CN114756021A - Path tracking method and device and path tracking equipment - Google Patents
Path tracking method and device and path tracking equipment Download PDFInfo
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- CN114756021A CN114756021A CN202210196496.9A CN202210196496A CN114756021A CN 114756021 A CN114756021 A CN 114756021A CN 202210196496 A CN202210196496 A CN 202210196496A CN 114756021 A CN114756021 A CN 114756021A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
- G05D1/0278—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using satellite positioning signals, e.g. GPS
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
Abstract
The application provides a path tracking method, which relates to the technical field of robots, wherein the method comprises the following steps: acquiring target planning path information and operation information of the robot in the current motion period, determining a path deviation model of the next motion period according to the target planning path, and determining the motion speed of the next motion period according to the current motion speed, the path deviation model and the path category. The target planning path information comprises a target planning path and a path category to which a target sub-path corresponding to the target planning path belongs, and the operation information comprises the current movement speed of the robot. The technical scheme that this application provided can reduce the driftage distance of robot when the robot drifts to reduce the actual movement track that the robot leads to because the driftage and the deviation of planning the route, and then improved the operating efficiency of robot.
Description
Technical Field
The present application relates to the field of robotics, and in particular, to a path tracking method, apparatus, and path tracking device.
Background
With the continuous development of artificial intelligence technology, the application of mobile robots is more and more extensive. Before the robot works, the working area of the robot and the planned path operated by the robot are generally confirmed and set in the working area, and then the robot is allowed to execute the operation according to the planned path.
When the robot tracks according to the planned path, due to factors such as terrain, self structure and positioning error, the actual moving track of the robot and the planned path are prone to have path deviation, so that the coverage rate of the robot to an operation area is reduced, and meanwhile, the path deviation is larger and larger along with the accumulation of time, so that more time is consumed in the process that the robot needs to return to the planned path after deviating from the planned path, and the operation efficiency is greatly influenced.
Disclosure of Invention
In view of this, the present application provides a path tracking method, a path tracking device, and a path tracking apparatus, so as to reduce a deviation between an actual moving trajectory and a planned path of a robot during a moving process, and improve coverage of the robot on a working area and working efficiency.
In order to achieve the above object, in a first aspect, an embodiment of the present application provides a path tracking method applied to a robot, including:
acquiring target planning path information and operation information of a current motion cycle of the robot, wherein the target planning path information comprises a target planning path and a path category to which a target sub-path corresponding to the target planning path belongs, and the operation information comprises a current motion speed of the robot;
determining a path deviation model of the next motion cycle according to the target planned path, wherein the path deviation model is used for calculating the deviation condition of the robot to the target planned path in the future moment;
and determining the target movement speed of the robot in the next movement period according to the current movement speed, the path deviation model and the path category.
As an optional implementation manner of the embodiment of the present application, the determining a path deviation model of a next motion cycle according to the target planned path includes:
acquiring a target sub-path corresponding to the current position in the target planning path;
acquiring the position information of the robot in each sampling moment;
acquiring the actual path offset of the position information deviating from the target sub-path;
and determining a path deviation model of the next motion period according to the actual path deviation.
As an optional implementation manner of this embodiment of this application, the determining, according to the current movement speed, the path deviation model, and the path category, a target movement speed of the robot in a next movement cycle includes:
acquiring motion information of the robot in the next motion period and an error increment coefficient of the path deviation model; the motion information comprises a current position and an actual path offset of the current position relative to a target planning path;
determining the predicted path offset of the robot in the next motion cycle according to the actual path offset and the path deviation model;
calculating a target movement speed according to the actual path offset, the predicted path offset, the error increment coefficient, the path category and the current movement speed;
and taking the target movement speed as the movement speed of the robot in the next movement period.
As an optional implementation manner of this embodiment of the present application, the calculating the target movement speed according to the actual path offset, the predicted path offset, the error increment coefficient, the path category, and the current movement speed includes:
calculating a target error increment coefficient according to the actual path offset, the predicted path offset and the error increment coefficient;
and calculating to obtain the target linear velocity according to the target error increment coefficient, the path category and the current motion velocity.
As an optional implementation manner of the embodiment of the present application, the target motion speed includes a target angular speed, and the calculating, according to the actual path offset, the predicted path offset, the error increment coefficient, the path category, and the current motion speed, obtains the target motion speed further includes:
acquiring the forward looking distance of the robot in the next motion cycle according to the path category;
acquiring the offset distance of the robot deviating from the target planning path;
acquiring an offset distance range;
when the offset distance is smaller than the minimum value in the offset distance range, calculating to obtain the target angular velocity according to the forward looking distance, the offset distance and the target linear velocity;
when the offset distance is within the offset distance range, determining a proportional increment according to the offset distance, and determining a target angular velocity according to the forward looking distance, the offset distance, the target linear velocity and the proportional increment.
As an optional implementation manner of the embodiment of the present application, the method further includes:
when the offset distance is larger than the offset distance range, constructing a first target planning path;
and updating the target planning path to the first target planning path, and returning to execute the step of determining the path deviation model of the next motion period according to the target planning path.
As an optional implementation manner of the embodiment of the present application, the method further includes:
acquiring all path points of the target planning path and slopes between adjacent path points;
dividing the target planning path into different target sub-paths according to the change condition of the slope, and determining the path category corresponding to the target sub-paths;
the path categories include straight path and curved path.
As an optional implementation manner of this embodiment of the present application, when the path category of the target sub-path is a curved path, the obtaining a forward-looking distance of the robot in a next movement cycle according to the path category includes:
acquiring the slope between adjacent path points in the curve path;
performing linear fitting on the slope between the adjacent path points to divide the curve path into curve sub-paths with different curvatures, wherein connecting points between the curve sub-paths are forward-looking distance points which are used for determining the end point position of a forward-looking distance;
acquiring a forward-looking distance point of the next motion cycle from the forward-looking distance points;
and acquiring the forward looking distance according to the current position and the forward looking distance point.
In a second aspect, an embodiment of the present application provides a path tracking apparatus, including:
an acquisition module: the system comprises a target planning path information acquisition unit, a target sub-path acquisition unit and a target planning path information acquisition unit, wherein the target planning path information acquisition unit is used for acquiring target planning path information and operation information of a current motion cycle of the robot, the target planning path information comprises a target planning path and a path category to which a target sub-path corresponding to the target planning path belongs, and the operation information comprises a current motion speed of the robot;
a path deviation model determination module: a path deviation model used for determining the next motion cycle according to the target planned path, wherein the path deviation model is used for calculating the deviation condition of the robot to the target planned path in the future moment;
a movement velocity determination module: the motion speed of the robot in the next motion cycle is determined according to the current motion speed, the path deviation model and the path category.
In a third aspect, an embodiment of the present application provides a path tracking device, including: a memory for storing a computer program and a processor; the processor is configured to perform the method of the first aspect or any of the embodiments of the first aspect when the computer program is invoked.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method described in the first aspect or any implementation manner of the first aspect.
In a fifth aspect, an embodiment of the present application provides a computer program product, which when run on the path tracking apparatus, causes the path tracking apparatus to execute the path tracking method according to any one of the first aspect.
The path tracking method, the path tracking device and the path tracking equipment obtain target planning path information and operation information of a robot in a current motion period, determine a path deviation model of a next motion period according to the target planning path, and determine a motion speed of the next motion period according to the current motion speed, the path deviation model and the path category. The target planning path information comprises a target planning path and a path category to which a target sub-path corresponding to the target planning path belongs, the running information comprises the current movement speed of the robot, and the path deviation model is used for calculating the deviation condition from the robot to the target planning path in the future. The path tracking method provided by the embodiment of the application can adjust the motion speed of the next motion period of the robot according to the motion speed of the current motion period of the robot and the path deviation model when the robot drifts, so that the actual moving track of the robot is closer to the planned path, and the path deviation between the actual moving track and the planned path caused by the drift of the robot is reduced, so that the robot can track the planned path more accurately, the coverage rate of the robot on a working area is improved, and meanwhile, the distance deviation between the actual moving track and the planned path is reduced, so that the robot can quickly return to the planned path by adjusting the motion speed of the robot, namely, the time of a return-to-right process is greatly reduced, and the working efficiency of the robot is improved.
Drawings
Fig. 1 is a schematic flowchart of a path tracking method according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram illustrating a method for calculating slopes of adjacent points in a planned path according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram of a planned path according to an embodiment of the present application;
FIG. 4 is a schematic graph of a curved path provided by an embodiment of the present application;
fig. 5 is a flowchart illustrating a process of determining a path deviation model according to an embodiment of the present disclosure;
fig. 6 is a schematic flowchart of a target movement speed determination process provided in an embodiment of the present application;
fig. 7 is a schematic flowchart of a target linear velocity calculation process provided in an embodiment of the present application;
fig. 8 is a schematic flowchart of a target angular velocity calculation process provided in an embodiment of the present application;
FIG. 9 is a schematic flow chart illustrating a forward-looking distance calculation process under curved path conditions according to an embodiment of the present disclosure;
fig. 10 is a schematic structural diagram of a path tracking apparatus according to an embodiment of the present application;
fig. 11 is a schematic structural diagram of a path tracking device according to an embodiment of the present application.
Detailed Description
The embodiments of the present application will be described below with reference to the drawings. The terminology used in the description of the embodiments herein is for the purpose of describing particular embodiments herein only and is not intended to be limiting of the application. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
The path tracking method provided by the embodiment of the application may be implemented by a path tracking device, where the path tracking device may be a robot, or may also be a chip or a circuit applied to the robot, or the path tracking device may also be an electronic device or a chip or a circuit in the electronic device, for example, the path tracking method may be used on a computer to determine a movement speed of the robot in a next movement cycle, and transmit the movement speed to the robot, so that the robot operates at the next movement cycle according to the received movement speed. The present embodiment will be described below by taking an example in which the path tracking method is applied to a robot. When the path tracking device is an electronic device, the path may interact with the robot according to the device, for example, the electronic device sends the determined movement speed of the robot to the robot through an instruction to control the robot to move according to the designated movement speed. For another example, the robot may report information such as an initial linear velocity and a planned path of the robot to the electronic device. Optionally, the path deviation model may also be sent to the robot after being trained by the electronic device, or may be trained by the robot itself, which is not limited in this embodiment of the application.
The robot may be a mowing robot, a sweeping robot, a mine clearance robot, a cruise robot, etc., which is not particularly limited in this embodiment.
Fig. 1 is a schematic flow chart of a path tracking method provided in an embodiment of the present application, and as shown in fig. 1, the method includes the following steps:
and S110, acquiring target planning path information and running information of the current motion cycle of the robot.
The target planning path information may include the target planning path and a path category to which a target sub-path corresponding to the target planning path belongs.
The operation information may include, but is not limited to, a current movement speed of the robot, a yaw angle and a pitch angle when the robot moves, and the like, and the current movement speed of the robot may include a current linear velocity and an angular velocity of the robot.
In the moving process of the robot, in order to enable the robot to travel along a planned path as much as possible, the moving speed of the robot can be adjusted according to the yaw condition of the robot at fixed time intervals, the fixed time intervals can be used as a moving cycle of the robot, and the current moving cycle can be the current fixed time interval of the robot.
The target planning path may be a complete planning path to be tracked by the robot this time, or may be a certain sub-path in the planning path to be tracked by the robot this time. Due to the influences of terrain, obstacles and the like in the working area, the shape of the formed target planning path may be an irregular geometric shape, and in order to enable the robot to quickly return to the target planning path, that is, enable the robot to quickly execute corresponding motion modes for different geometric shape paths, the path deviation of an actual moving path relative to the target planning path is reduced, for example, the robot runs at a certain speed in a straight path and a curved path, and rotates in situ. The target planning path is divided into a plurality of target sub-paths, and the target sub-paths are classified to form path categories including, but not limited to, curves, straight lines, and relay points connecting different target sub-paths.
In order to reduce the deviation between the robot and the planned path in the moving process, in this embodiment, the robot may divide the planned path according to the slope between each adjacent path point in the planned path, and generate at least one section of sub-path. The robot may track each segment of the sub-path in turn according to the order of each segment of the sub-path generated and the position of the robot.
The robot may determine the tracked sub-path according to the current position, where the currently tracked sub-path of the robot is the target planning path, that is, the target planning path may be any sub-path in at least one sub-path.
A common path planning scheme for a robot includes global coverage and edge, and a path planned for these two common situations generally includes three motion modes: the method comprises the steps of straight line driving, curve driving and in-situ rotation, and correspondingly, a planned path can be divided into two sub-paths of a straight line path and a curve path.
Specifically, the robot may sequentially traverse path points on the planned path from a starting point of the planned path to an end point of the planned path, and find a slope between any adjacent path points on the planned path. Fig. 2 is a schematic diagram of a slope calculation method for adjacent points in a planned path according to an embodiment of the present disclosure, as shown in fig. 2, a point A, B, C is an adjacent path point in the planned path, a slope of a vector AB is a slope between a path point a and a path point B, a vector BC is a slope between the path point B and a path point C, and α is an included angle between the vector AB and the vector BC, that is, α is an angle at which the slopes of the adjacent paths AB and BC change.
Fig. 3 is a schematic diagram of a planned path provided in an embodiment of the present application, and as shown in fig. 3, if a variation range of a slope between some adjacent points is smaller than or equal to a first preset angle, a path formed by the points may be divided, where the path formed by the points is a straight-line path, such as a straight-line path 1, a straight-line path 2, and a straight-line path 3 in fig. 3; if the variation range of the slope between some adjacent points is greater than the first preset angle and less than or equal to the second preset angle, the path formed by these points can also be divided, and the path formed by these points is a curved path, such as curved path 1 in fig. 3; if the variation range of the slope between some adjacent waypoints is greater than the second predetermined angle, the middle point of these adjacent waypoints may be used as the pivot point, that is, the relay point 2 and the relay point 3 shown in fig. 3.
The first preset angle and the second preset angle are fixed angles set in advance, for example, the first preset angle is 0.1 degree, the second preset angle is 60 degrees, and the first preset angle and the second preset angle may also be other fixed angles set in advance, which is not limited in this embodiment.
In the divided planned path, the point connecting any two sub-paths is a relay point, for example, relay point 1, relay point 2, and relay point 3 in fig. 3, where the two sub-paths connected by the relay point may both be a straight path or both may be a curved path, or one of the two sub-paths connected by the relay point may be a straight path or one of the two sub-paths is a curved path, and each relay point is not only an end point of one corresponding sub-path but also an end point of a forward-looking path of the corresponding sub-path.
Because the line pasting effect is better when the robot moves in the curved path close to the standard circular arc, the curved path can be further divided to obtain more sub-paths.
Fig. 4 is a schematic diagram of a curved path provided in the embodiment of the present application, and as shown in fig. 4, the curved path is divided into 3 segments of local curved paths: the local curved path 1, the local curved path 2, and the local curved paths 3, 3 are each closer to a standard circular arc, and a path point connecting each local curved path is taken as a relay point, for example, in fig. 4, the relay point 1 is an end point of the local curved path 1, the relay point 2 is an end point of the local curved path 2, and the relay point 3 is an end point of the local curved path 3.
When the robot tracks a certain section of local curve path (namely, a target planning path), the farthest forward-looking path point can be the end point of the target planning path, and when the robot detects that the forward-looking path point of the target planning path is an obstacle, the robot can adjust the forward-looking distance to avoid the obstacle.
In the embodiment, the movement speed of the robot may be adjusted when the robot deviates from the target planned path, so as to reduce the offset of the robot relative to the target planned path.
The deviation of the robot from the target planned path can be the deviation of the position of the robot from the target planned path or the deviation of the course of the robot from the target planned path.
When the robot deviates from the target planned path, the initial linear speed of the robot in the current motion cycle may be multiplied by a proportional parameter k to obtain the linear speed of the robot in the next motion cycle, where 0< k < 1.
In order to more accurately control the moving speed of the robot during the yaw, in this embodiment, a path deviation model for predicting the path deviation amount may also be established, and the moving speed of the robot in the current moving period may be determined by using the path deviation model.
And S120, determining a path deviation model of the next motion period according to the target planned path.
The path deviation model is used for calculating the deviation condition from the robot to the target planned path in the future time, and the path deviation model can predict the path deviation of the robot relative to the target planned path according to the actual path deviation and the error increment coefficient of the robot in the current motion cycle, and can be represented by the following formula (1):
wherein, the first and the second end of the pipe are connected with each other,
the predicted path offset of the robot relative to the target planning path is calculated after t seconds from the starting time of the current motion period,
in order to be the error increment factor,
is the actual path offset.
When the current movement period starts, the robot may determine, according to the current position, an actual path offset of the robot in the current movement period relative to the target planned path, where the position of the robot may be obtained through Real-time Kinematic (RTK), Global Positioning System (GPS), or other methods.
Fig. 5 is a flowchart of a process of determining a path deviation model according to an embodiment of the present application, and as shown in fig. 5, the process may include the following steps:
and S121, acquiring a target sub-path corresponding to the current position in the target planning path.
Specifically, a perpendicular line of the target planned path may be drawn through the current position, and a sub-path where an intersection of the perpendicular line and the target planned path is located is a target sub-path corresponding to the current position in the target planned path.
And S122, acquiring the position information of the robot in each sampling moment.
The sampling intervals corresponding to the N sampling moments may be equal, for example, the sampling interval corresponding to each sampling moment may be 1 second; the sampling intervals may also be unequal, for example, each sampling interval may gradually increase in sequence, which is not particularly limited in this embodiment. For example, the sampling interval for N sampling instants may be denoted as T ═ T1，t2，t3，...，tN]When the robot is knownWhen the current movement speed is v, the movement distance of the robot at N sampling points can be obtained, for example, v.T D1，d2，d3，...dN]。
Specifically, when the robot is at the start position of the target sub-path, the current position information (x) of the robot is acquired0，y0) And yaw angle theta, and according to
Acquiring the position of the robot in N sampling moments: [ (x)1，y1)，(x2，y2)，(x3，y3)，…，(xN-1，yN-1)，(xN，yN)]Wherein N is a positive integer.
And S123, acquiring the actual path offset of the position information deviating from the target sub-path.
Respectively calculating the distance from the position information of each sampling moment to the target planning sub-path, namely the actual path offset, for example, the actual path offset of the robot relative to the target planning path at N sampling moments can be represented as [ epsilon ]1，ε2…，εN]。
And S124, determining a path deviation model of the next motion period according to the actual path deviation amount.
Specifically, the predicted path offset and the error increment coefficient in the path deviation model may be calculated according to the following equation (2):
wherein, tiIs the time length between the ith sampling time and the start time of the current motion cycle, epsiloniAnd the actual path offset of the robot corresponding to the ith sampling moment.
The actual path deviation amount and the error increment coefficient are substituted into the formula (1), so that a specific path deviation model of the next movement period can be determined, and the actual deviation condition of the robot can be constantly changed due to the influence of external factors and the continuous adjustment of the speed of the robot, so that the path deviation model of the next movement period can be updated in each current movement period, and the deviation condition of the robot predicted by the path deviation model is closer to the actual condition.
And S130, determining the movement speed of the robot in the next movement period according to the current movement speed, the path deviation model and the path category.
Fig. 6 is a flowchart of a target movement speed determination process provided in an embodiment of the present application, and as shown in fig. 6, the process may include the following steps:
s131, acquiring motion information of the robot in the next motion period and an error increment coefficient of the path deviation model.
The motion information of the robot in the next motion cycle may include the current position coordinates of the robot and the actual path offset of the current position from the target planned path.
The error increment coefficient may be obtained according to the above equation (2).
And S132, determining the predicted path offset of the robot in the next motion period according to the actual path offset and the path deviation model.
Specifically, the actual path offset corresponding to the current position of the robot may be substituted into formula (1), and the predicted path offset corresponding to the position of the robot at the beginning of the next cycle may be calculated.
And S133, calculating to obtain the target movement speed according to the actual path offset, the predicted path offset, the error increment coefficient, the path type and the current movement speed.
The target moving speed may include a target linear speed and a target angular speed.
Fig. 7 is a schematic flowchart of a process of calculating a target linear velocity according to an embodiment of the present application, and as shown in fig. 7, the process may include the following steps:
and S13310, calculating to obtain a target error increment coefficient according to the actual path offset, the predicted path offset and the error increment coefficient.
Specifically, the target error increment coefficient of the path deviation model may be calculated according to the following formula (3):
where k is a target error increment coefficient of the path deviation model of the next cycle, E (epsilon') is an expectation of the actual path deviation amount, and the expectation of the actual path deviation amount may be determined according to the actual path deviation amount of each motion cycle.
And S13311, calculating the target linear speed according to the target error increment coefficient, the path type and the current movement speed.
Specifically, the target linear velocity may be determined according to the following formula (4):
wherein v' is the target linear velocity, and v is the current moving linear velocity of the robot.
Fig. 8 is a flowchart of a target angular velocity calculation process provided in an embodiment of the present application, and as shown in fig. 8, the process may include the following steps:
and S13320, acquiring the forward looking distance of the robot in the next motion cycle according to the path type.
It should be noted that, when the robot travels along a planned route, the travel speed of the robot needs to be calculated from the geometric information of the route, that is, the determined route type. Conventionally, a look-ahead waypoint (lookup head position) is found on a path according to a set look-ahead distance (lookup head distance). And then, geometrically resolving the path between the current position of the robot and the found forward-looking path point, and further issuing the speed information obtained by resolving to the robot so as to drive the robot at the speed. The process is carried out in a certain frequency cycle, namely, in each frequency cycle, the robot moves a short distance, the forward looking path point is changed, but the set forward looking distance is constant, so that the robot needs to continuously receive the issuing speed and run along the planned path. The mode depends on the set forward-looking distance, if the forward-looking distance is too large, the robot is easy to deviate the path when walking straight, and when walking arc, the path deviation is too large, for example, the robot can bring hidden trouble of obstacle avoidance failure when deviating to an obstacle; if the forward looking distance is too small, when the robot walks along an arc line, the moving fluency and the obstacle avoidance performance of the robot are greatly influenced; if the robot deviates from the planned path during driving due to positioning errors, terrain limitations, mechanical structure or electrical control, the robot realignment process may be slowed by the fixed forward-looking distance during the realignment process.
It can be seen that the look-ahead distance varies according to the path class. When the path type is a straight path, the foresight distance can be a set foresight distance, and when the path type is a curve path, the curvature of the curve is acquired at the moment, and then the foresight distance of the curve is calculated, so that the issuing speed of the robot can be adjusted according to the foresight distance, and the rapid correction is realized. Fig. 9 is a flowchart illustrating a forward-looking distance calculating process under a curved path condition according to an embodiment of the present application, and as shown in fig. 9, the process may include the following steps:
and S133201, acquiring the slope between adjacent path points in the curve path.
In particular, when a curved path is included in the sub-path, a slope δ between adjacent path points in the curved path may be recorded as δ ═ δ1，δ2，…，δn]Wherein, delta1Is the slope, δ, between the 1 st and 2 nd waypoints in the curve path2Is the slope between the 2 nd and 3 rd waypoints in the curve path, deltanIs the slope between the nth and the (n + 1) th waypoints in the curved path, where n is greater than or equal to 1 and n is less than the total number of waypoints on the curved path.
And S133202, performing linear fitting on the slope between the adjacent path points to divide the curve path into curve sub-paths with different curvatures.
And taking the connecting point between the curve sub-paths as a forward-looking distance point, wherein the forward-looking distance point is used for determining the end position of the forward-looking distance.
When the number n of waypoints is an integer greater than 2, the robot may pair [ δ ]1，δ2]Linear fitting is carried out, the model is recorded as the 1 st fitting model, and delta is calculated3Error beta from 1 st fitting model1Then to [ delta ]1，δ2，δ3]Linear fitting is carried out, the model is recorded as a 2 nd fitting model, and delta is calculated4Error beta between the 2 nd fitting model2Then to [ delta ]1，δ2，δ3，δ4]Linear fitting is performed, and the analogy is repeated, for [ delta ]1，δ2…，δn-1]Linear fitting is carried out, the model is recorded as the n-2 fitting model, and delta is calculatednError beta between n-2 fitting modeln-2。
The fitting model in this embodiment may be constructed by a least square curve fitting method, that is, based on the least square principle, a functional relationship of slope variables is obtained as the fitting model, so that the slope between the path points can be optimally approximated or fitted to the curve by the fitting model.
The robot can be based on [ beta ]1，β2，β3，…，βn-2]The curve path is divided into a plurality of sections of curve sub-paths with different curvatures, the curvature ranges of the adjacent curve sub-paths are different, the curvatures of all path points in each curve sub-path are approximately consistent, and all path points in each curve sub-path can form a standard circular arc.
The connecting point between the adjacent curve sub-paths is a forward-looking distance point of the previous section of the curve sub-path of the robot in the adjacent curve sub-paths, and the forward-looking distance point is used for determining the end point position of the forward-looking distance of the robot.
In addition, if a certain section of the curve sub-path is the last section of the curve path, the end point of the section of the curve sub-path is also a forward looking distance point, and the number of the forward looking distance points is the same as that of the curve sub-paths in the curve path.
And S133203, acquiring a forward looking distance point of the next motion cycle from the forward looking distance points.
Specifically, the robot may first determine a curve sub-path corresponding to the current position, then determine a forward looking distance point corresponding to the current cycle, and then predict whether the current cycle will reach the forward looking distance point according to the path deviation model of the current cycle, if not, the forward looking distance point of the next motion cycle is the forward looking distance point; if so, the look-ahead distance point for the next motion cycle is the next look-ahead distance point to the look-ahead distance point.
And S133204, acquiring the forward looking distance according to the current position and the forward looking distance point.
Specifically, a straight-line distance between the current position of the robot and a forward-looking distance point of the next movement cycle may be taken as the forward-looking distance of the robot in the next movement cycle.
And S13321, acquiring the offset distance of the robot deviating from the target planning path.
If the robot is currently located on a certain section of curve sub-path in the curve path, the section of curve sub-path can be taken as a section of standard circular arc first, then the circle center corresponding to the section of circular arc is determined, an intersection point exists between the connecting line of the geometric center of the robot and the circle center and the circular arc, and the straight line distance from the intersection point to the geometric center of the robot is the offset distance of the robot.
And S13322, acquiring the offset distance range.
The offset distance range of the robot may be a set range, for example, d-e, where d may be the vertical distance from the geometric center of the robot to the target planned path, e may be a set distance, such as 1 meter, or other set distances, and d < e.
And S13323, when the offset distance is smaller than the minimum value in the offset distance range, calculating to obtain the target angular speed according to the forward looking distance, the offset distance and the target linear speed.
In order to make the robot quickly return to the right when yawing, and simultaneously minimize the distance that the robot needs to move when returning to the right, when the current offset distance of the robot is less than d, the target angular velocity of the robot in the next motion cycle can be determined according to the following formula (5):
where ω is the target angular velocity, v' is the target linear velocity, and L is the forward-looking distance of the robot.
And S13324, when the offset distance is within the offset distance range, determining a proportional increment according to the offset distance, and determining a target angular velocity according to the forward looking distance, the offset distance, the target linear velocity and the proportional increment.
Specifically, when the offset distance of the robot is greater than or equal to d and less than or equal to e due to factors such as terrain, collision or structural defects, a proportional increment p may be introduced to increase the target angular velocity, quickly return the robot to the right, and reduce the distance that the robot needs to move to return the right, wherein the proportional increment p may be determined according to the following formula (6):
the target angular velocity of the robot in the current motion cycle may be determined according to the following equation (7):
in the process that the robot tracks the target planned path, there may be a case that the robot deviates far from the target planned path, and for this case, in this embodiment, when the offset distance is greater than e, a first target planned path may be planned for the robot, the target planned path may be updated to the first target planned path, and then a path deviation model of a next motion cycle may be re-determined, so that the robot returns to the right quickly.
For example, the robot may be rotated in situ by a certain angle, and returned to the target planned path along a perpendicular path from the robot position to the target planned path.
And S134, taking the target movement speed as the movement speed of the robot in the next movement period.
In the case that the target linear velocity and the target angular velocity are determined, the robot may adjust the linear velocity of the robot to the target linear velocity and the angular velocity of the robot to the target angular velocity in the next movement cycle.
The path tracking method, the path tracking device and the path tracking equipment obtain target planning path information and operation information of a robot in a current motion period, determine a path deviation model of a next motion period according to the target planning path, and determine a motion speed of the next motion period according to the current motion speed, the path deviation model and the path category. The target planning path information comprises a target planning path and a path category to which a target sub-path corresponding to the target planning path belongs, the operation information comprises the current movement speed of the robot, and the path deviation model is used for calculating the deviation condition from the robot to the target planning path in the future. The path tracking method provided by the embodiment of the application can adjust the motion speed of the next motion period of the robot according to the motion speed of the current motion period of the robot and the path deviation model when the robot drifts, so that the actual motion track of the robot is closer to the planned path, the path deviation between the actual motion track and the planned path caused by the drift of the robot is reduced, the tracking of the robot on the planned path is more accurate, the coverage rate of the robot on a working area is improved, meanwhile, the distance deviation between the actual motion track and the planned path is reduced, the robot can quickly return to the planned path by adjusting the motion speed of the robot, the time of the aligning process is greatly reduced, and the working efficiency of the robot is improved.
It will be appreciated by those skilled in the art that the above embodiments are exemplary and not intended to limit the present application. Where possible, the order of execution of one or more of the above steps may be adjusted, or selectively combined, to arrive at one or more other embodiments. The skilled person can select any combination of the above steps according to the needs, and all that does not depart from the essence of the scheme of the present application falls into the protection scope of the present application.
Based on the same inventive concept, as an implementation of the foregoing method, an embodiment of the present application provides a path tracking apparatus, where the apparatus embodiment corresponds to the foregoing method embodiment, and for convenience of reading, details in the foregoing method embodiment are not described again in the apparatus embodiment one by one, but it should be clear that the apparatus in this embodiment can correspondingly implement all contents in the foregoing method embodiment.
Fig. 10 is a schematic structural diagram of a path tracking apparatus provided in an embodiment of the present application, and as shown in fig. 10, the apparatus provided in the embodiment includes:
the acquisition module 101: the system comprises a target planning path information acquisition unit, a target sub-path acquisition unit and a target planning path information acquisition unit, wherein the target planning path information acquisition unit is used for acquiring target planning path information and operation information of a current motion cycle of the robot, the target planning path information comprises a target planning path and a path category to which a target sub-path corresponding to the target planning path belongs, and the operation information comprises a current motion speed of the robot;
the path deviation model determination module 102: a path deviation model used for determining the next motion cycle according to the target planning path, wherein the path deviation model is used for calculating the deviation condition of the robot to the target planning path in the future moment;
the movement velocity determination module 103: the motion speed of the robot in the next motion cycle is determined according to the current motion speed, the path deviation model and the path category.
As an optional implementation manner, the path deviation model determining module 102 is specifically configured to:
acquiring a target sub-path corresponding to the current position in the target planning path;
acquiring the position information of the robot in each sampling moment;
acquiring actual path offset of the position information deviating from the target sub-path;
and determining a path deviation model of the next motion period according to the actual path deviation.
As an optional implementation manner, the movement speed determination module 103 is specifically configured to:
acquiring motion information of the robot in the next motion period and an error increment coefficient of the path deviation model; the motion information comprises a current position and an actual path offset of the current position relative to a target planning path;
determining the predicted path offset of the robot in the next motion period according to the actual path offset and the path deviation model;
calculating a target movement speed according to the actual path offset, the predicted path offset, the error increment coefficient, the path category and the current movement speed;
and taking the target movement speed as the movement speed of the robot in the next movement period.
As an optional implementation manner, the target moving speed includes a target linear speed, and the moving speed determining module 103 is specifically configured to:
calculating to obtain a target error increment coefficient according to the actual path offset, the predicted path offset and the error increment coefficient;
and calculating to obtain the target linear velocity according to the target error increment coefficient, the path category and the current motion velocity.
As an optional implementation manner, the target movement speed includes a target angular speed, and the movement speed determination module 103 is specifically configured to:
acquiring the forward looking distance of the robot in the next motion cycle according to the path category;
acquiring the offset distance of the robot deviating from the target planning path;
acquiring an offset distance range;
when the offset distance is smaller than the minimum value in the offset distance range, calculating to obtain the target angular velocity according to the forward looking distance, the offset distance and the target linear velocity;
when the offset distance is within the offset distance range, determining a proportional increment according to the offset distance, and determining a target angular velocity according to the forward looking distance, the offset distance, the target linear velocity and the proportional increment.
As an optional implementation, the method further comprises:
when the offset distance is larger than the offset distance range, constructing a first target planning path;
and updating the target planning path to the first target planning path, and returning to the step of determining the path deviation model of the next motion cycle according to the target planning path.
As an optional implementation, the method further comprises:
acquiring all path points of the target planning path and slopes between adjacent path points;
dividing the target planning path into different target sub-paths according to the change condition of the slope, and determining the path category corresponding to the target sub-paths;
the path categories include straight path and curved path.
As an optional implementation manner, when the path category of the target sub-path is a curved path, the movement speed determination module 103 is specifically configured to:
acquiring the slope between adjacent path points in the curve path;
performing linear fitting on the slope between the adjacent path points to divide the curve path into curve sub-paths with different curvatures, wherein connecting points between the curve sub-paths are forward-looking distance points which are used for determining the end point position of a forward-looking distance;
acquiring a forward-looking distance point of the next motion cycle from the forward-looking distance points;
and acquiring the forward looking distance according to the current position and the forward looking distance point.
The path tracking apparatus provided in this embodiment may perform the method embodiments, and the implementation principle and technical effect thereof are similar, which are not described herein again.
It should be clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional units and modules is only used for illustration, and in practical applications, the above function distribution may be performed by different functional units and modules as needed, that is, the internal structure of the apparatus may be divided into different functional units or modules to perform all or part of the above described functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. For the specific working processes of the units and modules in the system, reference may be made to the corresponding processes in the foregoing method embodiments, which are not described herein again.
Based on the same inventive concept, the embodiment of the application also provides a path tracking device. Fig. 11 is a schematic structural diagram of a path tracking device according to an embodiment of the present application, and as shown in fig. 11, the path tracking device according to the embodiment includes: a memory 110 and a processor 120, the memory 110 for storing computer programs; the processor 120 is configured to perform the method according to the above method embodiment when the computer program is called.
The path tracking device provided in this embodiment may perform the above method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.
Embodiments of the present application further provide a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the computer program implements the method described in the above method embodiments.
The embodiment of the present application further provides a computer program product, when the computer program product runs on the path tracking device, the path tracking device implements the method described in the foregoing method embodiment when executed.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in or transmitted over a computer-readable storage medium. The computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optics, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more available media. The usable medium may be a magnetic medium (e.g., a floppy Disk, a hard Disk, or a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), among others.
One of ordinary skill in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by hardware related to instructions of a computer program, which may be stored in a computer-readable storage medium, and when executed, may include the processes of the above method embodiments. And the aforementioned storage medium may include: various media capable of storing program codes, such as ROM or RAM, magnetic or optical disks, etc.
The naming or numbering of the steps appearing in the present application does not mean that the steps in the method flow have to be executed in the chronological/logical order indicated by the naming or numbering, and the named or numbered process steps may be executed in a modified order depending on the technical purpose to be achieved, as long as the same or similar technical effects are achieved.
In the above embodiments, the description of each embodiment has its own emphasis, and reference may be made to the related description of other embodiments for parts that are not described or recited in any embodiment.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/device and method may be implemented in other ways. For example, the above-described apparatus/device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
In the description of the present application, a "/" indicates a relationship in which the objects associated before and after are an "or", for example, a/B may indicate a or B; in the present application, "and/or" is only an association relationship describing an association object, and means that there may be three relationships, for example, a and/or B, and may mean: a exists alone, A and B exist simultaneously, and B exists alone, wherein A and B can be singular or plural.
Also, in the description of the present application, "a plurality" means two or more than two unless otherwise specified. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, or c, may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein.
Reference throughout this specification to "one embodiment" or "some embodiments" or the like, described with reference to "one embodiment" or "some embodiments" or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A path tracking method, comprising:
acquiring target planning path information and operation information of a current motion cycle of the robot, wherein the target planning path information comprises a target planning path and a path category to which a target sub-path corresponding to the target planning path belongs, and the operation information comprises a current motion speed of the robot;
determining a path deviation model of the next motion cycle according to the target planned path, wherein the path deviation model is used for calculating the deviation condition of the robot to the target planned path in the future moment;
and determining the movement speed of the robot in the next movement period according to the current movement speed, the path deviation model and the path category.
2. The method of claim 1, wherein determining the path deviation model for the next motion cycle from the target planned path comprises:
acquiring a target sub-path corresponding to the current position in the target planning path;
acquiring the position information of the robot in each sampling moment;
acquiring the actual path offset of the position information deviating from the target sub-path;
and determining a path deviation model of the next motion period according to the actual path deviation amount.
3. The method of claim 1 or 2, wherein said determining the movement velocity of the robot in the next movement cycle from the current movement velocity, the path deviation model and the path classification comprises:
acquiring motion information of the robot in the next motion period and an error increment coefficient of the path deviation model; the motion information comprises a current position and an actual path offset of the current position relative to a target planning path;
determining the predicted path offset of the robot in the next motion period according to the actual path offset and the path deviation model;
calculating a target movement speed according to the actual path offset, the predicted path offset, the error increment coefficient, the path category and the current movement speed;
and taking the target movement speed as the movement speed of the robot in the next movement period.
4. The method of claim 3, wherein the target motion velocity comprises a target linear velocity, and wherein calculating the target motion velocity based on the actual path offset, the predicted path offset, the error delta coefficient, the path classification, and the current motion velocity comprises:
calculating to obtain a target error increment coefficient according to the actual path offset, the predicted path offset and the error increment coefficient;
and calculating to obtain the target linear velocity according to the target error increment coefficient, the path category and the current motion velocity.
5. The method of claim 3, wherein the target motion velocity comprises a target angular velocity, and wherein calculating the target motion velocity based on the actual path offset, the predicted path offset, the error delta coefficient, the path class, and the current motion velocity further comprises:
acquiring the forward looking distance of the robot in the next motion cycle according to the path category;
acquiring the offset distance of the robot deviating from the target planning path;
acquiring an offset distance range;
when the offset distance is smaller than the minimum value in the offset distance range, calculating to obtain the target angular velocity according to the forward looking distance, the offset distance and the target linear velocity;
when the offset distance is within the offset distance range, determining a proportional increment according to the offset distance, and determining a target angular velocity according to the forward looking distance, the offset distance, the target linear velocity and the proportional increment.
6. The method of claim 5, further comprising:
when the offset distance is larger than the offset distance range, constructing a first target planning path;
and updating the target planning path to the first target planning path, and returning to execute the step of determining the path deviation model of the next motion period according to the target planning path.
7. The method of claim 5, further comprising:
acquiring all path points of the target planning path and slopes between adjacent path points;
dividing the target planning path into different target sub-paths according to the change condition of the slope, and determining the path category corresponding to the target sub-paths;
the path categories include straight path and curved path.
8. The method of claim 7, wherein when the path category of the target sub-path is a curved path, the obtaining the forward-looking distance of the robot in the next motion cycle according to the path category comprises:
acquiring the slope between adjacent path points in the curve path;
performing linear fitting on the slope between the adjacent path points to divide the curve path into curve sub-paths with different curvatures, wherein connecting points between the curve sub-paths are forward-looking distance points which are used for determining the end point position of a forward-looking distance;
acquiring a forward-looking distance point of the next motion cycle from the forward-looking distance points;
and acquiring the forward looking distance according to the current position and the forward looking distance point.
9. A path tracking device, comprising: a memory for storing a computer program and a processor; the processor is adapted to perform the method of any of claims 1-8 when the computer program is invoked.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-8.
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| CN202210196496.9A CN114756021A (en) | 2022-03-01 | 2022-03-01 | Path tracking method and device and path tracking equipment |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116841307A (en) * | 2023-09-01 | 2023-10-03 | 中国民用航空飞行学院 | Flight trajectory prediction method and device based on Koopman neural network |
| CN117519212A (en) * | 2024-01-03 | 2024-02-06 | 杭州华橙软件技术有限公司 | Path tracking control method, device, terminal and computer readable storage medium |
| CN117647250B (en) * | 2024-01-29 | 2024-04-30 | 深圳市爱保护科技有限公司 | Navigation method and system based on intelligent bracelet |
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2022
- 2022-03-01 CN CN202210196496.9A patent/CN114756021A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116841307A (en) * | 2023-09-01 | 2023-10-03 | 中国民用航空飞行学院 | Flight trajectory prediction method and device based on Koopman neural network |
| CN116841307B (en) * | 2023-09-01 | 2023-12-01 | 中国民用航空飞行学院 | Flight trajectory prediction method and device based on Koopman neural network |
| CN117519212A (en) * | 2024-01-03 | 2024-02-06 | 杭州华橙软件技术有限公司 | Path tracking control method, device, terminal and computer readable storage medium |
| CN117519212B (en) * | 2024-01-03 | 2024-04-12 | 杭州华橙软件技术有限公司 | Path tracking control method, device, terminal and computer readable storage medium |
| CN117647250B (en) * | 2024-01-29 | 2024-04-30 | 深圳市爱保护科技有限公司 | Navigation method and system based on intelligent bracelet |
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