CN104932494B - The build mechanism of distribution of obstacles figure in a kind of probabilistic type room - Google Patents
The build mechanism of distribution of obstacles figure in a kind of probabilistic type room Download PDFInfo
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- CN104932494B CN104932494B CN201510206078.3A CN201510206078A CN104932494B CN 104932494 B CN104932494 B CN 104932494B CN 201510206078 A CN201510206078 A CN 201510206078A CN 104932494 B CN104932494 B CN 104932494B
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Abstract
The present invention proposes a kind of build mechanism of distribution of obstacles figure in probabilistic type room, and key step is as follows：1) room area is regarded as and is made of basic grid member, the grid member is square, its size is u；2) robot advance step-length is set to s, s=n*u, wherein n >=1, n are integer；3) center of maximum zone of ignorance in last time exploration result is set as starting point, and robot, which often takes a step forward, detects whether front has barrier；If so, the attribute of grid member barrier is set to；Otherwise, the attribute of grid member is set to clear.In detection process, the place for having obstacle in three faces to avoid robot turns not come out, and the direction turned to is selected by Probability p, if p>0.5.Meanwhile the step-length number preset value for exploring end can be adaptive to purging zone size.The distribution of obstacles figure build mechanism of the present invention can quickly verify indoor border, build irregular indoor distribution of obstacles figure, and the place that can have barrier from three faces, which is produced, to be come.
Description
Technical field
The present invention relates to the structure of distribution of obstacles figure, a kind of structure of distribution of obstacles figure in probabilistic type room is particularly related to
Mechanism.
Background technology
More and more extensive with the application of robot, requirement of the people to robot is continuously improved, service robot conduct
Important branch in robot application, its importance in production and living also gradually embody.Robot is complete indoors
Into a certain task, first have to build indoor distribution of obstacles figure, the structure of indoor distribution of obstacles figure be in robot research most
Basis, while be also sixty-four dollar question, how to cause robot quickly to verify indoor border, how to avoid robot from running into
During barrier rapidly produce come, how to be established in irregular interior distribution of obstacles figure be those skilled in the art need after
The problem of continuous research.
At present, mostly using straight trip cleaning method, can not keeping straight on just, a turn angle continues to keep straight on clean robot at random, should
Algorithm is simple, and hardware configuration is simple, but efficiency is than relatively low.Related data shows：The usual first pass of stochastic programming can cover
Clean the 65% of region, second time covering 85%, the 3rd time covering 92%, the 4th time covering 98%, not stinting if the time can be with
Tend to 100%.But in fact, since clean robot is from charged pool, electricity is limited, with reference to energy expenditure and cleaning replacement rate etc.
Parameter, it is difficult gratifying that the sweeping efficiency of the random cleaning method of this blindness, which is,.
So it is contemplated that the distribution map of barrier is explored by robot before cleaning, and cloud is stored that information in
Platform.When robot exploration next time distribution of obstacles, pervious information can be utilized, and then improve distribution of obstacles figure
Build speed and precision.
The content of the invention
The present invention proposes that a kind of build mechanism of distribution of obstacles figure in probabilistic type room solves robot in the prior art
It is difficult to quick contouring, running into barrier cannot have the place of three face barriers to produce from one, it is impossible to irregular
The problem of distribution of obstacles figure, is established in interior.
The technical proposal of the invention is realized in this way：The build mechanism of distribution of obstacles figure in a kind of probabilistic type room, bag
Include following steps：
Step 1:Room area is regarded as and is made of many basic grid members, the grid member is square, grid member
Size is u；Grid member is smaller, and distribution map is more accurate, and grid cell size is more than or equal to the error in robot direction, grid cell
Size can be set to 5cm or 10cm.
Step 2:Robot advance step-length is arranged to s, s=n*u, wherein n >=1, n is integer；
Step 3:The starting point of robot is set, robot, which often takes a step forward, detects whether front has barrier；Robot
Barrier is run into, the attribute status of grid member is provided with barrier；Robot does not run into barrier, by the category of grid member
Character state is arranged to clear.
Where whole indoor edge can not being learned for the first time due to robot, so, the entirety of barrier in target chamber
Distribution is including the indoor edge in constantly makeover process, i.e., indoor map can become complete in heuristic process.
Further, the build mechanism of distribution of obstacles figure further includes step 4 in the probabilistic type room, according to the last time
Result of detection determines the starting point of robot next time：The last center for exploring the maximum zone of ignorance in result of selection
As starting point, repeat step three.
Further, the build mechanism of distribution of obstacles figure further includes step 5 in the probabilistic type room：Robot returns to original
Point or institute of robot walking length reach preset value, and exploration then terminates.
Further, the attribute status of grid member is arranged to 0 or 1, wherein 0 represents clear, wherein 1 indicates obstacle
Thing.Robot is constantly explored, and the result explored every time and distribution map before are compared, and constantly refresh grid in distribution map
The attribute of lattice member, or the grid member with attribute is added, it 0 is also likely to be 1 that its attribute, which is probably,.So, by exploring many times
Cheng Hou, indoor edge, and distribution of obstacles situation can be confirmed, so as to build complete indoor distribution of obstacles figure.
Further, it is broken line that control system, which controls the route of travel of robot, and the starting point for setting robot is origin, will
The position coordinates of robot is set as D=(x, y), and the unit of x, y is u, and robot advances a step-length toward front, then x
1, y is added to remain unchanged；Toward dead astern advance a step-length, then x subtract 1, y is remained unchanged；Advance a step-length toward front-left, then x
Remain unchanged, y adds 1；Toward front-right advance a step-length, then x remain unchanged, y subtracts 1, what robot recorded during traveling
Information is I={ ∪ of (x, y)=0 (x, y)=1 }, and I is stored in cloud platform, then calculates distribution of obstacles figure according to I；Meanwhile
The distribution map is also stored in cloud platform, and follow-up exploration can be from learning to knowledge, and then improves distribution of obstacles figure structure
Speed and precision.
Further, the robot is by four-wheel drive, by control system control robot advance towards four direction or
Rotate.
Further, control system control robot, which often rotates, once rotates 90 °.
Further, n=1 is set, robot forward travel distance is more than 0.5u, and the attribute status of grid member is arranged to accessible
Thing.
Further, the probability for setting control system to control robot to rotate clockwise 90 ° when running into barrier is p, wherein p
>0.5。
Further, control system control robot advances in one direction, counterclockwise or clockwise until running into barrier
It is rotated by 90 °.
Beneficial effects of the present invention are：The build mechanism of distribution of obstacles figure is suitable for actual in the probabilistic type room of the present invention
Working environment, construction method simply preferably goes.Robot is controlled to advance or rotate towards four direction by control system, every time
In one direction, untill it cannot walk.In this mode, after robot runs into barrier, 90 degree are changed, then, along
This direction moves on.This process is carried out again and again, can rapidly verify indoor edge.In order to avoid robot
Turn-take and can't get out in the place that there is barrier in three faces, we select the direction turned to by Probability p.In order to avoid original
Ground is motionless, we set p>0.5.For irregular interior, using the thought similar to integration, robot forward travel distance exceedes
0.5u, you can think the grid member clear.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is attached drawing needed in technology description to be briefly described, it should be apparent that, drawings in the following description are only this
Some embodiments of invention, for those of ordinary skill in the art, without having to pay creative labor, may be used also
To obtain other attached drawings according to these attached drawings.
Fig. 1 is the method flow of build mechanism one embodiment of distribution of obstacles figure in a kind of probabilistic type room of the present invention
Figure；
Fig. 2 is the interior of one embodiment of the build mechanism structure of distribution of obstacles figure in a kind of probabilistic type room of the present invention
Distribution of obstacles figure；
Fig. 3 is that the state for inventing robot direction vector in the build mechanism of distribution of obstacles figure in a kind of probabilistic type room turns
Move figure.
Embodiment
Below in conjunction with the attached drawing in the embodiment of the present invention, the technical solution in the embodiment of the present invention is carried out clear, complete
Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art are obtained every other without creative efforts
Embodiment, belongs to the scope of protection of the invention.
The build mechanism of distribution of obstacles figure, comprises the following steps in a kind of probabilistic type room：
Step 1：Room area is regarded as and is made of many basic grid members, the grid member is square, grid member
Size is u；Grid member is smaller, and distribution map is more accurate, and grid cell size is more than or equal to the error in robot direction, grid cell
Size can be set to 5cm or 10cm；
Step 2：Robot advance step-length is arranged to s, s=n*u, wherein n >=1, n is integer；
Step 3：Robot, which often takes a step forward, detects whether front has barrier；Robot runs into barrier, by grid member
Attribute status be provided with barrier；Robot does not run into barrier, and the attribute status of grid member is arranged to accessible
Thing.In the present embodiment, n=1 is set, robot forward travel distance is more than 0.5u, and the attribute status of grid member is arranged to clear.
Where whole indoor edge can not being learned for the first time due to robot, so, the entirety of barrier in target chamber
Distribution is including the indoor edge in constantly makeover process, i.e., indoor map can become complete in heuristic process.
Step 4, the starting point of robot next time is determined according to last result of detection：Selection is last to explore knot
The center of maximum zone of ignorance in fruit is as starting point, repeat step three.
Step 5：Robot returns to origin or institute of robot walking length reaches preset value exploration and terminates.
Determine that ith explores the step-length number L terminated as followsi, LiDetermined by equation below：
Li=(1- δ) × (Li-1)+δ×(Qi-1* 1.1) (i=2,3,4 ...)
As i=1, i.e., when exploring for the first time, L1Equal to A, A be with explore the relevant constant of area size, 100 >=
A≥10。QiThe grid quantity summation of distribution map after expression ith is explored.δ is to update regulatory factor, 1 ＞ δ ＞ 0, this reality
Apply a δ and be set to 0.7.
The attribute status of grid member is arranged to 0 or 1, wherein 0 represents clear, wherein 1 indicates barrier.Machine
People constantly explores, and the result explored every time and distribution map before are compared, and constantly refresh grid member in distribution map
Attribute, or the grid member with attribute is added, it 0 is also likely to be 1 that its attribute, which is probably,.So, after heuristic process many times, room
Interior edge, and distribution of obstacles situation can be confirmed, so as to build complete indoor distribution of obstacles figure.The machine
People controls robot to advance or rotate towards four direction by four-wheel drive by control system.
It is broken line that control system, which controls the route of travel of robot, and the starting point for setting robot is origin, by robot
Position coordinates be set as D=(x, y), the unit of x, y is u, and robot advances a step-length, i.e. direction initialization toward front
Vector (E=1, N=0), then x adds 1, y to remain unchanged；Toward one step-length of dead astern advance, i.e. direction initialization vector (E=-1, N
=0), then x subtracts 1, and y is remained unchanged；Toward front-left one step-length of advance, i.e. direction initialization vector (E=0, N=1), then x holdings
Constant, y adds 1；Toward front-right one step-length of advance, i.e. direction initialization vector (E=0, N=-1), then x is remained unchanged, and y subtracts 1, machine
The information that device people records during traveling is I={ ∪ of (x, y)=0 (x, y)=1 }, and I is stored in cloud platform, is then counted according to I
Calculate distribution of obstacles figure.Meanwhile the distribution map is also stored in cloud platform, follow-up exploration can from learning to knowledge, into
And improve the speed and precision of distribution of obstacles figure structure.
As shown in Figure 1, in probabilistic type room distribution of obstacles figure build mechanism method flow diagram：
After beginning, the starting point for setting robot is origin, and robot advances in one direction, until running into barrier not
It can walk, direction vector is initialized：(E=0, N=1), robot does not turn to, a step-length of advancing；Setting direction vector (E,
N), as shown in figure 3, the state transition diagram of direction vector (E, N), runs into barrier, (x, y)=1, the attribute shape of grid member are set
State is arranged to 1；Barrier is not run into, sets (x, y)=0, and the attribute status of grid member is arranged to 0；Judge when running into barrier
Whether origin is reached, reach origin, i.e. x=0and y=0, end task, or during ith exploration, step-length number LSArrival is set
Definite value Li, end task；Not up to origin and LSSetting value is not arrived, i.e. (x ≠ 0ory ≠ 0) and LS<Li, control system control
Robot processed turns to 90 ° or turns to 90 ° counterclockwise clockwise, and wherein control system control robot turns to 90 ° clockwise
Probability is more than 0.5, then proceedes to not turn to a step-length of advancing, setting direction vector simultaneously judges whether to run into barrier；Do not meet
During to barrier, judge whether to reach origin, reach origin and then end task, or during ith exploration, step-length number LSArrival is set
Definite value Li, reach setting value and end task, not up to origin and LSSetting value is not arrived, i.e. (x ≠ 0ory ≠ 0) and LS<Li,
Do not turn to then, a step-length of advancing, repeats the above steps, finally obtain indoor distribution of obstacles figure as shown in Figure 2, black
Square expression runs into barrier, and the attribute status of grid member is arranged to 1, and white square represents not run into barrier, grid
The attribute status of lattice member is arranged to 0.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
With within principle, any modification, equivalent replacement, improvement and so on, should all be included in the protection scope of the present invention god.
Claims (6)
1. the build mechanism of distribution of obstacles figure in a kind of probabilistic type room, it is characterised in that comprise the following steps：
Step 1:Room area is regarded as and is made of many basic grid members, the grid member is square, the size of grid member
For u；
Step 2:Robot advance step-length is arranged to s, s=n*u, wherein n >=1, n is integer；
Step 3:The starting point of robot is set, robot, which often takes a step forward, detects whether front has barrier；Robot is run into
Barrier, barrier is provided with by the attribute status of grid member；Robot does not run into barrier, by the attribute shape of grid member
State is arranged to clear；
It is broken line that control system, which controls the route of travel of robot, and robot, which often rotates, once rotates 90 °；Control system control is set
Robot processed rotates clockwise 90 ° probability when running into barrier is p, wherein p>0.5；
The starting point for setting robot is origin, and the position coordinates of robot is set as D=(x, y), and the unit of x, y are u,
Robot adds 1, y to remain unchanged toward one step-length of front advance, then x；Toward dead astern advance a step-length, then x subtract 1, y keep
It is constant；Toward front-left advance a step-length, then x remain unchanged, y adds 1；Toward front-right advance a step-length, then x remain unchanged, y
Subtract 1；
The information that robot records during traveling is I={ ∪ of (x, y)=0 (x, y)=1 }, and I is stored in cloud platform, then
Distribution of obstacles figure is calculated according to I；Meanwhile the distribution map is also stored in cloud platform, follow-up exploration can from learning to
Knowledge, and then improve the speed and precision of distribution of obstacles figure structure；
Step 4:The starting point of robot next time is determined according to last result of detection：Selection is last to be explored in result
Maximum zone of ignorance center as starting point, repeat step three.
2. the build mechanism of distribution of obstacles figure in probabilistic type room as claimed in claim 1, it is characterised in that：The probabilistic type
The build mechanism of indoor distribution of obstacles figure further includes step 5：Robot returns to origin or institute of robot walking length reaches pre-
If value exploration terminates.
3. the build mechanism of distribution of obstacles figure in probabilistic type room as claimed in claim 1 or 2, it is characterised in that：By grid
The attribute status of member is arranged to 0 or 1, wherein 0 represents clear, wherein 1 indicates barrier.
4. the build mechanism of distribution of obstacles figure in probabilistic type room as claimed in claim 3, it is characterised in that：The robot
By four-wheel drive, robot is controlled to advance or rotate towards four direction by control system.
5. the build mechanism of distribution of obstacles figure in probabilistic type room as claimed in claim 4, it is characterised in that：
N=1 is set, robot forward travel distance is more than 0.5u, and the attribute status of grid member is arranged to clear.
6. the build mechanism of distribution of obstacles figure in probabilistic type room as claimed in claim 1, it is characterised in that：Control system control
Robot processed advances in one direction, until running into barrier counterclockwise or rotating clockwise 90 °.
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Families Citing this family (8)
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| CN105136155B (en) * | 2015-09-24 | 2018-12-14 | 联想(北京)有限公司 | A kind of air navigation aid and electronic equipment |
| CN107463168A (en) * | 2016-06-06 | 2017-12-12 | 苏州宝时得电动工具有限公司 | Localization method and system, map constructing method and system, automatic running device |
| CN106695790B (en) * | 2017-01-09 | 2018-04-17 | 广东宝乐机器人股份有限公司 | The control method for movement and robot of a kind of robot |
| EP3633478B1 (en) * | 2017-05-26 | 2023-10-18 | Hangzhou Hikrobot Co., Ltd. | Method and device for assessing probability of presence of obstacle in unknown position |
| CN109917791B (en) * | 2019-03-26 | 2022-12-06 | 深圳市锐曼智能装备有限公司 | Method for automatically exploring and constructing map by mobile device |
| CN112180910A (en) * | 2019-06-18 | 2021-01-05 | 北京京东尚科信息技术有限公司 | Obstacle sensing method and device for mobile robot |
| CN111813102B (en) * | 2020-06-06 | 2023-11-21 | 浙江中力机械股份有限公司 | Distributed autonomous robot environment map construction method |
| CN114148350A (en) * | 2021-12-21 | 2022-03-08 | 北京三快在线科技有限公司 | Control method and device for unmanned equipment |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101619985A (en) * | 2009-08-06 | 2010-01-06 | 上海交通大学 | Service robot autonomous navigation method based on deformable topological map |
| KR101207535B1 (en) * | 2010-12-31 | 2012-12-03 | 한양대학교 산학협력단 | Image-based simultaneous localization and mapping for moving robot |
| CN103278170A (en) * | 2013-05-16 | 2013-09-04 | 东南大学 | Mobile robot cascading map building method based on remarkable scenic spot detection |
| CN103512579A (en) * | 2013-10-22 | 2014-01-15 | 武汉科技大学 | Map building method based on thermal infrared camera and laser range finder |
| CN103809597A (en) * | 2014-02-18 | 2014-05-21 | 清华大学 | Flight path planning method for unmanned plane and unmanned plane |
| CN103914068A (en) * | 2013-01-07 | 2014-07-09 | 中国人民解放军第二炮兵工程大学 | Service robot autonomous navigation method based on raster maps |
| CN104199428A (en) * | 2014-09-17 | 2014-12-10 | 上海畔慧信息技术有限公司 | Swarm robot management server and method implemented by same |
-
2015
- 2015-04-27 CN CN201510206078.3A patent/CN104932494B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101619985A (en) * | 2009-08-06 | 2010-01-06 | 上海交通大学 | Service robot autonomous navigation method based on deformable topological map |
| KR101207535B1 (en) * | 2010-12-31 | 2012-12-03 | 한양대학교 산학협력단 | Image-based simultaneous localization and mapping for moving robot |
| CN103914068A (en) * | 2013-01-07 | 2014-07-09 | 中国人民解放军第二炮兵工程大学 | Service robot autonomous navigation method based on raster maps |
| CN103278170A (en) * | 2013-05-16 | 2013-09-04 | 东南大学 | Mobile robot cascading map building method based on remarkable scenic spot detection |
| CN103512579A (en) * | 2013-10-22 | 2014-01-15 | 武汉科技大学 | Map building method based on thermal infrared camera and laser range finder |
| CN103809597A (en) * | 2014-02-18 | 2014-05-21 | 清华大学 | Flight path planning method for unmanned plane and unmanned plane |
| CN104199428A (en) * | 2014-09-17 | 2014-12-10 | 上海畔慧信息技术有限公司 | Swarm robot management server and method implemented by same |
Non-Patent Citations (1)
| Title |
|---|
| 智能移动机器人路径规划方法研究;隋岩;《中国优秀硕士学位论文全文数据库信息科技辑》;20110515;论文第14-23页 * |
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