CN112327831A - Factory AGV track planning method based on improved artificial potential field method - Google Patents
Factory AGV track planning method based on improved artificial potential field method Download PDFInfo
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Abstract
The invention belongs to the technical field of factory AGV path planning, and provides a factory AGV track planning method based on an improved artificial potential field method, which is used for improving functions of attractive force and repulsive force applied to an unmanned transport vehicle in an artificial potential field. The defect that the unmanned transport vehicle is easy to collide at the initial stage of movement and the defect that the target is inaccessible at the final stage of movement is overcome. Meanwhile, the resultant force function borne by the unmanned transport vehicle is improved, a direction vector is introduced, and the memory effect of the resultant force function is increased. The local minimum point can be left out by generating an additional perturbation when being trapped. The method improves the algorithm of the artificial potential field method, is favorable for the unmanned transport vehicle to quickly find the optimal path from the starting point to the target point in the warehouse, and improves the working efficiency.
Description
Technical Field
The invention provides an AGV track planning method based on an artificial potential field method, and the AGV track planning method is correspondingly improved, and belongs to the technical field of unmanned vehicle path planning.
Background
Unmanned transport vechicle is because its advantage such as small, the goods weight of transport is big, efficient, autonomy is high, is used gradually in the construction of intelligent factory. People began to conduct relevant research on the path planning technology of unmanned transport vehicles. By planning the track of the unmanned transport vehicle, an optimal feasible track from a starting point to a terminal point can be found for the transport vehicle, and collision with static or dynamic obstacles in a factory is avoided, so that the functions of collision-free and efficient cargo carrying are achieved.
By knowing the environmental information, trajectory planning can be divided into global trajectory planning and local trajectory planning, wherein a common global trajectory planning method includes: dijkstra algorithm, A-algorithm and the like, and the local trajectory planning has an artificial potential field method, a genetic algorithm and the like. The artificial potential field method is a relatively mature and efficient planning method in a trajectory planning algorithm. The environment is considered as a potential field, and the repulsion field and the gravitational field which are respectively generated on the unmanned transport vehicle by the barrier and the target point jointly control the motion of the unmanned transport vehicle, so that the transport vehicle continuously approaches the target point. Compared with other algorithms, the method has the advantages that the obstacle avoidance effect is poor, the calculation mode is complex, the real-time performance is poor and the like in practical application, and the artificial potential field method has good real-time performance and is convenient for real-time control of the bottom layer. The method is widely applied to real-time obstacle avoidance, smooth track control and the like. However, in the manual potential field method, since the potential fields are overlapped and only local environmental information is grasped, collision is likely to occur at the initial stage of the movement of the unmanned transport vehicle, and a local minimum point and a target is not reached at the final stage at the middle stage.
In the conventional artificial potential field method, the size of the attractive force of the target point is influenced by the distance between the unmanned transport vehicle and the target point, and when the unmanned transport vehicle is far away from the target point, the attractive force may be too large to cause the unmanned transport vehicle to collide. In addition, when too many obstacles exist near the target point, the unmanned transport vehicle may not reach the target point due to too large obstacle repulsive force. Meanwhile, the situation that the attraction of a target point is equal to the repulsion of an obstacle easily occurs in the running process of the unmanned transport vehicle, so that the unmanned transport vehicle is locally optimal, has a local minimum phenomenon and is stagnated or vibrated.
Therefore, an improved algorithm is needed to solve the problems that the artificial potential field method is easy to generate collision in the initial stage, local minimum points occur in the middle stage and targets do not reach in the final stage.
The present invention has been made in view of solving the above problems.
Disclosure of Invention
The invention aims to provide an AGV track planning method based on an improved artificial potential field method.
The technical scheme of the invention is as follows:
a factory AGV track planning method based on an improved artificial potential field method comprises the following steps:
the method comprises the following steps: by improving an artificial potential field algorithm and improving a gravitational field function, the collision between an unmanned transport vehicle and an obstacle caused by overlarge partial gravitational force when the unmanned transport vehicle is far away from a target point is avoided. By improving the repulsive force field function, the phenomenon that a target cannot be reached due to the fact that a large number of obstacles exist near the target point is avoided. And judging whether the unmanned transport vehicle falls into a local minimum point in the running process.
If the unmanned transport vehicle falls into a local minimum point, the memory direction vector is introduced by improving the resultant force function, and the memory effect of the resultant force function is increased. The local minimum point can be left out by generating an additional perturbation when being trapped. And enabling the unmanned transport vehicle to escape from the local minimum point and continue to move towards the target. The method comprises the following steps:
s1: modeling a factory by adopting a grid method;
s2: acquiring environmental information such as initial state parameters, initial points, target points and the like of the unmanned transport vehicle;
s3: establishing a rectangular coordinate system by taking the target point as an origin, and establishing coordinates of the unmanned transport vehicle and the obstacle, wherein the distances between the unmanned transport vehicle and the target point and the obstacle are respectively as follows:
wherein (x, y) is the current position of the unmanned transport vehicle, (x)0,y0) Coordinates of the target point; (x)i,yi) Is the equivalent coordinate of the potential force field of the obstacle, i is 1,2,3,4,5 … … n
S4: an improved artificial potential field method is adopted to represent the resultant force applied to the unmanned transport vehicle;
s4.1 improved gravitational field Uatt(q) and repulsive force field UrepThe potential field function of (q) is as follows:
wherein KattIs a gravitational field gain constant, and r is a set distance threshold value
Wherein KrepIs a repulsive force field gain constant, rho is the maximum influence distance of the obstacle, thetaiThe included angle between the speed of the unmanned transport vehicle and the repulsion force is formed;
s4.2: the negative gradient of the potential field function is defined as the artificial force, which consists of the attractive and repulsive forces. Wherein the attractive force Fatt(q) is gravitational field UattNegative gradient of (q), repulsive force Frep(q) is a repulsive force field Urep(q) negative gradient, formula as follows:
Fatt＝-▽Uatt(q)
Frep＝-▽Urep(q)
s4.3: the attractive and repulsive forces are calculated by the modified attractive and repulsive force fields, respectively, as follows:
s4.4: calculating resultant force field UCombination of Chinese herbs(q) and the resultant force FCombination of Chinese herbs(q) is:
wherein n is the total number of barriers such as a goods shelf generating a repulsive force field, alpha is a gain coefficient of a direction vector,
is a unit direction vector.
S5: searching a point with the minimum total potential energy in the path, and judging whether the point is a final target point;
s6: if the target point is the final target point, the path planning is finished, the unmanned transport vehicle smoothly reaches the target point, and if the target point is not the target point, the unmanned transport vehicle sinks into the local minimum point;
s7: when the unmanned transport vehicle falls into a local minimum point, in the resultant force
And (4) changing the direction, adding additional disturbance, enabling the unmanned transport vehicle to be separated from the local minimum point, and returning to the step S3 until the unmanned transport vehicle finally reaches the position of the target point really, so that the path planning is completed.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, through improving the gravitational potential energy function and the repulsive potential energy function of the unmanned transport vehicle, the problems that the gravitational field is too large and collision is easy to occur due to too long distance in an artificial potential field method or the target cannot be reached due to too large repulsive field around a target point are solved; meanwhile, the resultant force function borne by the unmanned transport vehicle is improved, a direction vector is introduced, and the memory effect of the resultant force function is increased. The local minimum point can be left out by generating an additional perturbation when being trapped. The algorithm can quickly search a collision-free working path in the environment of the barrier, and ensures the safety of the unmanned transport vehicle and the barrier.
Drawings
FIG. 1 is a flowchart illustrating an AGV trajectory planning method based on an improved artificial potential field method according to the present invention;
FIG. 2 is a schematic diagram of a grid-method modeling of a plant;
FIG. 3 is a force diagram of the unmanned transport vehicle;
FIG. 4 is a diagram of a conventional artificial potential field method motion simulation;
fig. 5 is a diagram of a motion simulation of an improved artificial potential field method.
Detailed Description
The embodiments of the present invention will be described in further detail with reference to the accompanying drawings and technical solutions.
The process schematic diagram of the invention is shown in figure 1, and the specific steps are as follows:
s1: a grid method is adopted to model a factory, as shown in fig. 2, the regular modeling is carried out on the factory because the placement positions of large-scale articles such as shelves and the like in the factory are more regular;
s2: acquiring initial state parameters of the unmanned transport vehicle, environmental information such as an initial point, a target point and the like, and determining the target point according to which goods need to be transported to which goods shelf;
s3: establishing a rectangular coordinate system by taking the target point as an origin, and establishing coordinates of the unmanned transport vehicle and the obstacle, wherein the distances between the unmanned transport vehicle and the target point and the obstacle are respectively as follows:
wherein (x, y) is of unmanned transport vehicleCurrent position, (x)0,y0) Coordinates of the target point; (x)i,yi) Is the equivalent coordinate of the potential force field of the obstacle, i is 1,2,3,4,5 … … n
S4: an improved artificial potential field method is adopted to represent the resultant force applied to the unmanned transport vehicle, and the schematic diagram of the force applied to the unmanned transport vehicle is shown in FIG. 3;
s4.1 improved gravitational field Uatt(q) and repulsive force field Urep(q) a potential field function, wherein the magnitude of the gravitational field is determined by the distance between the unmanned transport vehicle and the target point, the gravitational field varies linearly when the distance between the unmanned transport vehicle and the target point is greater than r, and the gravitational field is the power of two times of the distance when the distance is less than r. The collision phenomenon caused by overlarge initial attraction due to the fact that the distance between the unmanned transport vehicle and the target point in the initial state is overlarge is avoided, and the formula is as follows:
wherein KattIs a gravitational field gain constant, and r is a set distance threshold value
Wherein KrepIs a repulsive force field gain constant, rho is the maximum influence distance of the obstacle, thetaiThe included angle between the speed of the unmanned transport vehicle and the repulsion force is formed;
wherein d is0Distance between the unmanned transport vehicle and the target point, diAnd p is the distance between the unmanned transport vehicle and the obstacle, the maximum influence distance of the obstacle is rho, and if the distance between the obstacle and the unmanned vehicle is greater than rho, the repulsive force field is regarded as 0. The value of eliminating the influence of the obstacle far away from the unmanned vehicle on the track of the unmanned vehicle by setting the rho depends on the speed and the acceleration of the unmanned vehicle. At the same time d0The arrangement of the target point can avoid the phenomenon that the target cannot be reached due to overlarge repulsive force near the target point;
because the positions of the goods shelves in the warehouse map are relatively orderly, the repulsive force between the goods shelves can be offset, only the resolution force of the repulsive force of the goods shelves and the speed direction of the trolley model needs to be considered, and the resolution schematic diagram is shown in fig. 3;
s4.2: defining the negative gradient of the potential field function as an artificial force, which consists of an attractive force and a repulsive force. Wherein the attractive force Fatt(q) is gravitational field UattNegative gradient of (q), repulsive force Frep(q) is a repulsive force field Urep(q) negative gradient, formula as follows:
Fatt＝-▽Uatt(q)
Frep＝-▽Urep(q)
s4.3: the attractive and repulsive forces are calculated by the modified attractive and repulsive force fields, respectively, as follows:
s4.4: calculating resultant force field UCombination of Chinese herbs(q) and the resultant force FCombination of Chinese herbs(q) is:
wherein n is the total number of barriers such as a goods shelf generating a repulsive force field, alpha is a gain coefficient of a direction vector,
is a unit direction vector. Wherein the vector
F according to the preceding stepCombination of Chinese herbs(q) determination ofDirection and FCombination of Chinese herbs(q) the same unit vector with length of 1, when the direction of the attraction force and the repulsion force applied to the unmanned transport vehicle is opposite and the magnitude of the attraction force and the repulsion force is equal, and
when the direction is collinear with the directions of the two forces, the unmanned transport vehicle is judged to be trapped in the local minimum, and at the moment, the vector is obtained
The direction of the vibration is randomly turned by 90 degrees, so that additional disturbance can be added in the vertical direction of stress, and the unmanned transport vehicle is helped to be separated from the balance state.
S5: searching a point with the minimum total potential energy in the path, and judging whether the point is a final target point;
s6: if the target point is the final target point, the path planning is finished, the unmanned transport vehicle smoothly reaches the target point, and if the target point is not the target point, the unmanned transport vehicle sinks into the local minimum point;
s7: when the unmanned transport vehicle falls into a local minimum point, in the resultant force
Changing the direction, adding additional disturbance, enabling the unmanned transport vehicle to be separated from the local minimum point, returning to the step S3 until the unmanned transport vehicle finally reaches the position of the target point really, and completing path planning;
s8: the simulation of the conventional artificial potential field method and the improved artificial potential field method by Matlab can be obtained as shown in the schematic diagrams of fig. 4 and 5. Compared with a simulation diagram, the improved artificial potential field method can solve the problem that collision is easy to occur in the early stage due to overlarge attraction force, the target is trapped in a local minimum point in the middle stage, and the target is inaccessible in the last stage due to overlarge repulsion force, so that the effectiveness of trajectory planning is improved.
Compared with the prior art, the method has the advantages that by improving the gravitational potential energy function and the repulsive potential energy function of the unmanned transport vehicle, the problems that an artificial potential field method is too large in gravitational field and easy to collide at the initial motion stage of the unmanned transport vehicle due to too long distance or the defect that the target cannot be reached due to too large repulsive field around a target point at the final motion stage of the unmanned transport vehicle are solved; meanwhile, the resultant force function borne by the unmanned transport vehicle is improved, a direction vector is introduced, and the memory effect of the resultant force function is increased. The local minimum point can be left out by generating an additional perturbation when being trapped. The algorithm can quickly search a collision-free working path in the environment of the barrier, and ensures the safety of the unmanned transport vehicle and the barrier.
In the specification, each step is described in a progressive manner, the detailed description of each step is different from that of other model examples, and the same and similar parts among the model examples can be referred to each other.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (1)
1. An improved artificial potential field method based AGV track planning algorithm is characterized in that the improved artificial potential field method is adopted, so that the phenomena that an unmanned transport vehicle is easy to collide in the initial driving stage of a factory and the target is easy to be inaccessible in the final driving stage are avoided; meanwhile, by introducing a direction vector, the memory effect of a path is increased, and the local minimum point can be separated when the local minimum point phenomenon is generated; planning a collision-free safe path from a starting point to a terminal point for the unmanned transport vehicle in the obstacle existing in the factory; the method comprises the following steps:
s1: modeling a factory by adopting a grid method;
s2: acquiring initial state parameters, initial points and target points of the unmanned transport vehicle;
s3: establishing a rectangular coordinate system by taking the target point as an origin, and establishing coordinates of the unmanned transport vehicle and the obstacle, wherein the distances between the unmanned transport vehicle and the target point and the obstacle are as follows:
wherein, (x, y) is the current position of the unmanned transport vehicle, (x)0,y0) Coordinates of the target point; (x)i,yi) Is an obstacle potential force field equivalent coordinate, i is 1,2,3,4,5 … … n;
s4: an improved artificial potential field method is adopted to represent the resultant force applied to the unmanned transport vehicle;
s4.1: improved gravitational field Uatt(q) and repulsive force field UrepThe potential field function of (q) is as follows:
wherein, KattIs a gravitational field gain constant, r is a set distance threshold;
wherein, KrepIs a repulsive force field gain constant, rho is the maximum influence distance of the obstacle, thetaiThe included angle between the speed of the unmanned transport vehicle and the repulsion force is formed;
s4.2: defining the negative gradient of the potential field function as an artificial force, wherein the artificial force consists of an attractive force and a repulsive force; wherein the attractive force Fatt(q) is gravitational field UattNegative gradient of (q), repulsive force Frep(q) is a repulsive force field Urep(q) negative gradient, formula as follows:
Fatt＝-▽Uatt(q)
Frep＝-▽Urep(q)
s4.3: the attractive and repulsive forces are calculated by the modified attractive and repulsive force fields, respectively, as follows:
s4.4: calculating resultant force field UCombination of Chinese herbs(q) and the resultant force FCombination of Chinese herbs(q) is:
wherein n is the total number of barriers such as a goods shelf generating a repulsive force field, alpha is a gain coefficient of a direction vector,
is a unit direction vector;
s5: searching a point with the minimum total potential energy in the path, and judging whether the point is a final target point;
s6: if the target point is the final target point, the path planning is finished, the unmanned transport vehicle smoothly reaches the target point, and if the target point is not the target point, the unmanned transport vehicle sinks into the local minimum point;
s7: when the unmanned transport vehicle falls into a local minimum point, in the resultant force
And (4) changing the direction, adding additional disturbance, enabling the unmanned transport vehicle to be separated from the local minimum point, and returning to the step S3 until the unmanned transport vehicle finally reaches the position of the target point really, so that the trajectory planning is completed.
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