欣宇 等: "基于Stanley算法的自适应最优预瞄模型研究", 《计算机工程》* |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111203870B (en)* | 2018-11-22 | 2021-12-17 | 深圳市优必选科技有限公司 | Steering engine motion control method and device and terminal equipment |
| CN111203870A (en)* | 2018-11-22 | 2020-05-29 | 深圳市优必选科技有限公司 | Steering gear motion control method, device and terminal equipment |
| CN109407674A (en)* | 2018-12-19 | 2019-03-01 | 中山大学 | The path following method of Pure Pursuit combination PI based on genetic algorithm setting parameter |
| CN109683616A (en)* | 2018-12-26 | 2019-04-26 | 芜湖哈特机器人产业技术研究院有限公司 | A kind of straight line path bootstrap technique of list steering wheel postposition driving mobile platform |
| CN109683616B (en)* | 2018-12-26 | 2021-07-09 | 芜湖哈特机器人产业技术研究院有限公司 | A linear path guidance method for a single steering wheel rear drive mobile platform |
| CN109726489A (en)* | 2019-01-02 | 2019-05-07 | 腾讯科技（深圳）有限公司 | A kind of method and system for establishing auxiliary driving data library |
| CN109726489B (en)* | 2019-01-02 | 2022-03-29 | 腾讯科技（深圳）有限公司 | Method and system for establishing driving assistance database |
| CN109901586A (en)* | 2019-03-27 | 2019-06-18 | 厦门金龙旅行车有限公司 | A kind of unmanned vehicle tracking control method, device, equipment and storage medium |
| CN110264586A (en)* | 2019-05-28 | 2019-09-20 | 浙江零跑科技有限公司 | L3 grades of automated driving system driving path data acquisitions, analysis and method for uploading |
| CN110187372A (en)* | 2019-06-20 | 2019-08-30 | 北京联合大学 | A low-speed unmanned vehicle integrated navigation method and system in the park |
| CN110187372B (en)* | 2019-06-20 | 2021-11-02 | 北京联合大学 | A method and system for integrated navigation in a low-speed unmanned vehicle park |
| CN110789526A (en)* | 2019-10-18 | 2020-02-14 | 清华大学 | A method to overcome the large pure lag in lateral control of driverless vehicles |
| CN110789526B (en)* | 2019-10-18 | 2021-05-18 | 清华大学 | Method for overcoming large pure lag of transverse control of unmanned automobile |
| CN112849222A (en)* | 2019-11-28 | 2021-05-28 | 中车株洲电力机车研究所有限公司 | Steering control method and device for following shaft |
| CN111193987A (en)* | 2019-12-27 | 2020-05-22 | 新石器慧通（北京）科技有限公司 | Method and device for directionally playing sound by vehicle and unmanned vehicle |
| CN111142527B (en)* | 2019-12-31 | 2023-08-11 | 陕西欧卡电子智能科技有限公司 | Tracking control method for arbitrary path of unmanned ship |
| CN111142527A (en)* | 2019-12-31 | 2020-05-12 | 陕西欧卡电子智能科技有限公司 | Tracking control method for arbitrary path of unmanned ship |
| CN111158379B (en)* | 2020-01-16 | 2022-11-29 | 合肥中科智驰科技有限公司 | Steering wheel zero-bias self-learning unmanned vehicle track tracking method |
| CN111158379A (en)* | 2020-01-16 | 2020-05-15 | 合肥中科智驰科技有限公司 | Steering wheel zero-bias self-learning unmanned vehicle track tracking method |
| CN111176298A (en)* | 2020-01-21 | 2020-05-19 | 广州赛特智能科技有限公司 | Unmanned vehicle track recording and tracking method |
| CN111176298B (en)* | 2020-01-21 | 2023-04-07 | 广州赛特智能科技有限公司 | Unmanned vehicle track recording and tracking method |
| CN111547066B (en)* | 2020-04-27 | 2021-11-30 | 中汽信息科技(天津)有限公司 | Vehicle trajectory tracking method, device, equipment and storage medium |
| CN111547066A (en)* | 2020-04-27 | 2020-08-18 | 中汽研(天津)汽车信息咨询有限公司 | Vehicle trajectory tracking method, device, equipment and storage medium |
| CN114089730B (en)* | 2020-07-30 | 2024-03-26 | 上海快仓智能科技有限公司 | Robot motion planning method and automatic guiding vehicle |
| CN114089730A (en)* | 2020-07-30 | 2022-02-25 | 上海快仓智能科技有限公司 | Robot motion planning method and automatic guided vehicle |
| CN111949036B (en)* | 2020-08-25 | 2022-08-02 | 重庆邮电大学 | Trajectory tracking control method and system and two-wheeled differential mobile robot |
| CN111949036A (en)* | 2020-08-25 | 2020-11-17 | 重庆邮电大学 | A trajectory tracking control method, system and two-wheel differential mobile robot |
| CN112578792B (en)* | 2020-11-12 | 2022-05-31 | 东风汽车集团有限公司 | Crossroad auxiliary control method and storage medium |
| CN112578792A (en)* | 2020-11-12 | 2021-03-30 | 东风汽车集团有限公司 | Crossroad auxiliary control method and storage medium |
| CN112945586A (en)* | 2021-01-29 | 2021-06-11 | 深圳一清创新科技有限公司 | Chassis deviation calibration method and device and unmanned automobile |
| CN112945586B (en)* | 2021-01-29 | 2023-10-27 | 深圳一清创新科技有限公司 | Chassis deflection calibration method and device and unmanned automobile |
| CN113701756A (en)* | 2021-08-04 | 2021-11-26 | 东南大学 | Novel self-adaptive method for planning and tracking parking path of unmanned vehicle |
| CN113701756B (en)* | 2021-08-04 | 2024-05-31 | 东南大学 | Novel self-adaptive unmanned vehicle reversing and warehousing path planning and tracking method |
| CN115542925A (en)* | 2022-11-28 | 2022-12-30 | 安徽中科星驰自动驾驶技术有限责任公司 | Accurate deviation estimation method for transverse control of unmanned vehicle |
| CN116520857A (en)* | 2023-07-05 | 2023-08-01 | 华东交通大学 | Vehicle track tracking method |
| CN116520857B (en)* | 2023-07-05 | 2023-09-08 | 华东交通大学 | Vehicle track tracking method |
| WO2025060782A1 (en)* | 2023-09-18 | 2025-03-27 | 上海联适导航技术股份有限公司 | Yaw rate-based trajectory tracking method for tracked vehicle |
| Publication | Publication Date | Title |
|---|---|---|
| CN108646748A (en) | A kind of place unmanned vehicle trace tracking method and system | |
| CN102393744B (en) | Navigation method of pilotless automobile | |
| CN102495631B (en) | Intelligent control method of driverless vehicle tracking desired trajectory | |
| CN107839683B (en) | A kind of automobile emergency collision avoidance control method considering moving obstacle | |
| CN107132563B (en) | Combined navigation method combining odometer and dual-antenna differential GNSS | |
| AU2017421869A1 (en) | Systems and methods for path determination | |
| CN115951678A (en) | Automatic driving system for electronic guide rubber-tyred vehicle | |
| CN111696339B (en) | Car following control method and system for automatic driving fleet and car | |
| CN211956223U (en) | A lane change trajectory planning system | |
| CN102591332A (en) | Device and method for local path planning of pilotless automobile | |
| CN111137298B (en) | Vehicle automatic driving method, device, system and storage medium | |
| US20190163201A1 (en) | Autonomous Vehicle Sensor Compensation Using Displacement Sensor | |
| CN109814550B (en) | A unmanned transport vechicle for sealing garden | |
| CN111413990A (en) | Lane change track planning system | |
| CN111452786B (en) | An obstacle avoidance method and system for an unmanned vehicle | |
| Péter et al. | Vision and odometry based autonomous vehicle lane changing | |
| CN111708010B (en) | Mobile equipment positioning method, device and system and mobile equipment | |
| Wang et al. | ECASS: Edge computing based auxiliary sensing system for self-driving vehicles | |
| CN108508899A (en) | A kind of automatic driving vehicle Trajectory Tracking Control driving system | |
| Ying et al. | A sliding mode control approach to longitudinal control of vehicles in a platoon | |
| CN117369450A (en) | Vehicle dynamic safety path planning method and system in V2X environment | |
| JP7591463B2 (en) | Vehicle position estimation device and vehicle position estimation method | |
| CN107544496A (en) | A kind of method for planning track for improving cosine lane-change model | |
| CN107992039A (en) | A kind of method for planning track based on flow field under dynamic environment | |
| Lu et al. | A hybrid fusion algorithm for integrated INS/UWB navigation and its application in vehicle platoon formation control |
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | Application publication date:20181012 | |
| RJ01 | Rejection of invention patent application after publication |