CN111752275A - Automatic cruise method and device for robot and storage medium - Google Patents
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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- G05D1/02—Control of position or course in two dimensions
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- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
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- 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
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
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Abstract
The application discloses an automatic cruise method, an automatic cruise device and a storage medium for a robot, wherein the robot acquires initial environment information, generates a global optimal path according to the initial environment information and a pre-stored raster map, and executes automatic cruise according to the global optimal path; in the process that the robot performs automatic cruising, if temporary obstacle information is detected from the global optimal path, generating a local path according to the temporary obstacle information and the raster map, and performing automatic cruising by the robot according to the local path; after the robot finishes moving the local path, automatic cruising is performed according to the global optimal path.
Description
Technical Field
The present application relates to the field of automatic cruise technology for robots, and more particularly, to an automatic cruise method, apparatus, and storage medium for a robot.
Background
At present, with the development of science and technology, automatic cruise technology for robots is increasingly applied, and an existing automatic cruise system for robots is usually equipped with a map database, scans an environment, identifies and matches environmental obstacles through the database, and generates a route capable of bypassing the environmental obstacles to realize automatic cruise for the robots. However, in the actual use process, the environment in the cruise route is not the same, and an obstacle may appear temporarily, and the existing scheme cannot generate an obstacle avoidance route in time, so that potential safety hazards are easily caused.
Disclosure of Invention
In order to overcome the defects of the prior art, the application aims to provide an automatic cruise method, an automatic cruise device and a storage medium for a robot, which can avoid temporary obstacles and obtain a reasonable cruise path.
The technical scheme adopted by the application for solving the problems is as follows: in a first aspect, the present application provides an auto-cruise method for a robot, comprising the steps of:
the method comprises the steps that a robot obtains initial environment information and generates a global optimal path according to the initial environment information and a pre-stored raster map, wherein the global optimal path is used for executing automatic cruising;
if the robot detects temporary obstacle information from the advancing direction in the process of executing automatic cruise according to the global optimal path, generating a local path according to the temporary obstacle information and the grid map, and executing automatic cruise according to the local path by the robot;
and after the robot finishes the movement of the local path, executing automatic cruising according to the global optimal path.
Further, the initial environment information and the temporary obstacle information are collected by a multi-threaded 3D lidar.
Further, the generating a global optimal path according to the initial environment information and a pre-stored grid map specifically includes:
the robot acquires a map database stored in advance;
the robot reads out initial obstacle information from the map database according to the initial environment information;
the robot acquires an initial obstacle area in the grid map according to the initial obstacle information;
and the robot generates a global optimal path according to the initial obstacle area and preset destination information.
Further, the generating a local path according to the temporary obstacle information and the grid map specifically includes:
the robot generates a temporary obstacle area in the grid map according to the temporary obstacle information and deletes a global optimal path in the temporary obstacle area;
and the robot acquires breakpoint coordinates formed by the global optimal path being intercepted by the temporary obstacle region from the grid map, and generates a local path according to the current position of the robot, the temporary obstacle region and the breakpoint coordinates.
Further, after the robot performs automatic cruising according to the local path, the method further includes: and saving the temporary obstacle information to a database.
Further, the robot performs the automatic cruise by an a-tracking algorithm.
Further, the robot acquires the current position from the grid map through a Monte Carlo positioning algorithm based on particle filtering.
Further, the robot acquires and records cruising mileage information in a linear automatic cruising process.
In a second aspect, the present application provides an automatic cruise apparatus for a robot, comprising at least one control processor and a memory for communicative connection with the at least one control processor; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform the auto-cruise method for a robot as described above.
In a third aspect, the present application provides a computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform the auto-cruise method for a robot as described above.
In a fourth aspect, the present application also provides a computer program product comprising a computer program stored on a computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the auto-cruise method for a robot as described above.
One or more technical schemes provided in the embodiment of the application have at least the following beneficial effects: according to the embodiment of the application, initial environment information is obtained through a robot, a global optimal path is generated according to the initial environment information and a pre-stored raster map, and automatic cruise is executed according to the global optimal path; in the process that the robot performs automatic cruising, if temporary obstacle information is detected from the global optimal path, generating a local path according to the temporary obstacle information and the raster map, and performing automatic cruising by the robot according to the local path; after the robot finishes moving the local path, automatic cruising is performed according to the global optimal path.
Drawings
The present application is further described below with reference to the following figures and examples.
FIG. 1 is a flow chart of an auto-cruise method for a robot provided in one embodiment of the present application;
FIG. 2 is a flow chart of an auto-cruise method for a robot according to another embodiment of the present application;
FIG. 3 is a flow chart of an auto-cruise method for a robot according to another embodiment of the present application;
fig. 4 is a schematic diagram of an apparatus for performing an auto-cruise method for a robot according to another embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
It should be noted that, if not conflicted, the various features of the embodiments of the present application may be combined with each other within the scope of protection of the present application. Additionally, while functional block divisions are performed in apparatus schematics, with logical sequences shown in flowcharts, in some cases, steps shown or described may be performed in sequences other than block divisions in apparatus or flowcharts.
Referring to fig. 1, a first embodiment of the present application provides an auto-cruise method for a robot, including the steps of:
s100, acquiring initial environment information by the robot, and generating a global optimal path according to the initial environment information and a pre-stored grid map, wherein the global optimal path is used for executing automatic cruise;
step S200, if the robot detects temporary obstacle information from the advancing direction in the process of executing automatic cruise according to the global optimal path, a local path is generated according to the temporary obstacle information and the grid map, and the robot executes automatic cruise according to the local path;
and step S300, after the robot finishes the movement of the local path, executing automatic cruising according to the global optimal path.
It should be noted that the automatic cruise method of this embodiment may also be used in devices that can realize automatic circulation, such as an intelligent car and a weeding machine, and will not be described herein again.
In an embodiment, the automatic cruise method for the robot may be implemented based on any software, and the embodiment is preferably implemented based on ROS software, because many core functions such as positioning movement, map building, path planning, and the like are built in the ROS software, and it is sufficient to implement steps S100 to S300 of the embodiment by improvement.
In an embodiment, the grid map may be input into the robot through a mobile device, such as a tablet computer, a mobile app, or a computer, or may be automatically scanned and acquired through the robot, which is not limited herein.
In an embodiment, step S200 may further determine the temporary obstacle information, for example, after the temporary obstacle information is acquired, the temporary obstacle is identified by using an image identification technology based on machine vision in the prior art, if the temporary obstacle cannot pass through, a local path is generated to bypass, for example, rocks and ravines, and if the temporary obstacle cannot pass through, the temporary obstacle may not be generated, but may directly pass through, for example, small stones or weeds that do not affect the passage of the robot.
In an embodiment, in step S300, the robot may be controlled to directly continue to perform the automatic cruise according to the global optimal path, or after bypassing the temporary obstacle, the robot may be rescanned to determine that there is no obstacle included in the middle of the other initial environment information in the global optimal path, and the specific execution mode may be selected according to the actual requirement.
It should be noted that the path planning method according to this embodiment may adopt any planning method in the prior art, and the area where the obstacle is located may be used as the unreachable area, which is not described herein again.
In another embodiment of the present application, the initial environment information and the temporary obstacle information are collected by a multi-threaded 3D lidar.
In an embodiment, the initial environment information and the temporary obstacle information may be acquired through any type of device, the 3D laser radar is preferably selected in this embodiment, information collected by the radar can be issued to a navigation system, an obstacle can be identified in time, and in order to reduce errors, a cartographer-based 3DSLAM algorithm may be used to achieve accurate positioning and navigation.
Referring to fig. 2, in another embodiment of the present application, step S100 further includes, but is not limited to, the following refinement steps:
step S110, the robot acquires a map database stored in advance;
step S120, the robot reads out initial obstacle information from a map database according to the initial environment information;
step S130, the robot acquires an initial obstacle area in the grid map according to the initial obstacle information;
in step S140, the robot generates a global optimal path based on the initial obstacle area and the preset destination information.
In an embodiment, the path planning method from step S110 to step S140 is only preferred in this embodiment, and a method capable of achieving similar effects in the prior art may also be adopted, and is not described herein again.
The map database may include information such as a topographic feature of the work area, or may store known obstacle information so as to compare the information with the acquired image and identify a specific obstacle.
In an embodiment, the initial obstacle area in step S130 may be an unreachable area, or an reachable area meeting a condition, for example, a muddy road, and the robot may pass through the unreachable area by controlling the robot at a certain speed per hour, and the use of the initial obstacle area in the path planning may be selected according to actual requirements, which is not described herein again.
In an embodiment, in order to implement path planning, a coordinate transformation module may be further disposed in the robot, which may be capable of implementing normalization of coordinates of different reference systems, for example, when a global optimal path is planned, a first coordinate system is established according to a global environment, when a local path is planned, a second coordinate system is established according to a local environment, and when the robot is actually used, the first coordinate system and the second coordinate system are normalized by the coordinate transformation module, so that the robot may obtain actual coordinates for cruising.
Referring to fig. 3, in another embodiment of the present application, step S200 further includes, but is not limited to, the following specific steps:
step S210, the robot generates a temporary obstacle area in the grid map according to the temporary obstacle information and deletes the global optimal path in the temporary obstacle area;
step S220, the robot acquires the breakpoint coordinates formed by the global optimal path being intercepted by the temporary obstacle area from the grid map, and generates a local path according to the current position of the robot, the temporary obstacle area and the breakpoint coordinates.
In an embodiment, step S210 and step S220 are preferred methods in this embodiment, and other similar path planning methods may also be adopted. It should be noted that, in step S210, the temporary obstacle may be identified first, and if the temporary obstacle can pass through, the path does not need to be re-planned, and a specific planning method is selected according to actual needs.
In an embodiment, the connection of the paths may be realized in the form of a breakpoint according to the method in step S220, or a local path may be separately generated for a temporary obstacle, and the global optimal path is re-planned after the temporary obstacle is bypassed, and is selected according to actual requirements.
In another embodiment of the present application, after the robot performs the automatic cruise according to the local path, the method further includes: and storing the temporary obstacle information into a database.
Based on the embodiment, when the temporary obstacle is encountered, the judgment can be carried out through image recognition, so that when the obstacle which is not pre-stored in the database is recognized, the specific obstacle type can be inquired and obtained through a server, the type can also be determined through a manual input mode, and the obstacle type is stored in the database of the robot after the determination, so that the robot is convenient to use next time.
In another embodiment of the present application, the robot performs automatic cruise through a tracking algorithm.
In one embodiment, the tracking algorithm a is the most efficient direct search method for solving the shortest path in the static road network, and is also an efficient algorithm for solving many search problems. The closer the distance estimation value in the algorithm is to the actual value, the faster the final search speed is, so that when the method is applied to the embodiment, the obstacle can be avoided quickly, and the safety of the robot is improved.
In another embodiment of the application, the robot acquires the current position from the grid map by a monte carlo localization algorithm based on particle filtering.
In an embodiment, the monte carlo positioning algorithm based on particle filtering can quickly acquire positioning information in a known map, and the timeliness of path planning can be effectively improved.
In another embodiment of the application, the robot acquires and records cruising mileage information during the linear automatic cruising process.
In an embodiment, the mileage information may be obtained through a relationship between speed and time, for example, the operation speed of the robot in a certain period of time is obtained, and then the operation speed is multiplied by the time to obtain the mileage, and the recorded mileage information may be used for analyzing the efficiency of the cruise path in the following process, which is not described herein again.
Referring to fig. 4, another embodiment of the present application also provides an automatic cruise apparatus 4000 for a robot, including: the memory 4100, the control processor 4200, and a computer program stored on the memory 4200 and executable on the control processor 4200, the control processor implementing the auto-cruise method for the robot as in any of the embodiments above when executing the computer program, e.g., performing the method steps S100 to S300 in fig. 1, the method steps S110 to S140 in fig. 2, and the method steps S210 to S220 in fig. 3 described above.
The control processor 4200 and the memory 4100 may be connected by a bus or other means, such as the bus connection shown in fig. 4.
The memory 4100 is one type of non-transitory computer readable storage medium that can be used to store non-transitory software programs as well as non-transitory computer executable programs. Further, memory 4100 can include high speed random access memory and can also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 4100 may optionally include memory remotely located from the control processor 4200, which may be connected to the auto cruise apparatus 4000 for a robot via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The above-described embodiments of the apparatus are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may also be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
Furthermore, another embodiment of the present application also provides a computer-readable storage medium storing computer-executable instructions, which are executed by one or more control processors, for example, by one control processor 4200 in fig. 4, and may cause the one or more control processors 4200 to perform the automatic cruise method for the robot in the above-described method embodiment, for example, perform the above-described method steps S100 to S300 in fig. 1, method steps S110 to S140 in fig. 2, and method steps S210 to S220 in fig. 3.
It should be noted that, since the device for executing the automatic cruise method for the robot in the present embodiment is based on the same inventive concept as the automatic cruise method for the robot described above, the corresponding contents in the method embodiment are also applicable to the present device embodiment, and are not described in detail herein.
Through the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be implemented by software plus a general hardware platform. Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by hardware related to instructions of a computer program, which may be stored in a computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a Random Access Memory (RAM), or the like.
While the preferred embodiments of the present invention have been described, the present invention is not limited to the above embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and such equivalent modifications or substitutions are included in the scope of the present invention defined by the claims.
Claims (10)
1. An auto-cruise method for a robot, comprising:
the method comprises the steps that a robot obtains initial environment information, and a global optimal path is generated according to the initial environment information and a pre-stored raster map, wherein the global optimal path is used for executing automatic cruising; if the robot detects temporary obstacle information from the advancing direction in the process of executing automatic cruise according to the global optimal path, generating a local path according to the temporary obstacle information and the grid map, and executing automatic cruise according to the local path by the robot;
and after the robot finishes the movement of the local path, executing automatic cruising according to the global optimal path.
2. An automatic cruise method for a robot according to claim 1, characterized in that: and acquiring the initial environment information and the temporary obstacle information through a multi-thread 3D laser radar.
3. The method as claimed in claim 1, wherein the generating a global optimal path according to the initial environment information and a pre-stored grid map specifically comprises:
the robot acquires a map database stored in advance;
the robot reads out initial obstacle information from the map database according to the initial environment information;
the robot acquires an initial obstacle area in the grid map according to the initial obstacle information;
and the robot generates a global optimal path according to the initial obstacle area and preset destination information.
4. The method as claimed in claim 1, wherein the generating a local path from the temporary obstacle information and the grid map specifically includes:
the robot generates a temporary obstacle area in the grid map according to the temporary obstacle information and deletes a global optimal path in the temporary obstacle area;
and the robot acquires breakpoint coordinates formed by the global optimal path being intercepted by the temporary obstacle region from the grid map, and generates a local path according to the current position of the robot, the temporary obstacle region and the breakpoint coordinates.
5. The auto-cruise method for a robot according to claim 4, wherein said robot, after performing auto-cruise according to said local path, further comprises: and saving the temporary obstacle information to a database.
6. An automatic cruise method for a robot according to claim 1, characterized in that: the robot performs the automatic cruise by an a-tracking algorithm.
7. An automatic cruise control method for a robot according to claim 4, characterized in that: and the robot acquires the current position from the grid map through a Monte Carlo positioning algorithm based on particle filtering.
8. The auto-cruise method for a robot according to claim 1, further comprising: the robot acquires and records cruising mileage information in a linear automatic cruising process.
9. An automatic cruise apparatus for a robot, comprising at least one control processor and a memory for communicative connection with the at least one control processor; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform the auto-cruise method for a robot according to any one of claims 1 to 8.
10. A computer-readable storage medium characterized by: the computer-readable storage medium stores computer-executable instructions for causing a computer to perform the auto-cruise method for a robot according to any one of claims 1 to 8.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112698653A (en) * | 2020-12-23 | 2021-04-23 | 南京中朗智能技术有限公司 | Robot autonomous navigation control method and system based on deep learning |
| CN113359714A (en) * | 2021-05-25 | 2021-09-07 | 国网江苏省电力有限公司电力科学研究院 | Routing inspection robot dynamic path planning method and device based on particle filter algorithm |
| CN113440074A (en) * | 2021-07-06 | 2021-09-28 | 上海高仙自动化科技发展有限公司 | Cleaning method, device, equipment and storage medium |
| WO2022233183A1 (en) * | 2021-05-06 | 2022-11-10 | 北京石头世纪科技股份有限公司 | Obstacle avoidance method and apparatus for self-walking device, and medium and electronic device |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106406320A (en) * | 2016-11-29 | 2017-02-15 | 重庆重智机器人研究院有限公司 | Robot path planning method and robot planning route |
| CN106708054A (en) * | 2017-01-24 | 2017-05-24 | 贵州电网有限责任公司电力科学研究院 | Inspection robot path planning method combining map grid with potential field method obstacle avoidance |
| CN108594843A (en) * | 2018-02-09 | 2018-09-28 | 深圳臻迪信息技术有限公司 | Unmanned plane autonomous flight method, apparatus and unmanned plane |
| CN110333714A (en) * | 2019-04-09 | 2019-10-15 | 武汉理工大学 | A kind of pilotless automobile paths planning method and device |
| CN110609557A (en) * | 2019-10-09 | 2019-12-24 | 中国人民解放军陆军装甲兵学院 | Unmanned vehicle mixed path planning algorithm |
| CN110658813A (en) * | 2019-09-17 | 2020-01-07 | 五邑大学 | Intelligent medical material supply robot based on Internet of things and SLAM technology |
-
2020
- 2020-06-19 CN CN202010564922.0A patent/CN111752275A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106406320A (en) * | 2016-11-29 | 2017-02-15 | 重庆重智机器人研究院有限公司 | Robot path planning method and robot planning route |
| CN106708054A (en) * | 2017-01-24 | 2017-05-24 | 贵州电网有限责任公司电力科学研究院 | Inspection robot path planning method combining map grid with potential field method obstacle avoidance |
| CN108594843A (en) * | 2018-02-09 | 2018-09-28 | 深圳臻迪信息技术有限公司 | Unmanned plane autonomous flight method, apparatus and unmanned plane |
| CN110333714A (en) * | 2019-04-09 | 2019-10-15 | 武汉理工大学 | A kind of pilotless automobile paths planning method and device |
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