CN111309031B - Robot, obstacle detection method and obstacle detection system - Google Patents
Robot, obstacle detection method and obstacle detection system Download PDFInfo
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- CN111309031B CN111309031B CN202010225132.XA CN202010225132A CN111309031B CN 111309031 B CN111309031 B CN 111309031B CN 202010225132 A CN202010225132 A CN 202010225132A CN 111309031 B CN111309031 B CN 111309031B
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- 238000004590 computer program Methods 0.000 claims description 7
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- 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
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0242—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using non-visible light signals, e.g. IR or UV signals
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- 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
- G05D1/0259—Control of position or course in two dimensions specially adapted to land vehicles using magnetic or electromagnetic means
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- 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
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
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Abstract
The invention provides a robot, an obstacle detection method and an obstacle detection system thereof, wherein detection data acquired in the current period of a plurality of infrared sensors are acquired; determining a suspected obstacle area of the robot according to the detection data acquired in the current period; and updating the obstacle detection state of the robot based on the suspected obstacle region and detection data acquired by the plurality of infrared sensors in the previous period of the current period. The obstacle detection method of the embodiment of the invention can update the obstacle detection result according to the running state of the robot in the actual environment, so that the running of the robot is safer and more reliable, and the trafficability of the robot is improved.
Description
[ field of technology ]
The present invention relates generally to the field of robotics, and more particularly, to a robot, and an obstacle detection method and an obstacle detection system thereof.
[ background Art ]
The robot obstacle detection technology has a very key function in the autonomous navigation system of the robot, and the quality of the robot obstacle detection technology can directly influence whether the robot can safely and reliably perform autonomous navigation when running in an actual environment. The sensors currently applied to the obstacle detection of the service robot are of a plurality of types, and mainly comprise a laser sensor, an RGBD sensor, an ultrasonic sensor and an infrared sensor. In order to enable the robot to have the three-dimensional obstacle avoidance capability, the above-mentioned multi-sensor data are generally fused for use. Since the working principle of the infrared sensor is a single-shot single-receipt mode, two common application schemes are provided: one is to use it as a single line ranging sensor, to use a single line ranging model to project obstacle ranging information, and the other is to use it as an ultrasonic-like sensor, to use a triangular beam model to project obstacle ranging information. Both of the two obstacle detection schemes based on infrared have a certain problem, the first scheme ignores most of the obstacles due to the fact that the single-line model is smaller in view, and the second scheme erroneously marks a larger part of non-obstacle areas as obstacles due to the fact that the approximate triangular beam model is larger along with the fact that the farther the obstacles are, so that the trafficability of the robot is reduced.
In view of this, there is an urgent need to propose a more accurate method for detecting an obstacle using an infrared sensor.
[ invention ]
An object of exemplary embodiments of the present invention is to provide a robot, an obstacle detection method and an obstacle detection system thereof, which overcome at least one of the above-mentioned drawbacks.
In one general aspect, there is provided a robot obstacle detection method, the robot including a plurality of infrared sensors, the obstacle detection method including:
acquiring detection data acquired in the current period of the infrared sensors;
determining a suspected obstacle area of the robot according to the detection data acquired in the current period;
and updating the obstacle detection state of the robot based on the suspected obstacle region and detection data acquired by the infrared sensors in the period before the current period.
Optionally, the robot includes a plurality of infrared sensors, including:
the infrared sensors are arranged on the outer surface of the robot outline in a fan-shaped array mode.
Optionally, the robot includes a plurality of infrared sensors, including:
each infrared sensor in the plurality of infrared sensors is arranged on the outer surface of the robot outline according to a preset step length.
Optionally, after the acquiring the detection data acquired by the current periods of the infrared sensors, the method further includes:
and filtering the detection data acquired in the current period by adopting a fluctuation amplitude value.
Optionally, the determining the suspected obstacle area of the robot according to the detection data acquired in the current period includes:
determining a suspected obstacle area of the robot by adopting a square matrix expansion division model to the detection data acquired in the current period; the square matrix expansion division model is established by adopting a preset expansion division rule according to the detection data acquired in the current period.
In another general aspect, the present invention provides a robot obstacle detection system, the robot including a plurality of infrared sensors, the obstacle detection system including:
the acquisition module is used for acquiring detection data acquired in the current period of the plurality of infrared sensors;
the determining module is used for determining a suspected obstacle area of the robot according to the detection data acquired in the current period;
and the updating module is used for updating the obstacle detection state of the robot based on the suspected obstacle region and detection data acquired by the infrared sensors in the period before the current period.
Optionally, the robot comprises a plurality of infrared sensors, and the plurality of infrared sensors are mounted on the outer surface of the robot contour in a fan-shaped array manner.
Optionally, the robot comprises a plurality of infrared sensors, and each infrared sensor of the plurality of infrared sensors is arranged on the outer surface of the robot outline according to a preset step length.
Optionally, the obstacle detection system further comprises a filtering module, wherein the filtering module is used for filtering the detection data acquired in the current period by adopting a fluctuation amplitude;
the determining module is specifically configured to determine a suspected obstacle region of the robot by using a square matrix expansion division model on the detection data acquired in the current period; the square matrix expansion division model is established by adopting a preset expansion division rule according to the detection data acquired in the current period.
In another general aspect, there is provided a robot including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the obstacle detection method as described above when executing the computer program.
By adopting the robot obstacle detection method and the obstacle detection system of the exemplary embodiment of the invention, detection data acquired in the current period of a plurality of infrared sensors are acquired; determining a suspected obstacle area of the robot according to the detection data acquired in the current period; and updating the obstacle detection state of the robot based on the suspected obstacle region and detection data acquired by the plurality of infrared sensors in the previous period of the current period. The obstacle detection method adopted by the invention can update the obstacle detection result according to the running state of the robot in the actual environment, so that the running of the robot is safer and more reliable, and the trafficability of the robot is improved.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 illustrates an installation schematic of an infrared sensor on a robot according to an exemplary embodiment of the present invention;
fig. 2 illustrates a flowchart of a robot obstacle detection method according to an exemplary embodiment of the present invention;
FIG. 3 shows a schematic diagram of building a matrix expansion division model according to an exemplary embodiment of the invention;
fig. 4 illustrates a schematic diagram of a method of determining a suspected obstacle region of a robot according to an exemplary embodiment of the invention;
fig. 5 illustrates a block diagram of a robot obstacle detection system according to an exemplary embodiment of the present invention;
fig. 6 shows a control block diagram of a robot according to an exemplary embodiment of the present invention.
[ detailed description ] of the invention
In order to make the objects, technical solutions and advantageous technical effects of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings and detailed description. It should be understood that the detailed description is intended to illustrate the invention, and not to limit the invention.
An embodiment of the present invention provides a robot obstacle detection method, wherein a robot includes a plurality of infrared sensors, and fig. 1 shows an installation schematic diagram of the infrared sensors on the robot according to an exemplary embodiment of the present invention.
For example, a plurality of infrared sensors may be installed on the outer surface of the robot contour in a fan-shaped array manner, as shown in fig. 1, when the left-handed system is adopted, the x-axis represents the positive direction of the robot, the y-axis represents the right hand side of the robot, each of the plurality of infrared sensors is installed on the outer surface of the robot contour according to a preset step length, generally, two front lateral vertexes of the robot contour are used as two side endpoints of the fan-shaped array, and the two front lateral vertexes are sequentially arranged in the fan-shaped area according to a fixed angle step length. The number of the sensors in the fan-shaped array is usually reversely determined according to a fixed angle or the distance between two adjacent infrared sensors, and is also related to the physical size of the robot; the infrared sensor can increase the accuracy and the sensing range along with the increase of the quantity, but the selected quantity needs to be considered in combination with the actual application condition.
Fig. 2 illustrates a flowchart of a robot obstacle detection method according to an exemplary embodiment of the present invention.
Referring to fig. 2, in step S10, detection data acquired at the current cycle of a plurality of infrared sensors is acquired.
In this step, detection data acquired by a plurality of infrared sensors in the current period, that is, infrared data acquired by each infrared sensor, for example, the acquisition period of the plurality of infrared sensors may be set according to the needs of the user, for example, acquired once every 25ms, or acquired once every 50 ms.
In step S20, a suspected obstacle region of the robot is determined based on the detection data acquired in the current cycle.
After the infrared data acquired in the current period are acquired, the suspected obstacle area of the robot is determined through a data processing algorithm.
The method for determining the suspected obstacle region of the robot may be exemplified by a method for determining the suspected obstacle region of the robot by using a square matrix expansion model at the end of each infrared measurement, wherein the square matrix expansion model is obtained according to a preset expansion rule, and a specific example will be described below.
Fig. 3 is a schematic diagram of a square matrix with a certain infrared end point as a center point of the corresponding square matrix, so as to establish 3*3, wherein the center point is set as S4, symbols in the remaining grids are numbered as S0, S1, S2, S3, S5, S6, S7, S8 in sequence from left to right and from top to bottom, and the set expansion rule is as follows: the single expansion of the grid corresponding to the central point S4 is 500, the single expansion of the outer layer of the central point S4 is 200, when the grid score in the square matrix is more than or equal to 1000, the attribute value of the grid is marked as a deadly obstacle state, otherwise, the attribute value is in a free state. In order to increase the safety of the robot operation, the size may be gradually increased,
in order to ensure the trafficability of the robot, the size can be gradually reduced, namely, a square matrix of 2 x 2, 5*5, 7*7 and the like formed by taking a certain infrared end point as the center point of the corresponding square matrix can be established, and the single expansion score of the grid in the square matrix can be properly adjusted according to the actual requirement of a user.
Referring to fig. 4, fig. 4 shows that the 4 th and 6 th infrared rays of the robot detect the obstacle in the current period, and the grid score of the area is 0 if all the infrared sensors in the area have not detected the obstacle before, and the scores are calculated for the grids affected by the 4 th and 6 th infrared rays in turn according to the above square matrix expansion division model: the 4 th infrared affected grid has: s0, S1, S2, S3, S5, S6, S7 and S8, wherein S4 is the center point of the grid corresponding to the infrared ranging end point, the score is accumulation 500, and the rest is accumulation 200; the 6 th infrared affected grid has: s0, S1, S2, S3, S4, S5, S6, S7, S8, wherein S4 is a center point of the grid corresponding to the end point of the infrared ranging, the score is an accumulated 500, the rest of accumulated 200, the 4 th and 6 th infrared overlapping regions are S2, S5, S8 (S0, S3, S6), and the accumulated score is 400, which is matched with the preset obstacle threshold. The comparison may be made with 1000, and the grid attributes are all free, and calculation is performed sequentially until the score of a grid is 1000 or more, and the attributes are converted into deadly barriers.
In step S30, the obstacle detection state of the robot is updated based on the suspected obstacle region and the detection data acquired by the plurality of infrared sensors in the period previous to the current period.
After the suspected obstacle area of the robot in the current period is determined, the obstacle detection state of the robot is updated according to the suspected obstacle area and detection data acquired in the previous period, and it can be understood that the suspected obstacle area can be determined through the detection data in the current period, and the obstacle detection state of the current robot can be updated in time by combining the historical detection data in the previous period.
The specific updating method may be that under the condition that the width of the minimum obstacle is greater than or equal to the distance between two adjacent infrared rays, that is, all the obstacles can be detected, infrared detection data without detecting the obstacle within a preset distance ρ is integrated into an infrared subset by sequentially traversing the infrared detection data, wherein the preset distance ρ can be between 30 cm and 35cm, according to actual requirements, when the number of elements (i.e., the number of infrared rays) in the infrared subset is greater than or equal to a preset number, the number can be set to be 3, and then all grid attributes corresponding to the outline area formed by the first infrared ray and the last infrared ray in the infrared subset are marked as free. For example, when the 3 rd, 4 th and 5 th infrared data exist in the infrared subset, the grid attribute is marked as free in the rectangle composed of the 3 rd and 5 th infrared data, so that the obstacle detection state of the robot is updated.
Illustratively, after acquiring the detection data acquired by the current periods of the infrared sensors, the method further includes: step S40, the detection data acquired in the current period are filtered by adopting the fluctuation amplitude.
In the step, invalid data can be filtered, so that the efficiency and accuracy of the subsequent data processing are improved, and the data volume is reduced. The specific fluctuation amplitude filtering rule is that when the variation R-diff of the infrared detection data R (i) of the current i-th frame of infrared data compared with the data value R (i-1) of the previous frame i-1 cannot be larger than the maximum allowable fluctuation amplitude sigma, if the variation R-diff is larger than the maximum allowable fluctuation amplitude sigma, the current frame of data is skipped, and the current frame of data is filtered as invalid data, and the calculation formula is as follows:
R-diff＝|R(i)-R(i-1)|
R(i)is valid,if R-diff≤σ
R(i)is invalid,if R-diff>σ
wherein R (i) represents infrared detection data of the current ith frame, R (i-1) is infrared detection data of the last frame, R-diff is the variation of the current frame R (i) and the last frame R (i-1), and sigma is the maximum allowable fluctuation amplitude.
According to the robot obstacle detection method provided by the embodiment of the invention, the detection data acquired in the current period of a plurality of infrared sensors are acquired; determining a suspected obstacle area of the robot according to the detection data acquired in the current period; and updating the obstacle detection state of the robot based on the suspected obstacle region and detection data acquired by the plurality of infrared sensors in the previous period of the current period. The obstacle detection method adopted by the invention can update the obstacle detection result according to the running state of the robot in the actual environment, so that the running of the robot is safer and more reliable, and the trafficability of the robot is improved.
The embodiment of the invention also provides a robot obstacle detection system 100, and fig. 5 shows a block diagram of the robot obstacle detection system according to an exemplary embodiment of the invention. The robot includes a plurality of infrared sensors, and the obstacle detection system 100 includes:
an acquisition module 101, configured to acquire detection data acquired in a current period of a plurality of infrared sensors;
a determining module 102, configured to determine a suspected obstacle region of the robot according to the detection data acquired in the current period;
and the updating module 103 is used for updating the obstacle detection state of the robot based on the suspected obstacle region and detection data acquired by the plurality of infrared sensors in the period before the current period.
Illustratively, a number of infrared sensors are mounted on the outer surface of the robot profile in a fan-like array.
Illustratively, each of the plurality of infrared sensors is mounted on an outer surface of the robot contour at a predetermined angle therebetween.
Illustratively, the obstacle detection system 100 further includes:
and the filtering module 104 is used for filtering the detection data acquired in the current period by adopting the fluctuation amplitude.
The determining module 102 is specifically configured to determine a suspected obstacle area of the robot by using a matrix expansion division model for the detection data acquired in the current period; the square matrix expansion division model is established by adopting a preset expansion division rule according to detection data acquired in the current period.
The robot obstacle detection system provided by the embodiment of the invention comprises a plurality of infrared sensors, wherein the robot comprises an acquisition module for acquiring detection data acquired by the plurality of infrared sensors in the current period; the determining module is used for determining a suspected obstacle area of the robot according to the detection data acquired in the current period; the updating module is used for updating the obstacle detection state of the robot based on the suspected obstacle area and the detection data acquired by the plurality of infrared sensors in the previous period of the current period, and acquiring the detection data acquired by the plurality of infrared sensors in the current period. The obstacle detection system adopted by the invention can update the obstacle detection result according to the running state of the robot in the actual environment, so that the running of the robot is safer and more reliable, and the trafficability of the robot is improved.
The invention also provides a robot comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the obstacle detection method as described above when executing the computer program.
As shown in fig. 6, a robot 200 according to an exemplary embodiment of the present invention includes: a processor 201 and a memory 202.
Specifically, the memory 202 is used to store a computer program that, when executed by the processor 201, implements the obstacle detection method described above.
While the invention has been shown and described with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes and modifications may be made to these embodiments without departing from the spirit and scope of the invention as defined by the following claims.
The present invention is not limited to the details and embodiments described herein, and thus additional advantages and modifications may readily be made by those skilled in the art, without departing from the spirit and scope of the general concepts defined in the claims and the equivalents thereof, and the invention is not limited to the specific details, representative apparatus and illustrative examples shown and described herein.
Claims (8)
1. A robot obstacle detection method, wherein the robot includes a plurality of infrared sensors, the obstacle detection method comprising:
acquiring detection data acquired in the current period of the infrared sensors;
determining a suspected obstacle area of the robot according to the detection data acquired in the current period;
updating the obstacle detection state of the robot based on the suspected obstacle region and detection data acquired by the infrared sensors in the period before the current period;
specifically, the determining the suspected obstacle area of the robot according to the detection data acquired in the current period includes:
determining a suspected obstacle area of the robot by adopting a square matrix expansion division model to the detection data acquired in the current period; the square matrix expansion division model is established by adopting a preset expansion division rule according to the detection data acquired in the current period;
the square matrix expansion division model takes a certain infrared end point as a center point of a corresponding square matrix to establish a 3*3 square matrix, the center point is set as S4, and symbols in other grids are sequentially numbered as S0, S1, S2, S3, S5, S6, S7 and S8 in sequence from left to right and from top to bottom; the set tension division rule is as follows: the single expansion of the grid corresponding to the central point S4 is 500, the single expansion of the outer layer of the central point S4 is 200, when the grid score in the square matrix is more than or equal to 1000, the attribute value of the grid is marked as a deadly obstacle state, otherwise, the attribute value is in a free state;
specifically, updating the obstacle detection state of the robot includes:
under the condition that the width of the minimum obstacle is larger than or equal to the distance between two adjacent infrared rays, namely all the obstacles can be detected, the infrared detection data without the obstacle detected within a preset distance rho are integrated into an infrared subset by traversing the infrared detection data in sequence,
wherein, the preset distance ρ can be between 30 cm and 35cm, and when the number of elements in the infrared subset is greater than or equal to the preset number, all grid attributes corresponding to the outline area formed by the first infrared ray and the last infrared ray in the infrared subset are marked as free.
2. The obstacle detection method as claimed in claim 1, wherein the robot includes a plurality of infrared sensors, comprising:
the infrared sensors are arranged on the outer surface of the robot outline in a fan-shaped array mode.
3. The obstacle detection method as claimed in claim 1, wherein the robot includes a plurality of infrared sensors, comprising:
each infrared sensor in the plurality of infrared sensors is arranged on the outer surface of the robot outline according to a preset step length.
4. The obstacle detecting method as claimed in claim 1, further comprising, after said acquiring the detection data acquired in the current cycle of the plurality of infrared sensors:
and filtering the detection data acquired in the current period by adopting a fluctuation amplitude value.
5. A robot obstacle detection system, wherein the robot includes a plurality of infrared sensors, the obstacle detection system comprising:
the acquisition module is used for acquiring detection data acquired in the current period of the plurality of infrared sensors;
the determining module is used for determining a suspected obstacle area of the robot according to the detection data acquired in the current period;
the updating module is used for updating the obstacle detection state of the robot based on the suspected obstacle region and detection data acquired by the infrared sensors in the period before the current period;
specifically, the obstacle detection system further comprises a filtering module, wherein the filtering module is used for filtering the detection data acquired in the current period by adopting a fluctuation amplitude;
the determining module is specifically configured to determine a suspected obstacle region of the robot by using a square matrix expansion division model on the detection data acquired in the current period; the square matrix expansion division model is established by adopting a preset expansion division rule according to the detection data acquired in the current period;
the square matrix expansion division model takes a certain infrared end point as a center point of a corresponding square matrix to establish a 3*3 square matrix, the center point is set as S4, and symbols in other grids are sequentially numbered as S0, S1, S2, S3, S5, S6, S7 and S8 in sequence from left to right and from top to bottom; the set tension division rule is as follows: the single expansion of the grid corresponding to the central point S4 is 500, the single expansion of the outer layer of the central point S4 is 200, when the grid score in the square matrix is more than or equal to 1000, the attribute value of the grid is marked as a deadly obstacle state, otherwise, the attribute value is in a free state
Specifically, the updating module is further configured to:
under the condition that the width of the minimum obstacle is larger than or equal to the distance between two adjacent infrared rays, namely all the obstacles can be detected, the infrared detection data without the obstacle detected within a preset distance rho are integrated into an infrared subset by traversing the infrared detection data in sequence,
wherein, the preset distance ρ can be between 30 cm and 35cm, and when the number of elements in the infrared subset is greater than or equal to the preset number, all grid attributes corresponding to the outline area formed by the first infrared ray and the last infrared ray in the infrared subset are marked as free.
6. The obstacle detection system as claimed in claim 5, wherein the robot includes a plurality of infrared sensors mounted in a fan-shaped array on an outer surface of the robot profile.
7. The obstacle detection system as claimed in claim 5, wherein the robot comprises a plurality of infrared sensors, each of the plurality of infrared sensors being mounted on an outer surface of the robot profile with a preset step therebetween.
8. Robot comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, carries out the method steps of obstacle detection according to any one of claims 1 to 4.
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| CN110850859A (en) * | 2018-08-01 | 2020-02-28 | 深圳市优必选科技有限公司 | Robot and obstacle avoidance method and obstacle avoidance system thereof |
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| CN110782506A (en) * | 2019-11-21 | 2020-02-11 | 大连理工大学 | Method for constructing grid map by fusing infrared camera and depth camera |
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