CN113847926A - Real-time path planning method based on edge micro-service cooperation - Google Patents
Real-time path planning method based on edge micro-service cooperation Download PDFInfo
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- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
Abstract
The invention discloses a real-time path planning method based on edge micro-service cooperation, which comprises the following steps: s1, dividing the edge micro service nodes of the urban area, and coding according to the longitude and latitude addresses; s2, judging the real-time path situation at the moment according to the pheromone recorded in the edge base station server, and making real-time path planning based on an improved ant colony algorithm, wherein the path planning refers to determining the direction of the next hop of regional edge service node; s3, when an obstacle is encountered, detecting a regional target and analyzing time; s4, finding the path direction of the edge micro server node making the decision most quickly through the analysis feedback of the edge micro service time; and performing area continuation, returning to the first step of iterative computation, and finally reaching the destination to finish the real-time path planning. The method finds the optimal driving path of the automatic driving vehicle by using the improved ant colony algorithm in the edge computing environment, so that the driving time of the vehicle is reduced, and the occupied edge computing resources can be reduced to the minimum.
Description
Technical Field
The invention relates to the field of edge calculation of smart cities, in particular to a real-time path planning method based on edge micro-service cooperation.
Background
The existing path planning using the ant colony algorithm mostly adopts a centralized processing mode in a remote cloud service center, and the optimal path is determined according to the amount of vehicle information stored in a cloud data center. Most of the existing research on micro-service scheduling cooperation only relates to a relatively fixed wireless network and a situation that a service request is sent to a remote center cloud, and dynamic availability resources and service requests to edge computing nodes caused by vehicle movement are rarely considered.
Disclosure of Invention
In view of the defects in the prior art, the invention designs an automatic driving (unmanned vehicle) optimal path planning method with minimum transmission and calculation time cost and real-time congestion avoidance by combining an ant colony algorithm and a Geohash coding method and according to the short-time driving information of the edge service node, and simultaneously, the vehicle completes the real-time obstacle avoidance function by means of the rapid calculation capability of the edge micro service node, and finally completes the automatic driving vehicle to reach the destination by the cooperation of the edge micro service node with the minimum time cost.
The technical purpose of the invention is realized by the following technical scheme:
in order to achieve the above object, the present invention provides a real-time path planning method based on edge micro-service cooperation, which includes:
s1, dividing the edge micro service nodes of the urban area, and coding according to the longitude and latitude addresses;
s2, judging the real-time path situation at the moment according to the pheromone recorded in the edge base station server, and making real-time path planning based on an improved ant colony algorithm, wherein the path planning refers to determining the direction of the next hop of regional edge service node;
s3, when an obstacle is encountered, detecting a regional target and analyzing time;
s4, finding the path direction of the edge micro server node making the decision most quickly through the analysis feedback of the edge micro service time; and performing area continuation, returning to the first step of iterative computation, and finally reaching the destination to finish the real-time path planning.
The invention is further improved in that: in step S1, the Geohash coding method is used to perform area coding on the edge micro service nodes, select candidate area edge micro service nodes closer to the vehicle, perform coding using longitude and latitude, and mark the same edge micro service.
The invention is further improved in that: in step S2, the situation of the pheromone at this time is analyzed according to the vehicle driving information of the base station microservice, and the vehicle driving direction is preliminarily obtained according to the situation of the pheromone.
The invention is further improved in that: when an obstacle is encountered during driving in step S3, target object detection is performed by using techniques such as opencv and yolo, and information such as an object image and a position is uploaded to the edge micro server, and the edge micro server performs information processing to feed back the size and shape of the obstacle, whether the obstacle is reached or not, and the obstacle avoidance direction to the vehicle.
The invention is further improved in that: in step S4, according to the edge micro-service feedback result calculated at the minimum cost, the edge micro-service node direction that is fed back first is the optimal path direction, that is, the optimal path with the strongest real-time performance and the minimum short-time vehicle congestion degree is obtained.
The beneficial technical effects of the invention are as follows: the method finds the optimal driving path of the automatic driving vehicle by using the improved ant colony algorithm in the edge computing environment, so that the driving time of the vehicle is reduced, the occupied edge computing resources can be reduced to the minimum, the edge micro-service processing method well solves the problems of low instantaneity and high transmission bandwidth consumption caused by the fact that information resources are transmitted to a remote cloud end at present, and the path planning method in the edge computing environment is the least in cost.
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FIG. 1 is a diagram of a deployed device location;
FIG. 2 is a position diagram of the target vehicle traveling through the edge-coordinated path;
FIG. 3 is a position diagram of the target vehicle after edge cooperation;
FIG. 4 is a position diagram of the target vehicle traveling through the edge-coordinated path;
FIG. 5 is a detail of the D4 area driving;
fig. 6 is a route travel diagram after the target vehicle has passed all the edge cooperation.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
The method realizes automatic driving (unmanned vehicle driving) real-time path planning through micro-service cooperation on the edge nodes. The micro-service is deployed at the edge base station node and used for searching and calculating the optimal path in real time. The invention discloses a real-time path planning method based on edge micro-service cooperation, which comprises the following steps:
s1, dividing the edge micro service nodes of the urban area, and coding according to the longitude and latitude addresses;
s2, judging the real-time path situation at the moment according to the pheromone recorded in the edge base station server, and making real-time path planning based on an improved ant colony algorithm, wherein the path planning refers to determining the direction of the next hop of regional edge service node;
s3, when an obstacle is encountered, detecting a regional target and analyzing time;
s4, finding the path direction of the edge micro server node making the decision most quickly through the analysis feedback of the edge micro service time; and performing area continuation, returning to the first step of iterative computation, and finally reaching the destination to finish the real-time path planning.
Specifically, as shown in fig. 1, in step S1, the Geohash coding method is used to perform area coding on the edge micro service nodes, select candidate area edge micro service nodes closer to the vehicle, perform coding by using longitude and latitude, and mark the same edge micro service.
Specifically, as shown in fig. 2, 3 and 4, the condition of the pheromone at this time is analyzed by the vehicle running information of the base station microservice in step S2, and the vehicle running direction is preliminarily obtained according to the condition of the pheromone.
Specifically, as shown in fig. 5, when an obstacle is encountered during the driving process in step S3, the target object is detected by using techniques such as opencv and yolo, and information such as an object image and a position is uploaded to the edge micro server, and the edge micro server performs information processing to feed back the size and shape of the obstacle, whether the obstacle is reached or not, and the obstacle avoidance direction to the vehicle.
Specifically, as shown in fig. 6, according to the edge micro-service feedback result calculated at the minimum cost in step S4, the edge micro-service node direction that makes feedback first is the optimal path direction, that is, the optimal path with the strongest real-time performance and the minimum short-time vehicle congestion degree is obtained.
In order to make the automatic driving vehicle path planning method have diversity and real-time performance, a method of edge micro-service cooperation is used. The technical scheme adopts an ant colony improvement algorithm and a Geohash method, and real-time adjustment and data information processing are carried out in the vehicle driving process. The automatic driving vehicle carries out path planning and selection in real time in the moving process, and the path decision is quicker and more accurate. The Geohash algorithm converts the geo-location code into a short string of letters and numbers. According to the algorithm principle, the longitude and the latitude are respectively halved and coded, continuous coding is carried out according to the region to which the longitude and the latitude belong, then the two groups of codes are mixed and subjected to Base32 coding, and finally the required Geohash code is generated. The specific process is as follows: (1) and respectively coding the longitude and the latitude to obtain a digital string (2), and recombining the longitude code degrees of the even-numbered positions and the latitude code degrees of the odd-numbered positions to obtain a new digital string from the 0 th position. (3) The regions are encoded in alphabetical plus numeric form. When in coding, a symbol (such as a vertical coordinate A, B, C, D, and horizontal coordinates 1, 2, 3 and 4) is added in front of the edge node of the same area, and a path in the edge micro-service cooperation direction with high pheromone is found in the edge node of the same area firstly, so that the edge node does not need to be crossed, and certain delay crossing the edge node is reduced; and if the pheromones of the edge nodes in the same region are all low, finding the edge micro-service node with the closest distance and high pheromone across the coding region. The method mainly considers the condition that the vehicle running path decision in the same area is optimal, when the vehicle needs to run across areas, path connection is carried out between different areas, and algorithm is used for carrying out loop iteration calculation until the vehicle reaches the destination. The following formula (1) is a calculation formula of the ant colony improvement algorithm, wherein time-cost heuristic information calculation is added.
Wherein:
τxy(t+1)＝(1-p)*τxy(t)+Δτxy(t)；
wherein tau isx,y(t) pheromone concentration of en-route (x, y) nodes, x being current edge micro-service, y being next edge micro-service,. etaxyIs a heuristic of timeThe formula information is the reciprocal of the minimum time. a and beta are respectively taux,y(t)，ηxyThe weight parameter of (2). A new selection update is then made and the process is repeated until the end point. p is the pheromone volatility number.
Pheromone increment of the kth ant at the (x, y) node. Q represents the absolute amount of information released by ants at one time, LkThe total path length of the kth ant for one turn. Delta taux,y(t) represents pheromone quantity of m ants at node (x, y).
The ant colony algorithm adopted by the invention abstracts the problems through natural ants, and takes the micro-services deployed on the edge nodes as ant colonies consisting of ant individuals. The vehicle data information stored on each edge micro server is an pheromone. The vehicle runs to the edge micro-service direction with the nearest high coding pheromone so as to avoid congestion and obstacle avoidance in real time. The starting nodes on the optimal path are the least, so that the edge micro-service nodes have the characteristics of real-time and quick response, less resource occupation, less energy consumption, good user experience and the like. The information processed before is processed in a remote cloud centralized mode, and the current smart city application requirement with high real-time performance is difficult to meet, so that the scheme adopts edge base station micro-service to process the information uploaded by the vehicle and timely makes feedback, and the time cost is minimum, so that the route planning of the automatic driving vehicle is high in real-time performance, and the route decision is optimal. In the existing situation that most of the research on micro-service scheduling cooperation only relates to a relatively fixed wireless network and service requests are sent to the cloud end of a remote center, the dynamically changed availability resources and the service requests to edge computing nodes caused by vehicle movement are rarely considered. However, according to the scheme, the candidate micro-service nodes are selected in the vehicle moving area, then the decision is made while driving, the optimized path with the minimum congestion is dynamically planned, if emergency conditions such as obstacles are met, obstacle avoidance reaction can be carried out in real time, and the vehicle reaches the final destination through micro-service loop iterative calculation in different areas.
The method finds the optimal driving path of the automatic driving vehicle by using the improved ant colony algorithm in the edge computing environment, so that the driving time of the vehicle is reduced, the occupied edge computing resources can be reduced to the minimum, the edge micro-service processing method well solves the problems of low instantaneity and high transmission bandwidth consumption caused by the fact that information resources are transmitted to a remote cloud end at present, and the path planning method in the edge computing environment is the least in cost.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (5)
1. A real-time path planning method based on edge micro-service cooperation is characterized by comprising the following steps:
s1, dividing the edge micro service nodes of the urban area, and coding according to the longitude and latitude addresses;
s2, judging the real-time path situation at the moment according to the pheromone recorded in the edge base station server, and making real-time path planning based on an improved ant colony algorithm, wherein the path planning refers to determining the direction of the next hop of regional edge service node;
s3, when an obstacle is encountered, detecting a regional target and analyzing time;
s4, finding the path direction of the edge micro server node making the decision most quickly through the analysis feedback of the edge micro service time; and performing area continuation, returning to the first step of iterative computation, and finally reaching the destination to finish the real-time path planning.
2. The real-time path planning method based on edge micro-service collaboration as claimed in claim 1, wherein: in step S1, the Geohash coding method is used to perform area coding on the edge micro service nodes, select candidate area edge micro service nodes closer to the vehicle, perform coding using longitude and latitude, and mark the same edge micro service.
3. The real-time path planning method based on edge micro-service collaboration as claimed in claim 1, wherein: in step S2, the situation of the pheromone at this time is analyzed according to the vehicle driving information of the base station microservice, and the vehicle driving direction is preliminarily obtained according to the situation of the pheromone.
4. The real-time path planning method based on edge micro-service collaboration as claimed in claim 1, wherein: when an obstacle is encountered during the driving in step S3, the target object detection is performed, and information such as an object image and a position is uploaded to the edge micro server, and the edge micro server performs information processing to feed back the size, shape, whether the obstacle is reached or not, and the obstacle avoidance direction to the vehicle.
5. The real-time path planning method based on edge micro-service collaboration as claimed in claim 1, wherein: in step S4, according to the edge micro-service feedback result calculated at the minimum cost, the edge micro-service node direction that is fed back first is the optimal path direction, that is, the optimal path with the strongest real-time performance and the minimum short-time vehicle congestion degree is obtained.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116678432A (en) * | 2023-08-04 | 2023-09-01 | 江西师范大学 | Vehicle real-time path planning method based on edge service |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111289007A (en) * | 2020-03-23 | 2020-06-16 | 南京理工大学 | Parking AGV path planning method based on improved ant colony algorithm |
| DE102018009904A1 (en) * | 2018-12-20 | 2020-06-25 | Volkswagen Aktiengesellschaft | Procedure for navigating a vehicle |
| CN111552313A (en) * | 2020-04-29 | 2020-08-18 | 南京理工大学 | Multi-unmanned aerial vehicle path planning method based on edge calculation dynamic task arrival |
| CN111694636A (en) * | 2020-05-11 | 2020-09-22 | 国网江苏省电力有限公司南京供电分公司 | Electric power Internet of things container migration method oriented to edge network load balancing |
| CN113391647A (en) * | 2021-07-20 | 2021-09-14 | 中国人民解放军国防科技大学 | Multi-unmanned aerial vehicle edge computing service deployment and scheduling method and system |
-
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- 2021-09-18 CN CN202111098455.8A patent/CN113847926B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102018009904A1 (en) * | 2018-12-20 | 2020-06-25 | Volkswagen Aktiengesellschaft | Procedure for navigating a vehicle |
| CN111289007A (en) * | 2020-03-23 | 2020-06-16 | 南京理工大学 | Parking AGV path planning method based on improved ant colony algorithm |
| CN111552313A (en) * | 2020-04-29 | 2020-08-18 | 南京理工大学 | Multi-unmanned aerial vehicle path planning method based on edge calculation dynamic task arrival |
| CN111694636A (en) * | 2020-05-11 | 2020-09-22 | 国网江苏省电力有限公司南京供电分公司 | Electric power Internet of things container migration method oriented to edge network load balancing |
| CN113391647A (en) * | 2021-07-20 | 2021-09-14 | 中国人民解放军国防科技大学 | Multi-unmanned aerial vehicle edge computing service deployment and scheduling method and system |
Non-Patent Citations (2)
| Title |
|---|
| 张晓玲;罗印升;张宝峰;王亚春;: "基于蚁群算法的移动机器人路径规划", 激光杂志, no. 11 * |
| 陈友东;胡嘉航;: "基于边缘计算的工业应用：自动导引小车控制***", 计算机集成制造***, no. 12 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116678432A (en) * | 2023-08-04 | 2023-09-01 | 江西师范大学 | Vehicle real-time path planning method based on edge service |
| CN116678432B (en) * | 2023-08-04 | 2023-10-24 | 江西师范大学 | Vehicle real-time path planning method based on edge service |
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