CN112578790B - Local path planning method and AGV - Google Patents
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Abstract
The invention discloses a local path planning method, which comprises the following steps: s1, periodically forming a plurality of initial sampling groups, and generating tracks corresponding to the initial sampling groups based on initial positions; s2, screening a sampling group meeting a speed constraint condition from the initial sampling group, and calling the sampling group I; s3, screening out a sampling group meeting the angular speed constraint condition from the sampling group I to obtain a candidate sampling group; s4, evaluating the track formed by the candidate sampling group to obtain the best evaluation track; and S5, performing angular velocity smoothing processing on the track with the optimal evaluation to form an optimal track. The sampling group is subjected to equal resolution sampling, more comprehensive linear velocity constraint and angular velocity constraint, the calculated amount of local navigation is greatly reduced, and meanwhile, the navigation accuracy can be ensured, so that the algorithm does not depend on the calculation force of a processor seriously, the method is suitable for adopting an embedded processor, and the method is more suitable for the operation scene of the industrial AGV.
Description
Technical Field
The invention belongs to the technical field of path planning, and particularly relates to a local path planning method and an AGV.
Background
Most of current AGVs adopt D-equal local path planning methods under ROS, the D-equal local path planning method adopts a traversal mode to search an optimal path from a task starting point to a task end point on a grid map, the task end point is taken as a starting point, 8 grids around a grid where the task end point is located are traversed, a grid which is nearest to the task starting point and has no obstacle is obtained, then the grid is taken as a starting point, 8 grids around the grid are traversed, and the like, the grid where the task starting point is located is traversed, and the local path planning methods have the problems of complex calculation and large calculation amount.
Disclosure of Invention
The invention provides a local path planning method, aiming at improving the problems.
The invention is realized in such a way, and a local path planning method specifically comprises the following steps:
s1, periodically forming a plurality of initial sampling groups (v) m ，w m ) Generating a track corresponding to each initial sampling group based on the initial position;
s2, screening a sampling group meeting a speed constraint condition from the initial sampling group, and calling the sampling group I;
s3, screening out a sampling group meeting the angular speed constraint condition from the sampling group I to obtain a candidate sampling group;
s4, evaluating the tracks formed by the candidate sampling groups to obtain the best track;
and S5, performing angular velocity smoothing processing on the track with the best evaluation to form an optimal track.
Further, the linear velocity of the AGV is constrained by the motor performance, the distance between the obstacles and the local path, and the linear velocity constraint condition is expressed as follows:
and (3) motor performance constraint: v. of m ∈{v 0 -v a ·Δt,v 0 +v a Δ t }, wherein v a Represents the maximum linear acceleration, v, that the AGV car can achieve 0 The current speed of the AGV trolley is represented, and delta t is the time interval of two times of sampling;
obstacle distance constraint:
wherein diast (v) m ,w m ) Is a sample group (v) m ,w m ) Corresponding to the distance of the trajectory from the nearest obstacle, v a Represents the maximum linear acceleration, w, that the AGV car can achieve a The maximum angular acceleration which can be achieved by the AGV trolley is represented;
local path constraint: v (w) × delta t-S | < delta > delta | 1 And | v (w) × Δ t-S- x ≤δ 2 Wherein v is t (w) denotes an AGV traveling angular velocity w m Linear velocity v of m S is v m (w m ) Corresponding to the local end point of the track on the global path, | v (w) × Δ t-S x Representing the projected distance of the straight line | v (w) × Δ t-S | on the transverse axis x.
Further, before generating the candidate sample group, the following operations are performed on the sample group i:
calculating a speed difference value v' between the maximum speed and the minimum speed in the sampling group I;
determining the sampling number N of the sampling group I based on the speed difference v', and sampling the sampling group I based on a method of equally dividing the resolution;
where ε is the velocity difference threshold, n s And f (×) is a set value of the number of sampling groups, a is a scaling factor, and n is the number of sampling groups in the sampling group I.
Further, the angular velocity constraint conditions are as follows:
the local course constraint is as follows:
wherein, theta c To take the course angle, θ, corresponding to the group g Local end course angle, w, of the corresponding track on the global path for the sampling group 0 Is a local course deviation threshold;
and (4) global course constraint:
wherein, theta c For the course angle, θ, corresponding to the sample group w Is the course angle, w, of the global end point on the current global path 1 Is a global course deviation threshold;
and (4) smooth constraint:
wherein,
θ p is a heading deviation threshold.
Further, the method for estimating the optimal angular velocity smoothing of the trajectory specifically includes:
smoothing treatment:
wherein,
θ c indicating the course angle, θ, corresponding to the sample group g And theta represents the course angle corresponding to the smoothed sampling group.
Further, the trajectory evaluation method specifically comprises the following steps:
obtaining the number of corners n of the track r And amplitude of rotation theta r The evaluation formula is mu (n) r ) And ρ (θ) r )；
Evaluating the distance between the AGV and the global terminal, wherein the distance evaluation formula is beta (dist);
optimal path T p Is T p ＝max{σ 1 ·μ(n r )+σ 2 ·ρ(θ r )+σ 3 ·β(dist)}，σ 1 、σ 2 And sigma 3 Is a specific gravity coefficient.
The invention is realized in such a way that the AGV comprises an embedded main board, the embedded main board comprises a memory and a processor, wherein,
a memory for storing a computer program;
and the executor is used for realizing the local path planning method when the computer program is executed.
The path planning method provided by the invention has the following beneficial technical effects: the sampling group is subjected to equal resolution sampling, more comprehensive linear velocity constraint and angular velocity constraint, the calculated amount of local navigation is greatly reduced, and the navigation accuracy can be ensured, so that the algorithm does not depend on the calculation power of a processor seriously, the method is suitable for adopting an embedded processor, and the method is more suitable for the operation scene of the industrial AGV.
Drawings
FIG. 1 is a flowchart of a method for planning a local path of an AGV according to an embodiment of the present invention.
Detailed Description
The following detailed description of the embodiments of the present invention will be given in order to provide those skilled in the art with a more complete, accurate and thorough understanding of the inventive concept and technical solutions of the present invention.
Fig. 1 is a flowchart of a local path planning method for an AGV provided in an embodiment of the present invention, where the local path planning method is mainly applied to local path planning of a grid map, and specifically includes the following steps:
before local path planning, a kinematic model of the AGV is first determined as:
namely, the motion track of the AGV in the adjacent time is regarded as a section of circular arc, the motion characteristic of the industrial wheeled AGV is better met, R is the instant center radius, v is the linear velocity, and w is the angular velocity. Therefore, by the model, it can be known that: the centrode radius R determines the trajectory of the motion, and v and w are related to R.
S1, acquiring the current position (x) of the AGV 0 ，y 0 ) Defined as the starting position, periodically generating several groups of samples (v) based on a fixed resolution m ，w m ) Called initial sampling group, and synchronously generating the track corresponding to each sampling group.
In the embodiment of the invention, the linear velocity v in each sampling group is used as the basis m And angular velocity w m The running track of each sampling group in the current period can be obtained through a speed displacement formula, and the terminal point coordinate of the track is (x) mf ，y mf ) The expression is specifically as follows:
wherein, theta t0 Is the starting course angle, θ, of the AGV t1 ＝θ t0 + w.DELTA.t, DELTA.t is the time interval between two samplings, i.e. the sampling period, v m ,w m Linear velocity and angular velocity of the mth sampling group are shown;
s2, filtering out sampling groups which do not meet speed constraint conditions from the initial sampling groups to form a sampling group I;
in the embodiment of the invention, the linear velocity of the AGV trolley is constrained by the motor performance, the distance of the obstacle and the local path, and a sampling group which meets the constraint condition is screened out from the initial sampling composition and is called as a sampling group I, wherein the linear velocity constraint condition is expressed as follows:
and (3) motor performance constraint: v. of m ∈{v 0 -v a ·Δt,v 0 +v a Δ t }, wherein v a Represents the maximum linear acceleration, v, that the AGV car can achieve 0 The current speed of the AGV trolley is represented, and delta t is the time interval of two times of sampling;
obstacle distance constraint: considering not only the straight-line distance from the obstacle but also the transverse distance from the obstacle, the straight-line distance and the transverse distance from the obstacle need to be kept within a safe distance, and if the vehicle runs at a certain course w, the speed v is coupled with the angular speed w in a relation mode. For straight-line distance of obstacle, v m Should satisfy the following:
wherein diast (v) m ,w m ) Is velocity (v) m ,w m ) Corresponding to the distance of the trajectory from the nearest obstacle, v a Represents the maximum linear acceleration, w, that the AGV car can achieve a The maximum angular acceleration that the AGV dolly can reach is shown, the lateral distance from the barrier is then very easy to acquire, and the limit based on barrier straight line distance satisfies the contained angle relation with the limit of barrier lateral distance. I.e. whether to travel at the current speed, stop before an obstacle;
local path constraint: the AGV runs at a certain course at the current speed, the distance between a position point reached within a sampling period delta t and a corresponding local terminal point on the global path is within a certain range, the distance comprises a linear distance and a transverse distance, and the linear distance between the position point and the corresponding local terminal point on the global path is smaller than a distance threshold value delta 1 I.e. | v (w) × Δ t-S | ≦ δ 1 And | v (w) × Δ t-S non-conducting phosphor x ≤δ 2 Wherein v is t (w) denotes an AGV traveling angular velocity w m Linear velocity v of m S is a local end point of the v (w) corresponding track on the global path, | v (w) × Δ t-S x Representing the projection distance of the straight line | v (w) × Δ t-S | on the transverse axis x.
The global path refers to the shortest path planned from the task starting position to the task ending position, the local ending point is a point on the global path, and is a point on the global path returned by the v (w) corresponding track,
the track formed by the sampling group meeting the constraint conditions meets the hardware performance of the motor, can not collide with an obstacle (has good safety), has short distance with a local path terminal point and has small lateral deviation of the local path (can not deviate).
In the embodiment of the invention, the following operations are performed on the sampling group I:
calculating the speed difference v 'between the maximum speed and the minimum speed in the sampling group I, and v' = | v max |-|v min L, wherein l v max | is the maximum velocity value in the sampling group I, | v min I is the minimum speed value in the sampling group I;
determining the sampling number N of the sampling group I based on the speed difference, and sampling the sampling group I based on an equal resolution method to generate a sampling group II;
where ε is the velocity difference threshold, n s For a set value of the number of sampling groups, f (×) is an integer function, a is a scale factor, N is the number of sampling groups in sampling group i, and if N has a value of 12 and N has a value of 6, every other sampling group in sampling group i is sampled, and if N has a value of 3, every other sampling group in sampling group i is sampled.
S3, screening out a sampling group meeting the angular velocity constraint condition from the sampling group I or the sampling group II to obtain a candidate sampling group;
when estimating the course angle, considering global path constraint, local path and smooth constraint, the constraint model is as follows:
1) Local course constraint:
wherein, theta c Is the course angle, theta, corresponding to the sample group g The local terminal course angle of the corresponding track of the sampling group on the global path (or the local terminal course angle of the corresponding track of the sampling group for short), the course angle in the invention refers to the included angle between the mass center speed (vector) of the AGV and the x axis, and w 0 Is a local course deviation threshold;
2) And (3) global course constraint:
wherein, theta c Is the course angle, theta, corresponding to the sample group w Is a global end course angle, w, on a global path 1 Is a global course deviation threshold;
3) And (4) smooth constraint:
wherein,
θ p is a heading deviation threshold.
In the local heading constraint, the global heading constraint and the smoothing constraint, if f is 1, the angular velocity in the corresponding sampling group is reserved, and if f is 0, the angular velocity in the corresponding sampling group is not reserved.
In the sampling group I or the sampling group II, the sampling group with the angle meeting the constraint condition is a candidate sampling group, and the track corresponding to the candidate sampling group does not rotate at a large angle (the smoothness is ensured); ensuring small deviation from the local terminal orientation (ensuring no deviation); and ensuring that the deviation from the global path orientation is small (ensuring that the deviation is not generated).
And S4, evaluating the track formed by the candidate sampling group to obtain the best track.
In the embodiment of the present invention, the track evaluation method specifically includes:
1) The problem that the AGV running efficiency is low due to the fact that broken line corners still exist in a path under a grid map and the number of the broken line corners is large possibly exists in the path under the grid map, the running problem of the path is comprehensively considered, a track is evaluated, two factors are considered, and the number n of the corners of the track T is considered r And a rotation amplitude theta r Has an evaluation formula of μ (n) r ) And ρ (θ) r ) The larger the number of corners, μ (n) r ) The smaller the value, the larger the amplitude of the rotation angle, ρ (θ) r ) The smaller the value;
2) The method is used for evaluating the distance between the AGV and the global end point in real time, dist represents the distance between two points of the robot on the current track, the distance evaluation formula is beta (dist), and the longer the distance is, the smaller the value of beta (dist) is.
3) Evaluating the optimal trajectory T p Is T p ＝max{σ 1 ·μ(n r )+σ 2 ·ρ(θ r )+σ 3 ·β(dist)}，σ 1 、σ 2 And sigma 3 For the proportion coefficient, the obtained path can better meet the application scene of the industrial AGV, and the AGV runs more stably.
And S5, performing angular velocity smoothing processing on the track with the optimal evaluation to form an optimal track.
Smoothing treatment:
wherein,
θ c indicating the course angle, θ, corresponding to the sample group g And theta represents the course angle corresponding to the smoothed sampling group.
In order to reduce the calculation amount and ensure that the algorithm does not depend on the calculation power of the processor seriously, in order to increase the portability, the industrial AGV is not based on an ROS system, an embedded processor is adopted, the calculation amount is reduced, the algorithm is more feasible, the accuracy is ensured, and the operation scene of the industrial AGV is more consistent.
Correspondingly, the invention also provides an AGV which comprises an embedded main board, wherein the embedded main board comprises a memory and a processor, the memory is used for storing a computer program, and the processor is used for realizing the local path planning method when executing the computer program.
The local path planning method provided by the invention has the following beneficial technical effects: the sampling group is subjected to equal resolution sampling, more comprehensive linear velocity constraint and angular velocity constraint, the calculated amount of local navigation is greatly reduced, and meanwhile, the navigation accuracy can be ensured, so that the algorithm does not depend on the calculation force of a processor seriously, the method is suitable for adopting an embedded processor, and the method is more suitable for the operation scene of the industrial AGV.
The invention has been described above with reference to the accompanying drawings, it is obvious that the invention is not limited to the specific implementation in the above-described manner, and it is within the scope of the invention to apply the inventive concept and solution to other applications without substantial modification.
Claims (5)
1. A local path planning method is characterized by comprising the following steps:
s1, periodically forming a plurality of initial sampling groups, and generating tracks corresponding to the initial sampling groups based on initial positions;
s2, screening a sampling group meeting a speed constraint condition from the initial sampling group, and calling the sampling group I;
s3, screening out a sampling group meeting the angular speed constraint condition from the sampling group I to obtain a candidate sampling group;
s4, evaluating the track formed by the candidate sampling group to obtain the best evaluation track;
s5, performing angular velocity smoothing processing on the track with the best evaluation to form an optimal track;
before generating the candidate sample set, the following operations are performed on the sample set i:
calculating a speed difference value v' between the maximum speed and the minimum speed in the sampling group I;
determining the sampling number N of the sampling group I based on the speed difference v', and sampling the sampling group I based on a method of equally dividing the resolution;
where ε is the velocity difference threshold, n s F (×) is a set value of the number of sampling groups, a is a scale factor, and n is the number of sampling groups in the sampling group I;
the method for estimating the optimal angular velocity smoothing of the track specifically comprises the following steps:
smoothing treatment:
wherein,
θ c indicating the course angle, θ, corresponding to the sample group g And e, representing the local end point course angle of the corresponding track of the sampling group on the global path, and using the course angle corresponding to the group after the theta represents smoothing.
2. The local path planning method of claim 1, wherein the linear velocity of the AGV is constrained by the motor performance, the distance to the obstacle, and the local path, and the linear velocity constraint is expressed as follows:
the motor performance constraint: v. of m ∈{v 0 -v a ·Δt,v 0 +v a Δ t }, wherein v a Represents the maximum linear acceleration, v, that the AGV car can achieve 0 The current speed of the AGV trolley is represented, and delta t is the time interval of two times of sampling;
obstacle distance constraint:
wherein diast (v) m ,w m ) Is a sample group (v) m ,w m ) Corresponding to the distance of the trajectory from the nearest obstacle, v a Represents the maximum linear acceleration, w, that the AGV car can achieve a Representing the maximum angular acceleration that the AGV car can achieve;
local path constraint: v (w) × delta t-S | < delta > delta | 1 And | v (w) × Δ t-S- x ≤δ 2 Wherein v (w) represents the AGV traveling angular velocity w m Lower linear velocity v m S is a local end point of the v (w) corresponding track on the global path, | v (w) × Δ t-S calculation x Representing the projected distance of the straight line | v (w) × Δ t-S | on the transverse axis x.
3. The local path planning method of claim 1, wherein the angular velocity constraints are as follows:
the local course constraint is as follows:
wherein, theta c To take the course angle, θ, corresponding to the group g For the local end-point course angle, w, of the corresponding track of the sampling group on the global path 0 Is a local course deviation threshold;
and (4) global course constraint:
wherein, theta c Is the course angle, theta, corresponding to the sample group w Is the course angle, w, of the global end point on the current global path 1 Is a global heading deviation threshold;
and (4) smooth constraint:
wherein,
θ p is a deviation of courseAnd (4) a threshold value.
4. The local path planning method of claim 1, wherein the trajectory evaluation method is specifically as follows:
obtaining the number of corners n of the track r And amplitude of rotation theta r The evaluation formula is mu (n) r ) And ρ (θ) r )；
Estimating the distance between the AGV and the global terminal, wherein the distance estimation formula is beta (dist);
optimal path T p Is T p ＝max{σ 1 ·μ(n r )+σ 2 ·ρ(θ r )+σ 3 ·β(dist)}，σ 1 、σ 2 And sigma 3 Is a specific gravity coefficient.
5. An AGV is characterized in that the AGV comprises an embedded main board, the embedded main board comprises a memory and a processor, wherein,
a memory for storing a computer program;
an executor for, when executing the computer program, implementing a local path planning method as claimed in any one of claims 1 to 4.
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