CN110895409A - Control method for avoiding barrier - Google Patents
Control method for avoiding barrier Download PDFInfo
- Publication number
- CN110895409A CN110895409A CN201810967285.4A CN201810967285A CN110895409A CN 110895409 A CN110895409 A CN 110895409A CN 201810967285 A CN201810967285 A CN 201810967285A CN 110895409 A CN110895409 A CN 110895409A
- Authority
- CN
- China
- Prior art keywords
- obstacle
- robot
- image information
- type
- image
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 46
- 230000004888 barrier function Effects 0.000 title claims description 8
- 230000008569 process Effects 0.000 claims abstract description 18
- 238000003062 neural network model Methods 0.000 claims abstract description 12
- 238000000605 extraction Methods 0.000 claims description 7
- 230000008859 change Effects 0.000 abstract description 6
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000004044 response Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000005034 decoration Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000001143 conditioned effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000010408 sweeping Methods 0.000 description 1
Images
Classifications
-
- 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
- G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
-
- 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
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Multimedia (AREA)
- Electromagnetism (AREA)
- Manipulator (AREA)
Abstract
The invention discloses a control method for avoiding obstacles. Wherein, the method comprises the following steps: acquiring image information in the process that the robot moves according to a preset path; identifying image information based on an identification model, and acquiring the type of an obstacle if the obstacle is identified, wherein the identification model is a neural network model based on a depth reconstruction model and is used for determining whether the image information contains an image of the obstacle; and controlling whether the robot needs to avoid the obstacle or not based on the type of the obstacle. The invention solves the technical problem that in the prior art, all obstacles need to be avoided in the process of moving the robot according to the set path, so that the path change is wrong.
Description
Technical Field
The invention relates to the field of intelligent control, in particular to a control method for avoiding obstacles.
Background
The artificial intelligence technology is rapidly developed, the influence of smart homes on the life of users is more and more, the application convenience is gradually upgraded, and the applicability is still to be further improved on some service details. For example, an existing sweeping robot implements new planning on a walking path of the robot according to detection of an obstacle, so as to avoid the obstacle. However, sometimes, the type of avoiding the obstacle is different, and there is a possibility that a paper roll or a doll will cause the path of the robot to change, and in fact, the machine does not need to avoid a paper roll or a doll and should operate according to the originally planned path. In the process that a robot in the prior art moves according to a set path, obstacles cannot be accurately classified, so that all the obstacles need to be avoided.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a control method for avoiding obstacles, which is used for at least solving the technical problem that in the prior art, all obstacles need to be avoided in the process that a robot moves according to a set path, so that errors exist in path change.
According to an aspect of an embodiment of the present invention, there is provided a control method for avoiding an obstacle, including: acquiring image information in the process that the robot moves according to a preset path; identifying the image information based on an identification model, and acquiring the type of the obstacle if the obstacle is identified, wherein the identification model is a neural network model based on a depth reconstruction model and is used for determining whether the image information contains an image of the obstacle; controlling whether the robot needs to avoid the obstacle based on the type of the obstacle.
Optionally, identifying the image information based on the identification model includes: performing feature extraction on the image information to obtain at least one image feature, wherein the feature extraction is performed based on a predetermined feature parameter; and detecting whether the image information contains the image parameters of the obstacle or not by inputting the extracted at least one image feature into the recognition model.
Optionally, the image parameter of the obstacle includes at least one of: mass, volume, type, density, hardness and strength of the obstacle.
Optionally, if an obstacle is identified, acquiring the type of the obstacle includes: if the image information is detected to contain the image parameters of the obstacle, determining that the image information contains the image of the obstacle; and acquiring the type of the obstacle based on the image parameters of the obstacle.
Optionally, controlling whether the robot needs to avoid the obstacle based on the type of the obstacle includes: based on the type of the obstacle, calling a control instruction; and controlling whether the robot needs to avoid the barrier or not based on the control instruction.
Optionally, based on the type of the obstacle, a control instruction is invoked: determining a damage degree when the robot collides with the obstacle based on the type of the obstacle; inquiring a corresponding control instruction based on the damage degree, wherein when the damage degree is within a preset range, the control instruction is to keep the current moving position of the robot; and when the damage degree is out of the preset range, the control command is that the obstacle needs to be avoided.
Optionally, when the control instruction is to control the robot to avoid the obstacle, the control instruction further includes information of an avoidance path for controlling the robot to avoid the obstacle.
According to another aspect of the embodiments of the present invention, there is provided a control apparatus for avoiding an obstacle, including: the acquisition module is used for acquiring image information in the process that the robot moves according to a preset path; the identification module is used for identifying the image information based on an identification model and acquiring the type of the obstacle if the obstacle is identified, wherein the identification model is a neural network model based on a depth reconstruction model and is used for determining whether the image information contains an image of the obstacle; and the control module is used for controlling whether the robot needs to avoid the obstacle or not based on the type of the obstacle.
According to another aspect of the embodiments of the present invention, there is provided a storage medium storing program instructions, wherein when the program instructions are executed, the storage medium is controlled by a device to execute any one of the above methods.
According to another aspect of the embodiments of the present invention, there is provided a processor for executing a program, wherein the program executes to perform the method of any one of the above.
In the embodiment of the invention, image information is collected in the process that the robot moves according to a preset path; identifying the image information based on an identification model, and acquiring the type of the obstacle if the obstacle is identified, wherein the identification model is a neural network model based on a depth reconstruction model and is used for determining whether the image information contains an image of the obstacle; based on the type of the obstacle, the mode of controlling whether the robot needs to avoid the obstacle is carried out, the type of the obstacle is identified through the identification model, and the purpose of classifying according to the type of the obstacle is achieved, so that the technical effects of accurately making a response according to the specific situation of the obstacle and improving the accuracy of the path are achieved, and the technical problem that in the prior art, in the process that the robot moves according to the set path, all obstacles need to be avoided, and the path change is wrong is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a flowchart of a control method of avoiding an obstacle according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a control device for avoiding an obstacle according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In accordance with an embodiment of the present invention, there is provided an embodiment of a method of controlling avoidance of an obstacle, it being noted that the steps illustrated in the flowchart of the drawings may be carried out in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be carried out in an order different than that presented herein.
Fig. 1 is a flowchart of a control method for avoiding an obstacle according to an embodiment of the present invention, as shown in fig. 1, the method including the steps of:
step S102, collecting image information in the process that the robot moves according to a preset path;
step S104, identifying image information based on an identification model, and acquiring the type of the obstacle if the obstacle is identified, wherein the identification model is a neural network model based on a depth reconstruction model and is used for determining whether the image information contains an image of the obstacle;
and step S106, controlling whether the robot needs to avoid the obstacle or not based on the type of the obstacle.
Through the steps, image information is collected in the process that the robot moves according to the preset path; identifying image information based on an identification model, and acquiring the type of an obstacle if the obstacle is identified, wherein the identification model is a neural network model based on a depth reconstruction model and is used for determining whether the image information contains an image of the obstacle; based on the type of the obstacle, the mode of controlling whether the robot needs to avoid the obstacle is realized, the type of the obstacle is identified through the identification model, and the purpose of classifying according to the type of the obstacle is achieved, so that the accurate response according to the specific situation of the obstacle is realized, the technical effect of improving the path accuracy is achieved, and the technical problem that in the prior art, all obstacles need to be avoided in the process that the robot moves according to the set path, and the path change is wrong is solved.
The robot moves along a predetermined path, that is, the robot moves, or the robot works. The predetermined path is obtained by planning the robot according to a preset electronic map, where the electronic map at least includes geographic features of an environment where the robot is located, for example, in a building, where the electronic map at least includes positions of walls and doors. In an outdoor environment, the electronic map at least comprises the positions of mountains, valleys and the like. The preset route can be a plurality of routes, and the preset route can be dynamically planned according to the obstacles.
The recognition model is a neural network model based on a depth reconstruction model and used for determining whether image information contains an image of an obstacle, the neural network model based on the depth reconstruction model is small in iteration times, small in calculated amount and high in operation speed, and recognition efficiency can be effectively improved. When the recognition model recognizes that the image information contains an obstacle, the type of the obstacle is determined, and whether the obstacle needs to be avoided is determined.
The types of the obstacles can be an obstacle needing to be avoided and an obstacle not needing to be avoided, for the obstacle needing to be avoided, the robot plans an avoidance path to be avoided, and replans the path through a preset path and the avoidance path, wherein the avoidance path comprises a fastest avoidance path and a shortest avoidance path, the fastest avoidance path is the fastest (shortest time) path for the robot to bypass the obstacle from the current position and return to the preset path, and the shortest avoidance path is the shortest (shortest path) path for the robot to bypass the obstacle from the current position and return to the preset path.
Optionally, identifying the image information based on the identification model includes: performing feature extraction on the image information to obtain at least one image feature, wherein the feature extraction is performed based on a predetermined feature parameter; and detecting whether the image information contains the image parameters of the obstacle or not by inputting the extracted at least one image feature into the recognition model.
In the above-described image information extraction, the determined image feature is an image feature associated with the obstacle, and the more the image features are, the more accurate the obstacle recognition is. The image information is recognized based on the recognition model by inputting the image features into the recognition model and outputting the image parameters of whether the image information contains the obstacle or not by the recognition model.
Optionally, the image parameter of the obstacle includes at least one of: mass, volume, type, density, hardness and strength of the obstacle.
And determining the image parameters of the obstacles through the recognition model according to the image characteristics in the image information, wherein the image parameters are used for classifying the obstacles so as to determine whether avoidance is needed according to the type of the obstacles. The mass, and/or volume, and/or density of the obstacle is indicative of how easily the obstacle is moved, e.g., the greater the mass, the more difficult the obstacle is to be moved; the type, hardness and/or strength of the obstacle is indicative of whether the obstacle will affect the robot when moved, for example, if the hardness is too high, the robot may be damaged.
Optionally, if the obstacle is identified, acquiring the type of the obstacle includes: if the image information is detected to contain the image parameters of the obstacle, determining that the image information contains the image of the obstacle; based on the image parameters of the obstacle, the type of the obstacle is obtained.
When the image parameters of the obstacle are identified to be contained in the image information, the image of the obstacle existing in the image information is determined, namely the obstacle existing on the forward path of the obstacle is confirmed, the type of the obstacle needs to be judged, and whether the obstacle belongs to the obstacle needing to be avoided or the obstacle not needing to be avoided is judged. The type of the obstacle may be determined based on image parameters of the obstacle.
Optionally, controlling whether the robot needs to avoid the obstacle based on the type of the obstacle includes: calling a control instruction based on the type of the obstacle; and controlling whether the robot needs to avoid the barrier or not based on the control instruction.
And when the obstacle belongs to the obstacle which does not need to be avoided, calling a control command for continuing to advance, and controlling the robot to continue to advance according to the preset path.
Optionally, based on the type of the obstacle, the control instruction is invoked: determining the damage degree when the robot collides with the obstacle based on the type of the obstacle; inquiring a corresponding control instruction based on the damage degree, wherein when the damage degree is within a preset range, the control instruction is to keep the current moving position of the robot; and when the damage degree is out of the preset range, controlling the command to avoid the barrier.
When the obstacle is determined to be an obstacle that does not need to be avoided, whether to avoid the obstacle is determined in consideration of the specific requirements of the robot. For example, based on the type of the obstacle, determining the damage degree when the robot collides with the obstacle; inquiring a corresponding control instruction based on the damage degree, wherein when the damage degree is within a preset range, the control instruction is to keep the current moving position of the robot; and when the damage degree is out of the preset range, controlling the command to avoid the barrier. The damage level may be power loss, travel time, etc. If the robot is conditioned on power loss and exceeds a certain power loss threshold, the robot should be controlled to avoid the obstacle. Under the condition that the moving time required by the robot to move the obstacle is conditional, and the moving time exceeds a certain moving time threshold value, the robot is controlled to avoid the obstacle.
Optionally, in a case that the control instruction is to control the robot to avoid the obstacle, the control instruction further includes information of an avoidance path for controlling the robot to avoid the obstacle.
The avoidance path is used for the robot to carry out avoidance behavior on the barrier. The avoidance path can be planned according to a path planning method.
The condition that the robot does not perform avoidance is that the robot has a sufficient driving force regardless of the obstacle and that damage to the robot is allowed in which case the robot can receive the obstacle when the obstacle comes into contact with the robot. For example, in a robot focusing on appearance, if the hardness of the outer shell of the robot is smaller than that of an obstacle, the robot cannot travel along the original path even if the robot can push the obstacle if the robot cannot receive a certain degree of damage; if the robot can receive the damage to the above-described extent, it is possible to consider whether or not the robot can travel along a predetermined route regardless of the obstacle. Therefore, the image parameters are used for determining the type of the obstacle according to the specific robot condition and the obstacle condition, and the obstacle belongs to the obstacle needing to be avoided or the obstacle not needing to be avoided.
Fig. 2 is a schematic structural diagram of a control device for avoiding an obstacle according to an embodiment of the present invention, and as shown in fig. 2, the control device 20 includes: an acquisition module 22, an identification module 24, and a control module 26, which are described in detail below.
The acquisition module 22 is used for acquiring image information in the process that the robot moves according to a preset path; the recognition module 24 is connected to the acquisition module 22, and is configured to recognize image information based on a recognition model, and if an obstacle is recognized, obtain a type of the obstacle, where the recognition model is a neural network model based on a depth reconstruction model, and is configured to determine whether the image information includes an image of the obstacle; and a control module 26 connected with the identification module 24 and used for controlling whether the robot needs to avoid the obstacle or not based on the type of the obstacle.
According to the device, the acquisition module 22 acquires image information in the process that the robot moves according to the preset path; the recognition module 24 recognizes the image information based on a recognition model, and if the obstacle is recognized, obtains the type of the obstacle, wherein the recognition model is a neural network model based on a depth reconstruction model and is used for determining whether the image information contains an image of the obstacle; the control module 26 controls the mode of whether the robot needs to avoid the obstacle or not based on the type of the obstacle, and identifies the type of the obstacle through the identification model, so that the purpose of classifying according to the type of the obstacle is achieved, the accurate response according to the specific situation of the obstacle is realized, the technical effect of improving the path accuracy is achieved, and the technical problem that in the prior art, in the process that the robot moves according to the set path, all obstacles need to be avoided, and the path change has errors is solved.
According to another aspect of the embodiments of the present invention, there is provided a storage medium storing program instructions, wherein when the program instructions are executed, the apparatus on which the storage medium is located is controlled to execute the method of any one of the above.
According to another aspect of the embodiments of the present invention, there is provided a processor for executing a program, wherein the program executes to perform the method of any one of the above.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A method of controlling avoidance of an obstacle, comprising:
acquiring image information in the process that the robot moves according to a preset path;
identifying the image information based on an identification model, and acquiring the type of the obstacle if the obstacle is identified, wherein the identification model is a neural network model based on a depth reconstruction model and is used for determining whether the image information contains an image of the obstacle;
controlling whether the robot needs to avoid the obstacle based on the type of the obstacle.
2. The method of claim 1, wherein identifying the image information based on an identification model comprises:
performing feature extraction on the image information to obtain at least one image feature, wherein the feature extraction is performed based on a predetermined feature parameter;
and detecting whether the image information contains the image parameters of the obstacle or not by inputting the extracted at least one image feature into the recognition model.
3. The method of claim 2, wherein the image parameters of the obstacle comprise at least one of: mass, volume, type, density, hardness and strength of the obstacle.
4. The method of claim 3, wherein if an obstacle is identified, obtaining the type of the obstacle comprises:
if the image information is detected to contain the image parameters of the obstacle, determining that the image information contains the image of the obstacle;
and acquiring the type of the obstacle based on the image parameters of the obstacle.
5. The method of claim 4, wherein controlling whether the robot needs to avoid the obstacle based on the type of the obstacle comprises:
based on the type of the obstacle, calling a control instruction;
and controlling whether the robot needs to avoid the barrier or not based on the control instruction.
6. The method of claim 5, wherein based on the type of obstacle, invoking a control instruction:
determining a damage degree when the robot collides with the obstacle based on the type of the obstacle;
inquiring corresponding control instructions based on the damage degree,
when the damage degree is within a preset range, the control instruction is to keep the current moving position of the robot;
and when the damage degree is out of the preset range, the control command is that the obstacle needs to be avoided.
7. The method of claim 5, wherein in the event that the control instruction is to control the robot to avoid the obstacle, the control instruction further includes information of an avoidance path to control the robot to avoid the obstacle.
8. A control device for avoiding an obstacle, comprising:
the acquisition module is used for acquiring image information in the process that the robot moves according to a preset path;
the identification module is used for identifying the image information based on an identification model and acquiring the type of the obstacle if the obstacle is identified, wherein the identification model is a neural network model based on a depth reconstruction model and is used for determining whether the image information contains an image of the obstacle;
and the control module is used for controlling whether the robot needs to avoid the obstacle or not based on the type of the obstacle.
9. A storage medium storing program instructions, wherein the program instructions, when executed, control an apparatus in which the storage medium is located to perform the method of any one of claims 1 to 7.
10. A processor, characterized in that the processor is configured to run a program, wherein the program when running performs the method of any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810967285.4A CN110895409B (en) | 2018-08-23 | 2018-08-23 | Control method for avoiding barrier |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810967285.4A CN110895409B (en) | 2018-08-23 | 2018-08-23 | Control method for avoiding barrier |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110895409A true CN110895409A (en) | 2020-03-20 |
| CN110895409B CN110895409B (en) | 2020-11-27 |
Family
ID=69784778
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810967285.4A Active CN110895409B (en) | 2018-08-23 | 2018-08-23 | Control method for avoiding barrier |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110895409B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113335527A (en) * | 2021-06-16 | 2021-09-03 | 宁波大学 | Wearable unmanned aerial vehicle |
| WO2021203784A1 (en) * | 2020-04-09 | 2021-10-14 | 北京石头世纪科技股份有限公司 | Image selection method, self-propelled device, and computer storage medium |
| CN114260891A (en) * | 2021-12-16 | 2022-04-01 | 珠海格力电器股份有限公司 | Robot control method and device, electronic equipment and storage medium |
| CN116203970A (en) * | 2023-04-27 | 2023-06-02 | 泰坦(天津)能源技术有限公司 | Intelligent obstacle avoidance method and system for inspection robot |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001037289A (en) * | 1999-05-20 | 2001-02-09 | Yaskawa Electric Corp | Abnormal load detecting/controlling method for servo motor and multi-joint robot |
| US6429617B1 (en) * | 1999-10-22 | 2002-08-06 | Kawasaki Jukogyo Kabushiki Kaisha | Drive-controlling method and apparatus and robot having the apparatus |
| WO2006049559A1 (en) * | 2004-11-02 | 2006-05-11 | Viktor Kaznov | Ball robot |
| KR100952893B1 (en) * | 2008-05-21 | 2010-04-16 | 성균관대학교산학협력단 | Mobile robot conrolling system using a racing wheel and method thereof |
| CN102339019A (en) * | 2011-07-26 | 2012-02-01 | 重庆邮电大学 | Intelligent wheel chair obstacle avoidance method based on fuzzy neural network |
| CN102914967A (en) * | 2012-09-21 | 2013-02-06 | 浙江工业大学 | Autonomous navigation and man-machine coordination picking operating system of picking robot |
| CN105362048A (en) * | 2015-10-15 | 2016-03-02 | 广东欧珀移动通信有限公司 | Mobile equipment and barrier information prompting method and device based on mobile equipment |
| CN105511478A (en) * | 2016-02-23 | 2016-04-20 | 百度在线网络技术（北京）有限公司 | Robot cleaner, control method applied to same and terminal |
| CN205375184U (en) * | 2015-11-24 | 2016-07-06 | 黑龙江中科诺晟自动化设备开发有限公司 | Anthropomorphic food delivery robot based on from main motion |
| CN106872987A (en) * | 2017-03-15 | 2017-06-20 | 桂林理工大学 | Obstacle detecting device and omnidirectional's car |
| CN106940565A (en) * | 2017-03-24 | 2017-07-11 | 西安旋飞电子科技有限公司 | A kind of obstacle detection system of anticollision unmanned plane |
| WO2017200303A2 (en) * | 2016-05-17 | 2017-11-23 | 엘지전자 주식회사 | Mobile robot and control method therefor |
| CN107427177A (en) * | 2015-02-13 | 2017-12-01 | 三星电子株式会社 | Clean robot and its control method |
| CN108227731A (en) * | 2016-12-15 | 2018-06-29 | 比亚迪股份有限公司 | Unmanned plane collision-proof method and device |
| CN108319982A (en) * | 2018-02-06 | 2018-07-24 | 贵州电网有限责任公司 | A kind of power-line patrolling unmanned plane Fusion obstacle detection method |
| US20180210445A1 (en) * | 2017-01-25 | 2018-07-26 | Lg Electronics Inc. | Moving robot and control method thereof |
| CN108344414A (en) * | 2017-12-29 | 2018-07-31 | 中兴通讯股份有限公司 | A kind of map structuring, air navigation aid and device, system |
-
2018
- 2018-08-23 CN CN201810967285.4A patent/CN110895409B/en active Active
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001037289A (en) * | 1999-05-20 | 2001-02-09 | Yaskawa Electric Corp | Abnormal load detecting/controlling method for servo motor and multi-joint robot |
| US6429617B1 (en) * | 1999-10-22 | 2002-08-06 | Kawasaki Jukogyo Kabushiki Kaisha | Drive-controlling method and apparatus and robot having the apparatus |
| WO2006049559A1 (en) * | 2004-11-02 | 2006-05-11 | Viktor Kaznov | Ball robot |
| KR100952893B1 (en) * | 2008-05-21 | 2010-04-16 | 성균관대학교산학협력단 | Mobile robot conrolling system using a racing wheel and method thereof |
| CN102339019A (en) * | 2011-07-26 | 2012-02-01 | 重庆邮电大学 | Intelligent wheel chair obstacle avoidance method based on fuzzy neural network |
| CN102914967A (en) * | 2012-09-21 | 2013-02-06 | 浙江工业大学 | Autonomous navigation and man-machine coordination picking operating system of picking robot |
| CN107427177A (en) * | 2015-02-13 | 2017-12-01 | 三星电子株式会社 | Clean robot and its control method |
| CN105362048A (en) * | 2015-10-15 | 2016-03-02 | 广东欧珀移动通信有限公司 | Mobile equipment and barrier information prompting method and device based on mobile equipment |
| CN205375184U (en) * | 2015-11-24 | 2016-07-06 | 黑龙江中科诺晟自动化设备开发有限公司 | Anthropomorphic food delivery robot based on from main motion |
| CN105511478A (en) * | 2016-02-23 | 2016-04-20 | 百度在线网络技术（北京）有限公司 | Robot cleaner, control method applied to same and terminal |
| WO2017200303A2 (en) * | 2016-05-17 | 2017-11-23 | 엘지전자 주식회사 | Mobile robot and control method therefor |
| CN108227731A (en) * | 2016-12-15 | 2018-06-29 | 比亚迪股份有限公司 | Unmanned plane collision-proof method and device |
| US20180210445A1 (en) * | 2017-01-25 | 2018-07-26 | Lg Electronics Inc. | Moving robot and control method thereof |
| CN106872987A (en) * | 2017-03-15 | 2017-06-20 | 桂林理工大学 | Obstacle detecting device and omnidirectional's car |
| CN106940565A (en) * | 2017-03-24 | 2017-07-11 | 西安旋飞电子科技有限公司 | A kind of obstacle detection system of anticollision unmanned plane |
| CN108344414A (en) * | 2017-12-29 | 2018-07-31 | 中兴通讯股份有限公司 | A kind of map structuring, air navigation aid and device, system |
| CN108319982A (en) * | 2018-02-06 | 2018-07-24 | 贵州电网有限责任公司 | A kind of power-line patrolling unmanned plane Fusion obstacle detection method |
Non-Patent Citations (3)
| Title |
|---|
| TOMAS BERGLUND 等: "Planning Smooth and Obstacle-Avoiding B-Spline Paths for Autonomous Mining Vehicles", 《IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING》 * |
| WEN-CHUNG CHANG 等: "Visual Navigation and Control of Mobile Robots Based on Environment Mapping", 《PROCEEDINGS OF THE 2014 INTERNATIONAL CONFERENCE ON ADVANCED MECHATRONIC SYSTEMS》 * |
| 曾岑: "室内清洁机器人的全区域路径规划及避障研究", 《中国优秀硕士学位论文全文数据库信息科技辑》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021203784A1 (en) * | 2020-04-09 | 2021-10-14 | 北京石头世纪科技股份有限公司 | Image selection method, self-propelled device, and computer storage medium |
| US11880967B2 (en) | 2020-04-09 | 2024-01-23 | Beijing Roborock Technology Co., Ltd. | Image selection method, self-propelled apparatus, and computer storage medium |
| CN113335527A (en) * | 2021-06-16 | 2021-09-03 | 宁波大学 | Wearable unmanned aerial vehicle |
| CN114260891A (en) * | 2021-12-16 | 2022-04-01 | 珠海格力电器股份有限公司 | Robot control method and device, electronic equipment and storage medium |
| CN114260891B (en) * | 2021-12-16 | 2024-06-11 | 珠海格力电器股份有限公司 | Robot control method and device, electronic equipment and storage medium |
| CN116203970A (en) * | 2023-04-27 | 2023-06-02 | 泰坦(天津)能源技术有限公司 | Intelligent obstacle avoidance method and system for inspection robot |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110895409B (en) | 2020-11-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110895409B (en) | Control method for avoiding barrier | |
| US11709058B2 (en) | Path planning method and device and mobile device | |
| CN104994360A (en) | Video monitoring method and video monitoring system | |
| CN113670292B (en) | Map drawing method and device, sweeper, storage medium and electronic device | |
| CN105519102A (en) | Video monitoring method, video monitoring system and computer program product | |
| JP7221183B2 (en) | Machine learning method, forklift control method, and machine learning device | |
| CN111603100B (en) | Storage and reuse method and storage and reuse device for sweeping drawing of sweeper | |
| CN110706247A (en) | Target tracking method, device and system | |
| CN110378942A (en) | Barrier identification method, system, equipment and storage medium based on binocular camera | |
| CN111295666A (en) | Lane line detection method, device, control equipment and storage medium | |
| CN113212456A (en) | Vehicle control method, device, vehicle, nonvolatile storage medium and processor | |
| CN110928282A (en) | Control method and device for cleaning robot | |
| CN113907663A (en) | Obstacle map construction method, cleaning robot and storage medium | |
| CN113936210A (en) | Anti-collision method for tower crane | |
| CN111374607A (en) | Target identification method and device based on sweeping robot, equipment and medium | |
| CN110245567A (en) | Barrier-avoiding method, device, storage medium and electronic equipment | |
| CN109063790A (en) | Object identifying model optimization method, apparatus and electronic equipment | |
| CN109886198B (en) | Information processing method, device and storage medium | |
| CN114637770A (en) | Vehicle track prediction method and device | |
| CN113205802B (en) | Updating method of voice recognition model, household appliance and server | |
| CN112667924B (en) | Robot map acquisition method and device, processor and electronic device | |
| CN111951543A (en) | Flow prediction method and device | |
| CN107225571B (en) | Robot motion control method and device and robot | |
| CN112220405A (en) | Self-moving tool cleaning route updating method, device, computer equipment and medium | |
| CN115661189A (en) | Scene dynamic information detection method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |