CN109213157A - Data center's crusing robot paths planning method based on improved Ant Colony System - Google Patents
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Abstract
The invention discloses data center's crusing robot paths planning methods based on improved Ant Colony System, comprising the following steps: S1, initialization information, while reading shortest distance matrix and arest neighbors matrix between the whole station all the points kept in advance；S2, it is numbered according to current task point, obtains corresponding shortest distance matrix.The present invention can be when task counts more, the time of planning path controlled within one second, duplicate paths significantly reduce, total path is obviously shortened, it can independently be determined according to the performance of patrol task after meeting barrier simultaneously and plan new polling path again, in addition the algorithm has preferable robustness, invention significantly improves the routing inspection efficiencies of data center's laser navigation crusing robot in a word, crusing robot can be made to complete task to be inspected with the shorter time, while improving the degree of intelligence and independence of crusing robot.
Description
Technical field
The present invention relates to crusing robot technical fields, more particularly to data center's inspection based on improved Ant Colony System
Robot path planning method.
Background technique
Data center replaces manually carrying out inspection to the instrumentation in data center using crusing robot at present, with
The problems such as overcoming the high heavy workload faced in manual inspection, danger coefficient, low efficiency and poor reliability.Wherein path planning is
Can crusing robot efficiently complete the basis of patrol task, while affect the intelligence degree and performance of crusing robot.
The local paths planning and global path planning of the crusing robot of data center's laser navigation at present all use dijkstra's algorithm,
This method is only being considered locally the optimal of path, but does not consider that path is optimal from the overall situation, and it is more that there are duplicate paths, always
The longer disadvantage of polling path, while encountering after obstacle regardless of whether patrol task terminates just to stop inspection, lack independence, nothing
Method fully demonstrates the intelligence of crusing robot.Data center's crusing robot also has using dijkstra's algorithm regional planning agency simultaneously
Global path is planned in portion path, simulated annealing, and this method is at task points fewer (being lower than 100), the consumption of planning
When and path length all relatively rationally, but when task point increases, this method is not suitable for large-scale inspection just than relatively time-consuming
Task.
In this field, it is complete that a kind of crusing robot based on topology point classification is introduced in Chinese 201510290471.5th invention
Office's paths planning method carries out classification merging according to said path by the topology point of anchor point, establishes the oriented of corresponding topology point
Oriented diagram data knot is then only inserted into path planning later in robot current location and target position topology point by data structure
Structure can significantly reduce and participate in calculating method topology point quantity, improve the computational efficiency of path planning and reduction disappears to memory space
Consumption, still, the path planning algorithm currently based on laser navigation crusing robot are the bases that relationship is digraph between topology point
It is planned on plinth, whether it is more that there are duplicate paths, and total path is longer, and terminate just to stop at once regardless of patrol task after meeting barrier
Only original place no longer inspection, cannot according to the executive condition of patrol task it is autonomous carry out judge and plan new polling path, and
And at present in the path planning algorithm scheme of some data center's crusing robots, when task point quantity is more, path rule
It draws just than relatively time-consuming, in addition, there are no realize that robot is met to hinder in the paths planning method of data center's crusing robot at present
Decide whether that there is also the task of non-inspection points according to the inspection situation of task afterwards, and its path is planned again, to guarantee to patrol
The satisfactory completion of inspection task, for this purpose, we have proposed data center's crusing robot path rule based on improved Ant Colony System
The method of drawing solves the above problems.
Summary of the invention
The purpose of the present invention is to solve disadvantage existing in the prior art, and propose based on improved Ant Colony System
Data center's crusing robot paths planning method.
To achieve the goals above, present invention employs following technical solutions:
Data center's crusing robot paths planning method based on improved Ant Colony System, comprising the following steps:
S1, initialization information, while reading shortest distance matrix and arest neighbors square between the whole station all the points kept in advance
Battle array；
S2, it is numbered according to current task point, obtains corresponding shortest distance matrix；
S3, improved ant group algorithm planning path, acquisition task point sequence are utilized；
S4, in conjunction with arest neighbors matrix, task point sequence is refined, with guarantee crusing robot can according to path into
The correct inspection of row, completes patrol task；
During S5, robot carry out inspection according to the path sequence of planning, the moment is detected whether with the presence of obstacle；Such as
Fruit does not encounter barrier, executes step 12；If there is barrier, step 6 is executed；
S6, judge whether that there are also the task points not detected, if so, thening follow the steps 7；If not, thening follow the steps 13；
S7, the chance barrier position that robot is presently in is obtained, is accurately updated between whole station all the points according to barrier position is met
Topological structure, while the closest point at robot chance barrier is obtained as new starting point and remaining not detecting for task
Point；
S8, judge whether isolated point occur, that is, after there is obstacle, if the task that no feasible path can reach occur
Point, if so, executing step 9；If not, executing step 10；
S9, the isolated point occurred is rejected, updates cartographic information, obtained the topological matrix between remaining all the points, then execute
Step 11；
S10, cartographic information is updated, obtains the topological matrix between whole station all the points, then execute step 11；
S11, newest distance matrix and arest neighbors matrix are obtained, then executes step 2；
S12, judge whether that inspection terminates, if inspection terminates, then follow the steps 13；If the not described end of inspection,
Execute step 5；
S13, end, robot stop inspection, wait and instructing in next step.
Preferably, whole station all the points are to rely on point and task point in described S1, S7, S9 and S10.
Preferably, in the S3, improved ant group algorithm is that ant group algorithm is combined with neighborhood search.
Preferably, in the S1, shortest path matrix and topological matrix between whole station all the points are main symmetrical matrix.
Preferably, in the S11, main symmetrical matrix uses dijkstra's algorithm operation.
Preferably, in the S1, task point quantity is 800-1500.
Preferably, in the S1, the initialization information includes starting point, terminal, task point quantity and task point number.
In the present invention, first by initialization information, i.e. starting point, terminal, task point quantity, task point number, read simultaneously
Shortest distance matrix and arest neighbors matrix between the whole station all the points kept in advance are numbered according to current task point, obtain phase
Corresponding shortest distance matrix recycles improved ant group algorithm planning path, task point sequence is obtained, in conjunction with arest neighbors square
Battle array, refines task point sequence, to guarantee that crusing robot can carry out correct inspection according to path, completes inspection and appoints
Business is detected during then robot carries out inspection according to the path sequence of planning using the self-contained sensor moment
Whether with the presence of obstacle, if not encountering barrier, step 13 is executed；If there is barrier, step 6 is executed, is then judged
Whether there are also the task points not detected, if so, thening follow the steps 7；If not, thening follow the steps 14, it is current to obtain robot
Locating chance hinders position, accurately updates the topological structure between whole station all the points according to barrier position is met, while obtaining robot chance
A closest point and the remaining task point not detected at barrier, judge whether isolated point occur, that is, after there is obstacle, are
The no task point that no feasible path occurs and can reach, if so, executing step 9；If not, executing step 10, then pick
Except the isolated point of appearance, cartographic information is updated, the topological matrix between remaining all the points is obtained, then executes step 12, then update
Cartographic information obtains the topological matrix between all the points, then executes step 12, is obtained with dijkstra's algorithm newest apart from square
Then battle array and arest neighbors matrix execute step 2, judge whether that inspection terminates, if inspection terminates, then follow the steps 14；If
Inspection is not finished, and thens follow the steps 5, finally terminates, and robot stops inspection, waits and instructing in next step, the present invention can be in office
When business points are more, the time of planning path was controlled within one second, and duplicate paths significantly reduce, and total path is obviously shortened,
It can independently be determined according to the performance of patrol task after meeting barrier simultaneously and plan new polling path again, the in addition calculation
Method has preferable robustness, in a word invention significantly improves the routing inspection efficiency of data center's laser navigation crusing robot,
Crusing robot can be made to complete task to be inspected with the shorter time, at the same improve crusing robot degree of intelligence and from
Main property.
Detailed description of the invention
Fig. 1 is the solution process of data center's crusing robot paths planning method based on improved Ant Colony System
Figure.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.
Referring to Fig.1, data center's crusing robot paths planning method based on improved Ant Colony System, including following step
It is rapid:
S1, initialization information, i.e. starting point, terminal, task point quantity, task point number, while reading keep complete in advance
Shortest distance matrix and arest neighbors matrix between all the points of standing, whole station all the points are by point and task point, whole station all the points
Between shortest path matrix and topological matrix be main symmetrical matrix, task point quantity is 800-1500, the premise of the step
The characteristics of being road information in combined data center, anchor point, task point and laser navigation mode, using topological structure logarithm
Mathematical modeling is carried out according to center environment map, by all anchor points and the undirected topological structure of task point in data center
Indicate the relationship between them, the distance between two points of direct neighbor are the actual range in scene；
S2, it is numbered according to current task point, obtains corresponding shortest distance matrix；
S3, using improved ant group algorithm planning path, obtain task point sequence, improved ant group algorithm is most
Neighborhood search is added on the basis of basic ant group algorithm, efficiency of algorithm can be greatly improved by adding neighborhood search strategy,
Allow to the large-scale task points of faster speed planning, to break through merely by the not applicable of original ant group algorithm
In the limitation of extensive task points, while it can solve the path planning of current data center's laser navigation crusing robot
The problem of algorithm；
S4, in conjunction with arest neighbors matrix, task point sequence is refined, with guarantee crusing robot can according to path into
The correct inspection of row, completes patrol task；
During S5, robot carry out inspection according to the path sequence of planning, the moment is detected whether with the presence of obstacle；Such as
Fruit does not encounter barrier, executes step 12；If there is barrier, step 6 is executed；
S6, judge whether that there are also the task points not detected, if so, thening follow the steps 7；If not, thening follow the steps 13；
S7, the chance barrier position that robot is presently in is obtained, is accurately updated between whole station all the points according to barrier position is met
Topological structure, while the closest point at robot chance barrier is obtained as new starting point and remaining not detecting for task
Point；
S8, judge whether isolated point occur, that is, after there is obstacle, if the task that no feasible path can reach occur
Point, if so, executing step 9；If not, executing step 10；
S9, the isolated point occurred is rejected, updates cartographic information, obtained the topological matrix between remaining all the points, then execute
Step 11；
S10, cartographic information is updated, obtains the topological matrix between whole station all the points, then execute step 11；
S11, newest distance matrix and arest neighbors matrix are obtained, then executes step 2, obtained main symmetrical matrix and use
Dijkstra's algorithm operation；
S12, judge whether that inspection terminates, if inspection terminates, then follow the steps 13；If the not described end of inspection,
Execute step 5；
S13, end, robot stop inspection, wait and instructing in next step.
In the present invention, first by initialization information, i.e. starting point, terminal, task point quantity, task point number, read simultaneously
Shortest distance matrix and arest neighbors matrix between the whole station all the points kept in advance are numbered according to current task point, obtain phase
Corresponding shortest distance matrix recycles improved ant group algorithm planning path, task point sequence is obtained, in conjunction with arest neighbors square
Battle array, refines task point sequence, to guarantee that crusing robot can carry out correct inspection according to path, completes inspection and appoints
Business is detected during then robot carries out inspection according to the path sequence of planning using the self-contained sensor moment
Whether with the presence of obstacle, if not encountering barrier, step 13 is executed；If there is barrier, step 6 is executed, is then judged
Whether there are also the task points not detected, if so, thening follow the steps 7；If not, thening follow the steps 14, it is current to obtain robot
Locating chance hinders position, accurately updates the topological structure between whole station all the points according to barrier position is met, while obtaining robot chance
A closest point and the remaining task point not detected at barrier, judge whether isolated point occur, that is, after there is obstacle, are
The no task point that no feasible path occurs and can reach, if so, executing step 9；If not, executing step 10, then pick
Except the isolated point of appearance, cartographic information is updated, the topological matrix between remaining all the points is obtained, then executes step 12, then update
Cartographic information obtains the topological matrix between all the points, then executes step 12, is obtained with dijkstra's algorithm newest apart from square
Then battle array and arest neighbors matrix execute step 2, judge whether that inspection terminates, if inspection terminates, then follow the steps 14；If
Inspection is not finished, and thens follow the steps 5, finally terminates, and robot stops inspection, waits and instructing in next step.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
Anyone skilled in the art in the technical scope disclosed by the present invention, according to the technique and scheme of the present invention and its
Inventive concept is subject to equivalent substitution or change, should be covered by the protection scope of the present invention.
Claims (7)
1. data center's crusing robot paths planning method based on improved Ant Colony System, which is characterized in that including following
Step:
S1, initialization information, while reading shortest distance matrix and arest neighbors matrix between the whole station all the points kept in advance；
S2, it is numbered according to current task point, obtains corresponding shortest distance matrix；
S3, improved ant group algorithm planning path, acquisition task point sequence are utilized；
S4, in conjunction with arest neighbors matrix, task point sequence is refined, with guarantee crusing robot can according to path carry out just
Patrol task is completed in true inspection；
During S5, robot carry out inspection according to the path sequence of planning, the moment is detected whether with the presence of obstacle；If not yet
Have and encounter barrier, executes step 12；If there is barrier, step 6 is executed；
S6, judge whether that there are also the task points not detected, if so, thening follow the steps 7；If not, thening follow the steps 13；
S7, the chance barrier position that robot is presently in is obtained, accurately updates the topology between whole station all the points according to barrier position is met
Structure, while the closest point at robot chance barrier is obtained as new starting point and the remaining task point not detected；
S8, judge whether isolated point occur, that is, after there is obstacle, if there is the task point that no feasible path can reach,
If so, executing step 9；If not, executing step 10；
S9, the isolated point occurred is rejected, updates cartographic information, obtained the topological matrix between remaining all the points, then execute step
11；
S10, cartographic information is updated, obtains the topological matrix between whole station all the points, then execute step 11；
S11, newest distance matrix and arest neighbors matrix are obtained, then executes step 2；
S12, judge whether that inspection terminates, if inspection terminates, then follow the steps 13；If the not described end of inspection, executes
Step 5；
S13, end, robot stop inspection, wait and instructing in next step.
2. data center's crusing robot paths planning method according to claim 1 based on improved Ant Colony System,
It is characterized in that, whole station all the points are by point and task point in described S1, S7, S9 and S10.
3. data center's crusing robot paths planning method according to claim 1 based on improved Ant Colony System,
It is characterized in that, improved ant group algorithm is that ant group algorithm is combined with neighborhood search in the S3.
4. data center's crusing robot paths planning method according to claim 1 based on improved Ant Colony System,
It is characterized in that, shortest path matrix and topological matrix between whole station all the points are main symmetrical matrix in the S1.
5. data center's crusing robot paths planning method according to claim 4 based on improved Ant Colony System,
It is characterized in that, the main symmetrical matrix uses dijkstra's algorithm operation.
6. data center's crusing robot paths planning method according to claim 1 based on improved Ant Colony System,
It is characterized in that, task point quantity is 800-1500 in the S1.
7. data center's crusing robot paths planning method according to claim 1 based on improved Ant Colony System,
It is characterized in that, the initialization information includes starting point, terminal, task point quantity and task point number in the S1.
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| CN110159935A (en) * | 2019-06-14 | 2019-08-23 | 哈尔滨工业大学 | Sampler, detection host, pipeline leak detection system and its leak detection method |
| CN110221608A (en) * | 2019-05-23 | 2019-09-10 | 中国银联股份有限公司 | A kind of method and device of inspection device |
| CN110826818A (en) * | 2019-11-21 | 2020-02-21 | 中冶华天工程技术有限公司 | Method for carrying out inspection task planning and path design on multiple sites by multiple inspectors |
| CN110928295A (en) * | 2019-10-16 | 2020-03-27 | 重庆邮电大学 | Robot path planning method integrating artificial potential field and logarithmic ant colony algorithm |
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| CN112817310A (en) * | 2020-12-30 | 2021-05-18 | 广东电网有限责任公司电力科学研究院 | Method and device for making substation inspection strategy |
| CN113485368A (en) * | 2021-08-09 | 2021-10-08 | 国电南瑞科技股份有限公司 | Navigation and line patrol method and device for line patrol robot of overhead transmission line |
| CN113485368B (en) * | 2021-08-09 | 2024-06-07 | 国电南瑞科技股份有限公司 | Navigation and line inspection method and device for overhead transmission line inspection robot |
| CN114326825A (en) * | 2021-11-09 | 2022-04-12 | 国网辽宁省电力有限公司铁岭供电公司 | Unmanned aerial vehicle routing inspection path planning and defect analysis cloud platform for power transmission line |
| CN115358681A (en) * | 2022-10-19 | 2022-11-18 | 睿羿科技(山东)有限公司 | Indoor multi-task point path planning method under static barrier |
| CN115560767B (en) * | 2022-12-01 | 2023-03-10 | 深圳市智绘科技有限公司 | Robot path generation method and device, storage medium and electronic device |
| CN115560767A (en) * | 2022-12-01 | 2023-01-03 | 深圳市智绘科技有限公司 | Robot path generation method and device, storage medium and electronic device |
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