CN107816996B - AGV flow time-space interference detection and avoidance method under time-varying environment - Google Patents
AGV flow time-space interference detection and avoidance method under time-varying environment Download PDFInfo
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Abstract
A method for detecting and avoiding time-space interference of AGV flows in a time-varying environment is characterized in that dynamic performance of AGV time-varying running tracks under time-varying uncertain factors and conduction effects of the time-varying uncertain factors is researched, behavior analysis is performed on AGV flows in a code head road network by using a Markov chain, characteristics of AGV flows and AGV running track road space-time occupation are analyzed from space dimensions, time dimensions and service dimensions, dynamic interaction relations among AGV space-time interference objects are analyzed, uncertainty and randomness of time-space interference are researched, and dynamic detection and avoidance of AGV time-space interference are performed. The invention solves the problem of operation task lag caused by the time-space interference of the traveling AGV, ensures the operation to continue, reduces and avoids the operation waiting of a shore bridge and a track crane, provides reference and support for the planning of the AGV path and the optimization of transportation, reduces the operation cost of the whole automatic container wharf, and improves the loading and unloading efficiency and the safety of the wharf.
Description
Technical Field
The invention relates to an AGV flow space-time interference detection and avoidance method under a time-varying environment.
Background
Automatic Guided Vehicles (AGVs) are currently the mainstream horizontal transport vehicles in Automated docks, and are responsible for the container handling operations between shore bridges and storage yards and between storage yards and storage yards. Different from the traditional wharf (the bottleneck of the operation system is a shore bridge and a field bridge), the automatic wharf is improved by the loading and unloading capacity of the shore bridge and the field bridge, the operation scale, the horizontal transportation distance, the greatly increased AGV number of parallel operation and other factors affect the horizontal transportation of the AGV, the horizontal transportation of the AGV becomes the new bottleneck of the operation system of the automatic wharf, and the whole loading and unloading efficiency of the wharf is determined.
Compared with a card concentrator, the AGV has intelligent characteristics of unmanned driving, automatic navigation, accurate positioning and the like, and has advantages in the aspects of operation cost, automation, energy conservation, environmental protection and the like. However, the reduction of human intelligent decision making and the continuation of the traditional production process and management system reduce the adaptability of the AGV to uncertain space-time environments (uncertainty of ship arrival time, randomness of task allocation, dynamics of wharf traffic and the like), and cause the dynamic change of the AGV running track. Meanwhile, the method is influenced by factors such as expansion of operation scale, increase of the number of AGV in parallel operation, limitation of operation space and task completion time and the like, and has the characteristics of complexity, time-varying property and the like of AGV operation. As shown in fig. 1, the AGVs of the automated terminal are prone to have time-space interference phenomena such as collision, waiting, queuing and even collision, which causes the formation of a horizontal transportation bottleneck, and research shows that throughput reduction caused by time-space interference may reach 85%, which seriously restricts the improvement of safety and efficiency of the automated terminal.
Disclosure of Invention
The invention provides a method for detecting and avoiding the time-space interference of an AGV flow under a time-varying environment, which solves the problem of operation task lag caused by the time-space interference of the running AGV, ensures the operation to continue, reduces and avoids the operation waiting of a shore bridge and a rail crane, provides reference and support for AGV path planning and transportation optimization, reduces the operation cost of the whole automatic container terminal, and improves the loading and unloading efficiency and the safety of the terminal.
In order to achieve the above object, the present invention provides a method for detecting and avoiding temporal-spatial interference of an AGV stream in a time-varying environment, comprising:
researching the time-varying uncertain factors and the dynamic performance of the AGV time-varying running track under the conduction effect of the time-varying uncertain factors, and performing dynamic analysis on the AGV flow of the code head network by using a Markov chain;
analyzing characteristics of AGV flow and AGV running track road space-time occupation from space dimension, time dimension and service dimension, and analyzing dynamic interaction relation between AGV space-time interference objects;
and researching uncertainty and randomness of the time-space interference, and performing dynamic detection and avoidance of the AGV time-space interference.
The method for detecting and avoiding the time-space interference of the AGV flow under the time-varying environment specifically comprises the following steps:
s1, acquiring an operation environment and an operation state data set according to the loading, unloading and carrying operation requirements of the automatic wharf and the influence of a time-varying uncertain environment on the operation of the AGV;
step S2, analyzing uncertainty characteristics of the automatic wharf operation environment according to the operation environment and the operation state data set, analyzing uncertainty sources influencing AGV operation, researching a conduction relation of uncertain time-space factors by applying a multivariate system theory, analyzing a cross conduction mechanism, selecting AGV operation state data from the operation environment and the operation state data set, and classifying according to expression forms and characteristic attributes of the AGV operation state;
step S3, analyzing the dynamics of the AGV running track to obtain the dynamic distribution characteristics of the AGV running track;
step S4, analyzing an evolution rule of the AGV flow by using a Markov chain;
step S5, analyzing characteristics of the AGV flow and the space-time occupation of the AGV running track road from the space dimension, the time dimension and the service dimension, and analyzing the dynamic interaction relation between the AGV space-time interference objects;
step S6, AGV path planning is carried out;
step S7, judging the type of AGV space-time interference according to the current state of the AGV, if the AGV space-time interference belongs to a fault type, performing step S10, and if the AGV space-time interference belongs to a non-fault type, performing step S8;
step S8, AGV space-time interference detection is carried out, and step S9 is carried out;
step S9, performing space-time interference avoidance on the non-fault AGV, and performing step S11;
and step S10, performing space-time interference avoidance on the fault AGV, and performing step S11.
In step S11, the next job instruction is received, and the process returns to step S6.
In step S1, the influence of the automated dock load/unload/transport operation requirement and the time-varying uncertain environment on the operation of the AGV includes: the operation tasks, operation routes, operation sequences, operation routes, wharf road network AGV flows, flow directions and flow rates of the AGVs, and the space-time interference often occurs between the AGVs, between the AGVs and other operation machines and between the AGVs and the ambient environment.
In step S2, the uncertainty characteristics of the automated dock operating environment include:
uncertain factors of ships, shore bridges, field bridges and road networks are combined with one another to form an external uncertain time-space environment for AGV operation;
the AGV can use quantity scheduling rules, running tracks, running speeds and mutual combination to form an internal uncertain time-space environment for the AGV to run.
In step S4, the method for analyzing the AGV flow evolution law using the markov chain specifically includes:
mapping the dynamic degree of each AGV moving track into errors, calculating AGV flows of each road section, loading and unloading nodes, intersections and probability distribution thereof through a covariance matrix by using the error transmission and synthesis thought based on multi-dimensional joint probability distribution, and establishing a general evolution dynamic equation of AGV flow average dynamic;
general evolutionary dynamic equations:
wherein W belongs to W, L belongs to LwF is epsilon t; w is the set of all OD (start and end of task) pairs of the AGV, LwThe method comprises the steps that a set of all paths between OD pairs w is obtained, and Γ is a set of all paths in a road network;
the flow of a path q between OD pairs and w is obtained, and f is a vector formed by all paths between OD pairs;
for the selection rate of the path/to be,
the correction rate for path l.
In step S6, the AGV path planning method specifically includes the following steps:
s6.1, distributing an AGV road network of the automatic container terminal by magnetic nails to form a corresponding distance matrix between nodes, searching an optimal path of a corresponding box area of each shore bridge, constructing a path library, and solving the shortest path value of the AGV by adopting a Dijkstra algorithm;
and S6.2, searching the generated multiple paths by adopting a depth-first search (DFS) algorithm according to the shortest path value, and screening out the optimal path.
In step S7, the method for determining the type of AGV spatiotemporal interference according to the current state of the AGV includes:
judging the current state of the AGV according to the formula (1):
wherein the content of the first and second substances,
indicating the type of state of the AGV at the present time,
for AGVkPlanned travel time, P, from node i and to node jlockFor AGVkRequested next travel path length, vijIndicating AGVkBy the speed between nodes i and j,
indicating AGVkPassing through the actual running time of the nodes i to j, wherein delta t is the buffering time of the AGV passing through the nodes i to j;
equal to 1 indicates that the travel time of the AGV through the nodes i to j is normal;
equal to 2 indicates that the AGV may be between nodes i, jConflict, the application for the next driving section fails;
equal to 3 indicates that the AGV has failed between nodes i, j.
The AGV space-time interference detection method and the non-fault AGV space-time interference avoidance method specifically comprise the following steps:
s8.1, calculating the length of the next driving section applied by the AGV according to the operation time and the driving speed of the AGV of each path and the time of passing through each node of the path to form a driving time table of the AGV passing through all the nodes of the path;
s8.2, judging whether nodes or road sections are overlapped among the paths, if so, performing the step S8.3, otherwise, indicating that the paths are not conflicted, and ending;
setting the node set A of the known path 1 and the node set B of the path 2, calculating the intersection C as A ═ N B, and judging whether C is an empty set or not
If the C is an empty set, no overlapping road section exists between the path 1 and the path 2, otherwise, the overlapping road section exists between the two paths, namely, the path conflict can exist;
step S8.3, judging whether time conflict exists in the overlapped road sections, namely whether the time difference T of the AGVs of different paths passing through a certain overlapped node is smaller than the time (L + L) of the AGV driving safety distances) V, where L is the AGV length, LsAnd v is the minimum safety distance between the two AGVs, and the driving speed of the AGVs. If yes, the AGV determines that the road section application fails and conflicts occur, and then the step S9.1 is carried out, and if not, the step S8.4 is carried out;
step S8.4, performing time conflict detection on all the AGVs in the same path by adopting the detection method of the step S8.3, if the time conflicts occur to the AGVs in the path, performing the step S9.1, and if no conflicts exist, ending the step;
s9.1, inputting the serial number, the conflict node and the running time of the conflict AGV into a conflict set;
s9.2, judging the priority of the conflicting AGV;
the method comprises the steps that operation priority levels are divided according to whether an AGV has a task or not, whether the AGV has a load or not and whether the AGV has an emergency or not, the AGV which executes the task, the AGV which has the load and the AGV which carries out emergency operation are higher than those of the AGV which does not execute the task, the AGV which does not have a load and the AGV which carries out conventional operation, and the AGV operation priority levels are adjusted according to real-time state feedback information conversion of task completion and fault emergency conditions;
s9.3, adopting speed control to enable the AGV with low priority to decelerate and wait for the AGV with high priority to pass through the conflict node preferentially;
and S9.4, after the AGV avoids the conflict under the speed control strategy, forming a new AGV running time table, returning to the step S8.1, carrying out conflict detection on the updated time table again, and circulating the steps until no conflict exists between the paths and the paths, and solving the conflict.
The method for avoiding the space-time interference of the fault AGV specifically comprises the following steps:
step S10.1, detecting nodes of a road section where a fault AGV is located, and locking an operation road section where the AGV is located as an infeasible road section;
step S10.2, judging whether the equipment fault of the AGV can be repaired, if so, performing step S10.3, and if not, performing step S10.4;
when the AGV breaks down, whether the fault AGV can be repaired or not is judged according to the formula (2):
wherein the content of the first and second substances,
for the longest repair time during which a fault can be repaired
Between
Then representThe AGV can be repaired; when time of failure
Exceed
It means that the repair is impossible within a short time;
s10.3, repairing the fault AGV, assigning a new AGV to the subsequent task, calling an alternative path, and performing the step S10.5;
step S10.4, rejecting the fault AGV out of the fleet and dragging out of the operation area, and performing step S10.8;
step S10.5, judging whether an alternative path of the fault AGV has an overlapped node with the road section, if so, performing step S10.7, otherwise, indicating that the alternative path is available, and performing step S10.6;
step S10.6, selecting an alternative path according to the principle of shortest running time, and performing step S10.11;
step S10.7, replanning the driving path of the AGV executing the subsequent task by adopting a Dijkstra algorithm, and performing step S10.11;
step S10.8, calculating the number of unrepairable fault AGVs, and performing step S10.9;
ngz＝ngz+1，ngzrepresenting the number of failed AGVs which cannot be repaired in a short time;
step S10.9, judging whether the number of unrepairable fault AGVs reaches an upper limit, if so, performing step S10.7, and if not, performing step S10.11;
judging ngz＞nGZWhether or not it is true, nGZRepresenting an upper limit of the number of failed AGVs that cannot be repaired;
step S10.11, adding a standby AGV into an operation fleet to perform operation, calling an alternative path, and performing step S10.5;
and step S10.11, carrying out boxing operation by the AGV.
The invention aims at the complex road space-time occupation requirement of AGV under the time-varying operation environment, corrects the definition of safe distance, safe speed and safe flow, establishes an automatic wharf AGV space-time interference dynamic interaction model, summarizes the AGV space-time interference detection and avoidance method facing the time-varying operation environment, solves the problem of operation task lag caused by the space-time interference of the AGV in driving, ensures the operation to continue, reduces and avoids the operation waiting of a shore bridge and a rail crane as much as possible, provides reference and support for AGV path planning and transportation optimization, reduces the operation cost of the whole automatic container wharf, and improves the loading and unloading efficiency and the safety of the wharf.
Drawings
FIG. 1 is a diagram illustrating a spatial-temporal interference phenomenon in the background art.
FIG. 2 is a flowchart of an AGV flow space-time interference detection and avoidance method under a time-varying environment provided by the present invention.
FIG. 3 is a schematic diagram of fault-like spatiotemporal interference.
FIG. 4 is a dynamic detection and avoidance process for non-fault-like spatiotemporal interference.
FIG. 5 is a flow chart of a fault AGV space-time interference avoidance method.
FIG. 6 is a graph of the propagation of uncertainty factors and elements affecting the operation of an AGV.
Detailed Description
The preferred embodiment of the present invention will be described in detail below with reference to fig. 2 to 6.
The inherent time-varying uncertain characteristics of the automatic wharf have obvious influence on the AGV operation, so that the AGV operation track presents time-space uncertainty, and the AGV flow (comprising flow density, flow speed, flow direction, occupancy and the like) dynamically changes, so that the traffic state and the time-space interference of the automatic wharf road network have new complexity.
Accurately reveals the evolution law of the AGV flow, establishes an accurate space-time interference interaction model, and performs effective and reliable interference degree detection, thereby being the premise and the basis for avoiding the AGV space-time interference.
The invention provides an AGV flow space-time interference detection and avoidance method under a time-varying environment, which comprises the following steps:
researching the time-varying uncertain factors and the dynamic performance of the AGV time-varying running track under the conduction effect of the time-varying uncertain factors, and performing dynamic analysis on the AGV flow of the code head network by using a Markov chain;
analyzing characteristics of AGV flow and AGV running track road space-time occupation from space dimension, time dimension and service dimension, and analyzing dynamic interaction relation between AGV space-time interference objects;
and researching uncertainty and randomness of the time-space interference, and performing dynamic detection and avoidance of the AGV time-space interference.
The AGV flow evolution law and the AGV space-time interference dynamic interaction are the basis of AGV space-time interference dynamic detection and avoidance. From the perspective of time advance, the dynamic and randomness of the time-space interference are researched by using a random process theory, and the time-space interference representation of different running tracks at different positions (loading and unloading nodes, intersections, general moving paths and the like) in an uncertain time-space environment is analyzed.
Specifically, as shown in fig. 2, the method for detecting and avoiding temporal-spatial interference of AGV streams in a time-varying environment provided by the present invention includes the following steps:
s1, acquiring an operation environment and an operation state data set according to the loading, unloading and carrying operation requirements of the automatic wharf and the influence of a time-varying uncertain environment on the operation of the AGV;
step S2, analyzing uncertainty characteristics of the automatic wharf operation environment according to the operation environment and the operation state data set, analyzing uncertainty sources influencing AGV operation, researching a conduction relation of uncertain time-space factors by applying a multivariate system theory, analyzing a cross conduction mechanism, selecting AGV operation state data from the operation environment and the operation state data set, and classifying according to expression forms and characteristic attributes of the AGV operation state;
step S3, analyzing the dynamics of the AGV running track to obtain the dynamic distribution characteristics of the AGV running track;
according to the collected real-time AGV running data, the AGV running tracks can be drawn according to a time sequence, and the AGV running track distribution can be excavated by using an automatic mechanism based on the data of the automatic dock management information system;
step S4, analyzing an evolution rule of the AGV flow by using a Markov chain;
step S5, analyzing characteristics of the AGV flow and the space-time occupation of the AGV running track road from the space dimension, the time dimension and the service dimension, and analyzing the dynamic interaction relation between the AGV space-time interference objects;
the AGV running track emphasizes the actual operation route of each AGV, and the AGV flow is the comprehensive performance of the operation AGV;
step S6, AGV path planning is carried out;
step S7, judging the type of AGV space-time interference according to the current state of the AGV, if the AGV space-time interference belongs to a fault type, performing step S10, and if the AGV space-time interference belongs to a non-fault type, performing step S8;
step S8, AGV space-time interference detection is carried out, and step S9 is carried out;
step S9, performing space-time interference avoidance on the non-fault AGV, and performing step S11;
and step S10, performing space-time interference avoidance on the fault AGV, and performing step S11.
In step S11, the next job instruction is received, and the process returns to step S6.
In step S1, the influence of the automated dock load/unload/transport operation requirement and the time-varying uncertain environment on the operation of the AGV includes: the method comprises the following steps of working tasks, working routes, working sequences, working routes, wharf road network AGV flow, flow direction and flow speed of the AGVs, and space-time interference often occurs between the AGVs, between the AGVs and other working machines, between the AGVs and the ambient environment.
The method for acquiring the working environment and working state data set comprises the following steps: the method comprises the steps of AGV running state real-time acquisition based on machine vision (infrared rays, laser, radar, visible light and the like), self-adaptive intelligent perception technology, navigation positioning technology, wireless transmission technology and the like.
Further, as shown in fig. 6, in step S2, the uncertainty characteristic of the operation environment of the automated dock includes:
1. uncertain factors of ships, shore bridges, field bridges and road networks and mutual combination form an external uncertain space-time environment for AGV operation, and the change of AGV operation task allocation, operation tracks and operation speed can be caused;
2. the usable number of the AGVs (the usable number is also uncertain due to the uncertainty of the AGV faults), the scheduling rule, the running track, the running speed and the mutual combination form an internal uncertain space-time environment for the AGV running.
In fig. 6, white circles indicate independent uncertainty elements, gray circles indicate dependent uncertainty elements, gray squares indicate AGV and road network uncertainty elements, and dashed arrows indicate the influence of the relationship between uncertainty elements.
Further, in step S4, the method for analyzing the AGV flow evolution law by using the markov chain specifically includes:
mapping the dynamic degree of each AGV moving track into errors, calculating AGV flows of each road section, loading and unloading nodes, intersections and probability distribution thereof through a covariance matrix by using the error transmission and synthesis thought based on multi-dimensional joint probability distribution, and establishing a general evolution dynamic equation of AGV flow even dynamic.
General evolutionary dynamic equations:
wherein W belongs to W, L belongs to LwF is epsilon t; w is the set of all OD (start and end of task) pairs of the AGV, LwThe method comprises the steps that a set of all paths between OD pairs w is obtained, and Γ is a set of all paths in a road network;
the flow of a path q between OD pairs and w is obtained, and f is a vector formed by all paths between OD pairs;
for the selection rate of the path/to be,
the correction rate for path l.
Based on the setting of the AGV flow state space and the time parameter set, the AGV flow is expressed as a Markov random process having parameters such as the number of devices and the scale of a road network, and states such as flow density, flow rate, flow velocity and flow direction. And calculating the state transition probability of the AGV flow according to the requirements of operation safety and completion time.
The Markov chain is mainly used for analyzing the future development and change trend of random events, and is successfully applied to the aspects of ocean wave prediction, water environment pollution state prediction, hydrometeorological prediction and the like. The method combines the probability vector with other vectors to observe the development situation behind the random event. Through the analysis of the horizontal transport operation of the AGV of the automatic wharf, the arrival number of the AGV streams and the distribution of the AGV streams at each road section, loading and unloading node and intersection can be found to be random and repeated. Therefore, the Markov chain can be used for analyzing the AGV flow dynamics, namely, the future state and the dynamics of the AGV flow are predicted by using the current state and the dynamics of variables such as flow density, flow speed, flow direction, occupancy and the like, so that the dynamic evolution law of the AGV flow is accurately disclosed, and how the road network state (AGV flow distribution) of a wharf is formed is explained.
As shown in fig. 4, in step S6, the AGV path planning method specifically includes the following steps:
s6.1, distributing an AGV road network of the automatic container terminal by magnetic nails to form a corresponding distance matrix between nodes, searching an optimal path of a corresponding box area of each shore bridge, constructing a path library, and solving the shortest path value of the AGV by adopting a Dijkstra algorithm;
and S6.2, searching the generated multiple paths by adopting a depth-first search (DFS) algorithm according to the shortest path value, and screening out the optimal path.
In step S7, the method for determining the type of AGV spatiotemporal interference according to the current state of the AGV includes:
judging the current state of the AGV according to the formula (1):
wherein the content of the first and second substances,
indicating the type of state of the AGV at the present time,
for AGVkPlanned travel time, P, from node i and to node jlockFor AGVkRequested next travel path length, vijIndicating AGVkBy the speed between nodes i and j,
indicating AGVkPassing through the actual running time of the nodes i to j, wherein delta t is the buffering time of the AGV passing through the nodes i to j;
equal to 1 indicates that the travel time of the AGV through the nodes i to j is normal;
equal to 2 indicates that the AGV may conflict between the nodes i and j and the next driving section is applied for failure;
equal to 3 indicates that the AGV has failed between nodes i, j.
The dynamic detection and avoidance of AGV space-time interference comprises three stages: an AGV path planning stage, an AGV space-time interference detection stage and an AGV space-time interference avoidance stage. The invention divides AGV space-time interference into two types: and if the fault type and the non-fault type are in the time-space interference of the fault type, executing a time-space interference evading stage of the fault type AGV after executing the AGV path planning stage, and if the non-fault type is in the time-space interference, continuously executing an AGV time-space interference detection stage and a non-fault type AGV time-space interference evading stage. And in the AGV path planning stage, combining an automatic wharf production management system and a production process to obtain a wharf production operation plan, and performing task allocation and path planning on the AGV so as to obtain an initial driving path of the AGV between each quayside crane and each container. In the AGV space-time interference detection stage, detecting overlapping road sections in the AGV driving path and the overlapping times of other paths by adopting a vector intersection operation method; if the paths are overlapped, detecting the time conflict; when detecting that the AGV applies for the next lineAnd if the road section fails to run, the fact that the AGV conflicts or the possibility of conflicts occurs is indicated. In the non-fault AGV space-time interference avoidance stage, operation priority levels are divided according to whether the AGV has a task or not, is loaded or not and is in emergency or not, the AGV operation priority levels are adjusted according to real-time state feedback information conversion of task completion and fault emergency conditions, a speed control strategy is adopted, path conflict is avoided by controlling the speed of the AGV in front of and behind a conflict node, space-time interference such as conflict collision and the like which are possibly generated in the process of running of the AGV is avoided, and after the space-time interference is detected and avoided. In the stage of evading space-time interference of fault AGVs, when an AGV fails, if the AGV cannot process the fault in time, the fault AGV may stay in the path for a long time, which results in failure of subsequent AGVs applying for a road section, queuing and congestion of the AGV, and the like, and thus, the fault AGV may have a great influence on production operation of an automatic dock, as shown in fig. 3. In FIG. 3, AGV1 has failed, and the safe travel distance between the AGVs is LsIf the subsequent AGVs in the same path are kept waiting in the safe distance queue, and the overlapped node of the path 2 and the path 1 is just the node of the road section where the conflicting AGVs are located, the AGV4 in the path 2 normally runs, and the subsequent AGV5 is located at the distance node LsWait. Thus, AGV1 affects not only the subsequent AGVs in path 1, but also the subsequent AGVs in path 2, causing the AGVs in the plurality of work lanes to collide. When the problem of AGV conflict in the route that leads to the AGV trouble, if there is the overlap between trouble AGV position and other AGV traveling paths, then can induce the space-time interference between the AGV. Therefore, when the AGV driving path is re-planned, the conflict problem of the operation path where the fault AGV is located is considered, the conflict problem of the AGV in other paths caused by the fault is avoided, and a method for calling the alternative path and the AGV path to re-plan is provided so as to solve the problems of operation face paralysis and traffic jam caused by the AGV fault in the container loading and unloading operation.
As shown in fig. 4, the AGV spatio-temporal interference detection method and the non-fault AGV spatio-temporal interference avoidance method specifically include the following steps:
s8.1, calculating the length of the next driving section applied by the AGV according to the operation time and the driving speed of the AGV of each path and the time of passing through each node of the path to form a driving time table of the AGV passing through all the nodes of the path;
s8.2, judging whether nodes or road sections are overlapped among the paths, if so, performing the step S8.3, otherwise, indicating that the paths are not conflicted, and ending;
setting the node set A of the known path 1 and the node set B of the path 2, calculating the intersection C as A ═ N B, and judging whether C is an empty set or not
If the C is an empty set, no overlapping road section exists between the path 1 and the path 2, otherwise, the overlapping road section exists between the two paths, namely, the path conflict can exist;
step S8.3, judging whether time conflict exists in the overlapped road sections, namely whether the time difference T of the AGVs of different paths passing through a certain overlapped node is smaller than the time (L + L) of the AGV driving safety distances) V, where L is the AGV length, LsAnd v is the minimum safety distance between the two AGVs, and the driving speed of the AGVs. If yes, the AGV determines that the road section application fails and conflicts occur, and then the step S9.1 is carried out, and if not, the step S8.4 is carried out;
step S8.4, performing time conflict detection on all AGVs in the same path (still adopting the detection method of the step S8.3), if the time conflicts (including time period overlapping and time period crossing) occur to the AGVs in the path, performing the step S9.1, and if no conflicts exist, ending the step;
s9.1, inputting the serial number, the conflict node and the running time of the conflict AGV into a conflict set;
s9.2, judging the priority of the conflicting AGV;
the method comprises the steps that operation priority levels are divided according to whether an AGV has a task or not, whether the AGV has a load or not and whether the AGV has an emergency or not, the AGV which executes the task, the AGV which has the load and the AGV which carries out emergency operation are higher than those of the AGV which does not execute the task, the AGV which does not have a load and the AGV which carries out conventional operation, and the AGV operation priority levels are adjusted according to real-time state feedback information conversion of task completion and fault emergency conditions;
s9.3, adopting speed control to enable the AGV with low priority to decelerate and wait for the AGV with high priority to pass through the conflict node preferentially;
in this embodiment, the AGV with low priority is made to be at the safe distance LsStarting to decelerate to a predetermined speed v1Then, the AGV runs at a constant speed, waits for the AGV with high priority to pass through the conflict point first, and accelerates the AGV with low priority back to v after the AGV with high priority passes through the conflict node0Then keeping constant speed running, wherein the acceleration distance is ls;
and S9.4, after the AGV avoids the conflict under the speed control strategy, forming a new AGV running time table, returning to the step S8.1, carrying out conflict detection on the updated time table again, and circulating the steps until no conflict exists between the paths and the paths, and solving the conflict.
As shown in fig. 5, the method for avoiding space-time interference of a fault AGV specifically includes the following steps:
step S10.1, detecting nodes of a road section where a fault AGV is located, and locking an operation road section where the AGV is located as an infeasible road section;
step S10.2, judging whether the equipment fault of the AGV can be repaired, if so, performing step S10.3, and if not, performing step S10.4;
when the AGV breaks down, whether the fault AGV can be repaired or not is judged according to the formula (2):
wherein the content of the first and second substances,
for the longest repair time during which a fault can be repaired
Between
It indicates that the AGV may be repaired; when time of failure
Exceed
It means that the repair is impossible within a short time;
s10.3, repairing the fault AGV, assigning a new AGV to the subsequent task, calling an alternative path, and performing the step S10.5;
after the fault AGV is repaired, the road section can pass through and continue to operate, and if the AGV exists in the path, the road section can pass through after queuing and waiting for the fault processing;
assigning subsequent operation tasks corresponding to the fault operation path according to the time sequence of the residual AGV completing the tasks in the current period, and calling the alternative path;
step S10.4, rejecting the fault AGV out of the fleet and dragging out of the operation area, and performing step S10.8;
after the fault AGV is dragged out of the operation area, the road section can pass through, other AGVs in the path continue to run along the set path, and if the road section to be run is overlapped with the road section where the fault AGV is located, the road section is arranged outside the safety distance for waiting; meanwhile, if the distance from the fault AGV to the adjacent node is less than the safe driving distance, the driving of the AGV in other paths may be influenced;
step S10.5, judging whether an alternative path of the fault AGV has an overlapped node with the road section, if so, performing step S10.7, otherwise, indicating that the alternative path is available, and performing step S10.6;
in an uncertain space-time environment, when one AGV has an equipment fault in operation, the number of affected operation paths may be multiple. In the time of processing the fault AGV, whether the AGV operating subsequent container loading and unloading tasks overlaps with the road section or not is detected, whether an alternative path of the fault AGV and the road section have an overlapping node or not is judged, if the overlapping node does not exist, the alternative path is called, after the fault AGV is processed, the road section is released to be a feasible road section, and the subsequent tasks continue to select the path for operation;
step S10.6, selecting an alternative path according to the principle of shortest running time, and performing step S10.11;
step S10.7, replanning the driving path of the AGV executing the subsequent task by adopting a Dijkstra algorithm, and performing step S10.11;
calling alternative paths fails, namely (K-1) alternative paths are overlapped with the road section where the fault AGV is located, a processing time window of the fault AGV is set, the driving road section where the fault AGV is located is locked into an inoperable road section in the time window range, the fault AGV is used as a fixed obstacle in the time window range, the distance of the road section where the fault AGV is located is set to be infinite, and the driving path of the AGV executing subsequent tasks is re-planned by adopting a Dijkstra algorithm;
step S10.8, calculating the number of unrepairable fault AGVs, and performing step S10.9;
ngz＝ngz+1，ngzrepresenting the number of failed AGVs which cannot be repaired in a short time;
step S10.9, judging whether the number of unrepairable fault AGVs reaches an upper limit, if so, performing step S10.7, and if not, performing step S10.11;
judging ngz＞nGZWhether or not it is true, nGZRepresenting an upper limit of the number of failed AGVs that cannot be repaired;
step S10.11, adding a standby AGV into an operation fleet to perform operation, calling an alternative path, and performing step S10.5;
step S10.11, carrying out boxing operation by the AGV;
and the AGV carries out boxing operation from the cache region to a boxing position, then is transported to a specified box unloading position, continues to operate a next instruction after finishing one instruction, and returns to the cache region to wait for a new operation instruction until all tasks are completely finished.
The invention aims at the complex road space-time occupation requirement of AGV under the time-varying operation environment, corrects the definition of safe distance, safe speed and safe flow, establishes an automatic wharf AGV space-time interference dynamic interaction model, summarizes the AGV space-time interference detection and avoidance method facing the time-varying operation environment, solves the problem of operation task lag caused by the space-time interference of the AGV in driving, ensures the operation to continue, reduces and avoids the operation waiting of a shore bridge and a rail crane as much as possible, provides reference and support for AGV path planning and transportation optimization, reduces the operation cost of the whole automatic container wharf, and improves the loading and unloading efficiency and the safety of the wharf.
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.
Claims (3)
1. A method for detecting and avoiding the time-space interference of an AGV flow under a time-varying environment is characterized by comprising the following steps:
researching the time-varying uncertain factors and the dynamic performance of the AGV time-varying running track under the conduction effect of the time-varying uncertain factors, and performing dynamic analysis on the AGV flow of the code head network by using a Markov chain;
analyzing characteristics of AGV flow and AGV running track road space-time occupation from space dimension, time dimension and service dimension, and analyzing dynamic interaction relation between AGV space-time interference objects;
researching uncertainty and randomness of the time-space interference, and performing dynamic detection and avoidance of the time-space interference of the AGV;
the method for detecting and avoiding the time-space interference of the AGV flow under the time-varying environment specifically comprises the following steps:
s1, acquiring an operation environment and an operation state data set according to the loading, unloading and carrying operation requirements of the automatic wharf and the influence of a time-varying uncertain environment on the operation of the AGV;
step S2, analyzing uncertainty characteristics of the automatic wharf operation environment according to the operation environment and the operation state data set, analyzing uncertainty sources influencing AGV operation, researching a conduction relation of uncertain time-space factors by applying a multivariate system theory, analyzing a cross conduction mechanism, selecting AGV operation state data from the operation environment and the operation state data set, and classifying according to expression forms and characteristic attributes of the AGV operation state;
step S3, analyzing the dynamics of the AGV running track to obtain the dynamic distribution characteristics of the AGV running track;
step S4, analyzing an evolution rule of the AGV flow by using a Markov chain;
step S5, analyzing characteristics of the AGV flow and the space-time occupation of the AGV running track road from the space dimension, the time dimension and the service dimension, and analyzing the dynamic interaction relation between the AGV space-time interference objects;
step S6, AGV path planning is carried out;
step S7, judging the type of AGV space-time interference according to the current state of the AGV, if the AGV space-time interference belongs to a fault type, performing step S10, and if the AGV space-time interference belongs to a non-fault type, performing step S8;
step S8, AGV space-time interference detection is carried out, and step S9 is carried out;
step S9, performing space-time interference avoidance on the non-fault AGV, and performing step S11;
step S10, performing space-time interference avoidance on the fault AGV, and performing step S11;
step 11, receiving the next job command, returning to step 6;
in step S1, the influence of the automated dock load/unload/transport operation requirement and the time-varying uncertain environment on the operation of the AGV includes: the method comprises the following steps that time-space interference often occurs among the operation tasks, the operation sequence, the operation route, the wharf road network AGV flow, the flow direction and the flow speed of the AGV, the AGV and other operation machines and the AGV and the surrounding environment of the AGV;
in step S2, the uncertainty characteristics of the automated dock operating environment include:
uncertain factors of ships, shore bridges, field bridges and road networks are combined with one another to form an external uncertain time-space environment for AGV operation;
the AGV can use quantity scheduling rules, running tracks, running speeds and mutual combination to form an uncertain space-time environment inside the AGV runs;
in step S4, the method for analyzing the AGV flow evolution law using the markov chain specifically includes:
mapping the dynamic degree of each AGV moving track into errors, calculating AGV flows of each road section, loading and unloading nodes, intersections and probability distribution thereof through a covariance matrix by using the error transmission and synthesis thought based on multi-dimensional joint probability distribution, and establishing a general evolution dynamic equation of AGV flow average dynamic;
general evolutionary dynamic equations:
wherein W belongs to W, L belongs to LwF is epsilon t; w is a set of all OD pairs of the AGV, and the OD is a starting point and an end point of a task; l iswThe method comprises the steps that a set of all paths between OD pairs w is obtained, and Γ is a set of all paths in a road network;
the flow of a path q between OD pairs and w is obtained, and f is a vector formed by all paths between OD pairs;
for the selection rate of the path/to be,
the correction rate for path l;
in step S6, the AGV path planning method specifically includes the following steps:
s6.1, distributing an AGV road network of the automatic container terminal by magnetic nails to form a corresponding distance matrix between nodes, searching an optimal path of a corresponding box area of each shore bridge, constructing a path library, and solving the shortest path value of the AGV by adopting a Dijkstra algorithm;
s6.2, searching the generated multiple paths by adopting a depth-first search (DFS) algorithm according to the shortest path value, and screening out the optimal path;
in step S7, the method for determining the type of AGV spatiotemporal interference according to the current state of the AGV includes:
judging the current state of the AGV according to the formula (1):
wherein the content of the first and second substances,
indicating the type of state of the AGV at the present time,
for AGVkPlanned travel time, P, from node i and to node jlockFor AGVkRequested next travel path length, vijIndicating AGVkBy the speed between nodes i and j,
indicating AGVkPassing through the actual running time of the nodes i to j, wherein delta t is the buffering time of the AGV passing through the nodes i to j;
equal to 1 indicates that the travel time of the AGV through the nodes i to j is normal;
equal to 2 indicates that the AGV may conflict between the nodes i and j and the next driving section is applied for failure;
equal to 3 indicates that the AGV has failed between nodes i, j.
2. The method for detecting and avoiding the temporal-spatial interference of the AGV streams under the time-varying environment according to claim 1, wherein the method for detecting the temporal-spatial interference of the AGV and the method for avoiding the temporal-spatial interference of the AGV with the non-fault type specifically comprise the following steps:
s8.1, calculating the length of the next driving section applied by the AGV according to the operation time and the driving speed of the AGV of each path and the time of passing through each node of the path to form a driving time table of the AGV passing through all the nodes of the path;
s8.2, judging whether nodes or road sections are overlapped among the paths, if so, performing the step S8.3, otherwise, indicating that the paths are not conflicted, and ending;
setting the node set A of the known path 1 and the node set B of the path 2, calculating the intersection C as A ═ N B, and judging whether C is an empty set or not
If the C is an empty set, no overlapping road section exists between the path 1 and the path 2, otherwise, the overlapping road section exists between the two paths, namely, the path conflict can exist;
step S8.3, judging whether time conflict exists in the overlapped road sections, namely whether the time difference T of the AGVs of different paths passing through a certain overlapped node is smaller than the time (L + L) of the AGV driving safety distances) V, where L is the AGV length, LsIf yes, it is indicated that one AGV fails to apply for the road section and conflicts occur, and the step S9.1 is performed, and if no, the step S8.4 is performed;
step S8.4, performing time conflict detection on all the AGVs in the same path by adopting the detection method of the step S8.3, if the time conflicts occur to the AGVs in the path, performing the step S9.1, and if no conflicts exist, ending the step;
s9.1, inputting the serial number, the conflict node and the running time of the conflict AGV into a conflict set;
s9.2, judging the priority of the conflicting AGV;
the method comprises the steps that operation priority levels are divided according to whether an AGV has a task or not, whether the AGV has a load or not and whether the AGV has an emergency or not, the AGV which executes the task, the AGV which has the load and the AGV which carries out emergency operation are higher than those of the AGV which does not execute the task, the AGV which does not have a load and the AGV which carries out conventional operation, and the AGV operation priority levels are adjusted according to real-time state feedback information conversion of task completion and fault emergency conditions;
s9.3, adopting speed control to enable the AGV with low priority to decelerate and wait for the AGV with high priority to pass through the conflict node preferentially;
and S9.4, after the AGV avoids the conflict under the speed control strategy, forming a new AGV running time table, returning to the step S8.1, carrying out conflict detection on the updated time table again, and circulating the steps until no conflict exists between the paths and the paths, and solving the conflict.
3. The method for detecting and avoiding the temporal-spatial interference of the AGV flows under the time-varying environment according to claim 2, wherein the method for avoiding the temporal-spatial interference of the fault AGV specifically comprises the following steps:
step S10.1, detecting nodes of a road section where a fault AGV is located, and locking an operation road section where the AGV is located as an infeasible road section;
step S10.2, judging whether the equipment fault of the AGV can be repaired, if so, performing step S10.3, and if not, performing step S10.4;
when the AGV breaks down, whether the fault AGV can be repaired or not is judged according to the formula (2):
wherein the content of the first and second substances,
for the longest repair time during which a fault can be repaired
Between
It indicates that the AGV may be repaired; when time of failure
Exceed
It means that the repair is impossible within a short time;
s10.3, repairing the fault AGV, assigning a new AGV to the subsequent task, calling an alternative path, and performing the step S10.5;
step S10.4, rejecting the fault AGV out of the fleet and dragging out of the operation area, and performing step S10.8;
step S10.5, judging whether an alternative path of the fault AGV has an overlapped node with the road section, if so, performing step S10.7, otherwise, indicating that the alternative path is available, and performing step S10.6;
step S10.6, selecting an alternative path according to the principle of shortest running time, and performing step S10.11;
step S10.7, replanning the driving path of the AGV executing the subsequent task by adopting a Dijkstra algorithm, and performing step S10.11;
step S10.8, calculating the number of unrepairable fault AGVs, and performing step S10.9;
ngz＝ngz+1，ngzrepresenting the number of failed AGVs which cannot be repaired in a short time;
step S10.9, judging whether the number of unrepairable fault AGVs reaches an upper limit, if so, performing step S10.7, and if not, performing step S10.11;
judging ngz＞nGZWhether or not it is true, nGZRepresenting an upper limit of the number of failed AGVs that cannot be repaired;
step S10.11, adding a standby AGV into an operation fleet to perform operation, calling an alternative path, and performing step S10.5;
and step S10.11, carrying out boxing operation by the AGV.
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