CN110515380B - Shortest path planning method based on turning weight constraint - Google Patents
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Abstract
The invention provides a shortest path planning method based on turning weight constraint. The method comprises the following steps: step 1, calculating the distances between a source point v and all other nodes, and storing the distances between the source point v and all other nodes in a node distance data table; step 2, selecting a node k with the shortest distance to the source point v from the U, and adding the k into the S; step 3, with the node k as an intermediate node, recalculating the distance from each node in the U to the source point v based on the turning weight, and updating and storing the recalculated distances between the source point v and all other nodes in a node distance data table; and 4, repeatedly executing the steps 2 and 3 until the end point w is added into the S, and obtaining the shortest path from the source point v to the end point w. According to the invention, while the shortest path is effectively calculated, the weighting punishment is carried out on the intersection turning, the obtained shortest path can avoid turning as much as possible on the basis of short road length, and the AGV is helped to guide the progress of the vehicle to effectively and quickly complete tasks.
Description
Technical Field
The invention relates to the technical field of path planning, in particular to a shortest path planning method based on turning weight constraint.
Background
An AGV (Automated Guided Vehicle) is a transport Vehicle equipped with an electromagnetic or optical automatic guide device, which is controlled by a computer, moves in wheels, and can automatically travel along a predetermined guide path. In the application of intelligent automatic services such as intelligent parking lot service or intelligent storage, the AGV is a main force vehicle, the problem that the turning difficulty of the vehicle is high is solved, and the driving in any direction of 360 degrees is realized.
The shortest path planning of the AGV in the intelligent scene needs to follow not only the current domestic basic traffic rules including but not limited to left-hand steering, right-hand driving, and turning courtesy, but also the rules of road conditions and related parking in the scene:
1. the other normal use lanes except the emergency lane are bidirectional lanes, and the vehicle runs towards the right side;
2. the AGV vehicles have consistent traveling speed, and adopt a waiting mode when encountering obstacles in front so as not to occupy an opposite lane for overtaking;
3. the straightway of marcing can have a plurality of AGV syntropy or subtend to travel, when having the AGV to turn round and drive into the parking stall, need occupy the subtend lane, and other AGVs wait, pass in proper order after the turn finishes.
At present, 1, 2 points of the rule are fully considered in planning the optimal traveling path through a mathematical algorithm (such as Floyd-Warshall algorithm, Dijkstra algorithm and the like), and the condition that the omnidirectional AGV turns to occupy two lanes and other vehicles need waiting is not considered in the shortest path planning algorithm, so that the parking and running speed of the whole AGV is seriously influenced.
The method for researching the shortest path is mostly based on graph theory, road key points and parking spaces are used as graph nodes, information such as road length or crowdedness is used as side weight, a corresponding adjacency matrix is constructed, the adjacency matrix is solved by an algorithm, and the shortest path from the initial node to the termination node is obtained. The shortest path algorithm includes heuristic search, cam basic method, dynamic programming, neural network, and the like, and currently, the shortest path algorithm in the prior art includes: floyd algorithm, Bellman-Ford algorithm, SPFA algorithm and Dijkstra algorithm.
For a graph G consisting of n nodes and m edges, the Floyd algorithm solves the problem of multi-source shortest cost path by using the idea of dynamic programming, wherein the time complexity is O (n3), the space complexity is O (n2), and the graph G is not suitable for mass data. The Bellman-Ford algorithm finds the shortest path of a single source based on dynamic programming, can judge whether a negative weight loop exists, has time complexity of O (n × m) and space complexity of O (m), and is suitable for a sparse graph and a more dense graph with an edge relation. The SPFA algorithm adds a queue optimization on the basis of the Bellman-ford algorithm, reduces redundant relaxation operation and improves efficiency. The Dijstra algorithm is based on the greedy algorithm idea, solves the problem of single-source shortest cost path, has time complexity and space complexity of O (n2), and is suitable for dense graphs. Because the intelligent scene path has no negative weight, and the Dijkstra algorithm can adapt to the change of the network topology, the performance is more stable, and therefore, the method is often applied to the actual path planning.
Dijkstra shortest path algorithm is widely applied and is mainly characterized in that an initial node is used as a center to diffuse outward layer by layer until a termination node. The Dijkstra algorithm divides all node sets V into two groups: a set of nodes S which have been determined for the shortest path; and the other group is nodes V-S of which the shortest path is not determined, and the nodes in the V-S are added into the S one by one according to the ascending sequence of the shortest path length, and in the adding process, the shortest path length of each node from the source point V to the S is always kept to be not more than the shortest path length from the source point V to any node in the V-S. The Dijkstra algorithm takes a road intersection and a parking space as graph nodes, measures road real-time traffic flow information by using an image radio frequency technology on the setting of a road weight, and realizes quick parking by taking a road section length as a side weight planning path.
The optimization algorithm for calculating the parking path by applying the Dijsktra algorithm in the prior art comprises the following steps: and respectively modeling a path entering the field and a path exiting the field, and calculating the shortest distance by using a Dijkstra algorithm and summing. In the parking path planning problem, road nodes and parking space nodes are distinguished, a layered search method is adopted, path search is carried out in a procedural mode, each procedure is solved through Dijsktra, and a global better solution is obtained. Aiming at the path planning and scheduling problems of multiple AGVs in an intelligent automation scene, the Dijkstar algorithm is used for carrying out path planning on the multiple AGVs, and the scheduling problem of the multiple AGVs is solved through updating of a time window.
The optimization algorithm for calculating the parking path by applying the Dijsktra algorithm in the prior art has the following defects: the application scenario is generally the task of stock distribution or manual parking in a large parking lot, so the weight of the algorithm only considers the length of the road. For the AGV automatic guidance device of the intelligent parking lot, when the intersection turns, the device occupies the opposite lane, so if the shortest path result includes too many turns, waiting of other AGV vehicles or intersection jam may be caused, and delay of vehicle access is caused.
Disclosure of Invention
The embodiment of the invention provides a shortest path planning method based on turning weight constraint, which overcomes the defects of the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme.
A shortest path planning method based on turning weight constraint comprises the following steps:
and 4, repeatedly executing the steps 2 and 3 until the end point w is added into the S, ending the process, and sequentially connecting all nodes in the S according to the time sequence of adding the nodes into the S to obtain the shortest path from the source point v to the end point w.
Preferably, step 1 further comprises:
the method comprises the steps that original data stored in a database comprise parking spaces, charging piles, entrances and exits, position information of roads and exit angle information of the parking spaces, the original data are stored in the database in a site coordinate mode, a site structure diagram is constructed according to coordinates of all sites, the sites are used as nodes, graph nodes and correct connecting sides are generated, a connecting relation is established, the length of the sides is calculated according to the coordinates of two end nodes connected by the sides in the site structure diagram, and the length of the sides is used as side weight.
Preferably, the method further comprises:
when a site structure chart is established, corresponding nodes are established according to attributes of an entrance, an exit, a charging position and a parking space, corresponding starting point nodes and end point nodes are generated for each section of road, each road and the established entrance, the charging position and the parking space nodes are judged one by one, intersection points are judged when the distance between each road and the established entrance, the charging position and the parking space nodes is smaller than a certain threshold value, the intersection points are started from the starting nodes of the roads, if the distance between the nodes at the back and the starting nodes is smaller than the certain threshold value, the nodes are deleted, and if not, the; and starting the next judgment by using the reserved nodes as initial nodes.
Preferably, the calculating of the distances between the source point v and all other nodes in step 1 includes:
if the node U in U is the border-out neighboring point of the source point v, the border v → U has border weight<v，u>The edge weight is the distance D between the source point v and the node uv→uIf the node u is not the adjacent point of the source point v, the edge weight value<v，u>Is infinite, i.e. the distance D between the source point v and the node uv→uIs infinite.
Preferably, the recalculating the distance from each node in U to the source point v based on the turn weight by using the node k as the intermediate node in step 3 includes:
selecting a node U from the U by taking the node k as an intermediate node, and calculating the distance D from v to U through the intermediate node kv→k→uFirst, it is determined whether or not a turn exists in the path v → k → u passing through the intermediate node k, and if the foot-clip between the side v → k and the side k → u is not zero, it is determined that a turn exists in the path v → k → u passing through the intermediate node k, and then the distance from v to u passing through the intermediate node k: dv→k→u＝<v，k>+<k，u>+2 × trunweight, where trunweight is a set turning weight;
if the edge v → k andif the angle between the sides k → u is zero, then it is determined that there is no turn in the path v → k → u passing through the middle node k from v to u, and the distance from v to u passing through the middle node k is: dv→k→u＝<v，k>+<k，u>；
The calculated distance D from v to u through the intermediate node kv→k→uDistance D from v to u stored in node distance data tablev→uMaking a comparison if Dv→k→u≤Dv→uUpdating the v to u distance stored in the node distance data table to Dv→k→u(ii) a If D isv→k→u＞Dv→uThen the v to u distance stored in the node distance data table is unchanged.
Preferably, the method is applied to path planning of an automated guided vehicle AGV in a smart parking system or a smart warehousing system.
According to the technical scheme provided by the embodiment of the invention, the method provided by the embodiment of the invention effectively calculates the shortest path, and combines the actual situation to punish the weight of the turning at the intersection, so that the shortest path obtained according to the new weight can avoid the turning as much as possible on the basis of shorter road length, thereby avoiding queuing and waiting of other AGV vehicles at the intersection and the road intersection, and helping the AGV guide vehicle process to effectively and quickly complete the task.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram illustrating an implementation principle of an omni-directional wheel AGV shortest path planning method based on weight constraint according to an embodiment of the present invention;
fig. 2 is a processing flow chart of a method for solving a shortest path between a known start node and a known end node by using Dijkstra algorithm according to an embodiment of the present invention;
fig. 3 is a processing flow chart of a method for solving a shortest path when an initial node and a termination node are known simultaneously by using a Dijkstra algorithm based on turning weight according to an embodiment of the present invention;
fig. 4 is an application implementation schematic diagram of an omni-directional wheel AGV shortest path planning method based on weight constraint according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
In order to solve the problem, the invention provides an improvement to Dijstra algorithm aiming at the shortest path planning based on an AGV system, considers the time delay possibly caused by vehicle turning, acquires whether the clamping foot between the front driving edge and the subsequent edge is zero degree or not when the determined node set of the shortest path is brought into a new node, and judges whether the two edges belong to the same road or not. If not, i.e., a turn is encountered, an additional weight penalty is added when a node is included.
The embodiment of the invention relates to a path planning system, which provides position distribution and optimal path calculation services for services such as intelligent parking lots or intelligent storage, and the like. The system allows a data server to select and awaken a specific AGV according to a scheduling instruction and an AGV state, establishes stable data connection, plans a path to form a path information table, reads designated information and path information, automatically drives the AGV to a first target point by using a self-positioning navigation system and the path information, executes a preset function, and updates state information. The data server plans an optimal path from the source point to the destination point again and updates a path information table, and the AGV automatically drives to the destination point according to the updated path information and completes a corresponding scheduling task; namely, the AGV performs the processes of automatic navigation, automatic driving and automatic loading and unloading twice when executing a task instruction. Therefore, the AGV shortest path planning algorithm needs to provide a more efficient and practical real-time corrected path information table (global path planning and local path planning) to assist the AGV in quickly completing corresponding tasks.
An implementation principle schematic diagram of the method for planning the shortest path of an omni-directional wheel AGV based on weight constraint provided by the embodiment of the invention is shown in fig. 1, and the method comprises two processing processes of structure diagram construction and path planning. The construction process of the structure diagram comprises the following steps of constructing a site structure diagram (graph) and allocating target positions (AGV parking spaces): the original data stored in the database comprise parking spaces, charging piles, entrances and exits, position information of roads, exit angle information of the parking spaces and the like. The original data in the database are stored in the form of coordinates of the stations, a site structure diagram is constructed according to the coordinates of all the stations, the stations are used as nodes, appropriate diagram nodes and correct connecting edges are generated, and a connection relation is established, wherein the structure diagram is the basis of subsequent path calculation and driving guidance. According to the coordinates of the two end nodes connected by the edge in the structure diagram, the length of the edge can be calculated, and the length of the edge is used as an edge weight, and the edge weight is also the distance between the two end nodes connected by the edge.
The system takes the database and the information of the starting point and the end point to be inquired as input, and outputs a sequence node set (namely a path) from the starting point to the end point. The initial map is drawn by AutoCAD, and an electronic map is generated through third-party application software Mapinfo. When a structure diagram of the parking lot is established, corresponding points are established according to attributes of an entrance, an exit, a charging position and a parking space, corresponding starting point nodes and end point nodes are generated for each section of road, each road and the established entrance, the charging position and the parking space nodes are judged one by one, intersection points are judged when the number of the road is smaller than a certain threshold value, and intersection points between the roads need to be considered.
In an actual scene, because each section of road is relatively close to each other, unnecessary calculation is often caused. In order to reduce the complexity of the algorithm, redundant nodes of the road are deleted. At this time, only nodes on the road are considered, and other points are all key points and need to be stored. Starting from the starting node, if the distance between the following nodes and the nodes is less than a certain threshold value, the nodes are deleted, otherwise, the nodes are reserved. Then, the reserved nodes are used as initial nodes to start next judgment. And initializing edges to obtain a relation set of parking, an entrance, a charging pile and all nodes. For each node, traversing all other nodes, and under the condition of meeting a certain distance, finding out the adjacent points and storing the related information. In addition, the roads have adjacent relation, and for each road, all nodes on the current road are found and sorted from small to large coordinates. For nodes on a straight line, if there is an adjacent relationship, they also need to be connected. There will of course be isolated points that should be parking places, entrances and exits, and charging posts. And finding the nearest nodes for the isolated points to be distributed.
When a relevant task arrives, the optimal idle target bit is selected according to the breadth-first strategy, and task allocation is carried out. The path planning process is based on Dijkstra algorithm, adds turning weight design, and is simultaneously applied to the following two tasks: 1. solving the shortest path problem when the starting node and the termination node are known at the same time; 2. and solving the global shortest path problem of the shortest paths among all nodes in the graph.
The quality of the AGV path planning method plays a crucial role in the efficiency of the whole AGV parking system, and the core of the AGV path planning is the shortest path problem in the fields of data structures and algorithms. The idea of the Dijkstra algorithm is as follows: let D ═ V, E be non-negative weight graph, V represent the set of nodes in the graph, E represent the set of edges in the graph that contain weights. Dividing all the node sets V into two groups, wherein the first group is the node set with the shortest path already obtained and is represented by S (only one source point in S is initially obtained, and the termination node of the path is added into the set S every time the shortest path is obtained later); the second group is the node set of the rest shortest paths to be determined and is represented by U. And sequentially adding the nodes of the U set into the S set one by one according to the increasing order of the shortest path length, wherein the constraint condition is that the shortest path length from the source point v to each node in the S is not greater than the shortest path length from the source point v to any node in the U. The termination condition of the algorithm is that the set U is an empty set, i.e. all nodes of the set U are added into the set S.
The processing flow of the method for solving the shortest path when the initial node and the termination node are known simultaneously by utilizing the Dijkstra algorithm provided by the embodiment of the invention is shown in FIG. 2, and comprises the following processing steps:
1: setting a starting node in a routing region containing a plurality of nodes as a source point v, setting an ending node as an end point w, initially, setting a path set S to only include the source point v, setting a candidate node combination U to include nodes other than v, namely S ═ v }, and setting a distance of v to be ds ═ 0. U contains other nodes than v, namely: if v and the node U in U have an edge, i.e. U is an edge neighboring point of v, the edge v → U has an edge weight<v，u>The edge weight is the distance D between the source point v and the node uv→uIf u is not the out-edge adjacent point of v, the edge weight value<v，u>Infinite, distance D between source point v and node uv→uIs infinite.
The distances between the source point v and all other nodes are stored in a node distance data table.
2: according to the distance information stored in the node distance data table, selecting a node k with the shortest distance to the source point v from the U, adding k into S, and selecting the shortest distance Dv→kIs the edge weight<v，k>。
3: using k as newly considered intermediate node, recalculating the distance from each node in U to source point v, selecting node U in U, and calculating the distance D from v to U through intermediate node kv→k→u＝<v，k>+<k，u>；
Then, the calculated distance D from v to u via the intermediate node k is calculatedv→k→uDistance D from v to u stored in node distance data tablev→uMaking a comparison if Dv→k→u≤Dv→uUpdating the v to u distance stored in the node distance data table to Dv→k→u(ii) a If D isv→k→u＞Dv→uThen the v to u distance stored in the node distance data table is unchanged.
4: and (3) repeating the steps (2) and (3) until the end point w serving as the termination node is added into the S, ending the process, and sequentially connecting all the nodes in the S according to the time sequence added into the S to obtain the shortest path from the source point v to the end point w.
The Dijkstra algorithm based on the turning weight fully considers the condition that other AGV wait due to the fact that the AGV occupies a bidirectional lane when turning, judges whether turning exists or not according to the relation from a father node to the next node side in a passing path, and adds the turning weight to the side weight if the turning exists; otherwise, the edge weight value is not changed, and the subsequent operation is continuously operated. The magnitude of the turning weight turnweight is a set fixed value and the unit is a length unit. Such as 10.
The processing flow of the method for solving the shortest path when the starting node and the ending node are known simultaneously by utilizing the Dijkstra algorithm based on the turning weight provided by the embodiment of the invention is shown in FIG. 3, and comprises the following processing steps:
1: setting a starting node in a routing region containing a plurality of nodes as a source point v, setting an ending node as an end point w, initially, setting a path set S to only include the source point v, setting a candidate node combination U to include nodes other than v, namely S ═ v }, and setting a distance of v to be ds ═ 0. U contains other nodes than v, namely: if v and the node U in U have an edge, i.e. U is an edge neighboring point of v, the edge v → U has an edge weight<v，u>The edge weight is the distance D between the source point v and the node uv→uIf u is not the out-edge adjacent point of v, the edge weight value<v，u>Infinite, distance D between source point v and node uv→uIs infinite.
The distances between the source point v and all other nodes are stored in a node distance data table.
2: according to the distance information stored in the node distance data table, selecting a node k with the shortest distance to the source point v from the U, adding k into S, and selecting the shortest distance Dv→kIs the edge weight<v，k>。
3. Using k as newly considered intermediate node, recalculating the distance from each node in U to source point v, selecting node U in U, and calculating the distance D from v to U through intermediate node kv→k→uFirst, it is determined whether or not a turn exists in the path v → k → u passing through the intermediate node k, and if the foot-clip between the side v → k and the side k → u is not zero, it is determined that a turn exists in the path v → k → u passing through the intermediate node k, and then the distance from v to u passing through the intermediate node k:Dv→k→u＝<v，k>+<k，u>+2×turnweight；
if the pinch between the side v → k and the side k → u is zero degrees, then it is determined that there is no turn in the path v → k → u passing through the intermediate node k from v to u, then the distance from v to u passing through the intermediate node k is: dv→k→u＝<v，k>+<k，u>；
Then, the calculated distance D from v to u via the intermediate node k is calculatedv→k→uDistance D from v to u stored in node distance data tablev→uMaking a comparison if Dv→k→u≤Dv→uUpdating the v to u distance stored in the node distance data table to Dv→k→u(ii) a If D isv→k→u＞Dv→uThen the v to u distance stored in the node distance data table is unchanged.
4. And (3) repeating the steps (2) and (3) until the end point w serving as the termination node is added into the S, ending the process, and sequentially connecting all the nodes in the S according to the time sequence added into the S to obtain the shortest path from the source point v to the end point w.
Example two:
fig. 4 is an application implementation schematic diagram of an omni-directional wheel AGV shortest path planning method based on weight constraint according to an embodiment of the present invention, and as shown in fig. 4, first to (r) () are graph nodes, and edge weights are given according to road lengths. The AGV drives from the initial node I to the final node I to calculate the shortest path.
According to the common Dijstra algorithm, the path 1 is obtained as phi-phi, and the path length is 45. Path 2 is (r) - ((c) and path length is 63). Path 1 is finally selected.
According to the improved Dijsktra algorithm provided by the invention, the path 1 is (r) - ((c) and (c)) passes through 4 turns, so that the final path length is 45+4 x 10 to 85; path 2 is (r) -c-b, and passes through 2 turns, and the final path length is 63+2 × 10 — 83, and path 2 is finally selected.
In the multiple AGV path scheduling, the average using time of the path 2 is shorter than that of the path 1, and the scheme is better.
In summary, the embodiment of the present invention provides a method for planning a shortest path of an omnidirectional AGV based on weight constraint, which relates to the fields of applications such as intelligent parking and intelligent storage, and shortest path algorithm design, and can implement scientific intelligent parking lot service and intelligent storage service, and complete real-time shortest path selection of an omnidirectional AGV in a relevant application scenario.
According to the invention, while the shortest path is effectively calculated, the weight punishment is carried out on the turning of the road junction by combining with the actual situation, the shortest path obtained according to the new weight can avoid the turning as much as possible on the basis of shorter road length, the queuing and waiting of other AGV vehicles at the intersection and the road intersection are avoided, and the AGV is helped to guide the vehicle progress to effectively and quickly complete the task.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (2)
1. A shortest path planning method based on turning weight constraint is characterized by comprising the following steps:
step 1, setting an initial node in a path planning region containing a plurality of nodes as a source point v, setting an end node as an end point w, initially setting a path set S to only include the source point v, calculating distances between the source point v and all other nodes by combining candidate nodes U including other nodes except the source point v, and storing the distances between the source point v and all other nodes in a node distance data table;
when a site structure chart is established, corresponding nodes are established according to attributes of an entrance, an exit, a charging position and a parking space, corresponding starting point nodes and end point nodes are generated for each section of road, each road and the established entrance, the charging position and the parking space nodes are judged one by one, intersection points are judged when the distance between each road and the established entrance, the charging position and the parking space nodes is smaller than a certain threshold value, the intersection points are started from the starting nodes of the roads, if the distance between the nodes at the back and the starting nodes is smaller than the certain threshold value, the nodes are deleted, and if not, the; starting to judge next time by using the reserved nodes as initial nodes;
the method comprises the steps that original data stored in a database comprise parking spaces, charging piles, entrances and exits, position information of roads and exit angle information of the parking spaces, the original data are stored in the database in a site coordinate mode, a site structure diagram is constructed according to coordinates of all sites, the sites are used as nodes, graph nodes and correct connecting edges are generated, a connecting relation is established, the length of the edges is calculated according to the coordinates of two end nodes connected with the edges in the site structure diagram, and the length of the edges is used as edge weight;
the calculating of the distance between the source point v and all other nodes includes: if the node U in U is the border-out neighboring point of the source point v, the border v → U has border weight<v，u>The edge weight is the distance D between the source point v and the node uv→uIf the node u is not the adjacent point of the source point v, the edge weight value<v，u>Is infinite, i.e. the distance D between the source point v and the node uv→uIs infinite;
step 2, selecting a node k with the shortest distance to the source point v from the U according to the distance information stored in the node distance data table, and adding the k into the S;
step 3, judging whether a turn exists according to whether an included angle from a father node to the next node side in the path is zero, and if so, adding a turn weight to the side weight; otherwise, continuing to operate subsequent operation while the weight value is unchanged, wherein the turning weight value turnweight is a set fixed value;
recalculating the distance from each node in the U to the source point v based on the turning weight by taking the node k as an intermediate node, and updating and storing the recalculated distances between the source point v and all other nodes in a node distance data table; step 4, repeatedly executing the steps 2 and 3 until the end point w is added into S, ending the process, and sequentially connecting all nodes in S according to the time sequence added into S to obtain the shortest path from the source point v to the end point w;
the step 3 of recalculating the distance from each node in the U to the source point v based on the turning weight by using the node k as the intermediate node includes:
selecting a node U from the U by taking the node k as an intermediate node, and calculating the distance D from v to U through the intermediate node kv→k→uFirst, it is judged whether or not there is a turn in the path v → k → u from v to u via the intermediate node k, if the side v →If the included angle between k and the side k → u is not zero, judging that a turn exists in the path v → k → u passing through the intermediate node k from v to u, and judging that the distance from v to u passing through the intermediate node k is: dv→k→u＝<v，k>+<k，u>+2 × trunweight, where trunweight is a set turning weight;
if the included angle between the side v → k and the side k → u is zero degrees, then it is determined that there is no turn in the path v → k → u passing through the intermediate node k from v to u, then the distance from v to u passing through the intermediate node k is: dv→k→u＝＜v，k>+<k，u>；
The calculated distance D from v to u through the intermediate node kv→k→uDistance D from v to u stored in node distance data tablev→uMaking a comparison if Dv→k→u≤Dv→uUpdating the v to u distance stored in the node distance data table to Dv→k→u(ii) a If D isv→k→u＞Dv→uThen the v to u distance stored in the node distance data table is unchanged.
2. The method according to claim 1, characterized in that the method is applied for path planning of an automated guided vehicle, AGV, in a smart parking system or a smart storage system.
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