CN113341999A - Forklift path planning method and device based on optimized D-x algorithm - Google Patents
Forklift path planning method and device based on optimized D-x algorithm Download PDFInfo
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Abstract
The invention provides a forklift path planning method and device based on an optimized D-x algorithm, and belongs to the technical field of forklift path planning. The method comprises the following steps: establishing a current forklift surrounding environment map; based on the limitation of a forklift kinematic model, drawing a plurality of paths from the current position to the target position on a current forklift surrounding environment map by using a D-x algorithm; screening a plurality of planned paths on an expanded forklift surrounding environment map according to the principle that the paths do not intersect with the expanded obstacles and the path length is shortest; smoothing the screened paths to obtain the current optimal path; and controlling the forklift to run along the current optimal path, updating a surrounding environment map of the forklift in real time in the running process, and replanning the path if a new obstacle appears on the current optimal path until the forklift reaches the target position. The method can plan the path of the forklift in real time in a dynamic environment, so that the forklift can efficiently pass through the planned path.
Description
Technical Field
The invention relates to a forklift path planning method and device based on an optimized D-x algorithm, and belongs to the technical field of forklift path planning.
Background
In recent years, the demands of automatic storage and logistics transportation are adopted by more and more industrial enterprises, the investment of intelligent forklifts is more and more, and the advantages of high working efficiency, strong logistics flexibility and the like of the intelligent forklifts are more and more recognized. The intelligent forklift is bound to encounter obstacles in work, needs to avoid the obstacles to continue to advance, and finally reaches a target point to complete a transportation task. The path planning capability of the intelligent forklift determines whether the intelligent forklift can work with high difficulty in a complex environment.
The purpose of path planning is to plan a collision-free path connecting the current position of the vehicle and the target position under certain constraint conditions. Aiming at the problem of path planning of unmanned vehicles, domestic and foreign scholars propose various methods, such as: artificial potential field methods, a-algorithms, D-algorithms, etc. The artificial potential field method is simple in structure, beneficial to real-time control and widely applied to the unmanned vehicle obstacle avoidance and track smooth direction, but the method is easy to fall into a local minimum value, is not suitable for planning under the condition of high degree of freedom, and is not ideal in effect in the direction of unmanned vehicle constraint; the a-algorithm is adapted to static environments; the D algorithm is suitable for dynamic environments.
The intelligent forklift is required to be fast enough to meet the requirement of real-time running of the forklift due to the fact that the environment where the intelligent forklift is located changes all the time, the D-type algorithm is used as the improvement of the A-type algorithm, when the environment changes, the searching efficiency can be improved by using the previous searching information, and compared with the method that the A-type algorithm is repeatedly used for re-planning, the searching efficiency is higher, and therefore the D-type algorithm is more suitable for solving the problem of dynamic path planning of the intelligent forklift. However, the effect of directly applying the D-x algorithm to path planning of the intelligent forklift is not good, the situation that the planned path cannot be driven often occurs, and the path passing rate is low.
Disclosure of Invention
The invention aims to provide a forklift path planning method and device based on an optimized D-x algorithm, which can improve the path passing rate and enable a forklift to efficiently pass through a planned path.
In order to achieve the above object, the present invention provides a forklift path planning method based on an optimized D-x algorithm, which includes the following steps:
step 1, acquiring a current position and a target position of a forklift, acquiring surrounding environment information of the current position of the forklift, and establishing a current forklift surrounding environment map based on the surrounding environment information of the current position of the forklift;
step 2, drawing a plurality of paths from the current position to the target position on the current forklift surrounding environment map by using a D-x algorithm based on the limitation of the forklift kinematic model;
step 3, screening the plurality of paths planned in the step 2 on the expanded forklift surrounding environment map according to the principle that the paths do not intersect with the expanded obstacles and the path length is shortest; the expanded forklift surrounding environment map is obtained by expanding the obstacles in the current forklift surrounding environment map;
step 4, smoothing the path screened out in the step 3 to obtain a current optimal path;
and 5, controlling the forklift to run along the current optimal path, updating a surrounding environment map of the forklift in real time in the running process, judging whether a new obstacle appears on the current optimal path or not by using the real-time updated surrounding environment map of the forklift, and returning to the step 2 to continue executing the step if the new obstacle appears until the forklift reaches the target position.
The forklift path planning method based on the optimized D-x algorithm has the beneficial effects that: the method comprises the steps that firstly, a plurality of paths from a current position to a target position are drawn on a current forklift surrounding environment map by using a D-x calculation rule, then the plurality of paths drawn by the D-x calculation rule are screened on the expanded forklift surrounding environment map according to the principles that the paths do not intersect with expanded obstacles and the path length is shortest, on one hand, the screened paths do not intersect with the expanded obstacles by ensuring that the screened paths do not intersect with real obstacles on the current forklift surrounding environment map, and the passing rate of the forklift running along the optimal path is improved; on the other hand, the shortest length of the screened path is ensured, so that the shortest running time of the forklift along the optimal path is ensured; and in addition, the surrounding environment map of the forklift is updated in real time in the driving process, and if a new obstacle appears on the current optimal path, the path planning is carried out again, so that the path of the forklift can be planned in real time in a dynamic environment, the path passing rate is further improved, and the forklift can efficiently pass through the planned path.
Further, in the above method, the step 3 is realized by: firstly, determining key nodes of an expanded obstacle in an effective area of an expanded forklift surrounding environment map, wherein the key nodes of the expanded obstacle are boundary points of the expanded obstacle; then, screening the plurality of paths planned in the step 2 by using key nodes of the obstacle after expansion in the effective domain, and taking paths which are not intersected with the region formed by the key nodes of the obstacle after expansion in the plurality of paths planned in the step 2 as paths which are not intersected with the obstacle after expansion; and finally, screening the path with the shortest path length from the paths which do not intersect with the expanded obstacle as the screened path in the step 3.
Further, in the above method, the expanded forklift surrounding environment map is obtained by: and rasterizing the current forklift surrounding environment map, and performing expansion processing on the obstacles in the rasterized forklift surrounding environment map to obtain an expanded forklift surrounding environment map.
Further, in the above method, the expanded forklift surrounding environment map is obtained by: and expanding the obstacles in the current forklift surrounding environment map, and rasterizing the expanded forklift surrounding environment map to obtain the expanded forklift surrounding environment map.
Further, in order to ensure safe and collision-free passage of the forklift, in the above method, the expansion radius of the obstacle is set to be at least half of the width of the forklift at the time of the expansion process.
Further, in the above method, the forklift kinematic model is:
wherein x is the motion state of the forklift, (x, y) are the current position coordinates of the forklift, theta is the heading angle of the forklift, delta is the rotation angle of the rear wheel of the forklift (delta is not more than 90 degrees at the maximum), l is the distance between the front axle and the rear axle of the forklift,
corresponding to the first derivatives of x, y, and theta, respectively.
Further, in the above method, the path filtered out in step 3 is smoothed by using a path smoothness function, where the path smoothness function is:
in the formula, mu is an environment factor and is used for describing influence information of known obstacles on the environment under different environments, the influence information is different along with the difference of the area ratio of the obstacles in the environment map, theta represents a forklift heading angle before the current node is corrected, and f (theta i) represents the forklift heading angle after the current node is corrected.
Further, in the above method, the path after the path smoothness function processing is also smoothed by a gradient descent method.
The invention also provides a forklift path planning device based on the optimized D algorithm, which comprises an environment sensing system and a controller, wherein the environment sensing system is used for acquiring the current position of the forklift and the surrounding environment information of the current position of the forklift and sending the information to the controller, and the controller realizes the forklift path planning method based on the optimized D algorithm based on the data sent by the environment sensing system.
The forklift path planning device based on the optimized D-x algorithm has the beneficial effects that: the device can plan the path of the forklift in real time in a dynamic environment, so that the path passing rate is improved, and the forklift can efficiently pass through the planned path.
Further, in the device, the environment sensing system comprises a multi-line laser radar, a main GPS, an auxiliary GPS, an inertia measuring unit and a ranging type scanning sensor, wherein the multi-line laser radar is arranged in the middle of the top of the forklift and has the same distance to the periphery of the forklift; the two GPS are arranged on the top of the forklift, are arranged in front and at the back respectively, are in the same longitudinal direction of the forklift with the multi-line laser radar and are collinear; the inertia measurement unit is arranged behind a forklift driving seat; the ranging type scanning sensor is arranged in the middle of the front of the forklift.
Drawings
Fig. 1 is a flowchart of a forklift path planning method based on an optimized D-x algorithm in an embodiment of the present invention;
FIG. 2 is a schematic view of a barrier cell prior to inflation in an embodiment of the apparatus of the present invention;
FIG. 3 is a schematic view of an expanded barrier cell in an embodiment of the device of the present invention;
FIG. 4 is a side view of a forklift truck in an embodiment of the apparatus of the present invention;
FIG. 5 is a top view of a forklift truck in an embodiment of the apparatus of the present invention;
in the figure, 1 is a multiline laser radar, 2 is a main GPS, 3 is a sub-GPS, 4 is an inertial measurement unit, and 5 is a ranging type scanning sensor.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments.
The embodiment of the device is as follows:
the forklift path planning device based on the optimized D-x algorithm of the embodiment includes: a context awareness system and a controller. The environmental sensing system on the forklift is arranged as shown in fig. 4 and 5, and the sensor used by the environmental sensing system comprises: the system comprises a multi-line laser radar 1, a main GPS2, a sub GPS3, an inertial measurement unit 4 and a ranging type scanning sensor 5. The multi-line laser radar 1 is arranged in the middle of the top of the forklift, the distances from the multi-line laser radar to the periphery of the forklift are equal, the forward direction of a cross shaft of a coordinate axis of a sensor is the forward direction of the forklift, and after a global coordinate system is established, the included angle between the forward direction of the cross shaft of the sensor and the forward direction of the cross shaft of the global coordinate system is the direction angle of a forklift body; the two GPS are arranged on the top of the forklift, are arranged in front and at the back respectively, are in the same longitudinal direction of the forklift with the multi-line laser radar and are collinear; the inertia measurement unit 4 is arranged behind a forklift driving seat; the distance measurement type scanning sensor 5 is arranged in the middle of the front head of the forklift, and the scanning range is generally set to be 0-180 degrees.
The environment sensing system is used for acquiring the current position of the forklift and the surrounding environment information of the current position of the forklift and sending the information to the controller, and the controller realizes the forklift path planning method (hereinafter referred to as the forklift path planning method) based on the optimized D-algorithm shown in fig. 1 based on the data sent by the environment sensing system.
As shown in fig. 1, the method for planning a forklift path of the present embodiment includes the following steps:
step 1, acquiring a current position and a target position of a forklift, acquiring surrounding environment information of the current position of the forklift, and establishing a current forklift surrounding environment map based on the surrounding environment information of the current position of the forklift;
step 2, drawing a plurality of paths from the current position to the target position on the current forklift surrounding environment map by using a D-x algorithm based on the limitation of the forklift kinematic model;
step 3, screening the plurality of paths planned in the step 2 on the expanded forklift surrounding environment map according to the principle that the paths do not intersect with the expanded obstacles and the path length is shortest;
the expanded forklift surrounding environment map is obtained by expanding the obstacles in the current forklift surrounding environment map, and the time of the expansion processing can be determined according to the actual situation before the step 2 or after the step 2.
Step 4, smoothing the path screened out in the step 3 to obtain a current optimal path;
and 5, controlling the forklift to run along the current optimal path in the step 4, updating a surrounding environment map of the forklift in real time in the running process, judging whether a new obstacle appears on the current optimal path or not by using the real-time updated surrounding environment map of the forklift, and returning to the step 2 to continue executing until the forklift reaches the target position if the new obstacle appears.
The specific implementation process of the step 1 is as follows:
acquiring the current position of the forklift and the surrounding environment information of the current position of the forklift by using an environment sensing system arranged on the forklift, and constructing a current forklift surrounding environment map based on the acquired surrounding environment information of the current position of the forklift; the method comprises the steps of scanning the current forklift surrounding environment by using a multi-line laser radar to establish a Cartesian global coordinate system, calculating the global coordinate of the current position point of each sensor according to the current global position information, the vehicle body size parameters and the position relation among the sensors which are obtained by the multi-line laser radar and a GPS, and converting the relative coordinate of the surrounding object obtained by the multi-line laser radar into the global coordinate. The distance measuring type scanning sensor is used for detecting the distance from the obstacle to the forklift, the current position of the forklift is obtained by combining information detected by the multi-line laser radar, the GPS and the inertia measuring unit, and the shape, the position and the size of the obstacle around the forklift are detected by combining the multi-line laser radar and the distance measuring type scanning sensor.
The specific implementation process of the step 2 is as follows:
based on the limitation of the forklift kinematic model, marking out the current pose X on the current forklift surrounding environment map by using a D-X algorithms＝(xs,ys,θs) To target pose Xe＝(xe,ye,θe) Each path represented by a series of vehicle poses.
Wherein, supposing that fork truck only moves on the plane, the wheel does not have slip relatively ground, then fork truck kinematics model is:
wherein x is the motion state of the forklift, (x, y) are the current position coordinates of the forklift, theta is the heading angle of the forklift, delta is the rotation angle of the rear wheel of the forklift (delta is not more than 90 degrees at the maximum), l is the distance between the front axle and the rear axle of the forklift,
corresponding to the first derivatives of x, y, and theta, respectively.
The specific method for planning the path by using the D-algorithm is the prior art, and this embodiment simply introduces the heuristic search function of the D-algorithm, and the rest of the method is not described again.
The heuristic search function of the D-algorithm is: where n denotes the cell to be expanded, f (n) is the cost value of the node, g (n) denotes the cost of movement from the starting point to cell n along the planned route, which is generally the physical length of the road, and h (n) denotes the estimated cost of movement from cell n to the target point. The list of the algorithm D stores the shortest path information from each node to the target node on the planning path, the father node of each node, f (n), g (n), h (n), and the track from the father node to the current node. When a father node is extended to a child node, each node only contains coordinate information of a vehicle, and the child node (x ', y') is extended in 4 directions of up, down, left and right or 8 directions of up, down, left, right, left up, left down, right up and right down by taking the father node (x, y) as a central point.
In this embodiment, the expanded forklift surrounding environment map is obtained through the following steps:
(1) rasterizing a current forklift surrounding environment map to obtain a rasterized forklift surrounding environment map (hereinafter referred to as a grid map, see fig. 2), defining each grid of the grid map as a cell according to a cell method, and extracting barrier cells in an effective domain;
specifically, the obstacle cells are extracted according to the shape, position and size of the obstacle in the effective domain of the grid map, wherein the effective domain refers to: and taking the current position of the forklift as the center of a circle and taking the distance between the current position of the forklift and the target position as the radius to form an effective area.
(2) The expansion processing method is used to perform expansion processing on the obstacle cells in the grid map, so as to obtain an expanded grid map (i.e., an expanded forklift surrounding environment map), as shown in fig. 3.
The expansion radius of the obstacle is set to be more than half of the width of the forklift so as to ensure that the forklift can safely pass through without collision.
In the embodiment, a grid map is obtained by rasterizing a current forklift surrounding environment map, an expanded grid map is obtained by expanding obstacles in the grid map, and the expanded grid map is used as the expanded forklift surrounding environment map; in another embodiment, the obstacle in the current forklift surrounding environment map may be expanded, and then the expanded map may be rasterized to obtain an expanded grid map. In another embodiment, the rasterizing step may be omitted, and only the obstacle in the current forklift surrounding environment map may be expanded to obtain an expanded forklift surrounding environment map.
The specific implementation process of the step 3 is as follows:
(1) determining key nodes of expanded obstacle cells in the effective domain of the expanded grid map (namely key nodes of the expanded obstacles in the effective domain);
the key nodes of the obstacle after expansion are boundary points of the obstacle after expansion, specifically, the obstacle after expansion is approximated to quadrilateral cells, four intersections of the boundaries of the obstacle cells after expansion and surrounding cells are taken as key nodes, as shown in fig. 3, there are two obstacle cells after expansion in fig. 3, the key nodes of each obstacle cell are numbered, the key nodes of the upper left obstacle cells are p7, p8, p9 and p10, and the key nodes of the lower right obstacle cells are p1, p2, p3, p4, p5 and p 6. And the 10 key nodes are used for representing the position information of the expanded obstacles in the effective domain of the expanded grid map.
(2) Screening the plurality of paths planned in the step 2 by using key nodes of the obstacle after expansion in the effective domain, and taking the paths which are not intersected with the region formed by the key nodes of the obstacle after expansion in the plurality of paths planned in the step 2 as paths which are not intersected with the obstacle after expansion;
(3) and (3) screening the path with the shortest path length from the paths which do not intersect with the expanded obstacle as the screened path in the step (3).
The specific implementation process of the step 4 is as follows:
and (4) smoothing the path screened out in the step (3) by using a path smoothness function and a gradient descent method, and eliminating redundant points to obtain the current optimal path.
Wherein the path smoothness function is:
in the formula, mu is an environment factor and is used for describing influence information of known obstacles on the environment under different environments, the influence information is different along with the difference of the area ratio of the obstacles in the environment map, theta represents a forklift heading angle before the current node is corrected, and f (theta i) represents the forklift heading angle after the current node is corrected.
In order to reduce the possible invalid turning phenomenon, a path smoothness function is introduced to punish the degree of deviation of the screened path from an ideal path (the ideal path is a connecting line between the current position and the target position), so that a path with a smaller deviation degree is obtained, and the invalid turning is avoided as much as possible.
And (3) distributing energy to each path point by adopting a gradient descent method, and firstly solving a first derivative:
Dxi＝0(i＝1,2,3…N)
i＝2Repeat
curretDxi＝-4×(xi+1-2xi+xi-1)
succDxi+1＝2×(xi+1-2xi+xi-1)
prev Dxi-1＝2×(xi+1-2xi+xi-1)
Dxi-1＝Dxi-1+prev Dxi-1
Dxi＝Dxi+curretDxi
Dxi+1＝Dxi+1+succDxi+1
Until>N-1
a constant mu is selected, and i is calculated to be 1
Repeat
xi＝xi-μ×Dxi
until i>N
And repeating the two cycles until the absolute value of the subtraction of the value of the previous energy function and the value of the next energy function is less than the preset epsilon.
Wherein the energy function is
Where N is the number of path points, Δ xi+1＝xi+1-xi,Δxi＝xi-xi-1，Dxi、Dxi+1、Dxi-1Respectively, the energy of the ith point, the (i + 1) th point and the (i-1) th point, (the energy here can also be understood as the distance). The processing flow of the gradient descent method is as follows: and setting a value epsilon, and comparing the energy of the current point with the energy of the next point, wherein the absolute value of the difference between the two is smaller than epsilon, namely the target point is reached.
The specific implementation process of the step 5 is as follows:
and (4) controlling the forklift to run along the current optimal path in the step (4), updating a surrounding environment map of the forklift in real time in the running process, judging whether a new obstacle appears on the current optimal path or not by using the real-time updated surrounding environment map of the forklift, continuing running along the current optimal path if no new obstacle appears, returning to the step (2) to continue executing if a new obstacle appears until the forklift reaches a target position, and finishing a real-time path planning task.
In summary, in the forklift path planning method of this embodiment, firstly, multiple paths from the current position to the target position are drawn by using the D-x algorithm on the current forklift surrounding environment map, then, on the expanded forklift surrounding environment map, multiple paths drawn by the D-x algorithm are screened according to the principle that the paths do not intersect with the expanded obstacle and the path length is shortest, on one hand, by ensuring that the screened paths do not intersect with the expanded obstacle, the screened paths are further ensured not to intersect with the real obstacle on the current forklift surrounding environment map, and the passing rate of the forklift traveling along the optimal path is improved; on the other hand, the shortest length of the screened path is ensured, so that the shortest running time of the forklift along the optimal path is ensured; and in addition, the surrounding environment map of the forklift is updated in real time in the driving process, and if a new obstacle appears on the current optimal path, the path planning is carried out again, so that the path of the forklift can be planned in real time in a dynamic environment, the path passing rate is further improved, and the forklift can efficiently pass through the planned path.
Claims (10)
1. A forklift path planning method based on an optimized D-x algorithm is characterized by comprising the following steps:
step 1, acquiring a current position and a target position of a forklift, acquiring surrounding environment information of the current position of the forklift, and establishing a current forklift surrounding environment map based on the surrounding environment information of the current position of the forklift;
step 2, drawing a plurality of paths from the current position to the target position on the current forklift surrounding environment map by using a D-x algorithm based on the limitation of the forklift kinematic model;
step 3, screening the plurality of paths planned in the step 2 on the expanded forklift surrounding environment map according to the principle that the paths do not intersect with the expanded obstacles and the path length is shortest; the expanded forklift surrounding environment map is obtained by expanding the obstacles in the current forklift surrounding environment map;
step 4, smoothing the path screened out in the step 3 to obtain a current optimal path;
and 5, controlling the forklift to run along the current optimal path, updating a surrounding environment map of the forklift in real time in the running process, judging whether a new obstacle appears on the current optimal path or not by using the real-time updated surrounding environment map of the forklift, and returning to the step 2 to continue executing the step if the new obstacle appears until the forklift reaches the target position.
2. The optimized D algorithm-based forklift path planning method according to claim 1, wherein the step 3 is implemented by: firstly, determining key nodes of an expanded obstacle in an effective area of an expanded forklift surrounding environment map, wherein the key nodes of the expanded obstacle are boundary points of the expanded obstacle; then, screening the plurality of paths planned in the step 2 by using key nodes of the obstacle after expansion in the effective domain, and taking paths which are not intersected with the region formed by the key nodes of the obstacle after expansion in the plurality of paths planned in the step 2 as paths which are not intersected with the obstacle after expansion; and finally, screening the path with the shortest path length from the paths which do not intersect with the expanded obstacle as the screened path in the step 3.
3. The optimized D-algorithm-based forklift path planning method according to claim 2, wherein the expanded forklift surrounding environment map is obtained by the following steps: and rasterizing the current forklift surrounding environment map, and performing expansion processing on the obstacles in the rasterized forklift surrounding environment map to obtain an expanded forklift surrounding environment map.
4. The optimized D-algorithm-based forklift path planning method according to claim 2, wherein the expanded forklift surrounding environment map is obtained by the following steps: and expanding the obstacles in the current forklift surrounding environment map, and rasterizing the expanded forklift surrounding environment map to obtain the expanded forklift surrounding environment map.
5. The method for planning a forklift path according to claim 3 or 4, wherein the expansion radius of the obstacle is set to be at least half of the forklift width when the expansion process is performed.
6. The optimized D algorithm-based forklift path planning method according to claim 5, wherein the forklift kinematic model is as follows:
wherein x is the motion state of the forklift, (x, y) are the current position coordinates of the forklift, theta is the heading angle of the forklift, delta is the rotation angle of the rear wheel of the forklift (delta is not more than 90 degrees at the maximum), l is the distance between the front axle and the rear axle of the forklift,
corresponding to the first derivatives of x, y, and theta, respectively.
7. The method for planning the forklift path based on the optimized D-x algorithm according to claim 6, wherein the paths screened out in step 3 are smoothed by using a path smoothness function, wherein the path smoothness function is:
in the formula, mu is an environment factor and is used for describing influence information of a known obstacle on the environment under different environments, the influence information is different along with the difference of the area ratio of the obstacle to the environment map, theta represents a current node before correction, and f (theta) represents the heading angle of the forklift before correctioni) Representing the corrected forklift heading angle of the current node.
8. The method for planning the forklift path based on the optimized D-x algorithm according to claim 7, wherein the path after the path smoothness function is processed is further smoothed by a gradient descent method.
9. A forklift path planning device based on an optimized D-algorithm is characterized by comprising an environment sensing system and a controller, wherein the environment sensing system is used for acquiring current position of a forklift and surrounding environment information of the current position of the forklift and sending the information to the controller, and the controller realizes the forklift path planning method based on the optimized D-algorithm according to any one of claims 1-8 on the basis of data sent by the environment sensing system.
10. The optimized D-algorithm-based forklift path planning device according to claim 9, wherein the environment sensing system comprises a multi-line laser radar, a main GPS, a secondary GPS, an inertial measurement unit and a ranging type scanning sensor, the multi-line laser radar is installed in the middle of the top of the forklift, and the distances to the periphery of the forklift are equal; the two GPS are arranged on the top of the forklift, are arranged in front and at the back respectively, are in the same longitudinal direction of the forklift with the multi-line laser radar and are collinear; the inertia measurement unit is arranged behind a forklift driving seat; the ranging type scanning sensor is arranged in the middle of the front of the forklift.
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