CN103344248B - Optimal path calculation method for vehicle navigation system - Google Patents
Optimal path calculation method for vehicle navigation system Download PDFInfo
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- CN103344248B CN103344248B CN201310297623.5A CN201310297623A CN103344248B CN 103344248 B CN103344248 B CN 103344248B CN 201310297623 A CN201310297623 A CN 201310297623A CN 103344248 B CN103344248 B CN 103344248B
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Abstract
The invention relates to an optimal path calculation method for a vehicle navigation system. The method refers to an optimal path algorithm for the vehicle navigation system, the turning point of a grid map is identified and selected only in the execution process, the intermediate node from one turning point to an adjacent turning point can be ignored, and the number of nodes required to be treated is greatly reduced. The provided algorithm is simple, the grid map is not required to be subjected to pretreatment and extra computer storage space overhead, the optimal path can be efficiently searched, the vehicle navigation efficiency is improved, the vehicle path can be well planned in a complex map environment, and the optimal path is rapidly searched for the vehicle.
Description
Technical field
The present invention relates to a kind of best path algorithm of Vehicular navigation system, particularly a kind of optimal path finding method based on Vehicular navigation system under the complicated map environment of TPA* algorithm.
Background technology
In Vehicular navigation system, topmost part determines the algorithm of optimal path, is used for determining the optimal path of vehicle in road traffic net from current location to destination.For most of Vehicular navigation system, the optimal path between starting point and terminal is normally defined the shortest path of expected travel time.Determine that in Vehicular navigation system, the main thought of optimal path is: calculate the fullpath of current vehicle position to destination with shortest path first, make its estimated travel time the shortest, finally the path calculated is supplied to vehicle driver's reference.
Optimal path finds nodes to a class problem of direct shortest path, and the method for normally employing graph theory and mathematical programming carries out the search to path.At present, optimal path problem is in various fields widespread uses such as computer science, operational research, Geographical Information Sciences.Embody rule comprises intelligent transportation system, Geographic Information System, path planning problem, computer network communication etc.
From the fifties in last century, the scholar in each field conducts in-depth research optimal path problem.Based on the Bellman principle of optimality, in solution node network, node is to the dijkstra's algorithm of every other node optimal path, Floyd algorithm arbitrarily to shortest path between node in solution node network, and the classic algorithm that is representative such as intelligent search A* algorithm has established the basis of meshed network best path algorithm.
In numerous best path algorithm, A* algorithm is the searching algorithm of a kind of optimal path popular at present, compare with Dijkstra scheduling algorithm, A* algorithm have employed heuristic search strategy, in the process of search, the node of each search is estimated and calculated, avoid the blindness of route searching, the node traveled through in search procedure can be reduced to a great extent, thus improve the execution speed of algorithm.
By relatively can find current best path algorithm performance, in time being applied in large complicated meshed network, even if the superior A* algorithm of Performance comparision also also exists the problems such as traverse node quantity is many, arithmetic speed is slow, when being applied in the Vehicular navigation system under complicated map environment, the requirement of Vehicular navigation system to real-time can not be met well, be necessary to propose the problem that modified hydrothermal process solves automobile navigation under complicated map environment therefore in fact.
Summary of the invention
The present invention proposes a kind of optimum path calculation method of Vehicular navigation system, to solve the problem of automobile navigation under complicated map environment, the speed of algorithm is improved with the reach home node of required traversal of the path of the best by identifying and reducing, there is algorithm realization simple, do not take the storage space that computing machine is extra, do not need, to advantages such as the pre-service of map, optimal path to be searched out rapidly under complicated map environment, navigate to vehicle.
The technical scheme that the present invention takes comprises the following steps:
Step one: set up the network node grating map that two dimension is directionless, weight is identical, wherein, 8 nodes are included in neighborhood node set neighbors (n) of each network node n, the different geographic entity of map is travelled according to vehicle, each node has and can to pass through and impassability two states, a node moves two kinds of modes by rectilinear movement and oblique line and arrives the adjacent node of another one, and the distance between adjacent node is defaulted as 1;
Step 2: adjacent node set neighbors (n) of node n is deleted, object to determine the incoherent node x of optimal path with arriving target, needing consideration two kinds of situations according to father node p (n) to the direction difference of node n, is that rectilinear movement and oblique line move respectively; In addition, if node n is starting point, because its father node is empty, so cannot carry out deleting of adjacent node;
(1), move linearly
In this case, to any node x(x ∈ neighbors (n) meeting constraint condition in formula (1)) delete:
len(<p(n),…,x>\n)≤len(<p(n),n,x>) (1)
(2), oblique line moves
Delete that rule is similar with above-mentioned straight line, but when oblique line moves, node x deleted that restrictive condition is more strict, the constraint condition in demand fulfillment formula (2):
len(<p(n),…,x>\n)<len(<p(n),n,x>) (2)
In above-mentioned two kinds of situations, in neighbors (n), all do not comprise obstacle nodes, the node now obtained after deleting is called the common adjacent node (NormalNeighbor) of node n; And when in neighbors (n) containing obstacle nodes time, there will be can not completely to the situation that non-generic adjacent node is deleted, at this moment claims these to be not special joint (Special Neighbor) by the node deleted; If node x(x ∈ neighbors (n)) be special joint, so it should meet: node x is not the ordinary node of node n, and meets the constraint condition in formula (3).
len(<p(n),n,x>)<len(<p(n),…,x>\n) (3)
Step 3: carry out the search work to path turning point, node n is to the moving direction of node x ∈ neighbor (n)
be a key factor, turning point be defined as follows:
Node m is the descendant node of node n, and the direction of n to m is
if n arrives m this side up after minimum k unit moves, namely
and meet one of following constraint, then title node m is the turning point of node n;
1). node m is terminal;
2). have at least one to be special joint in the adjacent node of node m;
3).
when being oblique line directions, there is a node
?
distance m node k on direction
iindividual unit distance, and node p is the turning point meeting above-mentioned condition 1 or condition 2;
Step 4: by what obtain according to definition in step 3, the turning point belonging to node n is stored in array ProcessList, arbitrary node x ∈ ProcessList is pending node, when inserting node each time in ProcessList, all need the order keeping ProcessList, the path cost value of first node namely in ProcessList: namely minimum to the travel distance of this node from starting point, the path cost value of last node is maximum, when upper once algorithm circulation, first node taken out in ProcessList processes,
Step 5: circulation performs above-mentioned steps two to step 4, as terminal n
goalwhen being inserted in ProcessList, complete the search to optimal path, terminate; From terminal n
goalstart, successively continue before it node and father node are added in the array PathList of path, until by starting point n
startadd in PathList, all path nodes on the optimal path that being of preserving in the array PathList of path searches out, Vehicular navigation system can carry out real-time navigation work according to this optimal path to the vehicle travelled on map.
Compared with existing algorithm, the best path algorithm of a kind of Vehicular navigation system of the present invention only identifies the turning point in grating map and chooses in the process performed, intermediate node from a turning point to its adjacent next turning point can be left in the basket, and then substantially reduces need number of nodes to be processed.The algorithm that the present invention proposes is simple, does not need to carry out any pre-service and extra Computer Storage space expense to grating map, high efficiencyly can search out optimal path, improves the efficiency of automobile navigation.Adopt the present invention, the planning to vehicle route can well be carried out under complicated map environment, rapidly for optimal path found by vehicle.
Accompanying drawing explanation
Fig. 1 is the schematic diagram in the isometric path related in algorithm of the present invention;
Fig. 2 is in varied situations to the schematic diagram that neighborhood node is deleted in algorithm of the present invention;
Fig. 3 is the search schematic diagram to turning point in algorithm of the present invention.
Embodiment:
Below by way of specific instantiation and accompanying drawings embodiments of the present invention, those skilled in the art can understand other advantage of the present invention and effect easily by content disclosed in the present specification.The present invention is also implemented by other different instantiation or is applied, and the every details in this instructions also can based on different viewpoints and application, carries out various modification and change not deviating under spirit of the present invention.
Before specifically introducing the present invention, the central core thought of the best path algorithm of lower a kind of Vehicular navigation system of the present invention is first described: by finding A* Algorithm Analysis, A* algorithm also exists a large amount of isometric paths in the process of search optimal path.In the present invention to the definition in isometric path be: if two paths have identical starting point and terminal, and another paths can be obtained by carrying out displacement restructuring to the composition subpath of a wherein paths, so claim this two paths to be isometric path.Several paths shown in Fig. 1 isometric path each other, can obtain by observing, each paths is all made up of the subpath of 9 vertical directions and 9 horizontal directions.
In order to reduce to the unnecessary search in isometric path and traversal in A* algorithm, improve efficiency and the travelling speed of algorithm further, the present invention proposes a kind of TPA*(Turning Point) algorithm.The central idea of TPA* algorithm deletes any adjacent node that can arrive in optimal manner from the father node of present node, can avoid a large amount of isometric paths in this way.In addition, in TPA* algorithm, all pending nodes are all that node of direction turnover may occur in the working direction of path for those, and the part of nodes between two turnover nodes does not process, and this just means that the number of nodes that needs travel through is compared can significantly reduce with A* algorithm.
Below in conjunction with accompanying drawing, the specific embodiment of the present invention is explained in detail.
Step one: in order to find the optimal path of Vehicular navigation system, first needs the concrete implementation environment setting up algorithm of the present invention.The enforcement of the optimal algorithm of a kind of Vehicular navigation system of the present invention carries out in the network node grating map that the directionless weight of two dimension is identical.Wherein, neighborhood node set neighbors (n) of each network node n, containing 8 nodes, travels the different geographic entity of map according to vehicle, and each node can have and can pass through and obstacle two states.A node can move two kinds of modes by rectilinear movement and oblique line and arrive another node, and the distance between adjacent node is defaulted as 1.
Step 2: delete adjacent node set neighbors (n) of node n, object to determine the incoherent node x of optimal path with arriving target.In order to reach above-mentioned requirements, need to compare the length of two class.paths: π, this path, from father node p (n) of node n, arrives node x via node n; π ', this path is equally from father node p (n) of node n, but arrives node x without node n.It should be noted that the node comprised in above-mentioned two paths is included in set neighbors (n).Needing consideration two kinds of situations according to father node p (n) to the direction difference of node n, is that rectilinear movement and oblique line move respectively.In addition, if node n is starting point, because its father node is empty, so cannot carry out deleting of adjacent node.
1, move linearly
In this case, to any node x(x ∈ neighbors (n) meeting constraint condition in formula (1)) delete:
len(<p(n),…,x>\n)≤len(<p(n),n,x>) (1)
Situation as shown in Figure 2 (a), p (n)=7, all nodes except node x=2 all will be deleted.
2, oblique line moves
Delete that rule is similar with above-mentioned straight line, but when oblique line moves, node x deleted that restrictive condition is more strict, the constraint condition in demand fulfillment formula (2):
len(<p(n),…,x>\n)<len(<p(n),n,x>) (2)
Situation as shown in Figure 2 (c), p (n)=1, except node x=5, all nodes outside x=7, x=8 all will be deleted.
The algorithm related in the present invention, in above-mentioned two kinds of situations, does not all comprise obstacle nodes in neighbors (n), the node now obtained after deleting is called the common adjacent node (Normal Neighbor) of node n.And when containing obstacle nodes in neighbors (n), there will be and can not, completely to the situation that non-generic adjacent node is deleted, at this moment claim these nodes to be special joint (Special Neighbor).If node x(x ∈ neighbors (n)) be special joint, so should meet: node x is not the ordinary node of node n, and meet the constraint condition in formula (3).
len(<p(n),n,x>)<len(<p(n),…,x>\n) (3)
X=1 node in Fig. 2 (b) and the x=3 node in Fig. 2 (d) are all the special joints meeting above-mentioned condition.
Algorithm involved in the present invention complete 8 neighborhood nodes around to node n delete work after can obtain neighborhood node set neighbor (n) of a node n.Consider in worst situation, namely node n is the starting point n in path
starttime, the interstitial content in set neighbor (n) is 8.Consider generalized case, and when not having special adjacent node (SpecialNeighbor) in the neighborhood of node n, the maximum node quantity in set neighbor (n) is 1 when moving linearly, and when oblique line moves is 3; When there is special joint, the maximum node quantity in set neighbor (n) is 3 when moving linearly, and is 5 when oblique line moves.Neighborhood number of nodes after deleting has had significant decline.
Step 3: in the follow-up work of algorithm of the present invention, not directly using the neighborhood node that obtains in step 2 as next step pending node, but carry out the search work to path turning point.In the search of this step, node n is to the moving direction of node x ∈ neighbor (n)
it is a key factor needing to consider.Algorithm involved in the present invention to the definition of turning point and search example as follows:
Node m is the descendant node of node n, and the direction of n to m is
if n arrives m this side up after minimum k unit moves, namely
and meet one of following constraint, then title node m is the turning point of node n.
1. node m is terminal.
2. in the adjacent node of node m, have at least one to be special joint.
3.
when being oblique line directions, there is a node
?
distance m node k on direction
iindividual unit distance, and node p is the turning point meeting above-mentioned condition 1 or condition 2.
Node m in Fig. 3 (a) is a turning point of node n, meets above-mentioned condition 2.Node m shown in Fig. 3 (b) be satisfy condition 3 turning point.Move along oblique line directions from node n, until run into node m, through k from m
i=2 sub-levels move to reach node p, and p point is a turning point (satisfying condition 2) of m point, and therefore, node m is a turning point of node n.
Step 4: by what obtain according to definition in step 3, the turning point belonging to node n is stored in array ProcessList, and arbitrary node x ∈ ProcessList is pending node.When inserting node each time in ProcessList, all need the order keeping ProcessList, the path cost value (from starting point to the travel distance of this node) of first node namely in ProcessList is minimum, and the path cost value of last node is maximum.When upper once algorithm circulation, first node taken out in ProcessList processes.
Step 5: circulation performs above-mentioned steps two to step 4, as terminal n
goalwhen being inserted in ProcessList, complete the search to optimal path, algorithm terminates.From terminal n
goalstart, successively continue before it node and father node are added in the array PathList of path, until by starting point n
startadd in PathList.All path nodes on the optimal path that being of preserving in the array PathList of path searches out.Vehicular navigation system can carry out real-time navigation work according to this optimal path to the vehicle travelled on map.
In sum, the best path algorithm of a kind of Vehicular navigation system of the present invention only identifies the path turning point in map and chooses in the process performed, intermediate node from a turning point to its adjacent next turning point can be left in the basket, and then substantially reduces need number of nodes to be processed.The algorithm that the present invention proposes is simple, does not need to carry out any pre-service and extra Computer Storage space expense to map, high efficiencyly can search out optimal path, improves the efficiency of automobile navigation.Adopt the present invention, the planning to vehicle route can well be carried out under complicated map environment, rapidly for optimal path found by vehicle.
The foregoing is only the preferred embodiment of the present invention; protection scope of the present invention is not limited in above-mentioned embodiment; every technical scheme belonging to principle of the present invention all belongs to the protection domain of this aspect; for a person skilled in the art; some improvements and modifications of carrying out under the premise of not departing from the present invention, these improvements and modifications also should be considered as protection scope of the present invention.
Claims (1)
1. an optimum path calculation method for Vehicular navigation system, is characterized in that comprising the following steps:
Step one: set up the network node grating map that two dimension is directionless, weight is identical, wherein, 8 nodes are included in neighborhood node set neighbors (n) of each network node n, the different geographic entity of map is travelled according to vehicle, each node has and can to pass through and impassability two states, a node moves two kinds of modes by rectilinear movement and oblique line and arrives the adjacent node of another one, and the distance between adjacent node is defaulted as 1;
Step 2: adjacent node set neighbors (n) of node n is deleted, object to determine the incoherent node x of optimal path with arriving target, needing consideration two kinds of situations according to father node p (n) to the direction difference of node n, is that rectilinear movement and oblique line move respectively; In addition, if node n is starting point, because its father node is empty, so cannot carry out deleting of adjacent node;
(1), move linearly
In this case, to any node x meeting constraint condition in formula (1), x ∈ neighbors (n) deletes:
len(<p(n),L,x>\n)≤len(<p(n),n,x>) (1)
(2), oblique line moves
Delete that rule is similar with above-mentioned straight line, but when oblique line moves, node x deleted that restrictive condition is more strict, the constraint condition in demand fulfillment formula (2):
len(<p(n),L,x>\n)<len(<p(n),n,x>) (2)
In above-mentioned two kinds of situations, in neighbors (n), all do not comprise obstacle nodes, the node now obtained after deleting is called the common adjacent node (NormalNeighbor) of node n; And when in neighbors (n) containing obstacle nodes time, there will be can not completely to the situation that non-generic adjacent node is deleted, at this moment claims these to be not special joint (Special Neighbor) by the node deleted; If a node x, x ∈ neighbors (n) is special joint, and so it should meet: node x is not the ordinary node of node n, and meets the constraint condition in formula (3);
len(<p(n),n,x>)<len(<p(n),L,x>\n) (3)
Step 3: carry out the search work to path turning point, node n is to the moving direction of node x ∈ neighbor (n)
be a key factor, turning point be defined as follows:
Node m is the descendant node of node n, and the direction of n to m is
if n arrives m this side up after minimum k unit moves, namely
and meet one of following constraint, then title node m is the turning point of node n;
1). node m is terminal;
2). have at least one to be special joint in the adjacent node of node m;
3).
when being oblique line directions, there is a node
distance m node k on direction
iindividual unit distance, and node p is the turning point meeting above-mentioned condition 1 or condition 2;
Step 4: by what obtain according to definition in step 3, the turning point belonging to node n is stored in array ProcessList, arbitrary node x ∈ ProcessList is pending node, when inserting node each time in ProcessList, all need the order keeping ProcessList, the path cost value of first node namely in ProcessList: namely minimum to the travel distance of this node from starting point, the path cost value of last node is maximum, when upper once algorithm circulation, first node taken out in ProcessList processes,
Step 5: circulation performs above-mentioned steps two to step 4, as terminal n
goalwhen being inserted in ProcessList, complete the search to optimal path, terminate; From terminal n
goalstart, successively continue before it node and father node are added in the array PathList of path, until by starting point n
startadd in PathList, all path nodes on the optimal path that being of preserving in the array PathList of path searches out, Vehicular navigation system can carry out real-time navigation work according to this optimal path to the vehicle travelled on map.
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| CN104464280B (en) * | 2014-09-05 | 2017-05-03 | 广州市香港科大霍英东研究院 | Vehicle advance expenditure prediction method and system |
| CN104717278A (en) * | 2014-12-04 | 2015-06-17 | 广西蕴藏科技有限公司 | Mark position guiding method and system |
| CN104917835A (en) * | 2015-06-10 | 2015-09-16 | 广西蕴藏科技有限公司 | Mark position guidance system of small network traffic and guidance method therefor |
| CN105606113B (en) * | 2016-01-28 | 2017-09-26 | 福州华鹰重工机械有限公司 | Quick planning optimal path method and device |
| CN105739495B (en) * | 2016-01-29 | 2019-05-14 | 大连楼兰科技股份有限公司 | Planning driving path planing method, device and automatic steering system |
| CN107272679B (en) * | 2017-06-15 | 2020-06-16 | 东南大学 | Path planning method based on improved ant colony algorithm |
| CN107092265A (en) * | 2017-06-22 | 2017-08-25 | 义乌文烁光电科技有限公司 | A kind of sorting trolley path planning method suitable for matrix form warehouse |
| CN112764413B (en) * | 2019-10-22 | 2024-01-16 | 广州中国科学院先进技术研究所 | Robot path planning method |
| CN111060127B (en) * | 2019-12-31 | 2021-10-26 | 深圳一清创新科技有限公司 | Vehicle starting point positioning method and device, computer equipment and storage medium |
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