CN108614554A - A kind of robot multi-source shortest path planning method based on region limitation - Google Patents
A kind of robot multi-source shortest path planning method based on region limitation Download PDFInfo
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- G05D1/02—Control of position or course in two dimensions
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
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Abstract
The present invention relates to a kind of robot multi-source shortest path firsts based on region limitation.There will be it is connected while mobile node between distance be set as connection while weights, there is no distances between the mobile node on connected side to be set as ∞, obtain the link information between mobile node and build be abstracted map；By comparing all starting points and the target point set constituted and the set for having found out shortest path mobile node, show that current time not yet determines the set on shortest path vertex；According to apex coordinate and shortest path confidence level in shortest path vertex set is not determined currently, the extreme value of apex coordinate in vertex set is obtained, and construct the rectangle restricted searching area comprising entire starting point and target point point set；Be directly connected to while starting point and the shortest path of target point connected for it while power；For other starting points and target point, shortest path is determined using dijkstra's algorithm.The present invention can be with simple, convenient, expeditiously realizing route be planned, and stability is good.
Description
Technical field
The invention belongs to mobile robot technology fields, and in particular to a kind of robot multi-source based on region limitation is most short
Paths planning method.
Background technology
With extensive use of the equipment such as robot, intelligent vehicle in life, production, people are for its intelligent requirement
Also higher and higher, and find basic demand and core technology that shortest path is mobile robot path planning.Especially in essence
Degree requires under high, movement environment complexity indoor conditions, how effectively to determine optimal path planning be one it is important and
Significant problem.
The paths planning method of comparative maturity includes mainly Floyd algorithms, dijkstra's algorithm, Bellman-Ford at present
Algorithm etc..Wherein, Floyd algorithms can calculate the shortest path between any two node, but algorithm complexity is higher.
Dijkstra's algorithm and bellman-ford algorithm are all typical signal source shortest path algorithms, can acquire starting point to arbitrarily
The shortest path of point, wherein bellman-ford algorithm can be applied to the figure containing negative power.A* algorithms are a kind of heuristic calculations
Method, for given beginning and end, it can usually quickly find shortest path, but according to the difference and reality of Heuristic Strategy
The difference of environment, the time complexity of A* algorithms are difficult to estimate, and for multi-source shortest route problem, can be made using A* algorithms
At the calculating largely repeated.On the whole, existing route planing method is low to multi-source path planning efficiency, and complexity is high.
Invention content
The present invention proposes a kind of robot multi-source shortest path first limited based on region, with simple and convenient, high efficiency
Ground realizing route planning, and stability is good.
In order to solve the above technical problem, the present invention provides a kind of robot multi-source shortest paths based on region limitation to advise
The method of drawing, includes the following steps：
Setting indoors meets the mobile node of robot operating path in advance；There will be the mobile node spacing on connected side
From the weights for being set as connection side, there is no distances between the mobile node on connected side to be set as ∞, obtains the connection letter between mobile node
Breath；According to mobile node link information, the abstract map being made of range information between mobile node is built；
By comparing all starting points and the target point set constituted and the set for having found out shortest path mobile node, obtain
Go out the set on current time not yet determining shortest path vertex；According to do not determine currently in shortest path vertex set apex coordinate with
And shortest path confidence level, obtain the extreme value of apex coordinate in vertex set, and construct comprising entire starting point and target point point
The rectangle restricted searching area of collection；
Be directly connected to while starting point and the shortest path of target point connected for it while power；For other starting points
And target point, determine shortest path using dijkstra's algorithm.
Further, according to the coordinate of each mobile node, the air line distance between any two mobile node is calculated, is introduced auxiliary
Vectorial D, each of which component D [i] [j] is helped to indicate a certain mobile node viTo other mobile nodes vjAir line distance, wherein if
From viTo vjBetween hindered without current, then D [i] [j] is viTo vjAir line distance, it is ∞ otherwise to set D [i] [j], to be remembered
Record the Distance matrix D of link information between each mobile node.
Further, according to Distance matrix D, air line distance by each mobile node of mark and therebetween, construction can be anti-
Answer the abstract map of link information between mobile node
Further, it would be desirable to which the whole starting points and target point for carrying out path planning constitute set V, will currently complete
Each starting point of path planning constitutes set S with target point, and currently to be planned set out is obtained by comparing described two set
Point and sets of target points T, i.e. T=V-S；
It introduces rectangle restricted searching area and builds confidence level τ, by the way that τ values are manually set, determine rectangle restricted searching area
Reliability；If all starting points and the collection of target point composition are combined into Vse={ v1, v2... vn, enable xmin’、xmax’、ymin’、ymax’
Respectively set VseIn cross, the maximum of ordinate, minimum value in each mobile node, then for VseIn all mobile nodes, wrap
It is contained in xmin’、xmax’、ymin’、ymax' it is to introduce variable in the rectangular area of vertex structureAndIt counts as the following formula
Calculate each apex coordinate x of rectangle restricted searching areamin、xmax、ymin、ymax, with xmin、xmax、ymin、ymaxThe rectangular area of composition is
For restricted searching area,
Further, using dijkstra's algorithm, V is constructed using the mobile node in rectangular search restricted areaseIn
Shortest path between each starting point and target point, and update mobile node set T to be planned...
Compared with prior art, the present invention its remarkable advantage is：
(1) simple and convenient：Compared with other shortest path planning methods, structure rectangle restricted searching area operand is small,
It is easy to operate；After excluding to have the source for being connected directly side, remittance vertex, rectangle restricted searching area model can be adjusted according to confidence level
It encloses, after the completion of structure, can substantially reduce determining shortest path calculation amount；For extreme case, or even only need O (n) times complicated
Path planning can be completed in degree；
(2) adaptable：Based on mobile node construct Visual Graph, it is small by the interference of environmental change, different scenes can
In due course adjustment is made, ensures efficiency of algorithm, it is more adaptable compared with other path planning algorithms；
(3) stability is good：Carry out adjustment region range due to introducing confidence level when constructing rectangle restricted searching area, carries
The high controllability of robot path planning's degree of reliability；Even if there is a certain range of deviation or error in certain planning,
It can still be calibrated by adjusting confidence level coefficient when planning next time, there is higher stability；
(4) real-time accuracy is high：On the basis of dijkstra's algorithm, part is selected to be planned from the Visual Graph overall situation,
Improve computational efficiency and real-time；The algorithm of intensive is needed compared to other, reality of invention during path planning
Apply precision higher.
Description of the drawings
Fig. 1 is the main flow of the robot multi-source shortest path first based on region limitation described in the embodiment of the present invention
Figure；
Fig. 2 is the flow chart that restricted area dijkstra's algorithm of the present invention determines path planning；
Fig. 3 is that abstractively diagram is intended to；
Fig. 4 is rectangular search restricted area schematic diagram.
Specific implementation mode
It is readily appreciated that, technical solution according to the present invention, in the case where not changing the connotation of the present invention, this field
Those skilled in the art can imagine a variety of of the robot multi-source shortest path planning method limited the present invention is based on region
Embodiment.Therefore, detailed description below and attached drawing are only the exemplary illustrations to technical scheme of the present invention, without answering
When the whole for being considered as the present invention or it is considered as limitation or restriction to technical solution of the present invention.
In conjunction with attached drawing, the present invention is based on the robot multi-source shortest-path methods of region limitation, include the following steps：
(1) Visual Graph is built according to the geometric distance between indoor moving node；So-called mobile node refers to being present in machine
Node in people's mobile route, is moved for guidance machine people, and node itself is motionless；Further, the step (1) is specifically wrapped
It includes：
(11) setting indoors in advance meets the mobile node of robot operating path；
(12) there will be it is connected while mobile node between distance be set as connection while weights, there is no be connected side movable joint
Distance is set as ∞ between point, obtains the link information between mobile node；
(13) according to mobile node link information, the abstract map being made of range information between mobile node is built；
(2) restricted searching area with rectangle is built according to the position coordinates of necessary mobile node；Further, step (2) tool
Body includes：
(21) current not determine that shortest path vertex set obtains：By the set for comparing all starting points and target point composition
With the set for having found out shortest path mobile node, show that current time not yet determines the set on shortest path vertex；
(22) current restricted searching area structure：According to apex coordinate in currently not determining shortest path vertex set and most
Short path confidence level, obtains the extreme value of apex coordinate in vertex set, and constructs comprising entire starting point and target point point set
Rectangle restricted searching area；
(3) its signal source shortest path is calculated separately to all necessary mobile nodes in restricted area.Further, described
Step (3) specifically includes：
(31) directly judgement have be directly connected to while the vertex Yuan Hui shortest path be its connection while power；
(32) other signal source shortest paths are determined using dijkstra's algorithm.
Further, the weights on the Visual Graph side obtained in the step (1) be practical Euclidean distance, satisfaction 2 points it
Between the shortest geometrical constraint of distance；
Further, the rectangular search restricted area is not determine that shortest path vertex set apex coordinate is true according to current
Fixed.
Further, the rectangular search restricted area range can be adjusted according to different confidence levels.
The previously-introduced auxiliary quantity of the present invention, component are length of a certain source point to other nodes；According to indoor practical ruler
Very little each component of configuration, constructs and is abstracted Visual Graph；It is connected directly the Origin And Destination on side for existing, it is believed that shortest path is
The power on side constructs rectangle restricted searching area further according to other node coordinates；Rectangle, which is handled, according to Dijkstra limits the field of search
Node in domain further determines that shortest path of the robot in full figure.The present invention passes through in robot node Visual Graph
Rectangle restricted searching area is constructed, and the current shortest path of robot is determined using dijkstra's algorithm.By to existing
Dijkstra's algorithm introduces the rectangle restricted searching area built according to target point, the point set that sets out, accurate dijkstra's algorithm
Shortest path finds region, further promotes shortest path and finds efficiency.
Embodiment
Specifically as shown in Figure 1, including the following steps：
(1) each vertex arrangement of Visual Graph：According to robot real work needs, artificially set in robot work region in advance
Fixed multiple and different mobile node, and each mobile node coordinate is recorded, the basis of shortest path is found as this algorithm, and really
Protecting robot can be by moving between set mobile node to traverse entire robot operating path.
(2) connection weight calculates between vertex：It is straight between calculating any two mobile node according to the coordinate of each mobile node
Linear distance, introduces auxiliary vector D, and each of which component D [i] [j] indicates a certain mobile node viTo other mobile nodes vjStraight line
Distance, wherein if from viTo vjBetween hindered without current, then D [i] [j] is viTo vjAir line distance, otherwise setting D [i] [j] is
∞.To which acquisition records the Distance matrix D of air line distance information between each mobile node；
Preferably, the weights on the Visual Graph side obtained in the step (2) are practical Euclidean distance, are met between 2 points
Apart from shortest geometrical constraint.
(3) nodal distance is built according to D and is abstracted map：According to record air line distance information between each mobile node away from
From matrix D, air line distance by each mobile node of mark and therebetween constructs link information between can reacting mobile node
Abstract map；
The weights on the side that is connected between mobile node in map are abstracted completely according to the practical Euclidean between two mobile nodes in practice
Distance is determined；Have caused by obstacle or other problems centre there is no the node for being connected directly side, is then weighed and be set to ∞；
It is equal to the practical Euclidean distance for considering each node according to the weights progress path planning on each connected side in abstract map.
(4) shortest path planning：According to the starting point and target point of robot movement, structure in map is abstracted in mobile node
Rectangle restricted searching area is made, and the current shortest path planning of robot is determined according to dijkstra's algorithm in region.
Complete step (1) and arrive step (3), that is, complete to be abstracted map construction work, next can by abstract map into
Row shortest path planning.The optimal selection of mobile route is determined by specified starting point and target point set, robot.Starting
Before building rectangle restricted searching area, the starting point being directly connected to there are straight line and target point are first detected, it is contemplated that meet two
Point between the shortest geometrical constraint of distance, can there will be direct straight line be connected to starting point and target point between the shortest distance it is direct
It is set to its air line distance, without comparing update again.For the rectangle restricted searching area of structure, the present invention extracts first to be located at
Mobile node set in rectangle restricted searching area, completes its shortest path planning.
As shown in Fig. 2, step (4) the path planning step includes：
(41) current not determine that shortest path vertex set obtains：It will need the whole starting points and target of progress path planning
Point constitutes set V, each starting point for currently having completed path planning is constituted set S with target point, by comparing the two
Set, it can be deduced that current starting point to be planned and sets of target points T, i.e. T=V-S.
(42) current restricted searching area structure：It introduces rectangle restricted searching area and builds confidence level τ, pass through artificial settings
τ values, determining rectangle restricted searching area reliability, (confidence level is higher, then rectangle restricted searching area is bigger, carries out in the zone
The probability that path planning obtains global optimum path is bigger, but calculation amount also will increase, to reduce search efficiency).If all
The set V of starting point and target point compositionse={ v1, v2... vn, enable xmin’、xmax’、ymin’、ymax' it is respectively set VseIn
The minimax extreme value of each mobile node transverse and longitudinal coordinate, then for VseIn all mobile nodes, be all contained in xmin’、xmax’、
ymin’、ymax ’In rectangular area for vertex structure, variable is introducedAndEasily card, for any angle θ, restricted searching area with rectangle should meet following limit
System：
Therefore each apex coordinate x of rectangle restricted searching area can be calculated as followsmin、xmax、ymin、ymax, with xmin、xmax、
ymin、ymaxThe rectangular area of composition is restricted searching area.Rectangle restricted searching area apex coordinate can be obtained：
Since this method is substantially exaggerated the restricted searching area of single starting point monocular punctuate problem, thus it is same
Reliability higher under τ values.In primal algorithm, the value of τ has to be larger than 1, to ensure B as real number.In multi-source, τ
Value need not be large, at this point, τ takesmax{A2, B2There is minimum value, that is, restricted searching area is minimum.
Preferably, the rectangular search restricted area is not determine that shortest path vertex set apex coordinate determines according to current
's.
Preferably, the rectangular search restricted area range can be adjusted according to different confidence levels.
Current shortest path determines：According to existing dijkstra's algorithm, the shifting in rectangular search restricted area is utilized
Dynamic joint structure VseIn shortest path between each starting point and target point, and update mobile node set T to be planned.
Claims (5)
1. a kind of robot multi-source shortest path planning method based on region limitation, which is characterized in that include the following steps：
Setting indoors meets the mobile node of robot operating path in advance；There will be distances between the mobile node on connected side to set
To connect the weights on side, there is no distances between the mobile node on connected side to be set as ∞, obtains the link information between mobile node；Root
According to mobile node link information, the abstract map being made of range information between mobile node is built；
By comparing all starting points and the target point set constituted and the set for having found out shortest path mobile node, obtains and work as
The preceding moment not yet determines the set on shortest path vertex；According to apex coordinate in currently not determining shortest path vertex set and most
Short path confidence level, obtains the extreme value of apex coordinate in vertex set, and constructs comprising entire starting point and target point point set
Rectangle restricted searching area；
Be directly connected to while starting point and the shortest path of target point connected for it while power；For other starting points and mesh
Punctuate determines shortest path using dijkstra's algorithm.
2. the robot multi-source shortest path planning method as described in claim 1 based on region limitation, which is characterized in that according to
The coordinate of each mobile node calculates the air line distance between any two mobile node, introduces auxiliary vector D, each of which component D
[i] [j] indicates a certain mobile node viTo other mobile nodes vjAir line distance, wherein if from viTo vjBetween hindered without current
Hinder, then D [i] [j] is viTo vjAir line distance, it is ∞ otherwise to set D [i] [j], is connected between recording each mobile node to acquisition
The Distance matrix D of information.
3. the robot multi-source shortest path planning method as described in claim 1 based on region limitation, which is characterized in that according to
Distance matrix D, air line distance by each mobile node of mark and therebetween, construction can react link information between mobile node
Abstract map.
4. the robot multi-source shortest path planning method as described in claim 1 based on region limitation, which is characterized in that
The whole starting points for carrying out path planning will be needed to constitute set V with target point, will currently complete path planning
Each starting point constitutes set S with target point, and current starting point and target point to be planned are obtained by comparing described two set
Set T, i.e. T=V-S；
It introduces rectangle restricted searching area and builds confidence level τ, by the way that τ values are manually set, determine that rectangle restricted searching area is reliable
Property；If all starting points and the collection of target point composition are combined into Vse={ v1, v2... vn, enable xmin’、xmax’、ymin’、ymax' respectively
For set VseIn cross, the maximum of ordinate, minimum value in each mobile node, then for VseIn all mobile nodes, be all contained in
With xmin’、xmax’、ymin’、ymax' it is to introduce variable in the rectangular area of vertex structureAndIt counts as the following formula
Calculate each apex coordinate x of rectangle restricted searching areamin、xmax、ymin、ymax, with xmin、xmax、ymin、ymaxThe rectangular area of composition is
For restricted searching area,
5. the robot multi-source shortest path planning method as described in claim 1 based on region limitation, which is characterized in that use
Dijkstra's algorithm constructs V using the mobile node in rectangular search restricted areaseIn between each starting point and target point
Shortest path, and update mobile node set T to be planned.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109668572A (en) * | 2018-12-28 | 2019-04-23 | 芜湖哈特机器人产业技术研究院有限公司 | A kind of laser fork truck method for searching path based on floyd algorithm |
| CN110553661A (en) * | 2019-09-16 | 2019-12-10 | 湖南科技大学 | R-tree-based user position-to-target area path recommendation method |
| CN110866629A (en) * | 2019-09-20 | 2020-03-06 | 合肥工业大学 | Heterogeneous task-oriented vehicle path optimization method and device |
| CN111459169A (en) * | 2020-04-27 | 2020-07-28 | 四川智动木牛智能科技有限公司 | Comprehensive pipe gallery inspection method based on wheeled robot |
| CN113312694A (en) * | 2021-05-25 | 2021-08-27 | 中国科学院计算技术研究所厦门数据智能研究院 | Method for generating dynamic line plan in shelter type building |
| CN113342002A (en) * | 2021-07-05 | 2021-09-03 | 湖南大学 | Multi-mobile-robot scheduling method and system based on topological map |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0782058A1 (en) * | 1995-12-26 | 1997-07-02 | Pioneer Electronic Corporation | System for seeking a route for an automotive navigation system |
| CN103278171A (en) * | 2013-05-23 | 2013-09-04 | 中国地质大学（武汉） | Indoor emergency path navigation method |
| CN105716613A (en) * | 2016-04-07 | 2016-06-29 | 北京进化者机器人科技有限公司 | Method for planning shortest path in robot obstacle avoidance |
| CN106403976A (en) * | 2016-08-30 | 2017-02-15 | 哈尔滨航天恒星数据***科技有限公司 | Dijkstra optimal traffic path planning method and system based on impedance matching |
| CN106780506A (en) * | 2016-11-21 | 2017-05-31 | 北京交通大学 | A kind of interactive image segmentation method based on multi-source shortest path distance |
-
2018
- 2018-05-03 CN CN201810414481.9A patent/CN108614554A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0782058A1 (en) * | 1995-12-26 | 1997-07-02 | Pioneer Electronic Corporation | System for seeking a route for an automotive navigation system |
| CN103278171A (en) * | 2013-05-23 | 2013-09-04 | 中国地质大学（武汉） | Indoor emergency path navigation method |
| CN105716613A (en) * | 2016-04-07 | 2016-06-29 | 北京进化者机器人科技有限公司 | Method for planning shortest path in robot obstacle avoidance |
| CN106403976A (en) * | 2016-08-30 | 2017-02-15 | 哈尔滨航天恒星数据***科技有限公司 | Dijkstra optimal traffic path planning method and system based on impedance matching |
| CN106780506A (en) * | 2016-11-21 | 2017-05-31 | 北京交通大学 | A kind of interactive image segmentation method based on multi-source shortest path distance |
Non-Patent Citations (2)
| Title |
|---|
| 王海梅，等: "一种限制搜索区域的最短路径改进算法", 《南京理工大学学报(自然科学版)》 * |
| 葛莉: "基于最短路径Dijkstra算法多尺度道路网中优化路径规划算法的研究", 《湖北民族学院学报(自然科学版)》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109668572A (en) * | 2018-12-28 | 2019-04-23 | 芜湖哈特机器人产业技术研究院有限公司 | A kind of laser fork truck method for searching path based on floyd algorithm |
| CN110553661A (en) * | 2019-09-16 | 2019-12-10 | 湖南科技大学 | R-tree-based user position-to-target area path recommendation method |
| CN110866629A (en) * | 2019-09-20 | 2020-03-06 | 合肥工业大学 | Heterogeneous task-oriented vehicle path optimization method and device |
| CN110866629B (en) * | 2019-09-20 | 2022-12-13 | 合肥工业大学 | Heterogeneous task-oriented vehicle path optimization method and device |
| CN111459169A (en) * | 2020-04-27 | 2020-07-28 | 四川智动木牛智能科技有限公司 | Comprehensive pipe gallery inspection method based on wheeled robot |
| CN111459169B (en) * | 2020-04-27 | 2023-11-24 | 四川智动木牛智能科技有限公司 | Comprehensive pipe gallery inspection method based on wheeled robot |
| CN113312694A (en) * | 2021-05-25 | 2021-08-27 | 中国科学院计算技术研究所厦门数据智能研究院 | Method for generating dynamic line plan in shelter type building |
| CN113342002A (en) * | 2021-07-05 | 2021-09-03 | 湖南大学 | Multi-mobile-robot scheduling method and system based on topological map |
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Application publication date: 20181002 |