CN108801261B - Automobile test field test path planning method based on ant colony algorithm - Google Patents

Automobile test field test path planning method based on ant colony algorithm Download PDF

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CN108801261B
CN108801261B CN201810517257.2A CN201810517257A CN108801261B CN 108801261 B CN108801261 B CN 108801261B CN 201810517257 A CN201810517257 A CN 201810517257A CN 108801261 B CN108801261 B CN 108801261B
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秦文虎
方阳
孙立博
李凡
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Abstract

The invention discloses an ant colony algorithm-based automobile test field test path planning algorithm, aiming at the characteristics of an automobile test field test, an ant colony intelligent algorithm is introduced into the test path planning, so that the test field is helped to effectively reduce the time cost and the oil consumption cost waste caused by adopting a low-efficiency redundant path for the test, the efficiency of the reliability test of a characteristic road section is improved, the overall effective utilization rate of the test field is improved, and the optimal path can be planned quickly and efficiently.

Description

Automobile test field test path planning method based on ant colony algorithm
Field of the invention
The invention relates to the field of test specification formulation of an automobile test field, in particular to an ant colony algorithm-based automobile test field test path planning method for a plurality of test road sections of an automobile test field.
Background
The automobile reliability test is an important means for checking and evaluating the durability of the automobile, and in order to ensure that the automobile components are not broken under the worst working condition, the automobile is required to complete the reliability test of not less than a set mileage on various reinforced roads. A tester determines the type and mileage of a strengthened road to be tested by a vehicle by establishing a reliability test specification, and the mileage index is represented by the passing times of each strengthened road section in the test specification because the length of the strengthened road section established in a test field is fixed.
Various reinforced road pavement characteristics are different in the automobile test field, and in order to flexibly configure various test road sections, the test field is built in various forms, some road sections are laid in parallel, and some road sections are connected with each other. And common road surface connection is built between different adjacent test road sections. The road arrangement of a test specification is mainly to determine which test road sections need to be connected together as a cycle and corresponding cycle times, and a test specification comprises dozens of small cycles. It can be seen that there are multiple test routes that meet the requirements for each test cycle in the reliability test specification. At present, a test field generally enables test drivers to plan driving routes by themselves, so that different test drivers can cause different overall test mileage for the same test cycle, and some test drivers spend a large amount of time to drive on useless contact roads, so that the oil consumption and the loss of vehicles are increased, and the normalization of the test is not facilitated.
Therefore, by combining the actual development of the current automobile test field test specification formulation field and based on the current situation of vehicle experiments in China, the method for researching the path specification of the test road section has very important application significance.
Disclosure of Invention
The invention provides an automobile test field test path planning method based on an ant colony algorithm aiming at the blank of the prior art in the field of automobile test field test path planning, and provides an optimized path according to the position information of an actual field and the specification set by a tester, namely, each characteristic road needs to pass through for several times respectively, so that the test vehicle can ensure the minimum total mileage while completing the test of the required characteristic road section, the invalid driving mileage is reduced, the efficiency of the reliability test of the characteristic road section is improved, the oil consumption and the vehicle body loss of the vehicle are reduced, and the effective utilization rate of the test field is improved.
In order to achieve the purpose, the invention adopts the technical scheme that: an automobile test field test path planning method based on an ant colony algorithm is characterized by comprising the following steps:
s1, determining the position and the length of a communication path: acquiring the relative positions of the start and stop point garage and each characteristic road section in the test field, and measuring the length of a communication road between the garage and each characteristic road section and between the garage and each characteristic road section;
s2, drawing a weighted directed graph: taking the garage of the starting point and the stopping point and the characteristic road section as nodes, taking the contact road as an edge, drawing a weighted directed graph G which corresponds to the test site, wherein N is {0,1,2,3, …, N }, E is { (i, j) | i, j belongs to N }, and the road length of the contact road is taken as the weight of the corresponding edge;
s3, generating an adjacency matrix: generating an adjacency matrix C according to the weighted directed graph obtained in the step S2, wherein the adjacency matrix is defined as the following formula;
Figure BDA0001673770680000021
wherein d isi,jWhen there is no edge (i, j), i node can not directly point to the distance weight of edge (i, j)When the node j is reached, the weight is set to-1.
S4, generating a constraint array: setting the required number of times of passing through of the corresponding node of each characteristic road section, and generating a constraint array R, wherein the length of the constraint array is equal to the number of the nodes, and the ith element is corresponding to the required number of times of passing through of the i node;
s5, setting an initialization global parameter: the method comprises the steps of parallel exploration thread number m, iteration times n, an pheromone attenuation coefficient r and an pheromone matrix T, wherein the pheromone matrix is defined as;
Figure BDA0001673770680000022
when there is an edge (i, j), the pheromone is initialized to 1.0, otherwise it is initialized to 0.0.
S6, completing each parallel exploration thread according to the behavior probability function:
s61: setting the initialization position as a starting node;
s62: selecting a next node to be reached according to the behavior probability function, and constructing the times of passing each node in the exploration thread;
s63: storing nodes which are sequentially passed by each exploration thread in a path memory vector path, and storing the times of passing each node by each exploration thread in a history time array Rant;
s64: completing each parallel exploration thread and making a record;
s7, when each parallel exploration thread finishes the requirement of the number of times of each node, namely each element in the Rant is not less than the corresponding element in the R, and returns to the starting node, the current exploration is finished; otherwise, step S6 is repeated.
And S8, when all the m threads finish the exploration, finding and recording the shortest route S in the m threads and the feasible path corresponding to the shortest route S.
S9, updating the pheromone matrix T according to the pheromone updating function;
and S10, judging whether the iteration is finished for n times, if so, stopping the iteration to obtain the optimal path, and outputting the optimal path, otherwise, turning to S6 and continuing to explore the iteration.
As a refinement of the present invention, the behavior probability function in step S62 is:
Figure BDA0001673770680000031
wherein alpha is an information elicitation factor, beta is a visibility factor, Rj(T) represents the number of times that the jth node still needs to pass at time T, Ti,j(t) represents the pheromone value at time (i, j) t.
As another improvement of the present invention, the pheromone updating function in step S9 is:
Figure BDA0001673770680000032
Figure BDA0001673770680000033
wherein r is pheromone attenuation coefficient, Q is pheromone constant, PathkThe path set of the kth thread is shown, and Sk shows the total mileage searched by the current iteration of the kth thread.
Compared with the prior art, the invention provides the automobile test field test path planning method based on the ant colony algorithm, which makes up the blank in the aspect of the automobile test field test path planning at present, and the general path planning method cannot solve the problems of inevitable nodes and the constraint of the number of times of experience, can ensure that a test vehicle completes the test of a required characteristic road section with a short total mileage, effectively reduces the time cost and the waste of oil consumption cost caused by adopting an inefficient redundant path for the test, improves the efficiency of the reliability test of the characteristic road section, and improves the overall effective utilization rate of a test field. The method is designed according to the ant colony principle, nodes with high frequency in the initial exploration period are more attractive to parallel exploration threads, the exploration probability of the exploration threads on target nodes is increased, the early search efficiency of the algorithm is improved, the exploration probability among the nodes tends to be consistent along with the completion of the access requirements of the nodes with high frequency in each exploration thread, the target solution is favorably searched in the global direction, the algorithm is effectively prevented from falling into a local solution, and the stability of the algorithm is improved.
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FIG. 1 is a flow chart of the operation of the ant colony algorithm of the present invention;
FIG. 2 is an exemplary illustration of a test site provided by the present invention.
Detailed Description
The invention will be explained in more detail below with reference to the drawings and examples.
An automobile test field test path planning method based on an ant colony algorithm is shown as fig. 1 and comprises the following steps:
s1, determining the position and the length of a communication path: acquiring the relative positions of the start and stop point garage and each characteristic road section in the test field, and measuring the length of a communication road between the garage and each characteristic road section and between the garage and each characteristic road section;
taking fig. 2 as an example, node 0 in the figure represents a garage of a start and stop point of a test vehicle, nodes 1 to 7 represent 7 characteristic road segments respectively, positions of nodes 0 to 7 are determined, and lengths of communication links of nodes 0 and 1 to 7 and lengths of communication links of nodes 1 to 7 are measured respectively, and the lengths of the communication links are specifically as follows:
Figure BDA0001673770680000041
Figure BDA0001673770680000051
s2, drawing a weighted directed graph: taking the garage of the starting point and the stopping point and the characteristic road section as nodes, taking the contact road as an edge, drawing a weighted directed graph G which corresponds to the test site, wherein N is {0,1,2,3, …, N }, E is { (i, j) | i, j belongs to N }, and the road length of the contact road is taken as the weight of the corresponding edge; as shown in fig. 2, the directional side (i, j) in the graph represents a one-way road connection road corresponding to the driving direction, and the weight value of the side represents the road length of the connection road, i.e. as shown in the table above;
s3, generating an adjacency matrix: generating an adjacency matrix C according to the weighted directed graph obtained in the step S2, wherein the adjacency matrix is defined as the following formula;
Figure BDA0001673770680000052
wherein d isi,jAnd (4) indicating the distance weight of the edge (i, j), and setting the weight as-1 when the edge (i, j) does not exist, namely the i node cannot directly reach the j node.
The adjacency matrix obtained in the example of fig. 2 is expressed by formula (1), where-1 in the matrix represents that no connection road exists between corresponding nodes, and a positive value represents the length of the connection road between corresponding nodes, for example, -1 at the (2,1) position represents that no connection road from the No. 2 characteristic road segment to the No. 1 characteristic road segment exists in the test site, and 29 at the (1,0) position in the matrix represents that the connection road from the No. 1 characteristic road segment to the No. 0 characteristic road segment with the length of 29 exists in the test site;
Figure BDA0001673770680000053
s4, generating a constraint array: setting the required number of times of passing through of the corresponding node of each characteristic road section, and generating a constraint array R, wherein the length of the constraint array is equal to the number of the nodes, and the ith element is corresponding to the required number of times of passing through of the i node;
assuming that the constraint array of this embodiment is as formula (2), in the array, 6 represents that the No. 1 road segment needs to be visited at least 6 times, the No. 2 road segment needs to be visited at least 8 times, the No. 3 road segment needs to be visited at least 2 times, the No. 4 road segment needs to be visited at least 4 times, the No. 5 road segment needs to be visited at least 9 times, the No. 6 road segment needs to be visited at least 5 times, and the No. 7 road segment needs to;
[0 6 8 2 4 9 5 3] (2)
s5, setting an initialization global parameter: the method comprises the steps of parallel exploration thread number m, iteration times n, an pheromone attenuation coefficient r and an pheromone matrix T, wherein the pheromone matrix is defined as;
Figure BDA0001673770680000061
assuming that m is 50, n is 1000, and r is 0.5 in this embodiment, in the ant colony algorithm theory, the number of exploration threads in this embodiment is the number of ants, and 50 parallel exploration threads are 50 ants performing path exploration simultaneously, which is 1000 exploration rounds in total. Initializing an pheromone matrix according to the initial adjacent matrix, wherein when edges (i, j) exist, the corresponding position of the existing contact path is set to be 1, otherwise, the corresponding position is set to be 0, and the initial pheromone matrix is obtained as shown in a formula (3);
Figure BDA0001673770680000062
s6, completing each parallel exploration thread according to the behavior probability function:
s61: setting the initialization position as a starting node;
s62: selecting a next node to be reached according to a behavior probability function, and constructing the times of passing each node in an exploration thread, wherein the behavior probability function is as follows:
Figure BDA0001673770680000063
wherein alpha is an information elicitation factor used for setting the importance degree of pheromone; beta is a visibility factor and is used for setting the importance degree of the distance between the nodes; rj(t) represents the number of times that the jth node still needs to be traversed at time t, so as to ensure that the node is accessed more frequently; t isi,j(t) represents the pheromone value at time (i, j) t. In this example, α is 1.0 and β is 2.0.
S63: storing nodes which are sequentially passed by each exploration thread in a path memory vector path, and storing the times of passing each node by each exploration thread in a history time array Rant;
s64: completing each parallel exploration thread and making a record;
s7, when each parallel exploration thread finishes the requirement of the number of times of each node, namely each element in the Rant is not less than the corresponding element in the R, and returns to the starting node, the current exploration is finished; otherwise, step S6 is repeated.
And S8, when all the m threads finish the exploration, finding and recording the shortest route S in the m threads and the feasible path corresponding to the shortest route S.
S9, pheromone matrix updating: the pheromone matrix T is updated as follows pheromone update function,
Figure BDA0001673770680000071
Figure BDA0001673770680000072
wherein r is a pheromone attenuation coefficient and is used for setting a pheromone attenuation rate; q is a pheromone constant used for setting a pheromone concentration level; pathkThe path set representing the kth ant, namely the ordered set of the edges passed by the kth ant; skAnd the total mileage explored by the kth ant in the iteration is shown. The updating of the pheromone demonstration can better serve the step S6, and the probability function is more accurate;
s10, determining whether or not to complete the iteration of this embodiment n-1000 times, if so, stopping the iteration to obtain the best path, and outputting the best path, otherwise, going to S6, and continuing to search the iteration.
The final path of the present example is 2062.0, and the planned path is as follows:
0-1-2-3-4-5-1-2-3-4-5-1-6-4-5-1-7-4-5-1-7-4-5-1-2-3-4-5-1-6-4-5-1-6-4-5-1-6-4-5-1-2-3-4-5-1-2-3-4-5-1-2-3-4-5-1-2-3-4-5-1-7-4-5-1-2-3-4-5-1-6-4-0
the foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited by the foregoing examples, which are provided to illustrate the principles of the invention, and that various changes and modifications may be made without departing from the spirit and scope of the invention, which is also intended to be covered by the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (1)

1. An automobile test field test path planning method based on an ant colony algorithm is characterized by comprising the following steps:
s1, determining the position and the length of a communication path: acquiring the relative positions of the start and stop point garage and each characteristic road section in the test field, and measuring the length of a communication road between the garage and each characteristic road section and between the garage and each characteristic road section;
s2, drawing a weighted directed graph: taking the garage of the starting point and the stopping point and the characteristic road section as nodes, taking the contact road as an edge, drawing a weighted directed graph G which corresponds to the test site, wherein N is {0,1,2,3, …, N }, E is { (i, j) | i, j belongs to N }, and the road length of the contact road is taken as the weight of the corresponding edge;
s3, generating an adjacency matrix: generating an adjacency matrix C according to the weighted directed graph obtained in the step S2, wherein the adjacency matrix is defined as the following formula;
Figure FDA0002974040640000011
wherein d isi,jThe distance weight of the edge (i, j) is set to be-1 when the edge (i, j) does not exist, namely the node i cannot directly reach the node j;
s4, generating a constraint array: setting the required number of times of passing through of the corresponding node of each characteristic road section, and generating a constraint array R, wherein the length of the constraint array is equal to the number of the nodes, and the ith element is corresponding to the required number of times of passing through of the i node;
s5, setting an initialization global parameter: the method comprises the steps of parallel exploration thread number m, iteration times n, an pheromone attenuation coefficient r and an pheromone matrix T, wherein the pheromone matrix is defined as;
Figure FDA0002974040640000012
when there is an edge (i, j), the pheromone is initialized to 1.0, otherwise, the pheromone is initialized to 0.0;
s6, completing each parallel exploration thread according to the behavior probability function:
s61: setting the initialization position as a starting node;
s62: selecting a next node to be reached according to a behavior probability function, and constructing the times of passing each node in an exploration thread, wherein the behavior probability function is as follows:
Figure FDA0002974040640000013
wherein alpha is an information elicitation factor, beta is a visibility factor, Rj(T) represents the number of times that the jth node still needs to pass at time T, Ti,j(t) indicates the pheromone value at time (i, j) t;
s63: storing nodes which are sequentially passed by each exploration thread in a path memory vector path, and storing the times of passing each node by each exploration thread in a history time array Rant;
s64: completing each parallel exploration thread and making a record;
s7, when each parallel exploration thread finishes the requirement of the number of times of each node, namely each element in the Rant is not less than the corresponding element in the R, and returns to the starting node, the current exploration is finished; otherwise, repeating step S6;
s8, when all m threads finish exploring, finding and recording the shortest route S in the m threads and the feasible path corresponding to the shortest route S;
s9, updating the pheromone matrix T according to a pheromone updating function, wherein the pheromone updating function is as follows:
Figure FDA0002974040640000021
Figure FDA0002974040640000022
wherein r is pheromone attenuation coefficient, Q is pheromone constant, PathkA path set representing the k-th thread, Sk representing the k-th threadThe total mileage explored in the iteration;
and S10, judging whether the iteration is finished for n times, if so, stopping the iteration to obtain the optimal path, and outputting the optimal path, otherwise, turning to S6 and continuing to explore the iteration.
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CN109489667A (en) * 2018-11-16 2019-03-19 楚天智能机器人（长沙）有限公司 A kind of improvement ant colony paths planning method based on weight matrix
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102289712A (en) * 2011-08-10 2011-12-21 天津商业大学 Method for optimizing minimum emergency logistic path based on fish-ant colony algorithm
CN105760954A (en) * 2016-02-15 2016-07-13 南通大学 Parking system path planning method based on improved ant colony algorithm
CN106225788A (en) * 2016-08-16 2016-12-14 上海理工大学 The robot path planning method of ant group algorithm is expanded based on path
CN107883973A (en) * 2016-09-29 2018-04-06 奥迪股份公司 Method for selecting route for emission test
CN107992051A (en) * 2017-12-26 2018-05-04 江南大学 Unmanned vehicle paths planning method based on improved multi-objective particle swarm algorithm

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
SG119169A1 (en) * 2003-01-20 2006-02-28 Nanyang Polytechnic Path searching system using multiple groups of cooperating agents and method thereof

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102289712A (en) * 2011-08-10 2011-12-21 天津商业大学 Method for optimizing minimum emergency logistic path based on fish-ant colony algorithm
CN105760954A (en) * 2016-02-15 2016-07-13 南通大学 Parking system path planning method based on improved ant colony algorithm
CN106225788A (en) * 2016-08-16 2016-12-14 上海理工大学 The robot path planning method of ant group algorithm is expanded based on path
CN107883973A (en) * 2016-09-29 2018-04-06 奥迪股份公司 Method for selecting route for emission test
CN107992051A (en) * 2017-12-26 2018-05-04 江南大学 Unmanned vehicle paths planning method based on improved multi-objective particle swarm algorithm

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
"蚁群算法在整车耐久试验中的应用研究";崔辰;《上海汽车》;20170331;第26-29页 *

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