CN104932494A - Probability type indoor barrier distribution map establishing mechanism - Google Patents
Probability type indoor barrier distribution map establishing mechanism Download PDFInfo
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- CN104932494A CN104932494A CN201510206078.3A CN201510206078A CN104932494A CN 104932494 A CN104932494 A CN 104932494A CN 201510206078 A CN201510206078 A CN 201510206078A CN 104932494 A CN104932494 A CN 104932494A
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Abstract
The invention provides a probability type indoor barrier distribution map establishing mechanism. The mechanism comprises main steps that 1) an indoor region is composed of square basic grid components of the size of u; 2) the forwarding step length of a robot is set as s, s=n*u, and n is an integer greater than or equivalent to 1; and 3) the central position of a maximal unknown area in a last search result is set as a starting point, whether there is a barrier in front is detected when the robot moves forwards in each step, if yes, the attribute of the grid component is set as barrier, and otherwise, the attribute of the grid component is set as non-barrier. In the detection process, to avoid the robot from being incapable of moving out of a position whose three sides include barriers, the rotation direction is selected according to the probability p, and p is set greater than 0.5. At the same time, the preset step length value after search can be adapted to the size of a cleaning area. The barrier distribution map establishing mechanism can rapidly detect the indoor boundary, establish an irregular indoor barrier distribution map, and thus, the robot can move out of the position whose three sides include barriers.
Description
Technical field
The present invention relates to the structure of distribution of obstacles figure, refer to the build mechanism of the indoor distribution of obstacles figure of a kind of probabilistic type especially.
Background technology
Along with the application of robot is more and more extensive, the requirement of people to robot improves constantly, and service robot is as the important branch in robot application, and its importance in productive life also embodies gradually.Robot will complete a certain task in indoor, first indoor distribution of obstacles figure will be built, the structure of indoor distribution of obstacles figure is the most basic in robot research, also be sixty-four dollar question simultaneously, robot how is made to verify indoor border fast, how to avoid robot to produce rapidly when running into barrier, how to set up distribution of obstacles figure in irregular indoor is the problem that those skilled in the art needs to continue research.
At present, clean robot mostly adopts craspedodrome cleaning mode, and the just random angle that turns of cannot keeping straight on continues to keep straight on, and the method algorithm is simple, and hardware configuration is simple and easy, but efficiency comparison is low.Related data shows: the usual first pass of stochastic programming can cover to cover to cover for the 85%, three time 65%, second time of cleaning area and cover 98% the 92%, four time, and the words of time do not stinted can tend to 100%.But in fact, because clean robot is from charged pool, electricity is limited, and in conjunction with parameters such as energy ezpenditure and clean replacement rates, the sweeping efficiency of the random cleaning mode of this blindness is very difficult gratifying.
So the present invention explores the distribution plan of barrier before being intended to robot cleaning, and this information is stored in cloud platform.When robot exploration next time distribution of obstacles, can former information be utilized, and then improve structure speed and the precision of distribution of obstacles figure.
Summary of the invention
The build mechanism that the present invention proposes the indoor distribution of obstacles figure of a kind of probabilistic type solves robot in prior art and is difficult to quick contouring, running into barrier can not have the place of three barriers to produce from one, can not set up the problem of distribution of obstacles figure to irregular indoor.
Technical scheme of the present invention is achieved in that the build mechanism of the indoor distribution of obstacles figure of a kind of probabilistic type, comprises the following steps:
Step one: regarded as by room area and be made up of a lot of basic grid unit, described grid unit is square, and the size of grid unit is u; Grid unit is less, and distribution plan is more accurate, and grid cell size is more than or equal to the error in robot direction, and grid cell size can be set to 5cm or 10cm.
Step 2: step-length of being advanced by robot is set to s, s=n*u, wherein n >=1, n is integer;
Step 3: setting robot starting point, robot often take a step forward detection front whether have barrier; Robot runs into barrier, and the attribute status of grid unit has been set to barrier; Robot does not run into barrier, and the attribute status of grid unit is set to clear.
Because robot first time cannot learn the place, edge of whole indoor, so the overall distribution of barrier is that comprise indoor edge, namely in heuristic process, indoor map can become complete in constantly makeover process in target chamber.
Further, the build mechanism of the indoor distribution of obstacles figure of described probabilistic type also comprises step 4, result of detection determination robot starting point next time according to the last time: select the center of the last maximum zone of ignorance explored in result as starting point, repeat step 3.
Further, the build mechanism of the indoor distribution of obstacles figure of described probabilistic type also comprises step 5: initial point is got back to by robot or the walking of institute of robot reaches preset value, explores and then terminates.
Further, the attribute status of grid unit is set to 0 or 1, wherein 0 represents clear, and wherein 1 indicates barrier.Robot is constantly explored, and the result at every turn explored and distribution plan before compare, and constantly refresh the attribute of grid unit in distribution plan, or the grid adding band attribute is first, and its attribute may be 0 also may be 1.So, after heuristic process many times, indoor edge, and distribution of obstacles situation can be confirmed, thus build complete indoor distribution of obstacles figure.
Further, the course of control system control is for discounting, and the starting point arranging robot is initial point, the position coordinates of robot is set as D=(x, y), and the unit of x, y is u, robot advances toward dead ahead a step-length, then x adds 1, y and remains unchanged; To advance a step-length toward dead astern, then x subtracts 1, y and remains unchanged; To advance a step-length toward front-left, then x remains unchanged, and y adds 1; To advance a step-length toward front-right, then x remains unchanged, and y subtracts 1, and the information that robot records in traveling process is that I={ (x, y)=0 ∪ (x, y)=1}, I is stored in cloud platform, then calculates distribution of obstacles figure according to I; Meanwhile, this distribution plan is also stored in cloud platform, and follow-up exploration from learning to knowledge, and then can improve speed and the precision of distribution of obstacles figure structure.
Further, described robot, by four-wheel drive, is advanced or rotation towards four direction by control system control.
Further, control system control often rotates and once rotates 90 °.
Further, setting n=1, robot forward travel distance is greater than 0.5u, and the attribute status of grid unit is set to clear.
Further, arranging the probability of 90 ° of turning clockwise when control system control runs into barrier is p, wherein p>0.5.
Further, control system control is advanced towards a direction, until run into barrier counterclockwise or turn clockwise 90 °.
Beneficial effect of the present invention is: the build mechanism of the indoor distribution of obstacles figure of probabilistic type of the present invention is applicable to actual working environment, and construction method simply should be gone.Advanced or rotation, at every turn towards a direction, until can not walk towards four direction by control system control.In this mode, after robot runs into barrier, change 90 degree, then, move on along this direction.Carry out this process again and again, indoor edge can be verified rapidly.Have the place of barrier to turn-take in order to avoid robot in three faces and can't get out, we select the direction turned to by Probability p.In order to avoid as you were, we establish p>0.5.For irregular indoor, adopt and be similar to the thought of integration, robot forward travel distance, more than 0.5u, can think this grid unit clear.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
Fig. 1 is the method flow diagram of a build mechanism embodiment of the indoor distribution of obstacles figure of a kind of probabilistic type of the present invention;
Fig. 2 is the indoor distribution of obstacles figure of the embodiment that the build mechanism of the indoor distribution of obstacles figure of a kind of probabilistic type of the present invention builds;
Fig. 3 is the state transition diagram of robot direction vector in the build mechanism of the indoor distribution of obstacles figure of a kind of probabilistic type of invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, be clearly and completely described the technical scheme in the embodiment of the present invention, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.
A build mechanism of the indoor distribution of obstacles figure of probabilistic type, comprises the following steps:
Step one: regarded as by room area and be made up of a lot of basic grid unit, described grid unit is square, and the size of grid unit is u; Grid unit is less, and distribution plan is more accurate, and grid cell size is more than or equal to the error in robot direction, and grid cell size can be set to 5cm or 10cm;
Step 2: step-length of being advanced by robot is set to s, s=n*u, wherein n >=1, n is integer;
Step 3: robot often take a step forward detection front whether have barrier; Robot runs into barrier, and the attribute status of grid unit has been set to barrier; Robot does not run into barrier, and the attribute status of grid unit is set to clear.In the present embodiment, setting n=1, robot forward travel distance is greater than 0.5u, and the attribute status of grid unit is set to clear.
Because robot first time cannot learn the place, edge of whole indoor, so the overall distribution of barrier is that comprise indoor edge, namely in heuristic process, indoor map can become complete in constantly makeover process in target chamber.
Step 4, the result of detection determination robot starting point next time according to the last time: select the center of the last maximum zone of ignorance explored in result as starting point, repeat step 3.
Step 5: initial point is got back to by robot or the walking of institute of robot reaches preset value exploration end.
Determine to explore the step-length number L terminated i-th time by following method
i, L
idetermined by following formula:
L
i＝(1-δ)×(L
i-1)+δ×(Q
i-1*1.1)(i＝2,3,4……)
As i=1, when namely first time is explored, L
1equal A, A is a constant relevant to exploring area size, 100>=A>=10.Q
irepresent and explore the grid quantity summation terminating rear distribution plan i-th time.δ upgrades regulatory factor, and 1 > δ > 0, the present embodiment δ is set to 0.7.
The attribute status of grid unit is set to 0 or 1, and wherein 0 represents clear, and wherein 1 indicates barrier.Robot is constantly explored, and the result at every turn explored and distribution plan before compare, and constantly refresh the attribute of grid unit in distribution plan, or the grid adding band attribute is first, and its attribute may be 0 also may be 1.So, after heuristic process many times, indoor edge, and distribution of obstacles situation can be confirmed, thus build complete indoor distribution of obstacles figure.Described robot, by four-wheel drive, is advanced or rotation towards four direction by control system control.
The course of control system control is for discounting, the starting point arranging robot is initial point, the position coordinates of robot is set as D=(x, y), the unit of x, y is u, and robot advances toward dead ahead a step-length, i.e. direction initialization vector (E=1, N=0), then x adds 1, y and remains unchanged; To advance a step-length, i.e. direction initialization vector (E=-1, N=0) toward dead astern, then x subtracts 1, y and remains unchanged; To advance a step-length, i.e. direction initialization vector (E=0, N=1) toward front-left, then x remains unchanged, and y adds 1; To advance a step-length toward front-right, i.e. direction initialization vector (E=0, N=-1), then x remains unchanged, and y subtracts 1, and the information that robot records in traveling process is I={ (x, y)=0 ∪ (x, y)=1}, I is stored in cloud platform, then calculates distribution of obstacles figure according to I.Meanwhile, this distribution plan is also stored in cloud platform, and follow-up exploration from learning to knowledge, and then can improve speed and the precision of distribution of obstacles figure structure.
As shown in Figure 1, the build mechanism method flow diagram of the indoor distribution of obstacles figure of probabilistic type:
After beginning, the starting point arranging robot is initial point, and robot advances towards a direction, and can not walk until run into barrier, by direction vector initialization: (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, setting (x, y)=1, and the attribute status of grid unit is set to 1; Do not run into barrier, setting (x, y)=0, the attribute status of grid unit is set to 0; Judge whether when running into barrier to arrive initial point, reach initial point, namely x=0 and y=0, ends task, or when exploring for i-th time, step-length number L
sarrive setting value L
i, end task; Do not reach initial point and L
sdo not arrive setting value, i.e. (or y ≠ 0, x ≠ 0) and L
s<L
icontrol system control turns to 90 ° or turn to 90 ° counterclockwise clockwise, wherein control system control turns to the probability of 90 ° to be greater than 0.5 clockwise, then continues not turn to advance step-length, and setting direction vector also judges whether to run into barrier; When not running into barrier, judge whether to reach initial point, arrive initial point and then end task, or when exploring for i-th time, step-length number L
sarrive setting value L
i, reach setting value and end task, do not reach initial point and L
sdo not arrive setting value, i.e. (or y ≠ 0, x ≠ 0) and L
s<L
i, then do not turn to, a step-length of advancing, repeat above-mentioned steps, finally obtain indoor distribution of obstacles figure as shown in Figure 2, the square of black represents and runs into barrier, the attribute status of grid unit is set to 1, and the square of white represents and do not run into barrier, and the attribute status of grid unit is set to 0.
The foregoing is only preferred embodiment of the present invention, not in order to limit the present invention, within the spirit and principles in the present invention all, any amendment done, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (10)
1. a build mechanism of the indoor distribution of obstacles figure of probabilistic type, is characterized in that, comprise the following steps:
Step one: regarded as by room area and be made up of a lot of basic grid unit, described grid unit is square, and the size of grid unit is u;
Step 2: step-length of being advanced by robot is set to s, s=n*u, wherein n >=1, n is integer;
Step 3: setting robot starting point, robot often take a step forward detection front whether have barrier; Robot runs into barrier, and the attribute status of grid unit has been set to barrier; Robot does not run into barrier, and the attribute status of grid unit is set to clear.
2. the build mechanism of the indoor distribution of obstacles figure of probabilistic type as claimed in claim 1, it is characterized in that: the build mechanism of the indoor distribution of obstacles figure of described probabilistic type also comprises step 4, result of detection determination robot starting point next time according to the last time: select the center of the last maximum zone of ignorance explored in result as starting point, repeat step 3.
3. the build mechanism of the indoor distribution of obstacles figure of probabilistic type as claimed in claim 2, is characterized in that: the build mechanism of the indoor distribution of obstacles figure of described probabilistic type also comprises step 5: initial point is got back to by robot or the walking of institute of robot reaches preset value exploration end.
4. the build mechanism of the indoor distribution of obstacles figure of the probabilistic type as described in claim 1 or 2 or 3, is characterized in that: the attribute status of grid unit is set to 0 or 1, wherein 0 represents clear, and wherein 1 indicates barrier.
5. the build mechanism of the indoor distribution of obstacles figure of probabilistic type as claimed in claim 4, it is characterized in that: the course of control system control is broken line, the starting point arranging robot is initial point, the position coordinates of robot is set as D=(x, y), the unit of x, y is u, and robot advances toward dead ahead a step-length, then x adds 1, y and remains unchanged; To advance a step-length toward dead astern, then x subtracts 1, y and remains unchanged; To advance a step-length toward front-left, then x remains unchanged, and y adds 1; To advance a step-length toward front-right, then x remains unchanged, and y subtracts 1, and the information that robot records in traveling process is that I={ (x, y)=0 ∪ (x, y)=1}, I is stored in cloud platform, then calculates distribution of obstacles figure according to I; Meanwhile, this distribution plan is also stored in cloud platform, and follow-up exploration from learning to knowledge, and then can improve speed and the precision of distribution of obstacles figure structure.
6. the build mechanism of the indoor distribution of obstacles figure of probabilistic type as claimed in claim 5, is characterized in that: described robot, by four-wheel drive, is advanced or rotation towards four direction by control system control.
7. the build mechanism of the indoor distribution of obstacles figure of probabilistic type as claimed in claim 5, is characterized in that: control system control often rotates and once rotates 90 °.
8. the build mechanism of the indoor distribution of obstacles figure of probabilistic type as claimed in claim 5, is characterized in that: setting n=1, robot forward travel distance is greater than 0.5u, and the attribute status of grid unit is set to clear.
9. the build mechanism of the indoor distribution of obstacles figure of probabilistic type as claimed in claims 6 or 7, is characterized in that: arranging the probability of 90 ° of turning clockwise when control system control runs into barrier is p, wherein p>0.5.
10. the build mechanism of the indoor distribution of obstacles figure of probabilistic type as claimed in claim 7, is characterized in that: control system control is advanced towards a direction, until run into barrier counterclockwise or turn clockwise 90 °.
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