CN110533234B - AGV optimization control method combined with collision avoidance strategy, terminal equipment and storage medium - Google Patents
AGV optimization control method combined with collision avoidance strategy, terminal equipment and storage medium Download PDFInfo
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Abstract
The invention relates to an AGV optimization control method, terminal equipment and a storage medium which are combined with a collision avoidance strategy, wherein the method comprises the following steps: setting an optional next path point set of each AGV in operation according to the relation between the pheromone concentration of each node in the transportation environment and a threshold value; judging whether the AGV stops waiting or the AGV drives into the next path point according to whether the selectable next path point set of the AGV is an empty set; and after the motion states of all the AGV trolleys in operation are judged, judging whether the motion states conflict or not, and adjusting the motion states by adopting a collision avoidance algorithm when the conflicts exist. The invention can control the AGV running state in real time, and avoid path conflict and road deadlock; the collision of the AGV can be avoided in an emergency; can guarantee that the transportation operation is stable to be gone on.
Description
Technical Field
The invention relates to the field of AGV trolley control, in particular to an AGV optimization control method combined with a collision avoidance strategy, terminal equipment and a storage medium.
Background
The AGV optimization control method combining the collision avoidance strategy commonly used in the prior art is mainly a dynamic path planning algorithm, such as a path planning technique based on an ant colony algorithm and a time window algorithm. The path planning technology based on the ant colony algorithm mainly utilizes the ant colony to search paths between an initial point and a target point, and finds an optimal path through iteration. The time window algorithm determines the sequence of passing nodes according to the priority of the AGV, and the conflict of paths is avoided. However, the above algorithm still has the following disadvantages: 1. the method comprises the steps that a path is planned before operation starts, real-time dynamic path planning cannot be achieved, and when an obstacle or a special condition is met, an AGV cannot achieve effective obstacle avoidance so as to guarantee normal operation of transportation operation; 2. only the path planning can be carried out aiming at a single starting point and a single target point, and the application requirement of a complex transportation network cannot be met.
Disclosure of Invention
In order to solve the above problems, the present invention provides an AGV optimization control method, a terminal device and a storage medium, which combine a collision avoidance strategy.
The specific scheme is as follows:
an AGV optimization control method combined with a collision avoidance strategy comprises the following steps:
setting an optional next path point set of each AGV in operation according to the relation between the pheromone concentration of each node in the transportation environment and a threshold value;
judging the motion state of each AGV according to whether the selectable next path point set of the AGV is an empty set, namely, the AGV stops waiting or the AGV drives into the next path point, wherein the next path point of the AGV is a node corresponding to the situation that the state transition probability from the node where the AGV is currently located to the node in the set is the maximum in the selectable next path point set;
and after the motion states of all the AGV trolleys which are in operation are judged, judging whether the motion states conflict or not, and adjusting the motion states by adopting a collision avoidance algorithm when the conflicts exist.
Further, the pheromone concentration τ (i,j) (t) has the calculation formula;
wherein i and j represent the abscissa and ordinate of the node, respectively, and τ (i,j) (t) represents the pheromone concentration at the node (i, j) at the t-th moment, lambda represents the pheromone concentration carried by the AGV, K represents the serial number of the AGV, K represents the number of the AGV operating in the transport environment at the t-th moment,
indicating the straight-line distance between the kth AGV and the node (i, j) at the time point t.
Further, the setting mode of the selectable next path point set is as follows: and according to the pheromone concentration of each node around the kth AGV in the transportation environment, forming all nodes with the pheromone concentration smaller than the pheromone concentration threshold value in each node around as a selectable next path point set corresponding to the kth AGV.
Further, the calculation process of the state transition probability is as follows:
(1) calculating the pheromone at node (i ', j') at time tAttraction function F for AGV 1 (i′,j′) ：
F 1 (i′,j′) ＝q-τ (i′,j′) (t)
Wherein q is pheromone concentration threshold, tau (i′,j′) (t) represents the pheromone concentration at node (i ', j') at time t;
(2) calculating a heuristic function F between the node (i ', j') at the t moment and the current node (i, j) where the AGV is located 2 (i′,j′) ：
Wherein, the first and the second end of the pipe are connected with each other,
represents the straight-line distance traveled by node (i, j) to node (i ', j');
(3) calculating a target point (e) of the node (i ', j') and the AGV at the t moment x ,e y ) Heuristic function F between 3 (i ′,j′) ：
Wherein the content of the first and second substances,
representing the node (i ', j') and the target point (e) x ,e y ) The linear distance therebetween;
(4) calculating the state transition probability of the kth AGV from the node (i, j) to the node (i ', j') at the t moment
Wherein α, β, γ respectively represent attraction functions F 1 (i′,j′) Heuristic function of the first kind F 2 (i′,j′) And a heuristic function F of the second kind 3 (i′,j′) The importance degree of (c) indicates the selectable next path point set, and (I ', J') indicates the nodes in the selectable next path point set.
Further, the collision avoidance algorithm is as follows: setting a node where two AGV trolleys generate node conflict as a conflict node, wherein the comprehensive attraction value of the conflict node to the AGV trolleys consists of a first attraction value, a second attraction value and a third attraction value, the first attraction value is a function of pheromone concentration of the conflict node, the second attraction value is a function of the distance between the node where the AGV trolleys are located at present and the conflict node, and the third attraction value is a function of the distance between the conflict node and a target point;
when the integrated attraction values of two AGV dollies with node conflict are different, determining the next path point of the AGV dolly as the conflict node according to the integrated attraction values, and when the integrated attraction values are the same, sequentially judging whether the corresponding values of the two dollies are equal according to the priority sequence of the first attraction value, the second attraction value and the third attraction value, and determining the next path point of the AGV dolly as the conflict node according to the size relation;
and setting a selectable next path point set of another car according to the relation between the pheromone concentration of each node in the transportation environment and the threshold value, and selecting the node with higher state transition probability from the current node of another AGV car to the nodes in the set and without node conflict as the next path point of another car.
Further, the integrated attraction value is a product of the first attraction value, the second attraction value and the third attraction value.
Further, determining which AGV car the collision node is at the next path point according to the magnitude of the integrated attraction value specifically comprises: and taking the conflict node as the next path point of the AGV with a larger comprehensive attraction value.
Further, whether the corresponding values of the two trolleys are equal or not is sequentially judged according to the priority sequence of the first attraction value, the second attraction value and the third attraction value, and the specific process of determining the next path point of the AGV trolley as the conflict node according to the size relationship is as follows:
step 1: redefining the first attraction degree P as the first attraction degree according to the priority of the first attraction value, the second attraction value and the third attraction value in the order from high to low 1k Second degree of attraction P 2k And a third attraction degree P 3k And respectively calculate two AGV trolleys which conflict a And AGV b First attraction degree P 1k Second degree of attraction P 2k And a third attraction degree P 3k ；
And 2, step: determine AGV a First attraction degree P 1a Whether equal to AGV b First attraction degree P 1b If yes, entering step 4; otherwise, entering step 3;
and step 3: determining AGV a First attraction degree P 1a Whether smaller than AGV b First attraction degree P of 1b If yes, go to step 10; otherwise, go to step 9;
and 4, step 4: determine AGV a Second degree of attraction P 2a Whether equal to AGV b Second degree of attraction P 2b If yes, go to step 6; otherwise, entering step 5;
and 5: determine AGV a Second degree of attraction P 2a Whether it is smaller than AGV b Second attraction degree P 2b If yes, go to step 10; otherwise, go to step 9;
and 6: determine AGV a Third attraction degree P 3a Whether equal to AGV b Third attraction degree P 3b If yes, go to step 8; otherwise, go to step 7;
and 7: determining AGV a Third attraction degree P 3a Whether smaller than AGV b Third attraction degree P 3b If yes, go to step 10; otherwise, go to step 9;
and step 8: taking any real number which is not 0, judging whether the real number is greater than 0, and if so, step 9; otherwise, step 10;
and step 9: setting node (i ', j') to AGV a The next waypoint of (a);
step 10: setting node (i ', j') to AGV b The next waypoint of (a).
An AGV optimization control terminal device incorporating a collision avoidance strategy includes a processor, a memory, and a computer program stored in the memory and operable on the processor, wherein the processor implements the steps of the above method according to the embodiment of the present invention when executing the computer program.
A computer-readable storage medium, in which a computer program is stored, wherein the computer program, when being executed by a processor, is adapted to carry out the steps of the method according to an embodiment of the present invention as described above.
The invention adopts the technical scheme and has the beneficial effects that:
1. the AGV driving state can be controlled in real time, and path conflict and road deadlock are avoided;
2. in an emergency, collision of the AGV is avoided;
3. the stable operation of transportation operation is guaranteed.
Drawings
Fig. 1 is an overall flowchart of a first embodiment of the present invention.
Fig. 2 is a flowchart of the collision avoidance algorithm in this embodiment.
Detailed Description
To further illustrate the various embodiments, the invention provides the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the embodiments. Those skilled in the art will appreciate still other possible embodiments and advantages of the present invention with reference to these figures.
The invention will now be further described with reference to the drawings and the detailed description.
The first embodiment is as follows:
the embodiment of the invention provides an AGV optimization control method combined with a collision avoidance strategy, which mainly comprises the following implementation modes:
setting an optional next path point set of each AGV in operation according to the relation between the concentration of the pheromones of all nodes in the transportation environment and a threshold value; judging the motion state of each AGV according to whether the selectable next path point set of the AGV is an empty set, namely, the AGV stops waiting or the AGV drives into the next path point, wherein the next path point of the AGV is a node corresponding to the situation that the state transition probability from the node where the AGV is currently located to the node in the set is the maximum in the selectable next path point set; and after the motion states of all the AGV trolleys which are in operation are judged, judging whether the motion states conflict or not, and adjusting the motion states by adopting a collision avoidance algorithm when the conflicts exist.
The above implementation method is described with reference to a specific flow, as shown in fig. 1, and includes the following steps:
s1: and (5) initializing and setting time t to be 0.
S2: the central control system determines whether an AGV is operating within the transport environment and proceeds to S3 if so, or to S16 otherwise.
The central control system is a common system in the existing AGV trolley control, and can acquire the position and the operation state (such as whether the current loading and unloading task is finished or not, whether the operation needs to be continued or not) of the AGV trolley in real time.
S3: calculating the number K of AGV trolleys operating in the transportation environment and the respective positions of the K AGV trolleys, and updating the pheromone concentration tau of each node in the transportation environment at the t moment (i,j) (t), let the intermediate variable k be 1.
Wherein i and j represent the abscissa and ordinate of the node, respectively, and τ (i,j) (t) represents the pheromone concentration at the node (i, j) at the t-th moment, lambda represents the pheromone concentration carried by the AGV, K represents the serial number of the AGV, K represents the number of the AGV operating in the transport environment at the t-th moment,
showing the k AGV at the t moment k And (ii) a linear distance from node (i, j).
S4: judging whether the kth AGV is located at the node, if so, entering S5; otherwise, the vehicle continues to travel to the corresponding next node, and the process proceeds to S11.
It should be noted here that when the AGV cart is not located at a node, the AGV cart is located on the path between two nodes at the time, and its corresponding next node is determined.
S5: and judging whether the k AGV trolley is positioned at the target point, if so, entering S6, and otherwise, entering S8.
S6: and judging whether the single loading and unloading transportation of the kth AGV is finished, if so, entering S7, and otherwise, entering S9.
S7: and judging whether the kth AGV needs to continue to work or not, if so, entering S8, and otherwise, entering S9.
S8: according to the pheromone concentration of each node in the transport environment at the t moment, all nodes with the pheromone concentration smaller than the pheromone concentration threshold value are formed to be used as the next optional path point set allowed corresponding to the kth AGV k Determine allowed k If it is an empty set, the process proceeds to S9, otherwise, the process proceeds to S10.
S9: and controlling the k AGV to stop and wait, and entering S11.
S10: collecting allowed next path points corresponding to the kth AGV k And the node with the highest transition probability of the middle state is the kth AGV, and the process is carried out to the next path point, and the step is S11.
The calculation process of the next waypoint in step S10 is:
(1) calculating an attraction function F of the pheromone at the node (i ', j') at the t-th moment on the AGV trolley 1 (i′,j′) ：
F 1 (i′,j′) ＝q-τ (i′,j′) (t)
Wherein q is pheromone concentration thresholdValue, τ (i′,j′) (t) represents the pheromone concentration at the node (i ', j') at the t-th time.
The collision of the AGV trolley at the node can be avoided through the set pheromone concentration threshold, namely if tau (i′,j′) (t) < q, then (i ', j'), (i ', j') belongs to allowed; if τ is (i′,j′) (t) is not less than q, then
(2) Calculating a heuristic function F between the t-th time node (i ', j') and the current node (i, j) where the AGV is located 2 (i′,j′) ：
Wherein the content of the first and second substances,
represents the straight-line distance traveled by node (i, j) to node (i ', j').
(3) Calculating the t-th time node (i ', j') and the target point (e) of the AGV trolley x ,e y ) Heuristic function F between 3 (i ′,j′) ：
Wherein the content of the first and second substances,
representing the node (i ', j') and the target point (e) x ,e y ) The linear distance therebetween.
(4) Calculating the state transition probability of the kth AGV from the node (i, j) to the node (i ', j') at the t moment
Wherein α, β, γ respectively represent attraction functions F 1 (i′,j′) Heuristic function of the first kind F 2 (i′,j′) And a heuristic function F of the second kind 3 (i′,j′) The importance degree of (c) indicates the selectable next path point set, and (I ', J') indicates the nodes in the selectable next path point set.
(5) Determining the next path point (i) of the k-th AGV for node (i, j) 0 ′,j 0 ′)：
S11: judging whether K is true or not, and if so, entering S12; otherwise, let k be k +1, return to S4.
S12: judging whether the motion states of the k AGV trolleys which are in operation conflict or not, and if yes, entering S13; otherwise, the process proceeds to S14.
S13: the motion state is adjusted using a collision avoidance algorithm, returning to S12.
S14: and sending a control instruction to the AGV in operation in the transportation environment, judging whether the transportation task is completed, if so, ending, otherwise, entering S15.
S15: and sending instructions to all AGV dollies in the transportation environment so as to control the AGV dollies which do not need to continue to operate to stop operating, and other AGV dollies to continue operating.
S16: let t be t +1, return to S2.
The following introduces the collision avoidance algorithm, and its main implementation is: setting a node where two AGV trolleys generate node conflict as a conflict node, wherein the comprehensive attraction value of the conflict node to the AGV trolleys consists of a first attraction value, a second attraction value and a third attraction value, the first attraction value is a function of pheromone concentration of the conflict node, the second attraction value is a function of the distance between the node where the AGV trolleys are located at present and the conflict node, and the third attraction value is a function of the distance between the conflict node and a target point.
When the integrated attraction values of two AGV dollies with node conflict are different, determining the next path point of the AGV dolly with the conflict node according to the integrated attraction values, and when the integrated attraction values are the same, sequentially judging whether the corresponding values of the two dollies are equal according to the priority sequence of the first attraction value, the second attraction value and the third attraction value, and determining the next path point of the AGV dolly with the conflict node according to the size relation.
And setting a selectable next path point set of another car according to the relation between the pheromone concentration of each node in the transportation environment and the threshold value, and selecting the node with higher state transition probability from the current node of another AGV car to the nodes in the set and without node conflict as the next path point of another car.
The above implementation method is described below with reference to a specific flow, as shown in fig. 2, which includes the following steps:
s1: and the central control system determines the total number C of the AGV trolleys which conflict with each other, and sets the intermediate variable C to be 1.
S2: and determining the conflict type of the c-th conflicting vehicle, if the conflict type is a node conflict, entering S3, and if the conflict type is a path congestion, controlling the two AGV vehicles to stop after the interval reaches a safe distance, and entering S22.
S3: calculating two AGV trolleys according to pheromone concentration threshold q a And AGV b Corresponding selectable next path point set allowed a And allowed b And according to two AGV trolleys a And AGV b Corresponding selectable next path point set allowed a And allowed b Sequencing the nodes in the sequence of the state transition probability of each node from large to small, wherein the state transition probability is the state transition probability from the node where the AGV currently locates to the node in the set, and N is set km Indicating the k-th AGV k Corresponding selectable next path point set allowed k In which nodes in order m are selected.
Wherein, the next path that can be selected that the k AGV dolly corresponds toPoint set allowed k The calculating method comprises the following steps: setting pheromone concentration threshold q, tau will be satisfied (i,j) A set formed by all nodes (i, j) with the value (t) < q is used as an available next path point set allowed of the kth AGV k 。
S4: according to the conflict node (i ', j'), the AGV of the kth AGV is carried out k Integrated attraction value of
The calculation formula calculates two AGV trolleys which conflict a And AGV b Integrated attraction value of
And
and judging whether or not the conditions are satisfied
If so, S6 is entered, otherwise, S5 is entered.
Integrated attraction value
The calculation formula of (c) is:
wherein the content of the first and second substances,
for the concentration of pheromone at node (i ', j') versus AGV k The value of the attraction function of (a),
is node (i ', j') and AGV k The heuristic function values between the current nodes (i, j),
is a section ofPoint (i ', j') and AGV k Target point (e) x ,e y ) The heuristic function value in between.
S5: judging whether the requirements are met
If yes, entering 15; otherwise, 14 is entered.
S6: according to setting
The weight coefficients alpha, beta and gamma of the weight coefficient are determined from big to small
And redefining the priority as the first attraction degree P according to the priority from high to low 1k Second degree of attraction P 2k And a third attraction degree P 3k I.e. setting the maximum weighting coefficient to P 1k Setting the weight coefficient at the minimum to P 3k And calculate two AGV carts a And AGV b First attraction degree P 1k Second degree of attraction P 2k And a third attraction degree P 3k Wherein the subscript k represents a serial number.
S7: judging whether P is satisfied 1a ＝P 1b If so, go to S9; otherwise, the process proceeds to S8.
S8: judgment of P 1a ＜P 1b If yes, go to S15; otherwise, the process proceeds to S14.
S9: judgment of P 2a ＝P 2b If yes, go to S11; otherwise, the process proceeds to S10.
S10: judgment of P 2a ＜P 2b If yes, go to S15; otherwise, the process proceeds to S14.
S11: judgment of P 3a ＝P 3b If yes, go to S13; otherwise, the process proceeds to S12.
S12: judgment of P 3a ＜P 3b If yes, go to S15; otherwise, the process proceeds to S14.
S13: taking any real number from the section (- ∞,0) U (0, + ∞), judging whether it is greater than 0, if yes, entering S14; otherwise, the process proceeds to S15.
S14: setting the node (i ', j') as the a-th AGV a The next path point, at this time, the second AGV car AGV needs to be determined b So that k is b, the process proceeds to S16.
S15: setting the node (i ', j') as the AGV of the second AGV b The next path point, at this time, the AGV of the a-th AGV needs to be determined a So that k is equal to a, the process proceeds to S16.
S16: let m equal 2.
S17: AGV (automatic guided vehicle) for judging kth AGV k Corresponding selectable next path point set allowed k Whether there is a node N with the selection order m km If so, go to S18; otherwise, the process proceeds to S20.
S18: judging node N km If so, entering S19; otherwise, the process proceeds to S21.
S19: judging whether m is 3, if so, entering S20; otherwise, let m be m +1, return to S17.
S20: kth AGV dolly AGV k The parking wait is performed, and the process proceeds to S22.
S21: setting N km For the kth AGV k S22.
S22: judging whether C is true or not, if so, ending; otherwise, let c be c +1, return to S2.
The first embodiment of the invention has the following beneficial effects:
1. by updating the pheromone concentration of each node of the transport environment in each time cycle, the running state of the AGV is controlled in real time, so that the AGV runs towards the direction with lower congestion degree, and can reach a target point, thereby not only completing the transport task, but also avoiding path conflict and road deadlock;
2. under the condition that the control algorithm generates node conflict or emergency, comparing the comprehensive attraction values and the priority function values of the node pairs of different AGV trolleys, providing basis for instruction adjustment of the control algorithm, avoiding collision of the AGV trolleys in transportation and ensuring stable transportation operation;
3. the stable operation of transportation operation is guaranteed.
The present embodiment has the following improvements over the prior art:
1. a node pheromone concentration model is designed by utilizing the idea of visibility;
2. pheromones of ants are used as a negative feedback mechanism to repel the companions so as to reduce the possibility of collision;
3. judging whether the periphery of the node to be selected is overcrowded by using a toxin threshold q so as to avoid node conflict;
4. heuristic functions between the nodes to be selected and the target nodes are designed by utilizing the idea of visibility, so that the algorithm is prevented from falling into local optimization;
5. utilizing a multiplier of an attraction function and an heuristic function to quantify the attraction of the node to be selected to the two AGV which generate conflict, and carrying out collision avoidance decision;
6. the importance degrees of the attraction function and the heuristic function are used for determining the respective priorities, and the priorities are compared step by step from high to low so as to increase the scientificity of collision avoidance decisions.
In addition, the application range of the present invention may be the AGV car of the logistics transportation system, and may also be applied to the control of multiple transportation carriers in a multi-starting-point and multi-target-point road network and the real-time path planning thereof, such as unmanned aerial vehicle transportation, robot path real-time planning, unmanned vehicle control, etc., which are not limited herein.
Example two:
the invention also provides AGV optimization control terminal equipment combined with a collision avoidance strategy, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of the method embodiment of the first embodiment of the invention.
Further, as an executable scheme, the AGV optimization control terminal device combined with the collision avoidance policy may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The AGV optimization control terminal device incorporating the collision avoidance strategy may include, but is not limited to, a processor and a memory. It can be understood by those skilled in the art that the above-mentioned configuration of the AGV optimal control terminal device incorporating the collision avoidance policy is only an example of the AGV optimal control terminal device incorporating the collision avoidance policy, and is not limited to the AGV optimal control terminal device incorporating the collision avoidance policy, and may include more or less components than the above, or combine some components, or different components, for example, the AGV optimal control terminal device incorporating the collision avoidance policy may further include an input/output device, a network access device, a bus, and the like, which is not limited in this embodiment of the present invention.
Further, as an executable solution, the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, and the like. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, and the processor is a control center of the AGV optimal control terminal device incorporating the collision avoidance policy, and various interfaces and lines are used to connect various parts of the AGV optimal control terminal device incorporating the collision avoidance policy as a whole.
The memory can be used for storing the computer program and/or the module, and the processor can realize various functions of the AGV optimization control terminal equipment combined with the collision avoidance strategy by running or executing the computer program and/or the module stored in the memory and calling the data stored in the memory. The memory can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system and an application program required by at least one function; the storage data area may store data created according to the use of the mobile phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The invention also provides a computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the above-mentioned method of an embodiment of the invention.
The integrated modules/units of the AGV optimization control terminal device combined with the collision avoidance strategy can be stored in a computer readable storage medium if the modules/units are realized in the form of software functional units and sold or used as independent products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), software distribution medium, and the like.
While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. An AGV optimization control method combined with a collision avoidance strategy is characterized by comprising the following steps:
setting an optional next path point set of each AGV in operation according to the relation between the pheromone concentration of each node in the transportation environment and a threshold value;
the pheromone concentration tau (i,j) The formula for calculation of (t) is:
wherein i and j represent the abscissa and ordinate of the node, respectively, and τ (i,j) (t) represents the pheromone concentration at the node (i, j) at the t-th moment, lambda represents the pheromone concentration carried by the AGV, K represents the serial number of the AGV, K represents the number of the AGV operating in the transport environment at the t-th moment,
the linear distance between the kth AGV and the node (i, j) at the t-th moment is represented;
judging the motion state of each AGV according to whether the selectable next path point set of the AGV is an empty set, namely, the AGV stops waiting or the AGV drives into the next path point, wherein the next path point of the AGV is a node corresponding to the situation that the state transition probability from the node where the AGV is currently located to the node in the set is the maximum in the selectable next path point set;
the calculation process of the state transition probability comprises the following steps:
(1) calculating an attraction function F of the pheromone at the node (i ', j') at the t-th moment on the AGV trolley 1 (i′,j′) ：
F 1 (i′,j′) ＝q-τ (i′,j′) (t)
Wherein q is the pheromone concentration threshold, tau (i′,j′) (t) represents the pheromone concentration at node (i ', j') at time t;
(2) calculating the current node (i, j) of the AGV trolley and the node (i ', j') at the t-th timeHeuristic function F of (2) 2 (i ′,j′) ：
Wherein the content of the first and second substances,
represents the straight-line distance traveled by node (i, j) to node (i ', j');
(3) calculating a target point (e) of the node (i ', j') and the AGV at the t moment x ,e y ) Heuristic function F between 3 (i′,j′) ：
Wherein, the first and the second end of the pipe are connected with each other,
representing the node (i ', j') and the target point (e) x ,e y ) The linear distance therebetween;
(4) calculating the state transition probability of the kth AGV transferring from the node (i, j) to the node (i ', j') at the t moment
Wherein α, β, γ respectively represent attraction functions F 1 (i′,j′) Heuristic function of the first kind F 2 (i′,j′) And a heuristic function F of the second kind 3 (i′,j′) The importance degree of (A) represents the selectable next path point set, and (I ', J') represents the nodes in the selectable next path point set;
after the motion states of all the AGV trolleys which are in operation are judged, judging whether the motion states conflict or not, and adjusting the motion states by adopting a collision avoidance algorithm when the conflicts exist; the collision avoidance algorithm is as follows: setting a node where two AGV trolleys generate node conflict as a conflict node, wherein the comprehensive attraction value of the conflict node to the AGV trolleys consists of a first attraction value, a second attraction value and a third attraction value, the first attraction value is a function of pheromone concentration of the conflict node, the second attraction value is a function of the distance between the node where the AGV trolleys are located at present and the conflict node, and the third attraction value is a function of the distance between the conflict node and a target point;
when the comprehensive attraction values of two AGV trolleys with node conflict are different, determining which AGV trolley the conflict node is the next path point according to the size of the comprehensive attraction values, and when the conflict node is the next path point, sequentially judging whether the corresponding values of the two trolleys are equal according to the priority sequence of the first attraction value, the second attraction value and the third attraction value, and determining which AGV trolley the conflict node is the next path point according to the size relationship;
and setting a selectable next path point set of another car according to the relation between the pheromone concentration of each node in the transportation environment and the threshold value, and selecting the node with higher state transition probability from the current node of another AGV car to the nodes in the set and without node conflict as the next path point of another car.
2. The AGV optimization control method combining a collision avoidance strategy according to claim 1, wherein: the setting mode of the selectable next path point set is as follows: and according to the pheromone concentration of each node around the kth AGV in the transportation environment, forming all nodes with pheromone concentration smaller than the pheromone concentration threshold value in each node around as a selectable next path point set corresponding to the kth AGV.
3. The AGV optimization control method combining a collision avoidance strategy according to claim 1, wherein: the comprehensive attraction value is the product of the first attraction value, the second attraction value and the third attraction value.
4. The AGV optimization control method combining a collision avoidance strategy according to claim 1, wherein: determining the next path point of the AGV trolley which is the conflict node according to the size of the comprehensive attraction value specifically comprises the following steps: and taking the conflict node as the next path point of the AGV with a larger comprehensive attraction value.
5. The AGV optimization control method combining a collision avoidance strategy according to claim 1, wherein: whether the corresponding values of the two trolleys are equal or not is sequentially judged according to the priority sequence of the first attraction value, the second attraction value and the third attraction value, and the specific process of determining the next path point of the AGV trolley as the conflict node according to the size relationship is as follows:
step 1: redefining the first attraction degree P as the first attraction degree according to the priority of the first attraction value, the second attraction value and the third attraction value in the order from high to low 1k Second degree of attraction P 2k And a third attraction degree P 3k And respectively calculate two AGV trolleys which conflict a And AGV b First attraction degree P 1k Second degree of attraction P 2k And a third attraction degree P 3k ；
Step 2: determine AGV a First attraction degree P 1a Whether equal to AGV b First attraction degree P 1b If yes, entering step 4; otherwise, entering step 3;
and step 3: determine AGV a First attraction degree P 1a Whether it is smaller than AGV b First attraction degree P of 1b If yes, go to step 10; otherwise, go to step 9;
and 4, step 4: determine AGV a Second attraction degree P 2a Whether equal to AGV b Second degree of attraction P 2b If yes, go to step 6; otherwise, entering step 5;
and 5: determine AGV a Second degree of attraction P 2a Whether it is smaller than AGV b Second degree of attraction P 2b If yes, go to step 10; otherwise, go to step 9;
step 6: determine AGV a Third attraction degree P 3a Whether equal to AGV b Third attraction degree P 3b If yes, go to step 8; otherwise, entering step 7;
and 7: determining AGV a Third attraction degree P 3a Whether smaller than AGV b Third attraction degree P 3b If yes, go to step 10; otherwise, go to step 9;
and 8: taking any real number which is not 0, judging whether the real number is greater than 0, and if so, step 9; otherwise, step 10;
and step 9: setting node (i ', j') to AGV a The next waypoint of (a);
step 10: setting node (i ', j') to AGV b The next waypoint of.
6. The utility model provides a AGV optimal control terminal equipment who combines collision avoidance strategy which characterized in that: comprising a processor, a memory and a computer program stored in the memory and running on the processor, the processor implementing the steps of the method according to any of claims 1 to 5 when executing the computer program.
7. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
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