CN110057360A - A kind of paths planning method and its system based on Distributed Parallel Computing - Google Patents
A kind of paths planning method and its system based on Distributed Parallel Computing Download PDFInfo
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- CN110057360A CN110057360A CN201910174544.2A CN201910174544A CN110057360A CN 110057360 A CN110057360 A CN 110057360A CN 201910174544 A CN201910174544 A CN 201910174544A CN 110057360 A CN110057360 A CN 110057360A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
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- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
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Abstract
The present invention relates to a kind of paths planning method and its system based on Distributed Parallel Computing, a kind of paths planning method based on Distributed Parallel Computing of the invention, include the following steps: that mobile robot reads data from laser radar sensor by data acquisition module timing, then processing, data storage to local data base are formatted；Sensing data is encapsulated as message by mobile robot, calls remote interface that message is submitted to cloud；Cloud executes Distributed Parallel Computing paths planning method, and message is taken out from message queue, laser radar data is grouped, each grouping parallel build environment map, the region of search is constructed by starting point line, by improving A*Algorithm parallel construction Visual Graph and accessed path, merging path post package is that message returns to mobile robot；Mobile robot obtains return path, mobile towards target point.The speed of mobile robot path planning of the present invention is fast, solves the problems, such as itself airborne scarce capacity when mobile robot path planning.
Description
Technical field
It is specially a kind of based on Distributed Parallel Computing the present invention relates to the application in mobile robot autonomous navigation field
Paths planning method and its system.
Background technique
Robotics is one of most active field of current high-tech research, and the research of mobile robot is related to multiple
Section is widely used in different fields.Independent navigation is the basic function that mobile robot should have, path planning
Execution efficiency be restrict mobile robot whether can independent navigation key factor.
Traditional paths planning method is to rely on mobile robot itself and calculate power calculate, and path planning is typical
Computation-intensive task, airborne equipment is required high.Benefit from the rapid growth of network data transmission rate, cloud computing technology
Start to be applied in robot field.The part of independent navigation intensive calculations is put into cloud to execute, it is big by streaming computing
Data platform can make up when mobile robot autonomous navigation airborne ability not using Distributed Parallel Computing planning path
Foot and the short slab that over-burden, improve the efficiency and precision of mobile robot path planning.Execution route is planned beyond the clouds simultaneously
Mobile robot performance requirement is reduced, part airborne equipment can be unloaded, improve the Mobile portable performance of robot.
Summary of the invention
The purpose of the present invention is to provide a kind of for improving the speed of mobile robot path planning, makes up mobile machine
The paths planning method and its system based on Distributed Parallel Computing of airborne scarce capacity when people's path planning.
The present invention uses following technical scheme to solve above-mentioned technical problem: one kind of the invention is based on distributed parallel
The paths planning method of calculation, includes the following steps:
Step 1: data acquisition,
Mobile robot reads data from laser radar sensor by data acquisition module timing, is then formatted
Processing, data storage to local data base；
Step 2: message transmission,
Sensing data is encapsulated as message by mobile robot, calls remote interface that message is submitted to cloud；
Step 3: path planning,
Distributed Parallel Computing paths planning method, takes out message from message queue, and laser radar data is grouped, often
A grouping parallel build environment map merges environmental map, the region of search is constructed by starting point line, by improving A*Algorithm
Parallel construction Visual Graph and accessed path, merging path post package is that message returns to mobile robot；
Step 4: path clustering,
Mobile robot obtains the message returned, generates control sequence according to planning path, so that mobile robot is towards mesh
Punctuate is mobile.
Further, above-mentioned steps 3 include the following steps:
Step 3a: taking out message from message queue, and radar data is grouped, and is distributed to multiple calculate nodes；
Step 3b: parallel environment map generates, and packet data is fitted to line segment group in each calculate node, according to movement
Robot anticollision radial expansion is polygon；
Step 3c: receiving the polygon information of upper calculate node transmitting, and construction starting point line takes out two vertex, structure
Build section route searching task；
Step 3d: in each calculate node, improvement A is used based on polygon*Algorithm parallel construction Visual Graph and lookup area
Between optimal path；
Step 3e: merging path, takes out global optimum path according to evaluation function；
Step 3f: global optimum path is encapsulated as message and returns to mobile robot.
Further, above-mentioned steps 1 include the following steps:
Step 1a: after mobile robot starting, load laser radar driving；
Step 1b: corresponding driving method is called to acquire data according to the sampling period；
Step 1c: formatted storage is into local data base.
Further, above-mentioned steps 1 include the following steps:
Step 1a: when mobile robot starts, the sensor and communication protocol for loading support are registered in Virtual table, in table
Including laser radar type, data format, communication means and parameter；
Step 1b: after mobile robot is connected to radar, communication protocol and laser radar are identified according to link information
The method that type calls Virtual table registration obtains laser radar data；
Step 1c: laser radar data is formatted, and is stored into the local data base of mobile robot.
Further, above-mentioned steps 2 include the following steps:
Step 2a: laser radar data and target point are encapsulated as message by mobile robot, upload to cloud by IOT gateway
End；
Step 2b: cloud stores the message received into message queue.
A kind of path planning system based on Distributed Parallel Computing of the invention, the mobile robot autonomous navigation
In path planning system include mobile robot end and cloud；
The mobile-robot system operates on embedded OS, and mobile robot end includes four parts: being disappeared
Cease acquisition module, path management module, IoT gateway and information client side；
The information of message collection module acquisition derives from sensor, including laser radar and inertial sensor；
Cloud includes four parts: message service cluster module, path planning module, distributed memory system and application association
Adjust service module.
Further, the mobile robot end includes
For acquiring laser radar data, it is formatted into the laser radar data acquisition module of unified form；
It is control sequence, the control mobile robot path management module mobile towards target by the path integration received；
IoT gateway for the encapsulation of message, deblocking and forwarding；
For message to be pushed in the message queue of cloud to and obtained from message queue the client component of message.
Further, the cloud includes
The message service cluster module of the messaging service of High Availabitity and load balancing is realized by trunking mode；
The distributed memory system of the storage of mobile robot message data and map datum is realized by distributed way；
The path planning module of distributed parallel path planning is realized by Distributed Parallel Computing cluster；
Realize the application coordination service of coordination, monitoring and the management in computing cluster between main controlled node, N number of calculate node
Module.
The utility model has the advantages that the speed of mobile robot path planning of the present invention is high, mobile robot path planning when, is airborne
Ability foot.A kind of improved path planning algorithm and cloud computing technology are combined, used based on streaming computing technology distributed
Parallel processing makes the present invention have the advantages that handy and safe, efficient, precision is high, is easy to the advantages of software and hardware realization, Ke Yishi
Existing mobile robot real-time route planning.
Detailed description of the invention
Fig. 1 is the paths planning method and its equipment architecture diagram of Distributed Parallel Computing of the invention；
Fig. 2 is the paths planning method flow chart of Distributed Parallel Computing of the invention；
Fig. 3 is the real-time collecting flowchart figure of mobile robot laser radar data of the invention；
Fig. 4 is that mobile robot laser radar information of the invention uploads flow chart in real time；
Fig. 5 is Distributed Parallel Computing paths planning method analysis flow chart diagram of the present invention；
Fig. 6 is that the present invention is based on improve A*The parallel Visual Graph construction and path searching method flow chart of algorithm；
Fig. 7 is the paths planning method of Distributed Parallel Computing of the present invention in laboratory corridor experimental result picture；
Fig. 8 is display interface of the paths planning method in mobile phone end of Distributed Parallel Computing of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
As shown in Figure 1, a kind of path planning system based on Distributed Parallel Computing of the invention, the mobile machine
Path planning system in people's independent navigation includes mobile robot end and cloud；
The mobile-robot system operates on embedded OS, and mobile robot end includes four parts: being disappeared
Cease acquisition module, path management module, IoT gateway and information client side；
The information of message collection module acquisition derives from sensor, including laser radar and inertial sensor；
Cloud includes four parts: message service cluster module, path planning module, distributed memory system and application association
Adjust service module.
The mobile robot end includes
For acquiring laser radar data, it is formatted into the laser radar data acquisition module of unified form；
It is control sequence, the control mobile robot path management module mobile towards target by the path integration received；
IoT gateway for the encapsulation of message, deblocking and forwarding；
For message to be pushed in the message queue of cloud to and obtained from message queue the client component of message.
The cloud includes
The message service cluster module of the messaging service of High Availabitity and load balancing is realized by trunking mode；
The distributed memory system of the storage of mobile robot message data and map datum is realized by distributed way；
The path planning module of distributed parallel path planning is realized by Distributed Parallel Computing cluster；
Realize the application coordination service of coordination, monitoring and the management in computing cluster between main controlled node, N number of calculate node
Module.
Embodiment 1
In the present embodiment, the Distributed Parallel Computing cluster in Fig. 1 passes through large-scale distributed streaming computing system
Apache Storm platform is realized, Distributed Parallel Computing ability is provided；Hadoop HDFS provides distributed text for path planning
Part system completes the storage in path and laser radar information；Application program coordination service is realized by Zookeeper, is path
Plan that distributed computing provides Consistency service.Main controlled node is realized in Storm platform by Nimbus.Working node is responsible for holding
Row calculating task runs Supervisor monitoring process on each working node, is responsible for receiving the task of Nimbus distribution, open
The progress of work that dynamic and stopping to one's name managing.The progress of work will start two kinds of component, and one kind is Spout, be to produce
The component of source data stream；One kind is Bolt, is the component for receiving data and then executing processing.Mobile robot disposes ROS
(Robot Operating System), encapsulates the hardware of robot, provides identical expression way to upper layer application.
As shown in Fig. 2, a kind of paths planning method and its system based on Distributed Parallel Computing, processing step be divided into
Lower four processes: data acquisition, message transmission, distributed parallel path planning and path clustering.One kind being based on distributed parallel
The paths planning method and its equipment of calculating, comprising the following steps:
Step 1: in the present embodiment, mobile robot configuration is single line laser radar, realizes laser radar as shown in Figure 3
Data acquisition.Mobile robot completes Virtual table initialization, by different types of laser radar data to unite when starting
One mode is handled.Robot judges sensor states every 10ms, when state can be used, reads laser radar data, completes
After the laser radar data of one scan period is read, by the following form of data formatization, and stores and arrive local data data
Library；
Step 2: message transmission,
Laser radar data and target point are encapsulated as message by mobile robot, by IOT gateway passes to Messaging clients
End；Information client side is as shown in figure 4, transmit the message to cloud.Here the distributed post that message transmission passes through high-throughput
Message system Kafka is subscribed to realize.Cloud message trunking server stores the message received into message queue.
Step 3: distributed parallel path planning,
Distributed Parallel Computing paths planning method from message queue as shown in figure 5, take out message, by laser thunder first
Several groups are divided into up to data, each grouping parallel generates the environmental map comprising polygon vertex information, merge environmental map,
The region of search is constructed by starting point line, by improving A*Algorithm parallel construction Visual Graph and accessed path, after merging path
It is encapsulated as message and returns to mobile robot.
Step 3a: the step is realized that the task is realized by Spout component by the ReadTask task in Fig. 5.The task
Message is taken out from message queue, traversal is apart from array data, when the distance of point of proximity is more than mobile robot anticollision radius
R sends multiple calculate nodes in a manner of tuple (tuple) for grouping information as a new grouping.
Step 3b: the step is completed parallel by multiple MapTask in Fig. 5, and MapTask is realized by Bolt component, should
Packet data is fitted to line segment group by least square method in each node by task, and every group of line segment is expanded to a polygon.
Step 3c-3d: task is executed parallel by multiple SVGATask in Fig. 5, is realized by Bolt component.Each calculating
Node realizes certain section route searching, uses improvement A based on polygon*The algorithm parallel construction Visual Graph road optimal with lookup
Diameter.Improve A*The process of algorithm search is as shown in Figure 6.
Improve A*The process of algorithm search is as follows:
(1) it initializes.Target point is put into OPEN table, starting point is stored in CLOSED table, adjacent in barrier
While being deposited into Visual Graph.OPEN table is used to store node to be extended, and CLOSED table is used to improve search efficiency and storage road
Diameter.
(2) evaluation minimum node extension is taken out.Evaluation function is defined as follows:
F (x)=gn+hn+vc × MAX
Wherein gn indicates start node to the actual path length of present node, and hn is heuristic function, is that present node arrives
The estimation of destination node shortest path.MAX indicates that possible maximum distance, vc indicate the visit of the node from starting point to target point
Ask number, it can be ensured that will not the identical node of repeated accesses.
(3) according to node state accessed path, it is divided into 2 branches and executes.
First is that explore a upper node to the path of present node, the structurally line of a node to present node.If should
Line be present in Visual Graph or with barrier Lothrus apterus, update node state, if the line line not in Visual Graph, by it
It is added in Visual Graph as visual side.Conflict if line has with barrier, taking-up be crossed in barrier both direction away from
The vertex farthest from line is added in OPEN table.
Second is that exploring present node to the path of target point.Line of the construction present node to target point.If the line is
Do not conflict in Visual Graph or with barrier, expression has found path, outgoing route, and algorithm terminates.If the line with
Barrier has conflict, which is added in CLOSED table, and addition is crossed the vertex of barrier into OPEN table.
Step 3f: the step is realized by CombineTask task, and optimal path is encapsulated as message by kafka interface
Return to mobile robot.
Step 4: path clustering,
Mobile robot obtains the message returned, and planning path is generated control sequence, so that mobile robot is towards target
Point movement.
Fig. 7 is shown in the corridor in laboratory, and robot reaches point of destination from the starting point in figure.Mobile robot passes through one kind
Path planning algorithm based on Distributed Parallel Computing realizes that real-time path planning avoiding obstacles arrive safe and sound point of destination.Figure
8 show the display interface on smart phone, contain the line segment information of laser radar scanning, the physiologic information of operator
Deng.
The present invention combines a kind of improved path planning algorithm and cloud computing technology, is based on cloud computing technology, uses
Distributed variable-frequencypump makes the present invention have the advantages that efficient, precision is high, be easy to software and hardware realization, and mobile machine may be implemented
The planning of people's real-time route.
The basic principles, main features and advantages of the present invention have been shown and described above.The technology of the industry
Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and the above embodiments and description only describe this
The principle of invention, without departing from the spirit and scope of the present invention, various changes and improvements may be made to the invention, the present invention
Claimed range is delineated by the appended claims, the specification and equivalents thereof from the appended claims.
Claims (8)
1. a kind of paths planning method based on Distributed Parallel Computing, it is characterised in that include the following steps:
Step 1: data acquisition,
Mobile robot reads data from laser radar sensor by data acquisition module timing, is then formatted place
Reason, data storage to local data base；
Step 2: message transmission,
Sensing data is encapsulated as message by mobile robot, calls remote interface that message is submitted to cloud；
Step 3: path planning,
Distributed Parallel Computing paths planning method, takes out message from message queue, and laser radar data is grouped, Mei Gefen
Group parallel generation environmental map, merges environmental map, the region of search is constructed by starting point line, by improving A*Algorithm is parallel
Visual Graph and accessed path are constructed, merging path post package is that message returns to mobile robot；
Step 4: path clustering,
Mobile robot obtains the message returned, generates control sequence according to planning path, so that mobile robot is towards target point
It is mobile.
2. as described in claim 1 based on the paths planning method of Distributed Parallel Computing, it is characterised in that: above-mentioned steps 3
Include the following steps:
Step 3a: taking out message from message queue, and radar data is grouped, and is distributed to multiple calculate nodes；
Step 3b: parallel environment map generates, and packet data is fitted to line segment group in each calculate node, according to mobile machine
People's air defense impact radius is expanded to polygon；
Step 3c: receiving the polygon information of upper calculate node transmitting, and construction starting point line takes out two vertex, constructs area
Between route searching task；
Step 3d: in each calculate node, improvement A is used based on polygon*Algorithm parallel construction Visual Graph and lookup range are optimal
Path；
Step 3e: merging path, takes out global optimum path according to evaluation function；
Step 3f: global optimum path is encapsulated as message and returns to mobile robot.
3. as described in claim 1 based on the paths planning method of Distributed Parallel Computing, it is characterised in that: above-mentioned steps 1
Include the following steps:
Step 1a: after mobile robot starting, load laser radar driving；
Step 1b: corresponding driving method is called to acquire data according to the sampling period；
Step 1c: formatted storage is into local data base.
4. as claimed in claim 3 based on the paths planning method of Distributed Parallel Computing, it is characterised in that: above-mentioned steps 1
Include the following steps:
Step 1a: when mobile robot starts, the sensor and communication protocol for loading support are registered in Virtual table, include in table
Laser radar type, data format, communication means and parameter；
Step 1b: after mobile robot is connected to radar, communication protocol and laser radar type are identified according to link information
The method for calling Virtual table registration obtains laser radar data；
Step 1c: laser radar data is formatted, and is stored into the local data base of mobile robot.
5. as described in claim 1 based on the paths planning method of Distributed Parallel Computing, it is characterised in that: above-mentioned steps 2
Include the following steps:
Step 2a: laser radar data and target point are encapsulated as message by mobile robot, upload to cloud by IOT gateway；
Step 2b: cloud stores the message received into message queue.
6. a kind of path planning system based on Distributed Parallel Computing, it is characterised in that: the mobile robot is certainly leading
Path planning system in boat includes mobile robot end and cloud；
The mobile-robot system operates on embedded OS, and mobile robot end includes four parts: message is adopted
Collect module, path management module, IoT gateway and information client side；
The information of message collection module acquisition derives from sensor, including laser radar and inertial sensor；
Cloud includes four parts: message service cluster module, path planning module, distributed memory system and application coordination clothes
Business module.
7. the path planning system according to claim 6 based on Distributed Parallel Computing, it is characterised in that: the shifting
Mobile robot end includes
For acquiring laser radar data, it is formatted into the laser radar data acquisition module of unified form；
It is control sequence, the control mobile robot path management module mobile towards target by the path integration received；
IoT gateway for the encapsulation of message, deblocking and forwarding；
For message to be pushed in the message queue of cloud to and obtained from message queue the information client side of client message.
8. the path planning system according to claim 6 based on Distributed Parallel Computing, it is characterised in that: the cloud
End includes
The message service cluster module of the messaging service of High Availabitity and load balancing is realized by trunking mode；
The distributed memory system of the storage of mobile robot message data and map datum is realized by distributed way；
The path planning module of distributed parallel path planning is realized by Distributed Parallel Computing cluster；
Realize that the application coordination of coordination, monitoring and the management in computing cluster between main controlled node, N number of calculate node services mould
Block.
Priority Applications (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910174544.2A CN110057360A (en) | 2019-03-08 | 2019-03-08 | A kind of paths planning method and its system based on Distributed Parallel Computing |
| PCT/CN2019/102439 WO2020181729A1 (en) | 2019-03-08 | 2019-08-26 | Path planning method employing distributed parallel computation and system thereof |
| ZA2021/07292A ZA202107292B (en) | 2019-03-08 | 2021-09-28 | Path planning method and system based on distributed parallel computing |
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| CN201910174544.2A CN110057360A (en) | 2019-03-08 | 2019-03-08 | A kind of paths planning method and its system based on Distributed Parallel Computing |
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| WO (1) | WO2020181729A1 (en) |
| ZA (1) | ZA202107292B (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110879594A (en) * | 2019-11-25 | 2020-03-13 | 广西科技师范学院 | Big data-based robot path planning data management system |
| WO2020181729A1 (en) * | 2019-03-08 | 2020-09-17 | 江苏海事职业技术学院 | Path planning method employing distributed parallel computation and system thereof |
| CN111885550A (en) * | 2020-06-06 | 2020-11-03 | 浙江中力机械有限公司 | Distributed autonomous mobile robot scheduling system |
| CN112698944A (en) * | 2020-12-29 | 2021-04-23 | 乐陵欧曼电子科技有限公司 | Distributed cloud computing system and method based on human brain simulation |
| WO2021077769A1 (en) * | 2019-10-25 | 2021-04-29 | 江苏海事职业技术学院 | Streaming computing-based map creation method and system therefor |
| CN114029944A (en) * | 2021-10-08 | 2022-02-11 | 智动时代(北京)科技有限公司 | Three-dimensional space grain orientation positioning algorithm |
| WO2022127829A1 (en) * | 2020-12-15 | 2022-06-23 | 美智纵横科技有限责任公司 | Self-moving robot, and path planning method, apparatus and device therefor, and storage medium |
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| CN112833905B (en) * | 2021-01-08 | 2022-09-27 | 北京大学 | Distributed multi-AGV collision-free path planning method based on improved A-x algorithm |
| CN112925307B (en) * | 2021-01-20 | 2023-03-24 | 中国科学院重庆绿色智能技术研究院 | Distributed multi-robot path planning method for intelligent warehousing robot system |
| CN113503884B (en) * | 2021-04-13 | 2023-09-15 | 上海擎朗智能科技有限公司 | Path planning method, device, equipment and storage medium |
| CN113255870B (en) * | 2021-04-19 | 2023-08-04 | 北京市遥感信息研究所 | Region revisit path optimization method, device, equipment and medium |
| CN113218383A (en) * | 2021-05-10 | 2021-08-06 | 山东新一代信息产业技术研究院有限公司 | Cloud robot map construction method and system |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107515002A (en) * | 2016-06-17 | 2017-12-26 | 趣之科技（深圳）有限公司 | A kind of systems approach and device that the real-time indoor map structure of robot and location navigation are realized based on LiDAR and cloud computing |
| CN108981695A (en) * | 2018-07-26 | 2018-12-11 | 浙江大学 | A kind of carriage navigation system based on ROS |
| CN110057360A (en) * | 2019-03-08 | 2019-07-26 | 江苏海事职业技术学院 | A kind of paths planning method and its system based on Distributed Parallel Computing |
-
2019
- 2019-03-08 CN CN201910174544.2A patent/CN110057360A/en not_active Withdrawn
- 2019-08-26 WO PCT/CN2019/102439 patent/WO2020181729A1/en active Application Filing
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2021
- 2021-09-28 ZA ZA2021/07292A patent/ZA202107292B/en unknown
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020181729A1 (en) * | 2019-03-08 | 2020-09-17 | 江苏海事职业技术学院 | Path planning method employing distributed parallel computation and system thereof |
| WO2021077769A1 (en) * | 2019-10-25 | 2021-04-29 | 江苏海事职业技术学院 | Streaming computing-based map creation method and system therefor |
| CN110879594A (en) * | 2019-11-25 | 2020-03-13 | 广西科技师范学院 | Big data-based robot path planning data management system |
| CN111885550A (en) * | 2020-06-06 | 2020-11-03 | 浙江中力机械有限公司 | Distributed autonomous mobile robot scheduling system |
| CN111885550B (en) * | 2020-06-06 | 2023-09-19 | 浙江中力机械股份有限公司 | Distributed autonomous mobile robot scheduling system |
| WO2022127829A1 (en) * | 2020-12-15 | 2022-06-23 | 美智纵横科技有限责任公司 | Self-moving robot, and path planning method, apparatus and device therefor, and storage medium |
| CN112698944A (en) * | 2020-12-29 | 2021-04-23 | 乐陵欧曼电子科技有限公司 | Distributed cloud computing system and method based on human brain simulation |
| CN114029944A (en) * | 2021-10-08 | 2022-02-11 | 智动时代(北京)科技有限公司 | Three-dimensional space grain orientation positioning algorithm |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2020181729A1 (en) | 2020-09-17 |
| ZA202107292B (en) | 2022-02-23 |
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Application publication date: 20190726 |