CN112596513B - AGV navigation system and AGV dolly - Google Patents
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Abstract
The invention discloses an AGV navigation system, comprising: the system comprises a global path planning unit, a local path planning unit connected with the global path planning unit and a path optimization unit connected with the global path planning unit and the local path planning unit, wherein the global path planning unit plans a shortest path from a task starting point to a task ending point, namely a global path; in the driving process, if the obstacle is detected to exist on the global path, planning and avoiding the obstacle to return to the shortest path on the global path through the local path planning unit, namely obtaining the local path; and the path optimization unit is used for smoothing the formed global path and the local path. When local navigation is carried out, the sampling group adopts equal resolution sampling, more comprehensive linear velocity constraint and angular velocity constraint for processing, the calculated amount of the local navigation is greatly reduced, and meanwhile, the navigation accuracy can be ensured.
Description
Technical Field
The invention belongs to the technical field of AGV navigation, and particularly relates to an AGV navigation system and an AGV.
Background
When an AGV navigation system generates a global path, the best driving path is mostly generated based on an existing obstacle of a field, in the driving process, when a newly-added obstacle appears on the global path, obstacle avoidance can be carried out only by local path planning, most of the current AGV adopts local path planning methods such as D and the like under ROS, the D-local path planning method searches the optimal path from a task starting point to a task terminal point on a grid map in a traversal mode, the task terminal point is taken as a starting point, 8 grids around the grid where the task terminal point is located are traversed, the grid which is nearest to the task starting point and has no obstacle is obtained, then the 8 grids around the grid are traversed by taking the grid as the starting point, and the grid where the task starting point is located is traversed by analogy in sequence, and the local path planning methods have the problems of complex calculation and large calculation amount.
Disclosure of Invention
The present invention provides an AGV navigation system that aims to improve the above-mentioned problems.
The present invention is achieved as described above, and an AGV navigation system includes:
a global path planning unit, a local path planning unit connected with the global path planning unit, and a path optimizing unit connected with the global path planning unit and the local path planning unit, wherein,
the global path planning unit is used for planning a shortest path from a task starting point to a task ending point, namely a global path;
in the driving process, if the obstacle is detected to exist on the global path, planning and avoiding the obstacle to return to the shortest path on the global path through the local path planning unit, namely obtaining the local path;
and the path optimization unit is used for smoothing the formed global path and the local path.
Further, the local planning unit includes: a sampling group generation module, a linear velocity constraint module, an angular velocity constraint module, a track evaluation module and a smooth processing module which are connected in sequence, wherein,
a sample group generation module: periodically forming a plurality of initial sampling groups, and generating a track corresponding to each initial sampling group based on an initial position;
a linear velocity constraint module; outputting a sampling group with the linear velocity meeting the motor performance constraint, the obstacle distance constraint and the local path constraint to an angular velocity constraint module;
an angular velocity constraint module; outputting the sampling group of which the angular speed meets the local course constraint and the global course constraint and the smooth constraint to a track evaluation module;
a trajectory evaluation module: evaluating the tracks formed by each adopted group, and outputting the tracks with the best evaluation to a smoothing processing unit;
a smoothing module: performing angular speed smoothing on the track with the best evaluation to form an optimal track;
further, the linear velocity constraint conditions of the linear velocity constraint module are as follows:
and (3) motor performance constraint: v. of m ∈{v 0 -v a ·Δt , v 0 +v a Δ t }, wherein v a Indicates the maximum linear acceleration, v, that the AGV can achieve 0 Representing the current speed of the AGV trolley, wherein 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 | 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 calculation x Representing the projected distance of the straight line | v (w) × Δ t-S | on the transverse axis x.
Further, the angular velocity constraint conditions of the angular velocity constraint module are as follows:
the local course constraint is as follows:
wherein, theta c Is the course angle, theta, corresponding to the sample 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 (3) global course constraint:
wherein, theta c Is the course angle, theta, corresponding to the sample group w Is the global end course angle, w, on the current global path 1 Is a global course deviation threshold;
and (4) smoothing constraint:
wherein,
θ p is a heading deviation threshold.
Further, the smoothing module performs smoothing based on the optimal trajectory estimated by formula (1), where formula (1) is expressed as follows:
wherein,
θ c indicating the heading angle, θ, corresponding to the sample group g And (4) regarding the local end point course angle of the corresponding track of the sampling group on the global path, wherein theta represents the course angle corresponding to the sampling group after smoothing.
Further, the trajectory evaluation module performs estimation evaluation based on the following method:
obtaining the number of corners n of the track r And a rotation amplitude 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.
Further, the AGV navigation system further includes:
the linear velocity sampling module, linear velocity restraint module pass through the linear velocity sampling module and are connected with angular velocity restraint module, and wherein, the linear velocity sampling module samples the sampling group of linear velocity restraint module output, exports the adoption group of sampling to the angular velocity restraint module, and the sampling method of linear velocity sampling module specifically is as follows:
calculating a speed difference value v' between the maximum speed and the minimum speed in the output sampling group of the linear speed constraint unit;
determining the sampling number N based on the speed difference v', and sampling the sampling group output by the linear speed constraint unit based on an equal resolution method;
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 output sampling groups of the linear velocity constraint unit.
The invention is realized by the AGV, the AGV is provided with an AGV navigation system, the AGV navigation system is provided with the system.
The AGV navigation method provided by the invention has the following beneficial technical effects: when local navigation is carried out, the sampling group adopts equal resolution sampling, more comprehensive linear velocity constraint and angular velocity constraint for processing, the calculated amount of the local navigation is greatly reduced, and meanwhile, the navigation accuracy can be ensured, so that the algorithm does not seriously depend on the calculation power of a processor, the method is suitable for adopting an embedded processor, and the method is more suitable for industrial AGV operation scenes.
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FIG. 1 is a schematic diagram of an AGV navigation system 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 schematic diagram of an AGV navigation system according to an embodiment of the present invention, and for convenience of illustration, only the portions related to the embodiment of the present invention are shown.
This AGV navigation mainly is applicable to the AGV navigation in the grid map, and this AGV navigation includes: a global path planning unit, a local path planning unit connected with the global path planning unit, and a path optimizing unit connected with the global path planning unit and the local path planning unit, wherein,
the global path planning unit is used for planning a shortest path from a task starting point to a task ending point, namely a global path; in the driving process, if the obstacle is detected to exist on the global path, planning and avoiding the obstacle to return to the shortest path on the global path through the local path planning unit, namely obtaining the local path; a path optimization unit for smoothing the global path and the local path,
the global path planning unit adopts a shortest path algorithm to plan a shortest path from a task starting point to a task ending point, such as a Floyd algorithm; the path optimization unit adopts a Bezier curve to smoothly process the global path and the local path, so that the driving is stable and smooth.
In an embodiment of the present invention, the local planning unit includes: a sampling group generation module, a linear velocity constraint module, an angular velocity constraint module, a track evaluation module and a smooth processing module which are connected in sequence, wherein,
a sample group generation module: periodically forming a plurality of initial sampling groups, and generating a track corresponding to each initial sampling group based on an initial position; a linear velocity constraint module; outputting a sampling group with the linear velocity meeting the motor performance constraint, the obstacle distance constraint and the local path constraint to an angular velocity constraint module; an angular velocity constraint module; outputting the sampling group of which the angular speed meets the local course constraint and the global course constraint and the smooth constraint to a track evaluation module; a trajectory evaluation module: evaluating the tracks formed by each adopted group, and outputting the tracks with the best evaluation to a smoothing processing unit; a smoothing module: performing angular speed smoothing on the track with the best evaluation to form an optimal track;
a sample group generation module: periodically forming a plurality of initial sampling groups, and generating a track corresponding to each initial sampling group based on an initial position;
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.
Obtaining 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. Based on the linear velocity v in each sampling group m And an 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 coordinate of the end point 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.Deltat, deltat 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;
the linear velocity constraint module: outputting a sampling group with the linear velocity meeting the motor performance constraint, the obstacle distance constraint and the local path constraint to an angular velocity constraint module;
in the embodiment of the present invention, the linear velocity constraint condition of the linear velocity constraint module is expressed as follows:
1) The motor performance constraint: v. of m ∈{v 0 -v a ·Δt,v 0 +v a Δ t }, wherein v a Indicates the maximum linear acceleration, v, that the AGV can achieve 0 Representing the current speed of the AGV trolley, wherein delta t is the time interval of two times of sampling;
2) 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:
3) 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) 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.
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 an embodiment of the present invention, the AGV system further includes:
the linear velocity sampling module is connected with the angular velocity constraint module through the linear velocity sampling module, wherein the linear velocity sampling module samples a sampling group output by the linear velocity constraint module, the sampling group is output to the angular velocity constraint module, and the sampling method of the linear velocity sampling module is as follows:
the wire harness speed constraint module outputs a speed difference value v ', v ' = | v ' of the maximum speed and the minimum speed in the set 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 a method of equally dividing the resolution 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, N is, a is a scaling 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.
An angular velocity constraint module: screening the angular speed of the adopted group output by the linear speed constraint module or the linear speed sampling module, enabling the angular speed to meet the local course constraint and the global course constraint, and outputting the sampling group with the smooth constraint to the track evaluation module;
estimating the angular velocity based on the course angle, wherein the global path constraint, the local path constraint and the smooth constraint are as follows:
1) Local course constraint:
wherein, theta c Is the course angle, theta, corresponding to the sample group g The local end point course angle of the corresponding track of the sampling group on the global path (or the local end point course angle of the corresponding track of the sampling group for short) is obtainedThe heading angle in the Ming is the included angle between the mass center speed (vector) of the AGV and the x axis, w 0 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 on a global path, w 1 Is a global heading deviation threshold;
3) And (4) smooth constraint:
wherein,
θ p is a heading deviation threshold.
In the local course constraint, the global course 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.
The corresponding track of the sampling group which can meet the constraint condition has no large-angle rotation (the smoothness is ensured); ensuring small deviation (ensuring no deviation) from the local path terminal point orientation; and ensuring that the deviation from the global path orientation is small (ensuring that the deviation is not generated).
A trajectory evaluation module: and receiving a sampling group output by the angular velocity constraint module, and evaluating a track formed by the sampling group to obtain an optimal track for evaluation. The track evaluation method specifically comprises the following steps:
1) The method has the advantages that as the paths under the grid map still have broken line corners and are likely to have more broken corner numbers, the paths cause the problem that the AGV has low operation efficiency, the operation problem of the paths is comprehensively considered, the tracks are evaluated, two factors are considered, and the corner number n of the tracks T is considered r And amplitude of rotation 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 distance estimation method is used for estimating 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 estimation formula is beta (dist), and the longer the distance is, the smaller the beta (dist) value is.
3) Evaluating the best 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 specific gravity coefficient, the obtained path can better meet the application scene of the industrial AGV, and the AGV runs more stably.
A smoothing module: and performing angular velocity smoothing processing on the track with the best evaluation to form an optimal track.
Smoothing treatment:
wherein,
θ c indicating the heading angle, θ, corresponding to the sample group g And theta represents the course angle corresponding to the smoothed sampling group.
Correspondingly, the invention also provides an AGV, wherein the AGV is provided with an AGV navigation system, and the AGV navigation system is provided with the system.
The AGV navigation method provided by the invention has the following beneficial technical effects: when local navigation is carried out, the sampling group adopts equal resolution sampling, more comprehensive linear velocity constraint and angular velocity constraint for processing, the calculated amount of the local navigation is greatly reduced, and meanwhile, the navigation accuracy can be ensured, so that the algorithm does not seriously depend on the calculation power of a processor, the method is suitable for adopting an embedded processor, and the method is more suitable for industrial AGV operation scenes.
The present invention has been described in detail with reference to the accompanying drawings, and it is to be understood that the invention is not limited to the specific embodiments described above, and that various insubstantial modifications of the inventive concepts and solutions, or their direct application to other applications without modification, are intended to be covered by the scope of the invention.
Claims (6)
1. An AGV navigation system, comprising:
a global path planning unit, a local path planning unit connected with the global path planning unit, and a path optimizing unit connected with the global path planning unit and the local path planning unit, wherein,
the global path planning unit is used for planning a shortest path from a task starting point to a task ending point, namely a global path;
in the driving process, if the obstacle is detected to exist on the global path, planning and avoiding the obstacle to return to the shortest path on the global path through the local path planning unit, namely obtaining the local path;
the path optimization unit is used for smoothing the formed global path and the local path;
the smoothing module performs smoothing based on the optimal trajectory estimated by formula (1), wherein formula (1) is expressed as follows:
wherein,
θ c indicating the heading angle, θ, corresponding to the sample group g For a local end point course angle of a corresponding track of the sampling group on the global path, representing a course angle corresponding to the sampling group after smoothing by theta;
the AGV navigation system further includes:
the linear velocity sampling module, linear velocity restraint module pass through the linear velocity sampling module and are connected with angular velocity restraint module, and wherein, the linear velocity sampling module samples the sampling group of linear velocity restraint module output, exports the adoption group of sampling to the angular velocity restraint module, and the sampling method of linear velocity sampling module specifically is as follows:
calculating a speed difference value v' between the maximum speed and the minimum speed in the output sampling group of the linear speed constraint unit;
determining the sampling number N based on the speed difference v', and sampling the sampling group output by the linear speed constraint unit based on an equal resolution method;
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 output sampling groups of the linear velocity constraint unit.
2. The AGV navigation system of claim 1, wherein the local planning unit includes: a sampling group generation module, a linear velocity constraint module, an angular velocity constraint module, a track evaluation module and a smooth processing module which are connected in sequence, wherein,
a sample group generation module: periodically forming a plurality of initial sampling groups, and generating a track corresponding to each initial sampling group based on an initial position;
a linear velocity constraint module; outputting a sampling group with the linear velocity meeting the motor performance constraint, the obstacle distance constraint and the local path constraint to an angular velocity constraint module;
an angular velocity constraint module; outputting the sampling group of which the angular speed meets the local course constraint and the global course constraint and the smooth constraint to a track evaluation module;
a trajectory evaluation module: evaluating the tracks formed by each adopted group, and outputting the tracks with the best evaluation to a smoothing processing unit;
a smoothing module: performing angular speed smoothing on the track with the best evaluation to form an optimal track;
3. the AGV navigation system of claim 2, wherein the linear velocity constraint of the linear velocity constraint module is specified by:
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 (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.
4. The AGV navigation system of claim 2, wherein the angular velocity constraints of the angular velocity constraint module are specified as follows:
the local course constraint is as follows:
wherein, theta c Is the course angle, theta, corresponding to the sample 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 Is the course angle, theta, corresponding to the sample group w Is the global end course angle, w, on the current global path 1 Is a global heading deviation threshold;
and (4) smoothing constraint:
wherein,
θ p is a heading deviation threshold.
5. The AGV navigation system of claim 2, wherein the trajectory evaluation module evaluates the estimate based on:
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 the specific gravity coefficient.
6. An AGV according to any one of claims 1 to 5, wherein an AGV navigation system is provided on the AGV.
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| CN111457929A (en) * | 2019-12-31 | 2020-07-28 | 南京工大数控科技有限公司 | Logistics vehicle autonomous path planning and navigation method based on geographic information system |
| CN111399506A (en) * | 2020-03-13 | 2020-07-10 | 大连海事大学 | Global-local hybrid unmanned ship path planning method based on dynamic constraints |
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