CN110975288B - Geometric container data compression method and system based on jump point path search - Google Patents
Geometric container data compression method and system based on jump point path search Download PDFInfo
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Abstract
A geometric container data compression method based on jump point path search comprises the following steps: carrying out structural analysis on the uniform grid map, identifying all axial jump points, and generating an axial jump point set; and a second step of: identifying all active diagonal jumping points according to the axial jumping point set, and generating an active diagonal jumping point set; and a third step of: and searching the whole uniform grid map by taking all generated axial jump points and all generated active diagonal jump points as source nodes, and carrying out geometric container identification preprocessing on each collapsed position so as to finish the optimal path searching between any two passable nodes. A system based on the method of the invention: the method comprises a memory and a processor, wherein the memory stores a uniform grid map and a geometric container data compression program of the jump point path search, and the processor executes the steps of the method when running the geometric container data compression program of the jump point path search.
Description
Technical Field
The basic application field of the invention is rapid path planning of an intelligent body in robot navigation and game artificial intelligence, and the technical field mainly relates to classical heuristic search problem solving technology, map space topological structure extracting technology and the like in the artificial intelligence.
Background
As a basis for technologies in various aspects of robots and game non-player characters, the limited computing resources in many scenarios require the simultaneous response of a large number of agent path planning requests, and thus the rapid response capability of planning technologies is of greatest concern.
One of the most well known algorithms is known as skip point search (JPS, jump Point Search). When the agent moves on the grid map, the jump point searches symmetrical paths with the same starting point and end point and the same length, the diagonal movement priority path is determined to be the main partial order, and all other paths which do not meet the principle are eliminated from the search space. The point search applies the a algorithm without adding intermediate nodes to the Open table unless nodes are encountered that need to extend the forward direction to other branches, such nodes are added to the Open table to be extended. The jump point search algorithm only needs online processing, does not relate to map preprocessing, and improves the speed by one order of magnitude compared with a simple A-type algorithm.
The main disadvantage of the jump point search is that a great number of line-by-line and column-by-column intermediate node scans and judgments are required when expanding nodes. An improvement to the jump point search is the jps+, jps+ calculating and storing the distance to the first jump point or obstacle reachable through that direction for each passable direction of each passable node during the offline phase. This pre-stored information further increases the search speed. Furthermore, the JPS+ (P) adopts a transit node pruning technology when searching, and the diagonal jump points are treated as transit nodes and are not added into the Open table for processing, so that compared with the JPS+, the operation times of the Open table are reduced to a certain extent.
The performance of the jump point search is further greatly improved by combining with the pruning technique of the geometric container (Geometric Containers). Geometric container pruning is a class of object-oriented acceleration techniques used to prune edges in the search phase that are not likely to guide the current node through the shortest path to the target location, with the simplest and most efficient version of the geometric container being called a rectangular Bounding box (Bounding Boxes). The jump point searching method combined with the bounding box technology is called JPS+BB for short.
The offline stage jps+bb first initializes a blank rectangular box for each edge in the graph, and then runs Dijkstra algorithm from each passable node that uses diagonal movement priority order to cover all nodes that it passes. When the Dijkstra algorithm is extended to a certain node, it is added to the rectangular box of the corresponding edge of the source node. When the preprocessing of all nodes is completed, a rectangular range surrounding all reachable through the optimal diagonal movement priority path is formed for each edge, although redundant nodes may be contained therein for reasons of shape simplicity.
The pruning technology based on off-line pre-calculation improves the performance of JPS+ algorithm by about an order of magnitude, so that the pruning technology can obtain the searching speed of tens of microseconds under a classical test set. However, this significant advancement comes at the cost of having to devote a significant amount of CPU time and memory space during the preprocessing stage. Jps+bb requires performing a modified Dijkstra search between all passable point pairs. Assuming that a grid map contains n passable nodes, the algorithm has both time and space complexity of O (n 2 ) I.e. the secondary complexity. From the experimental results, it usually takes about ten hours and hundreds of megamemory to complete the calculation of a 1024 x 1024 scale StarCraft map. Such a cost is not acceptable in applications where the CPU time and memory space specified for path planning is limited, as is typically the case in robotic navigation and gaming AI.
At present, no research results can realize the ultra-fast search speed on the premise of acceptable pretreatment cost. Therefore, the present invention aims to solve the serious map preprocessing complexity problem faced by the jps+bb algorithm by using map space structure analysis.
Disclosure of Invention
The present invention is based on the fundamental observation that only the number of all hop positions in the map is much smaller than the number of all navigable nodes, so that the overhead of preprocessing in both space-time terms will be greatly reduced if Dijkstra search is initiated from only all hop positions.
A geometric container data compression method based on jump point path search comprises the following specific steps:
s1, carrying out structural analysis on a uniform grid map, identifying all axial jump points, and generating an axial jump point set;
s2, identifying all active diagonal jumping points according to the axial jumping point set, and generating an active diagonal jumping point set;
s3, searching the whole uniform grid map by taking all generated grid positions collapsed by the axial jump points and all active diagonal jump points as source nodes, and preprocessing geometric container identification aiming at each collapsed position to complete optimal path searching between any two passable nodes.
The S1 axial jump point identification step comprises the following steps:
s11, scanning all nodes in the uniform grid map, and identifying obstacle points existing in all the nodes;
s12, obtaining feasible diagonal nodes of all obstacle points;
s13, when two public neighborhood nodes comprising an obstacle point and a feasible diagonal node of the obstacle point are passable nodes, identifying the feasible diagonal node of the obstacle point as an axial jump point;
s14, repeating the step S13 to obtain all the axial jump points in the uniform grid map.
The active diagonal jumping point in the step S2 is provided with an axial jumping point serving as a father node, and the active diagonal jumping point is reached by the axial jumping point through barrier-free movement.
The active diagonal jump point identification step comprises the following steps:
s21, calculating an axial jump distance table of all passable nodes on the uniform grid map, wherein the axial jump distance table shows the distance from each passable node to other axial jump points or obstacles in the axial direction;
s22, calculating diagonal jump distance tables of all passable nodes on the uniform grid map, wherein the diagonal jump distance tables display the distance from each passable node to other passable nodes in the diagonal direction;
and S23, traversing by taking each axial jump point as a source, repeatedly reading the diagonal distance of the current node, wherein the diagonal distance is positive to indicate that the diagonal movement can be transferred to axial movement at the position so as to reach the axial jump point, and judging the current node as an active diagonal jump point.
The pre-calculation method of the rectangular bounding box of the uniform grid map in the S3 of the invention comprises the following steps:
s31, traversing the axial jump point set and the active diagonal jump point set, and initializing a rectangular bounding box aiming at each side of the grid positions where the axial jump points and the active diagonal jump points in the set collapse;
s32, searching the full map by taking the current jump point as a source node, recording the initial edge of a path of the source jump point to the node every time a node is generated in the searching process, and pressing the node into an Open table;
s33, when the optimal path from the source jump point to the node is found, the node is taken out from the Open table for expansion, and the node is added into a bounding box of the corresponding edge of the source node;
s34, when the OpenTable is empty, the search of the current jump point is ended, and the search of the grid positions collapsed by the next axial jump point and the active diagonal jump point is entered.
In step S33 of the present invention:
one source node to one target node can correspond to a plurality of optimal paths, all the optimal paths should be recorded, and finally the target node is added into all rectangular bounding boxes meeting the conditions.
A system based on the method of the invention:
comprising a memory storing a uniform grid map and a geometric container data compression program for a hop path search, and a processor executing the steps of the method of any of claims 1-6 when running the geometric container data compression program for a hop path search.
The invention can achieve the following technical effects:
the invention solves the serious map preprocessing complexity problem faced by the structural analysis and geometric container identification pre-calculation of the uniform grid map. The uniform grid map is subjected to structural analysis to identify axial jump points and active diagonal jump points, geometric container pretreatment is performed on grid positions collapsed by the axial jump points and the active diagonal jump points, and the pretreatment cost of the uniform grid map is greatly reduced. In an optimal path with preferential diagonal movement, other key nodes are axial jump points and active diagonal jump points except the starting point and the ending point. Therefore, the route searching can be successfully completed by only preprocessing the positions and connecting the starting point to the adjacent axial jump point during the route searching.
Drawings
FIG. 1 is a schematic diagram of a geometrical container data compression method and system main sequence path classification based on jump point path search according to the present invention;
FIG. 2 is an example of a geometric container data compression method and system distance table based on hop path search according to the present invention;
FIG. 3 is a schematic diagram of a geometric container data compression method and a system equivalent main sequence path based on a jump point path search according to the present invention;
FIG. 4 is a diagram of a method and system for compressing geometric container data based on skip path search, which is a Starcraft map set TheFrozenSeaMap map.
Detailed Description
A geometric container data compression method based on jump point path search comprises the following specific steps:
s1, carrying out structural analysis on a uniform grid map, identifying all axial jump points, and generating an axial jump point set;
the axial jump point can be regarded as a tripletWhich contains a grid position n and two axial directions of movement. The mesh position and the two axial movement directions satisfy the following conditions: (1)/>And->Is two trafficable movements; (2)/>(3)/>The traffic is impossible.
Grid position n is the corner of the obstacle and is orientedThe parent direction, called the hop, is the direction of the search. The eligible n is generally attached with more than two axial hops, so there are more than two parent directions. Will->Andthe main order expansion direction called the axial jump point.
S2, identifying all active diagonal jumping points according to the axial jumping point set, and generating an active diagonal jumping point set;
a diagonal jump point can be represented as a tupleComprising a grid position n and a diagonal directionPosition n is derived from->Movement in the direction arrives and can go through + ->Or->Arrive at a certain +.>Or->An axial jump point in the parent direction, or to a target node. The same axial jump point can be reached by multiple diagonal jump points at the same time, even different diagonal jump points can be attached at the same time at the same position, and all the diagonal jump points can reach the same axial jump point.
Active diagonal hopsIs provided with an axial jump point->As its parent node, whereIs the diagonal main order expansion direction of the axial jump point, n can be passed by s through zero barrier +.>The directional movement arrives. The remaining diagonal hops that do not meet this condition are passive diagonal hops.
All paths conforming to the diagonal movement priority rule can be classified into two types. As shown in FIG. 1, the first category is a simple main sequence path, as in FIG. 1<s 1 ,t 1 >The search direction conversion at the jump point position is not involved, and the optimal path can be judged by one-time depth-first search when searching. The second type, called a composite main sequence path, can be expressed as<s,(d s ),s 1 ,(d 1 ) x1 ,s 2 ,(d 2 ) x2 ,s 3 ,…,s n ,(d t ),t>As in FIG. 1<s 2 ,t 2 >As shown, it can be divided into three parts: (1) an initial part: from the start point s to the first axial jump point s 1 Possibly including a passive diagonal jump point d s The method comprises the steps of carrying out a first treatment on the surface of the (2) an intermediate portion: from the first axial jump point s 1 To the last axial jump point s n With x between adjacent two i Active diagonal hops; (3) an end portion: from s n To the end point t, a passive jump point may be included.
S3, searching the whole uniform grid map by taking all generated grid positions collapsed by the axial jump points and all active diagonal jump points as source nodes, and preprocessing geometric container identification aiming at each collapsed position to complete optimal path searching between any two passable nodes.
The reason for geometric container pre-processing for collapsed grid positions is that: the total number of jump point collapse positions is far smaller than the total number of passable nodes in the map, so that the preprocessing cost is greatly reduced; in an optimal path with preferential diagonal movement, other key nodes are axial jump points and active diagonal jump points except the starting point and the ending point. Therefore, the route searching can be successfully completed by only preprocessing the positions and connecting the starting point to the adjacent axial jump point during the route searching.
The S1 axial jump point identification step comprises the following steps:
s11, scanning all nodes in the uniform grid map, and identifying obstacle points existing in all the nodes;
s12, obtaining feasible diagonal nodes of all obstacle points;
s13, when two public neighborhood nodes comprising an obstacle point and a feasible diagonal node of the obstacle point are passable nodes, identifying the feasible diagonal node of the obstacle point as an axial jump point;
s14, repeating the step S13 to obtain all the axial jump points in the uniform grid map.
The active diagonal jumping point in the step S2 is provided with an axial jumping point serving as a father node, and the active diagonal jumping point is reached by the axial jumping point through barrier-free movement.
The active diagonal jump point identification step comprises the following steps:
the axial hops and active diagonal hops are critical locations that make up the main-order path, so the preprocessing cost is primarily aimed at these axial hops and active diagonal hops. After identifying all axial hops in the map, the key is to quickly extract the active diagonal hops. The invention first calculates a jump distance table for all passable nodes on the map, as shown in fig. 2. The calculation process is linear with the number of nodes, more precisely, each node is not visited more than 8 times, at most once per direction.
S21, calculating an axial jump distance table of all passable nodes on the uniform grid map, wherein the axial jump distance table shows the distance from each passable node to other axial jump points or obstacles in the axial direction;
s22, calculating diagonal jump distance tables of all passable nodes on the uniform grid map, wherein the diagonal jump distance tables display the distance from each passable node to other passable nodes in the diagonal direction;
and S23, traversing by taking each axial jump point as a source, repeatedly reading the diagonal distance of the current node, wherein the diagonal distance is positive to indicate that the diagonal movement can be transferred to axial movement at the position so as to reach the axial jump point, and judging the current node as an active diagonal jump point.
The pre-calculation method of the rectangular bounding box of the uniform grid map in the S3 of the invention comprises the following steps:
according to the invention, only two types of jump points are used as source nodes to initiate Dijkstra search after being corrected by a main sequence thought. Multiple different types or types of skip points are possibly attached to the same grid position, but the tuple represents different skip points, repeated calculation is not needed according to the difference of the grid position and the father direction in the tuple representation, and the invention only performs precomputation on the collapsed grid position where the relevant skip points are located by collapsing the relevant skip points at the position.
S31, traversing the axial jump point set and the active diagonal jump point set, and initializing a rectangular bounding box aiming at each side of the grid positions where the axial jump points and the active diagonal jump points in the set collapse;
s32, searching the full map by taking the current jump point as a source node, recording the initial edge of a path of the source jump point to the node every time a node is generated in the searching process, and pressing the node into an Open table;
s33, when the optimal path from the source jump point to the node is found, the node is taken out from the Open table for expansion, and the node is added into a bounding box of the corresponding edge of the source node;
s34, when the OpenTable is empty, the search of the current jump point is ended, and the search of the grid positions collapsed by the next axial jump point and the active diagonal jump point is entered.
In step S33 of the present invention:
one source node to one target node can correspond to a plurality of optimal paths, all the optimal paths should be recorded, and finally the target node is added into all rectangular bounding boxes meeting the conditions.
The method emphasizes that the tie breaking mode of the Dijkstra algorithm is consistent with the diagonal movement priority order rule of the jump point search. This is not only because the main order search can avoid consuming a lot of computation resources on the symmetric path, but also importantly, maintain the consistency of offline computation and online search, and ensure the completeness and optimality of the search results. If the Dijkstra algorithm does not adopt the main order, but breaks the tie at will, it may result in that the target point cannot be included in the main order extension direction of the current node, because it is considered that the target can be reached by searching through other edges at the time of pre-calculation.
In addition, when a jump point of a certain grid position in different father directions has a main sequence path reaching a certain target point, or more than one main sequence path is also present under the same jump point, we refer to the jump point as having an equivalent main sequence path. Two equivalent main order paths between the solid line and the dashed line shown in fig. 3, i.e. s and t. If the preprocessing stage only stores t into the bounding box of one of the initial edges, it will result in a search failure starting from the other jump point. Therefore, we store it in all reasonable bounding boxes at the same time here, ensuring the completeness of the search.
A system based on the method of the invention:
comprising a memory storing a uniform grid map and a geometric container data compression program for a hop path search, and a processor executing the steps of the method of any of claims 1-6 when running the geometric container data compression program for a hop path search.
As previously mentioned, the number of axial and active diagonal skip points collapsed positions in a typical grid map is much less than the total number of all navigable nodes. The present invention thus provides a substantial savings in the pretreatment time and space required. The invention takes the general test set as a map, and the basic information is shown in table 1. Including both typical game maps and synthetic rooms and labyrinths.
The experimental results of the pretreatment are shown in table 2. Wherein each rectangular bounding box requires four coordinates up, down, left and right to calibrate its position, and each coordinate is stored in 16 bytes. The results clearly show that the pre-calculated Dijkstra search times required for the present invention are on average 1-2 orders of magnitude less than jps+bb, thus saving on average 1-2 orders of time and space. Taking the most complex Starcraft map as an example, the preprocessing time of the 75 maps by the original algorithm is approximately 8 days in total, and the storage space is consumed for 1 GB. The invention only needs about one day and more than one hundred megadays, and has extremely obvious benefit.
Table 1 map for experiment
Table 2 pretreatment results are compared.
Dijk represents the total number of Dijkstra searches by the algorithm, and corresponds to the total number of passable nodes and the total number of jumping points of JPS+BB+ in the JPS+BB algorithm respectively, wherein the units are millions; time represents the total Time required for pretreatment in hours; mem represents the total memory space required to store the bounding box in MB.
As shown in fig. 4, a specific embodiment is provided, which is a system StarCraft map set TheFrozenSeaMap map, with a size of 1024 x 1024. The jps+bb processes the table by calculating the distance table first and restarting Dijkstra search by APSP method, and calculates the table using 754304 nodes as the source, which takes 11.5 hours, and the memory of the geometric container needs 44.4MB. In contrast, JPS+BB+ performed 107624 searches as described above, taking 2.7 hours, the memory space required was 2.9MB.
Claims (5)
1. The geometric container data compression method based on the jump point path search is characterized by comprising the following steps:
s1, carrying out structural analysis on a uniform grid map, identifying all axial jump points, and generating an axial jump point set;
s2, identifying all active diagonal jumping points according to the axial jumping point set, and generating an active diagonal jumping point set, wherein the active diagonal jumping point is provided with an axial jumping point as a father node, and is reached by the axial jumping point through barrier-free movement; the step of identifying the active diagonal jump points comprises the following steps:
s21, calculating an axial jump distance table of all passable nodes on the uniform grid map, wherein the axial jump distance table shows the distance from each passable node to other axial jump points or obstacles in the axial direction;
s22, calculating diagonal jump distance tables of all passable nodes on the uniform grid map, wherein the diagonal jump distance tables display the distance from each passable node to other passable nodes in the diagonal direction;
s23, traversing by taking each axial jump point as a source, repeatedly reading the diagonal distance of the current node, wherein the diagonal distance is positive to indicate that the diagonal movement can be transferred to axial movement at the position so as to reach the axial jump point, and judging the current node as an active diagonal jump point;
and S3, searching the whole uniform grid map by taking all generated axial jump points and all generated active diagonal jump points as source nodes, and preprocessing geometric container identification aiming at each collapsed position to complete optimal path searching between any two passable nodes.
2. The geometric container data compression method based on the hop path search according to claim 1, wherein in the step S1, the axial hop identification includes the steps of:
s11, scanning all nodes in the uniform grid map, and identifying barrier vertices existing in all nodes;
s12, obtaining feasible diagonal nodes of all barrier vertexes;
s13, when two public neighborhood nodes comprising the barrier vertex and the feasible diagonal node of the barrier point are passable nodes, the feasible diagonal node of the barrier point is identified as an axial jump point;
s14, repeating the step S13 to obtain all the axial jump points in the uniform grid map.
3. The geometric container data compression method based on the hop path search according to claim 1, wherein in the step S3, geometric container identification preprocessing includes the following steps:
s31, traversing the axial jump point set and the active diagonal jump point set, and initializing a rectangular bounding box aiming at each side of the grid positions where the axial jump points and the active diagonal jump points in the set collapse;
s32, searching the full map by taking the current jump point as a source node, recording the initial edge of a path of the source jump point to the node every time a node is generated in the searching process, and pressing the node into an Open table;
s33, when the optimal path from the source jump point to the node is found, the node is taken out from the Open table for expansion, and the node is added into a bounding box of the corresponding edge of the source node;
s34, when the OpenTable is empty, the search of the current jump point is ended, and the search of the grid positions collapsed by the next axial jump point and the active diagonal jump point is entered.
4. A geometric container data compression method based on hop path search according to claim 3, wherein in the step S33:
one source node to one target node can correspond to a plurality of optimal paths, all the optimal paths should be recorded, and finally the target node is added into all rectangular bounding boxes meeting the conditions.
5. A geometric container data compression system based on jump point path search is characterized in that:
the geometric container data compression method based on the jumping point path search comprises a memory and a processor, wherein the memory stores a geometric container data compression program of the uniform grid map and the jumping point path search, and the processor executes the steps of the geometric container data compression method based on the jumping point path search according to any one of claims 1-4 when the geometric container data compression program of the jumping point path search is operated.
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