WO2021098663A1 - 一种四轮独立转向-独立驱动车辆轨迹跟踪方法和系统 - Google Patents

一种四轮独立转向-独立驱动车辆轨迹跟踪方法和系统 Download PDF

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WO2021098663A1
WO2021098663A1 PCT/CN2020/129215 CN2020129215W WO2021098663A1 WO 2021098663 A1 WO2021098663 A1 WO 2021098663A1 CN 2020129215 W CN2020129215 W CN 2020129215W WO 2021098663 A1 WO2021098663 A1 WO 2021098663A1
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vehicle body
point
foresight
trajectory
vehicle
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French (fr)
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徐坤
肖卫星
李慧云
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中国科学院深圳先进技术研究院
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2720/00—Output or target parameters relating to overall vehicle dynamics
    • B60W2720/24—Direction of travel
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2720/00—Output or target parameters relating to overall vehicle dynamics
    • B60W2720/28—Wheel speed

Definitions

  • the invention relates to the technical field of vehicle automatic control, in particular to a four-wheel independent steering-independent driving vehicle trajectory tracking method and system.
  • Vehicle trajectory tracking refers to a given target trajectory, control the vehicle to quickly converge on the target trajectory, and follow the target trajectory.
  • Current trajectory tracking algorithms can be roughly divided into three categories: model-based control algorithms, feedback error-based control algorithms, and geometric relationship-based control algorithms.
  • Model-based control algorithms need to establish accurate vehicle kinematics and dynamics models, taking into account the influence of tire cornering force and slip rate on trajectory tracking performance.
  • the control algorithm based on feedback error does not need to establish a system model, and treats the controlled system as a black box, and adjusts the controlled quantity according to the feedback error, so that the system error tends to zero.
  • the geometric relationship-based control algorithm calculates the steering angle control amount according to the geometric relationship between the body pose and the target trajectory, and controls the vehicle to travel along the target trajectory.
  • the geometric relationship-based control algorithm calculates the steering angle control amount according to the geometric relationship between the body pose and the target trajectory. The calculation is simple and easy to implement. It is commonly used to solve the problem of vehicle trajectory tracking.
  • Typical geometric relationship tracking algorithms include Pure Pursuit (pure tracking) method, Vector Pursuit (vector tracking) method and Stanley method.
  • the pure tracking method selects the foresight point on the target trajectory according to the foresight distance constraint.
  • the foresight distance constraint refers to the Euclidean distance between the reference point of the vehicle body and the foresight point.
  • a circular arc trajectory that can be executed by the vehicle is fitted, and the vehicle speed and rotation angle control amount are calculated according to the curvature information of the fitted circular arc to control the vehicle to travel along the target trajectory.
  • the performance of the pure tracking method mainly depends on the selection of the foresight point. If the foresight distance is too small, the vehicle body will oscillate inside and outside near the target trajectory. When the forward distance is too large, the vehicle body cannot fit the target trajectory well in the curve, resulting in a decrease in trajectory tracking accuracy.
  • the initial position of the vehicle is not necessarily on the target trajectory, and there is an initial lateral error between the vehicle and the target trajectory.
  • the existing pure tracking method only considers the constraint of the foresight distance when selecting the foresight point on the target trajectory.
  • the lateral error is large, the vehicle body will have a large yaw rate, which will cause the vehicle body to swing inside and outside near the target trajectory, the error convergence time is long, and the initial stage trajectory tracking error is large.
  • the traditional pure tracking method usually adopts a fixed forward distance constraint, which cannot automatically adapt to the curvature change of the target trajectory, resulting in the phenomenon of "curve edge trimming" at the curve.
  • the purpose of the present invention is to overcome the above-mentioned defects of the prior art and provide a four-wheel independent steering-independently driven vehicle trajectory tracking method and system.
  • a four-wheel independent steering-independent driving vehicle trajectory tracking method includes:
  • a point on the target trajectory that satisfies both the foresight distance constraint and the radial distance constraint is selected as the foresight point, where the radial distance constraint is the product of the radial distance coefficient and the lateral error, and the lateral error is the result.
  • the rotational speed control amount and the turning angle control amount of the wheels are obtained according to the angular velocity and the linear velocity at the reference point of the vehicle body to control the vehicle to travel along the target trajectory.
  • the foresight distance constraint is dynamically adjusted according to the curvature information of the target trajectory.
  • the foresight distance constraint is set according to the following steps:
  • the foresight distance constraint is dynamically adjusted according to the curvature information of the multiple waypoints and the curvature information of the closest waypoint of the vehicle body.
  • the multiple waypoints include a waypoint 1.5m ahead of the closest waypoint of the vehicle body and a waypoint 3m ahead of the closest waypoint of the vehicle body.
  • dynamically adjusting the foresight distance constraint according to the curvature information of the multiple path points and the curvature information of the closest path point of the vehicle body includes:
  • the association relationship is used to output the foresight distance constraint in an anti-fuzzy manner as a basis for selecting the foresight point.
  • the radial distance coefficient is preset to 0.6 or 0.9 or 1.2.
  • a four-wheel independent steering-independently driven vehicle trajectory tracking system includes:
  • Foresight point selection module used to select points on the target trajectory that satisfy both the foresight distance constraint and the radial distance constraint as the foresight point, where the radial distance constraint is the difference between the radial distance coefficient and the lateral error product;
  • Arc trajectory calculation module used to fit an arc trajectory passing through the foresight point and the car body reference point according to the current position relationship between the foresight point and the vehicle body and calculate the radius of the arc trajectory;
  • Angular velocity calculation module used to calculate the angular velocity at the reference point of the vehicle body according to the set vehicle speed and the radius of the arc trajectory;
  • Steering control module used to obtain the rotational speed control quantity and the turning angle control quantity of the wheels according to the angular velocity and linear velocity at the reference point of the vehicle body to control the vehicle to travel along the target trajectory.
  • the foresight point selection module is also used to execute:
  • the foresight distance constraint is dynamically adjusted according to the curvature information of the multiple waypoints and the curvature information of the closest waypoint of the vehicle body.
  • the present invention has the advantage that: in view of the problems of vehicle body yaw and "curve edge trimming" in the trajectory tracking process of the existing pure tracking method, the present invention introduces the introduction when selecting the foresight point.
  • the radial distance constraint effectively solves the vehicle body yaw problem that occurs during the error convergence process; for the "curve edge trimming problem", the present invention builds a forward distance adaptive fuzzy controller, which is automatically based on the curvature information of the target trajectory.
  • the foresight distance is dynamically adjusted adaptively to ensure the tracking accuracy of the algorithm on the variable curvature trajectory.
  • Fig. 1 is a flowchart of a four-wheel independent steering-independently driven vehicle trajectory tracking method according to an embodiment of the present invention
  • FIG. 2 is an overall architecture diagram of a method for tracking vehicle trajectory according to an embodiment of the present invention
  • FIG. 3 is a schematic diagram of a kinematics model of a four-wheel independent steering-independent drive vehicle according to an embodiment of the present invention
  • Figure 4 is a schematic diagram of the vehicle body yaw
  • Fig. 5 is a schematic diagram of a vehicle trajectory tracking method according to an embodiment of the present invention.
  • Fig. 6 is a schematic diagram of a membership function of path point curvature information according to an embodiment of the present invention.
  • the vehicle trajectory tracking method proposed in the present invention is an adaptive pure tracking method under radial distance constraints, which can realize the trajectory tracking of four-wheel independent steering-independently driven unmanned ground vehicles.
  • Distance constraint and radial distance constraint are defined as the distance between the path point closest to the vehicle body on the target trajectory and the foresight point.
  • the introduction of radial distance constraint can solve the problem of internal and external swing of the car body when the lateral error is large.
  • the present invention builds a forward distance adaptive fuzzy controller, which dynamically adjusts the forward distance adaptively and dynamically according to the curvature information of the target trajectory, thereby ensuring the tracking accuracy at the curve.
  • the vehicle trajectory tracking method of the embodiment of the present invention includes: step S110, selecting a point on the target trajectory that satisfies both the foresight distance constraint and the radial distance constraint as the foresight point; step S120, according to the previous The current position relationship between the probe point and the vehicle body is fitted to fit a circular arc trajectory passing through the forward probe point and the vehicle reference point, and the radius of the circular arc trajectory is calculated; step S130, calculation is performed according to the set vehicle speed and the radius of the circular arc trajectory Obtain the angular velocity at the reference point of the vehicle body; step S140, obtain the rotational speed control amount and the rotation angle control amount of the wheel according to the angular velocity and the linear velocity at the reference point of the vehicle body to control the vehicle to travel along the target trajectory.
  • step S110 selecting a point on the target trajectory that satisfies both the foresight distance constraint and the radial distance constraint as the foresight point
  • step S120 according to the previous The current position
  • Figure 2 is the overall architecture of the technical solution of the present invention, in which lidar is used to obtain environmental information, the SLAM module outputs the pose information of the vehicle based on point cloud data, and the target trajectory outputs curvature information.
  • the trajectory tracking method calculates the curvature of the fitted trajectory in the current control cycle based on the pose information and the curvature information.
  • the motion decomposition module calculates the wheel speed and the control amount of the rotation angle according to the output curvature information to control the vehicle's progress toward the target trajectory.
  • lidar provides real-time pose information of the vehicle, and the target trajectory is a discrete path point. Cubic spline interpolation is performed on the path point to approximate the curvature information of the target trajectory.
  • the vehicle trajectory in the current control is calculated according to the geometric relationship between the body pose and the target trajectory. Based on the established vehicle kinematics model, the motion decomposition is performed to calculate the wheel speed and the control amount of the turning angle. In practical applications, the control value can be sent to the vehicle controller through the CAN line, and the vehicle controller controls the vehicle to travel along the target trajectory.
  • a kinematics model of a four-wheel independent steering-independent drive vehicle as shown in FIG. 3 is built.
  • the instantaneous steering of the vehicle is preset on the extension line of the vehicle's central axis.
  • the relationship between the wheel speed, the angle of rotation and the speed and angular velocity at the reference point can be derived:
  • [alpha] i is the i-th wheel steering angle
  • ⁇ is the angular velocity at the reference point of the vehicle body
  • v i is the i-th wheel rotational speed
  • L is the vehicle wheel base
  • B is a vehicle track .
  • the pure tracking method will calculate a trajectory passing through the foresight point and the car body reference point.
  • R is the radius of the fitted trajectory
  • V is the speed at the car body reference point
  • the car body reference point is the geometric center of the car body.
  • the existing pure tracking method selects the foresight point, and only considers the foresight distance constraint.
  • the specific algorithm process includes: starting from the path point closest to the vehicle body, calculating whether the distance between the path point and the reference point of the vehicle body is greater than the foresight distance constraint, until finding a path point whose linear distance from the reference point of the vehicle body is greater than the foresight distance, the path The point is set as the foresight point; according to the current position relationship between the foresight point and the car body, a circular trajectory passing through the foresight point and the car body reference point is fitted, and the radius of the arc trajectory is calculated; the movement of the vehicle is set in advance Speed, the angular velocity can be calculated according to the vehicle speed and the radius of the arc trajectory; based on the built kinematics model, the angular velocity and linear velocity at the reference point of the car body can be decomposed into the rotation speed of the wheel and the control amount of the turning angle.
  • the vehicle body when the lateral error is large, the vehicle body will have a large yaw, which causes the vehicle body to swing in and out near the target trajectory, as shown in Fig. 4.
  • the reason for the large yaw of the vehicle body is the insufficient radial distance of the forward detection point.
  • the vehicle must converge to the target trajectory within a limited radial distance. Therefore, the existing pure tracking algorithm will control the vehicle body to a large yaw rate. Go on the target trajectory.
  • a larger yaw rate can help the lateral error converge quickly, but due to the large yaw rate, the lateral error will increase in the opposite direction after converging to near zero, causing the vehicle body to swing inside and outside near the target trajectory.
  • the radial distance constraint is embodied in the selection of the foresight point.
  • the traditional pure tracking method only has the foresight distance constraint when selecting the foresight point.
  • the embodiment of the present invention introduces the radial distance constraint into the selection of the foresight point, and the foresight point needs to satisfy the foresight distance constraint and the radial distance constraint at the same time.
  • the radial distance coefficient can be set based on experience, for example, set to 0.6, 0.9, 1.2, etc. After setting the radial distance coefficient, the radial distance constraint can be calculated, and then the foresight point can be selected.
  • the radial distance is defined as the distance between the closest path point and the foresight point.
  • the closest path point refers to the point on the target trajectory that is closest to the vehicle reference point, and the lateral error refers to the vehicle reference point. The distance between the point and the closest path point.
  • the foresight distance constraint is set to a fixed value.
  • the fixed foresight distance cannot meet the requirements of the variable curvature trajectory for the foresight distance
  • it is preferable to set the A relatively small foresight distance is used to ensure the accuracy of trajectory tracking.
  • a relatively large foresight distance is set at the straight line section with small curvature, and the foresight distance constraint is dynamically set by adapting to the curvature information, thereby improving the body motion Ride comfort.
  • the embodiment of the present invention builds a fuzzy controller.
  • the input of the fuzzy controller is the distance between the nearest path point of the vehicle body, the path point 1.5m in front of the vehicle body (that is, the path point directly in front of the vehicle body nearest path point on the target trajectory), and the path point 3m in front of the vehicle body
  • Curvature information the output is real-time forward distance constraints.
  • the input interval of curvature information is [0m -1 , 0.4m -1 ]
  • the fuzzy domain is small (small), middle (middle), and larger (large).
  • the membership function is shown in Figure 6.
  • the foresight distance range of the fuzzy controller output is [0.6m, 1.5m], and the correlation (or inference rules) between the input and output of the fuzzy controller can be obtained from the actual vehicle test.
  • the set fuzzy controller inference rules are shown in Table 1 below:
  • the foresight distance constraint can be obtained by the anti-fuzzy method.
  • the foresight distance constraint adopts the weighted average calculation method.
  • the membership function the current path point can be known.
  • the curvature membership and the fuzzy amount at 1.5m and 3m ahead The probability.
  • the real value corresponding to the small, medium, and large blur of the forward distance is 0.6m, 1.2m, and 1.5m.
  • the calculation formula for anti-blurring distance is:
  • u Ai is the membership degree of the i-th inference rule with curvature at the nearest path point
  • u Bi is the membership degree of the i-th inference rule with curvature at 1.5m
  • u Ci is the membership of the i-th inference rule with curvature at 3m
  • Z i is the value of the foresight distance of the i-th inference rule.
  • a curvature point of the current path 0.12m -1, front curvature of 1.5m to 0.28m -1, 3m at the front curvature of 0.35m -1.
  • the membership degree function can be used to calculate the small, medium and large fuzzy membership degree of the trajectory curvature at the nearest path point as 0.8, 0.4, 0.
  • the membership degrees of the small, medium, and large ambiguity of the trajectory curvature at 1.5m ahead are 0, 0.6, 0.8.
  • the membership degree of the small, medium, and large fuzzy amount of the trajectory curvature 3m ahead is 0, 0, 1, then:
  • the foresight distance constraint in the embodiment of the present invention ensures the accuracy of trajectory tracking
  • the radial distance constraint ensures the smoothness of the body movement
  • the dynamic and adaptive adjustment of the foresight distance constraint according to the curvature information ensures the accuracy of trajectory tracking.
  • the present invention also provides a four-wheel independent steering-independent drive vehicle trajectory tracking system for implementing one or more aspects of the above method.
  • the system includes: a foresight point selection module, which is used to select a point on the target trajectory that meets both the foresight distance constraint and the radial distance constraint as the foresight point;
  • the current position relationship between the probe point and the vehicle body is fitted to fit an arc trajectory passing through the forward probe point and the reference point of the vehicle body and calculate the radius of the arc trajectory;
  • the angular velocity calculation module is used to set the vehicle speed and the circle
  • the arc trajectory radius calculates the angular velocity at the reference point of the vehicle body;
  • the steering control module is used to obtain the rotational speed control value and the turning angle control value of the wheel according to the angular velocity and linear velocity at the vehicle reference point to control the vehicle to travel along the target trajectory.
  • the average lateral error of the existing pure tracking method is 0.23m, and the time for the vehicle body to converge on the target trajectory is 15.3s ,
  • the maximum wheel steering angle during trajectory tracking is 56.9°, while the average lateral error of the technical solution proposed by the present invention is 0.209m, the time for the vehicle body to converge on the target trajectory is 7.5s, and the maximum vehicle steering angle is 30.2°.
  • the initial lateral error is set to 1m
  • the vehicle speed is 0.6m/s
  • the average lateral error of the traditional pure tracking method is 0.121m
  • the average lateral error of the technical solution proposed by the present invention is 0.085m. It can be seen from the experimental results that the trajectory tracking accuracy, error convergence speed and ride comfort of the vehicle body during the tracking process of the present invention are all higher than those of the existing pure tracking method.
  • the present invention may be a system, a method and/or a computer program product.
  • the computer program product may include a computer-readable storage medium loaded with computer-readable program instructions for enabling a processor to implement various aspects of the present invention.
  • the computer-readable storage medium may be a tangible device that holds and stores instructions used by the instruction execution device.
  • the computer-readable storage medium may include, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing, for example.
  • Non-exhaustive list of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) Or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device, such as a printer with instructions stored thereon
  • RAM random access memory
  • ROM read-only memory
  • EPROM erasable programmable read-only memory
  • flash memory flash memory
  • SRAM static random access memory
  • CD-ROM compact disk read-only memory
  • DVD digital versatile disk
  • memory stick floppy disk
  • mechanical encoding device such as a printer with instructions stored thereon

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Abstract

本发明提供一种四轮独立转向-独立驱动车辆轨迹跟踪方法和系统。该方法包括：在目标轨迹上选择同时满足前探距离约束和径向距离约束的点作为前探点，其中，所述径向距离约束是径向距离系数和横向误差之间的乘积，所述横向误差是所述目标轨迹上与车身基准点之间的最短距离；根据所述前探点和车身当前的位置关系，拟合出一条经过所述前探点和车身基准点的圆弧轨迹并计算圆弧轨迹的半径；根据设定车速和所述圆弧轨迹半径计算出车身基准点处的角速度；根据车身基准点处的角速度和线速度获得车轮的转速控制量和转角控制量以控制车辆沿所述目标轨迹行进。本发明的方法和系统改善了轨迹跟踪精度、误差收敛速度以及跟踪过程中车身的平顺性。

Description

一种四轮独立转向-独立驱动车辆轨迹跟踪方法和系统 技术领域
本发明涉及车辆自动控制技术领域，尤其涉及一种四轮独立转向-独立驱动车辆轨迹跟踪方法和系统。
背景技术
车辆轨迹跟踪是指给定一条目标轨迹，控制车辆快速收敛到目标轨迹上，并沿着目标轨迹行进。当前的轨迹跟踪算法大致可以分为三类：基于模型的控制算法、基于反馈误差的控制算法以及基于几何关系的控制算法。基于模型的控制算法需要建立准确的车辆运动学模型和动力学模型，考虑轮胎的侧偏力和滑移率对轨迹跟踪性能的影响。基于反馈误差的控制算法，不需要建立系统模型，将被控系统看作黑箱，根据反馈误差来调整被控量，使得系统误差趋于零。基于几何关系的控制算法根据车身位姿与目标轨迹之间的几何关系计算转角控制量，控制车辆沿目标轨迹行进。通常情况下，建立准确的车辆运动学和动力学模型难度较大，而且车辆模型都是非线性的，这导致基于模型的控制算法计算复杂，难以应用于实际场景中。基于反馈误差的控制算法，根据反馈误差来修正控制量，系统的输出控制量存在延迟，难以满足轨迹跟踪的实时性要求。基于几何关系的控制算法，根据车身位姿与目标轨迹之间的几何关系，计算转角控制量，计算简单易于实现，普遍用于解决车辆的轨迹跟踪问题。
典型的几何关系跟踪算法有Pure Pursuit(纯追踪)方法、Vector Pursuit(矢量追踪)方法和Stanley方法。纯追踪方法根据前探距离约束在目标轨迹上选取前探点。前探距离约束是指车身基准点与前探点之间的欧式距离。基于前探点和车身基准点之间的位置关系，拟合出一条车辆可以执行的圆弧轨迹，根据拟合圆弧的曲率信息计算车辆转速、转角控制量来控制车辆沿目标轨迹行进。纯追踪方法的性能主要取决于前探点的选取，前探距离过小时，车身会在目标轨迹附近内外震荡。前探距离过大时，车身在弯道处不能很好地贴合目标轨迹，导致轨迹跟踪精度下降。
在实际应用场景中，车辆初始位置不一定在目标轨迹上，车辆与目标轨迹之间存在初始横向误差。现有纯追踪方法在目标轨迹上选取前探点时，只考虑了前探距离约束。在横向误差较大时，车身会出现一个较大的横摆角速度，导致车身在目标轨迹附近内外摆动，误差收敛时间长，初始阶段轨迹跟踪误差大。此外，传统的纯追踪方法通常采用固定的前探距离约束，无法自动适应目标轨迹的曲率变化，导致在弯道处会出现“弯道切边”现象。
发明内容
本发明的目的在于克服上述现有技术的缺陷，提供一种四轮独立转向-独立驱动车辆轨迹跟踪方法和系统。
根据本发明的第一方面，提供了一种四轮独立转向-独立驱动车辆轨迹跟踪方法。该方法包括：
在目标轨迹上选择同时满足前探距离约束和径向距离约束的点作为前探点，其中，所述径向距离约束是径向距离系数和横向误差之间的乘积，所述横向误差是所述目标轨迹与车身基准点之间的最短距离；
根据所述前探点和车身当前的位置关系，拟合出一条经过所述前探点和车身基准点的圆弧轨迹并计算圆弧轨迹的半径；
根据设定车速和所述圆弧轨迹半径计算出车身基准点处的角速度；
根据车身基准点处的角速度和线速度获得车轮的转速控制量和转角控制量以控制车辆沿所述目标轨迹行进。
在一些实施例中，适应于所述目标轨迹的曲率信息动态地调整所述前探距离约束。
在一些实施例中，根据以下步骤设置所述前探距离约束：
在所述目标轨迹上，选择与车身基准点距离最短的车身最近路径点，并在所述车身最近路径点的前方选择多个路径点；
计算所述车身最近路径点的曲率信息和所述多个路径点的曲率信息；
根据所述多个路径点的曲率信息和所述车身最近路径点的曲率信息动态调整所述前探距离约束。
在一些实施例中，所述多个路径点包括所述车身最近路径点前方1.5m处的路径点和所述车身最近路径点前方3m处的路径点。
在一些实施例中，根据所述多个路径点的曲率信息和所述车身最近路 径点的曲率信息动态调整所述前探距离约束包括：
利用模糊控制器构建所述多个路径点的曲率信息、所述车身最近路径点的曲率信息与所述前探距离约束之间的关联关系；
利用所述关联关系通过反模糊方式输出所述前探距离约束，作为选择前探点的依据。
在一些实施例中，所述径向距离系数预先设置为0.6或0.9或1.2。
根据本发明的第二方面，提供一种四轮独立转向-独立驱动车辆轨迹跟踪系统。该系统包括：
前探点选择模块：用于在目标轨迹上选择同时满足前探距离约束和径向距离约束的点作为前探点，其中，所述径向距离约束是径向距离系数和横向误差之间的乘积；
圆弧轨迹计算模块：用于根据所述前探点和车身当前的位置关系，拟合出一条经过所述前探点和车身基准点的圆弧轨迹并计算圆弧轨迹的半径；
角速度计算模块：用于根据设定车速和所述圆弧轨迹半径计算出车身基准点处的角速度；
转向控制模块：用于根据车身基准点处的角速度和线速度获得车轮的转速控制量和转角控制量以控制车辆沿所述目标轨迹行进。
在一些实施例中，所述前探点选择模块还用于执行：
在所述目标轨迹上，选择与车身基准点距离最短的车身最近路径点，并在所述车身最近路径点的前方选择多个路径点；
计算所述车身最近路径点的曲率信息和所述多个路径点的曲率信息；
根据所述多个路径点的曲率信息和所述车身最近路径点的曲率信息动态调整所述前探距离约束。
与现有技术相比，本发明的优点在于：针对现有的纯追踪方法在轨迹跟踪过程中会出现车身横摆和“弯道切边”的问题，本发明在选取前探点时引入了径向距离约束，有效地解决车身在误差收敛过程中出现的车身横摆问题；针对“弯道切边问题”，本发明搭建了前探距离自适应模糊控制器，根据目标轨迹的曲率信息自适应地动态调整前探距离，保证算法在变曲率轨迹上的跟踪精度。
附图说明
以下附图仅对本发明作示意性的说明和解释，并不用于限定本发明的范围，其中：
图1是根据本发明一个实施例的四轮独立转向-独立驱动车辆轨迹跟踪方法的流程图；
图2是实现本发明实施例的车辆轨迹跟踪方法的总体架构图；
图3是根据本发明一个实施例的四轮独立转向-独立驱动车辆运动学模型的示意图；
图4是车身横摆示意图；
图5是根据本发明一个实施例的车辆轨迹跟踪方法的示意图；
图6是根据本发明一个实施例的路径点曲率信息隶属度函数的示意图。
具体实施方式
为了使本发明的目的、技术方案、设计方法及优点更加清楚明了，以下结合附图通过具体实施例对本发明进一步详细说明。应当理解，此处所描述的具体实施例仅用于解释本发明，并不用于限定本发明。
在本文示出和讨论的所有例子中，任何具体值应被解释为仅仅是示例性的，而不是作为限制。因此，示例性实施例的其它例子可以具有不同的值。
对于相关领域普通技术人员已知的技术、方法和设备可能不作详细讨论，但在适当情况下，所述技术、方法和设备应当被视为说明书的一部分。
本发明提出的车辆轨迹跟踪方法，是一种径向距离约束下的自适应纯追踪方法，能够实现四轮独立转向-独立驱动无人地面车辆的轨迹跟踪，选取前探点时同时考虑前探距离约束和径向距离约束。在本文中，径向距离约束定义为目标轨迹上距离车身最近的路径点与前探点之间的距离。引入径向距离约束能够解决横向误差较大时，车身出现的内外摆动问题。此外，针对“弯道切边”问题，本发明搭建前探距离自适应模糊控制器，根据目标轨迹的曲率信息，自适应地动态调整前探距离，从而保证了在弯道处的跟踪精度。
参见图1所示，本发明实施例的车辆轨迹跟踪方法包括：步骤S110，在目标轨迹上选择同时满足前探距离约束和径向距离约束的点作为前探点；步骤S120，根据所述前探点和车身当前的位置关系，拟合出一条经过 所述前探点和车身基准点的圆弧轨迹并计算圆弧轨迹的半径；步骤S130，根据设定车速和所述圆弧轨迹半径计算出车身基准点处的角速度；步骤S140，根据车身基准点处的角速度和线速度获得车轮的转速控制量和转角控制量以控制车辆沿所述目标轨迹行进。以下结合图2至图6具体介绍。
图2是本发明技术方案的总体架构，其中使用激光雷达获取环境信息，SLAM模块基于点云数据输出车辆的位姿信息，目标轨迹输出曲率信息。轨迹跟踪方法基于位姿信息和曲率信息计算当前控制周期内拟合轨迹的曲率，运动分解模块根据输出的曲率信息计算车轮转速、转角控制量来控制整车向目标轨迹的行进。例如，激光雷达提供车辆的实时位姿信息，目标轨迹为离散的路径点，对路径点进行三次样条曲线插值，可以近似目标轨迹的曲率信息。根据车身位姿和目标轨迹之间几何关系计算当前控制内的车辆轨迹，基于建立的车辆运动学模型，进行运动分解，计算出车轮的转速、转角控制量。在实际应用中，可将控制量通过CAN线下发至整车控制器，由整车控制器控制车辆沿着目标轨迹行进。
在本发明实施例中，基于阿克曼转向原理，搭建了如图3所示的四轮独立转向-独立驱动车辆的运动学模型。将车辆的瞬时转向预设在车辆中轴的延长线上。此外，选定车身底盘的几何中心为基准点，可以推导出车轮转速、转角与基准点处的速度、角速度之间的关系：
Figure PCTCN2020129215-appb-000001
Figure PCTCN2020129215-appb-000002
Figure PCTCN2020129215-appb-000003
Figure PCTCN2020129215-appb-000004
Figure PCTCN2020129215-appb-000005
在公式(1)-(5)中α i为第i个车轮的转向角，ω为车身基准点处角速度，v i为第i个车轮的转速，L为车辆轴距，B为车辆轮距。纯追踪方 法会计算出一条通过前探点和车身基准点的轨迹，R为拟合轨迹的半径，V为车身基准点处的速度，车身基准点是车身的几何中心。
现有的纯追踪方法选取前探点，只考虑到了前探距离约束。具体的算法流程包括：从距离车身最近的路径点开始，计算路径点与车身基准点的距离是否大于前探距离约束，直至找到与车身基准点直线距离大于前探距离的路径点，将该路径点设定为前探点；根据前探点和车身当前的位置关系，拟合出一条经过前探点和车身基准点的圆弧轨迹，并计算圆弧轨迹的半径；提前设定车辆的运动速度，根据车速和圆弧轨迹半径可以计算出角速度；基于搭建的运动学模型，可以将车身基准点处的角速度和线速度分解为车轮的转速、转角控制量。
现有的纯追踪方法在横向误差较大时，车身会出现较大的横摆，导致车身会在目标轨迹附近内外摆动，如图4所示。车身出现大幅度横摆的原因是前探点的径向距离不足，车辆必须在有限的径向距离内收敛到目标轨迹上，因此现有纯追踪算法会控制车身以很大的横摆角速度向目标轨迹行进。较大的横摆角速度可以帮助横向误差快速收敛，但是由于横摆角速度大，横向误差收敛到零附近之后会反向增长，导致车身在目标轨迹附近内外摆动。
本发明实施例在纯追踪方法的基础上，引入径向距离约束L r，径向距离约束设置为径向距离系数K r与横向误差d之间的乘积(即L r＝K r·d)。径向距离约束体现在前探点的选取上，传统的纯追踪方法选取前探点时只有前探距离约束。本发明实施例将径向距离约束引入到前探点的选择，前探点需要同时满足前探距离约束和径向距离约束。
在一个实施例中，径向距离系数可以根据经验设定，例如，设置为0.6、0.9、1.2等，设定径向距离系数之后，可以计算出径向距离约束，进而选取前探点。
如图5所示，在本文中，径向距离定义为最近路径点与前探点之间的距离，最近路径点是指目标轨迹上与车身基准点距离最近的点，横向误差是指车身基准点和最近路径点之间的距离。在目标轨迹上选取前探点时，候选路径点需要同时满足前探距离约束和径向距离约束才可以成为前探点。
在一个实施例中，将前探距离约束设置为固定值，然而考虑到固定的前探距离是无法满足变曲率轨迹对前探距离的要求，优选地，在曲率较大 的弯道处，设置相对小的前探距离，以保证轨迹跟踪的精度，在曲率较小的直线段处，设置相对较大的前探距离，通过适应于曲率信息来动态设置前探距离约束，从而提高车身运动的平顺性。
进一步地，为获得目标轨迹的曲率信息与前探距离之间的对应关系以及实现前探距离约束自适应动态调整。本发明实施例搭建了模糊控制器，模糊控制器的输入量为车身最近路径点、车身前方(即目标轨迹上车身最近路径点的正前方)1.5m处路径点、车身前方3m处路径点的曲率信息，输出量为实时的前探距离约束。曲率信息的输入区间为[0m -1，0.4m -1]，模糊论域为small(小)，middle(中)，larger(大)三个区间，隶属度函数如图6所示。模糊控制器输出的前探距离范围为[0.6m，1.5m]，模糊控制器输入输出之间的关联(或称推理规则)可以从实车试验中获取。例如，设定的模糊控制器推理规则如下表1所示：
表1：模糊控制器推理规则
推理规则序号 最近路径点曲率 前方1.5m处曲率 前方3m处曲率 前探距离
1 小 小 小 大
2 小 小 中 中
3 小 小 大 中
4 小 中 / 中
5 大 / / 小
6 中 小 / 中
7 中 中 / 中
8 中 大 / 小
基于上表1，前探距离约束可通过反模糊方式获得，前探距离约束采用加权平均的计算方法，根据隶属度函数可以知道当前路径点，前方1.5m处，3m处的曲率隶属与模糊量的概率。前探距离模糊量小，中，大对应的真实数值为0.6m，1.2m，1.5m。例如，前探距离反模糊的计算公式为：
Figure PCTCN2020129215-appb-000006
其中，u Ai为最近路径点处曲率的第i条推理规则隶属度，u Bi为1.5m 处曲率的第i条推理规则隶属度，u Ci为3m处曲率的第i条推理规则隶属度。Z i为第i条推理规则前探距离数值。
例如，当前路径点曲率为0.12m -1，前方1.5m处的曲率为0.28m -1，前方3m处的曲率为0.35m -1。由隶属度函数可以计算出最近路径点处轨迹曲率的小，中，大模糊量隶属度为0.8，0.4，0。前方1.5m处轨迹曲率的小，中，大模糊量隶属度为0，0.6，0.8。前方3m处轨迹曲率的小，中，大模糊量隶属度为0，0，1，则：
La＝(0.8×0.6×1.2+0.4×0.6×1.2+0.4×0.8×0.6)/(0.8×0.6+0.4×0.6+0.4×0.8)＝1.015m。
在上述La计算中没有列出的其他项是因为存在隶属度为0的模糊量。
综上，本发明实施例的前探距离约束保证了轨迹跟踪的准确性，径向距离约束保证了车身运动的平顺性，根据曲率信息动态自适应调整前探距离约束保证了轨迹跟踪的精度。
相应地，本发明还提供一种四轮独立转向-独立驱动车辆轨迹跟踪系统，用于实现上述方法的一个或多个方面。例如，该系统包括：前探点选择模块，用于在目标轨迹上选择同时满足前探距离约束和径向距离约束的点作为前探点；圆弧轨迹计算模块，其用于根据所述前探点和车身当前的位置关系，拟合出一条经过所述前探点和车身基准点的圆弧轨迹并计算圆弧轨迹的半径；角速度计算模块，其用于根据设定车速和所述圆弧轨迹半径计算出车身基准点处的角速度；转向控制模块，其用于根据车身基准点处的角速度和线速度获得车轮的转速控制量和转角控制量以控制车辆沿所述目标轨迹行进。
为了验证本发明技术方案的可行性，在实验场地中进行了实车试验。设置了跟踪直线目标轨迹，和跟踪曲线目标轨迹两组实验。对比了现有纯追踪方法与本发明提出方法之间的性能差异。在直线目标轨迹的跟踪实验中，设定初始横向误差为1.5m，车速为0.6m/s，其中现有纯追踪方法的平均横向误差为0.23m，车身收敛到目标轨迹上的时间为15.3s，轨迹跟踪过程中的最大车轮转向角为56.9°，而本发明提出的技术方案的平均横向误差为0.209m，车身收敛到目标轨迹上的时间为7.5s，最大车辆转向角为30.2°。在曲线目标轨迹跟踪实验中，初始横向误差设定为1m，车速为0.6m/s，传统纯追踪方法的平均横向误差为0.121m，而本发明提出技术方案的平均横向误差为0.085m。从实验结果可以看出，本发明的轨迹跟踪精 度、误差收敛速度和跟踪过程中车身的平顺性均高于现有的纯追踪方法。
需要说明的是，虽然上文按照特定顺序描述了各个步骤，但是并不意味着必须按照上述特定顺序来执行各个步骤，实际上，这些步骤中的一些可以并发执行，甚至改变顺序，只要能够实现所需要的功能即可。
本发明可以是系统、方法和/或计算机程序产品。计算机程序产品可以包括计算机可读存储介质，其上载有用于使处理器实现本发明的各个方面的计算机可读程序指令。
计算机可读存储介质可以是保持和存储由指令执行设备使用的指令的有形设备。计算机可读存储介质例如可以包括但不限于电存储设备、磁存储设备、光存储设备、电磁存储设备、半导体存储设备或者上述的任意合适的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、静态随机存取存储器(SRAM)、便携式压缩盘只读存储器(CD-ROM)、数字多功能盘(DVD)、记忆棒、软盘、机械编码设备、例如其上存储有指令的打孔卡或凹槽内凸起结构、以及上述的任意合适的组合。
以上已经描述了本发明的各实施例，上述说明是示例性的，并非穷尽性的，并且也不限于所披露的各实施例。在不偏离所说明的各实施例的范围和精神的情况下，对于本技术领域的普通技术人员来说许多修改和变更都是显而易见的。本文中所用术语的选择，旨在最好地解释各实施例的原理、实际应用或对市场中的技术改进，或者使本技术领域的其它普通技术人员能理解本文披露的各实施例。

Claims (10)

  1. 一种四轮独立转向-独立驱动车辆轨迹跟踪方法，包括：
    在目标轨迹上选择同时满足前探距离约束和径向距离约束的点作为前探点，其中，所述径向距离约束是径向距离系数和横向误差之间的乘积，所述横向误差是所述目标轨迹与车身基准点之间的最短距离；
    根据所述前探点和车身当前的位置关系，拟合出一条经过所述前探点和车身基准点的圆弧轨迹并计算圆弧轨迹的半径；
    根据设定车速和所述圆弧轨迹半径计算出车身基准点处的角速度；
    根据车身基准点处的角速度和线速度获得车轮的转速控制量和转角控制量以控制车辆沿所述目标轨迹行进。
  2. 根据权利要求1所述的四轮独立转向-独立驱动车辆轨迹跟踪方法，其中，适应于所述目标轨迹的曲率信息动态地调整所述前探距离约束。
  3. 根据权利要求2所述的四轮独立转向-独立驱动车辆轨迹跟踪方法，其中，根据以下步骤设置所述前探距离约束：
    在所述目标轨迹上，选择与车身基准点距离最短的车身最近路径点，并在所述车身最近路径点的前方选择多个路径点；
    计算所述车身最近路径点的曲率信息和所述多个路径点的曲率信息；
    根据所述多个路径点的曲率信息和所述车身最近路径点的曲率信息动态调整所述前探距离约束。
  4. 根据权利要求3所述的四轮独立转向-独立驱动车辆轨迹跟踪方法，其中，所述多个路径点包括所述车身最近路径点前方1.5m处的路径点和所述车身最近路径点前方3m处的路径点。
  5. 根据权利要求3所述的四轮独立转向-独立驱动车辆轨迹跟踪方法，其中，根据所述多个路径点的曲率信息和所述车身最近路径点的曲率信息动态调整所述前探距离约束包括：
    利用模糊控制器构建所述多个路径点的曲率信息、所述车身最近路径点的曲率信息与所述前探距离约束之间的关联关系；
    利用所述关联关系通过反模糊方式输出所述前探距离约束，作为选择前探点的依据。
  6. 根据权利要求1所述的四轮独立转向-独立驱动车辆轨迹跟踪方法，其中，所述径向距离系数预先设置为0.6或0.9或1.2。
  7. 一种四轮独立转向-独立驱动车辆轨迹跟踪系统，包括：
    前探点选择模块：用于在目标轨迹上选择同时满足前探距离约束和径向距离约束的点作为前探点，其中，所述径向距离约束是径向距离系数和横向误差之间的乘积；
    圆弧轨迹计算模块：用于根据所述前探点和车身当前的位置关系，拟合出一条经过所述前探点和车身基准点的圆弧轨迹并计算圆弧轨迹的半径；
    角速度计算模块：用于根据设定车速和所述圆弧轨迹半径计算出车身基准点处的角速度；
    转向控制模块：用于根据车身基准点处的角速度和线速度获得车轮的转速控制量和转角控制量以控制车辆沿所述目标轨迹行进。
  8. 根据权利要求7所述的四轮独立转向-独立驱动车辆轨迹跟踪系统，其中，所述前探点选择模块还用于执行：
    在所述目标轨迹上，选择与车身基准点距离最短的车身最近路径点，并在所述车身最近路径点的前方选择多个路径点；
    计算所述车身最近路径点的曲率信息和所述多个路径点的曲率信息；
    根据所述多个路径点的曲率信息和所述车身最近路径点的曲率信息动态调整所述前探距离约束。
  9. 一种计算机可读存储介质，其上存储有计算机程序，其中，该程序被处理器执行时实现根据权利要求1至7中任一项所述方法的步骤。
  10. 一种计算机设备，包括存储器和处理器，在所述存储器上存储有能够在处理器上运行的计算机程序，其特征在于，所述处理器执行所述程序时实现权利要求1至7中任一项所述的方法的步骤。
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