CN111207754A - Multi-robot formation positioning method and robot equipment based on particle filter - Google Patents
Multi-robot formation positioning method and robot equipment based on particle filterDownload PDFInfo
- Publication number
- CN111207754A CN111207754ACN202010128966.9ACN202010128966ACN111207754ACN 111207754 ACN111207754 ACN 111207754ACN 202010128966 ACN202010128966 ACN 202010128966ACN 111207754 ACN111207754 ACN 111207754A
- Authority
- CN
- China
- Prior art keywords
- robot
- formation
- particle
- particle filter
- average weight
- 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.)
- Pending
Links
Images
Classifications
- 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
- 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
- G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching
- 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
- G05D1/0088—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours
- 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
- 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
- G05D1/0257—Control of position or course in two dimensions specially adapted to land vehicles using a radar
- 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
- G05D1/0268—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
- G05D1/0272—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means comprising means for registering the travel distance, e.g. revolutions of wheels
- 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
- G05D1/0287—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
- G05D1/0291—Fleet control
- G05D1/0293—Convoy travelling
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- General Physics & Mathematics (AREA)
- Aviation & Aerospace Engineering (AREA)
- Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Game Theory and Decision Science (AREA)
- Medical Informatics (AREA)
- Electromagnetism (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
Description
Technical Field
The invention relates to the technical field of robots and navigation, in particular to a particle filter-based multi-robot formation positioning method and robot equipment.
Background
With the improvement of embedded computing and communication capabilities and the development of distributed or decentralized ideas, it is increasingly recognized that multi-agent systems can accomplish more complex tasks at a lower cost than single agents, and that multi-robot formation has an increasing demand for applications, especially autonomous navigation functions of robot formation. The automatic navigation system firstly loads a grid map generated by the mapping system and realizes the self-positioning and navigation of the robot on the map. Therefore, in whatever environment, the robot needs to be positioned very accurately to its own position in order for the automatic navigation system to be effective quickly.
Laser-based single-robot self-localization algorithms typically employ a particle filtering algorithm, the common method being AMCL (adaptive monte carlo localization). The pose (usually a point) of the 2D laser includes position and orientation information of the robot, which then self-locates and navigates within the map. The problems with this approach are:
a single robot only depends on the odometer information and the radar information of the robot to carry out particle filtering, and the pose of the robot cannot be effectively corrected;
in the prior art, CN 201811205306.5 (a multi-robot cooperative positioning and control method) provides a positioning method based on multi-robot cooperative control, which optimizes the position of a robot through a gradient descent algorithm according to coordinates of a world coordinate system in a plurality of robots and ranging information obtained by sensors, and the method requires the robot to acquire a lot of data, has strong requirements on conditions, and simultaneously requires a lot of global calculations.
Therefore, those skilled in the art are dedicated to develop a particle filter-based multi-robot formation positioning method and a robot device to solve the problems of inaccurate positioning and insufficient correction caused by single-robot odometer error, inaccurate laser radar scanning and other factors in the prior art.
Disclosure of Invention
In view of the above defects in the prior art, the technical problem to be solved by the present invention is to solve the problems of inaccurate positioning and insufficient correction caused by the odometer error of a single robot, inaccurate scanning of laser radar, etc. in the prior art.
In order to achieve the purpose, the invention provides a particle filter-based multi-robot formation positioning method, which comprises the following steps of:
step 1, establishing a grid map;
step 2, realizing cooperative formation movement by multiple robots;
step 3, iterative updating of the particle filter, including iterative updating of a sampling particle generation process and iterative updating of an importance sampling process;
step 4, the robot acquires the average weight of the particles adjacent to the robot in the formation in real time;
step 5, the robot corrects the average weight of the robot in real time by using the average weight of the particles adjacent to the robot;
and 6, repeating the step 3, and outputting the pose of the robot in real time until the iteration termination condition is reached, and stopping the iteration updating.
Further, the method for establishing the grid map comprises the following steps: based on laser or visual sensors, the robot moves autonomously or is controlled by a user to construct the grid map in a working environment.
Further, the step 2 implementation method comprises: centralized, distributed, or hybrid.
Further, the iterative update of the sampling particle generation process specifically includes: and generating new sampling particles according to the current particle distribution or the particle distribution obtained in the importance sampling process.
Further, the importance sampling process specifically includes: and calculating the weight of the sampling particles according to the observation of the positions of the sampling particles and the observation of the real position of the robot, and performing low-variance resampling to obtain the updated particle distribution.
Further, the implementation method of step 4 includes:
step 4.1, the average weight of the particles obtained by the robot is specifically as follows: the average weight of the particles obtained by fusing the prediction information of the odometer and the measurement data of the laser radar is used for removing the particles with low confidence coefficient and updating the pose of the robot;
and 4.2, receiving data from the centralized processor in real time or directly communicating with the adjacent robots in real time, and if the robots realize communication with the adjacent robots in the form of an adjacency matrix, acquiring the average weight of the particles of the adjacent robots.
Further, step 5 comprises: using weighted fusion or difference correction, such as a method of correcting the average weight using the difference:
wherein, aijThe value of (a) indicates whether or not communication is between robots numbered i and j in the robot formation,
is the average weight of the particles at time k for robot j,
corrected particle weight, r, for robot j at time kijRepresenting parameters related to the relative positions of robot i and robot j in the formation.
Further, the iteration termination condition at least comprises that the number of iterations is reached or the variance of the particle distribution is smaller than a certain threshold value.
The invention also provides a particle filter-based multi-robot formation positioning robot device, which is characterized by comprising the following components: the device comprises a grid map building module, a formation communication module, a formation control module, a particle filter module and a correction module.
Further, the grid map building module is used for building the grid map; the formation communication module is used for communicating with adjacent robots or communicating with a centralized processor; the formation control module is used for realizing multi-robot cooperation and moving according to a fixed formation; the particle filter module is used for iteratively updating the particle filter, and comprises iterative updating of a sampling particle generation process and an importance sampling process; the correction module is used for correcting the particle average weight of the robot by using the particle average weight of the adjacent robot.
The multi-robot formation can quickly and accurately position the position of the multi-robot formation at any position in navigation, can correct the lost robot, obtains more accurate position estimation through sampling and resampling of the particle filter in a static state, and can realize more accurate formation control based on the positioning method.
The conception, the specific structure and the technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, the features and the effects of the present invention.
Drawings
FIG. 1 is a flow chart of a multi-robot formation positioning method based on a particle filter according to the present invention;
fig. 2 is a schematic structural diagram of the multi-robot formation positioning robot device based on the particle filter.
Detailed Description
The technical contents of the preferred embodiments of the present invention will be more clearly and easily understood by referring to the drawings attached to the specification. The present invention may be embodied in many different forms of embodiments and the scope of the invention is not limited to the embodiments set forth herein.
Example one
FIG. 1 is a flow chart of the multi-robot formation positioning method based on particle filter of the present invention, which includes:
step 1, establishing a grid map.
The method for establishing the grid map comprises the following steps: based on laser or vision sensors, the robot moves autonomously or is controlled by the user to perform mapping in the work environment.
And 2, realizing the cooperative formation movement by a plurality of robots.
The method for multi-robot collaborative formation movement comprises the following steps: centralized, distributed or hybrid, e.g. multiple robots are moved in a working environment in some fixed team type by a distributed control method of pilots and followers.
And 3, iteratively updating the particle filter, including a sampling particle generation process and an importance sampling process.
The sampling particle generation process specifically comprises the following steps: and generating new sampling particles according to the current particle distribution or the particle distribution obtained in the importance sampling process.
The importance sampling process specifically comprises the following steps: and calculating the weight of the sampling particles according to the observation of the positions of the sampling particles and the observation of the real position of the robot, and performing low variance resampling until the updated particle distribution is reached.
And 4, acquiring the average weight of the particles of the adjacent robots in the formation in real time by the robots.
The method for acquiring the average weight of the particles of the adjacent robots in the formation in real time by the robot comprises the following steps: and receiving data in real time from the centralized processor or directly communicating with the adjacent robot in real time, for example, the robot realizes the communication with the adjacent robot in the form of an adjacency matrix, and acquiring the average weight of particles of the adjacent robot.
The average weight of the particles obtained by the robot is specifically as follows: and the average weight of the particles obtained by fusing the prediction information of the odometer and the measurement data of the laser radar is used for removing the particles with low confidence coefficient and updating the pose of the robot.
And 5, the robot corrects the average weight of the robot in real time by using the average weight of the particles of the adjacent robots, repeats the step 3, and outputs the pose of the robot in real time until an iteration termination condition is reached, and then stops iteration updating.
A method for a robot to correct its own average weight of particles by using the average weights of particles of neighboring robots, comprising: using weighted fusion or difference correction, such as a method of correcting the average weight using the difference:
wherein, aijThe value of (a) indicates whether or not communication is between robots numbered i and j in the robot formation,
is the average weight of the particles at time k for robot j,
corrected particle weight, r, for robot j at time kijRepresenting parameters related to the relative positions of robot i and robot j in the formation.
And the iteration termination condition at least comprises that the iteration number is reached or the variance of the particle distribution is less than a certain threshold value.
According to the method provided by the embodiment of the invention, the multi-robot formation can quickly and accurately position the position of the multi-robot formation at any position in navigation, and the lost robot can be corrected to obtain more accurate position estimation through sampling and resampling by a particle filter in a static state.
Example two
Fig. 2 is a schematic structural diagram of the multi-robot formation positioning robot device based on particle filter, which comprises:
the grid map building module is used for building a grid map;
a formation communication module for communicating with an adjacent robot or a centralized processor;
the formation control module is used for realizing the cooperation of multiple robots and moving according to a fixed formation;
the particle filter module is used for iteratively updating the particle filter, and comprises a sampling particle generation process and an importance sampling process; stopping iteration updating when an iteration termination condition is reached, and outputting the position information of the robot;
and the correction module is used for correcting the average weight of the particles of the adjacent robots by using the average weight of the particles of the adjacent robots.
The specific detail information of each module refers to the first embodiment.
It should be noted that, in this embodiment, each module (or unit) is in a logical sense, and in particular, when the embodiment is implemented, a plurality of modules (or units) may be combined into one module (or unit), and one module (or unit) may also be split into a plurality of modules (or units).
By the robot equipment of the particle filter-based multi-robot formation positioning method, the multi-robot formation can quickly and accurately position the position of the multi-robot formation at any position in navigation, lost robots can be corrected, and more accurate position estimation can be obtained through particle filter sampling and resampling in a static state.
It will be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments may be implemented by hardware instructions related to a program, and the program may be stored in a computer-readable storage medium, and when executed, may include the processes of the above embodiments of the methods. The storage medium may be a magnetic disk, an optical disk, a Read-only Memory (ROM), a Random Access Memory (RAM), or the like.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (10)
1. A multi-robot formation positioning method based on a particle filter is characterized by comprising the following steps:
step 1, establishing a grid map;
step 2, realizing cooperative formation movement by multiple robots;
step 3, iterative updating of the particle filter, including iterative updating of a sampling particle generation process and iterative updating of an importance sampling process;
step 4, the robot acquires the average weight of the particles adjacent to the robot in the formation in real time;
step 5, the robot corrects the average weight of the robot in real time by using the average weight of the particles adjacent to the robot;
and 6, repeating the step 3, and outputting the pose of the robot in real time until the iteration termination condition is reached, and stopping the iteration updating.
2. The particle-filter-based multi-robot formation positioning method of claim 1, wherein the method for establishing the grid map comprises: based on laser or visual sensors, the robot moves autonomously or is controlled by a user to construct the grid map in a working environment.
3. The particle filter-based multi-robot formation positioning method as claimed in claim 1, wherein the step 2 implementation method comprises: centralized, distributed, or hybrid.
4. The particle filter-based multi-robot formation positioning method of claim 1, wherein the iterative update of the sampling particle generation process specifically comprises: and generating new sampling particles according to the current particle distribution or the particle distribution obtained in the importance sampling process.
5. The particle filter-based multi-robot formation positioning method as claimed in claim 1, wherein the importance sampling process specifically comprises: and calculating the weight of the sampling particles according to the observation of the positions of the sampling particles and the observation of the real position of the robot, and performing low-variance resampling to obtain the updated particle distribution.
6. The particle filter-based multi-robot formation positioning method as claimed in claim 1, wherein the implementation method of step 4 comprises:
step 4.1, the average weight of the particles obtained by the robot is specifically as follows: the average weight of the particles obtained by fusing the prediction information of the odometer and the measurement data of the laser radar is used for removing the particles with low confidence coefficient and updating the pose of the robot;
and 4.2, receiving data from the centralized processor in real time or directly communicating with the adjacent robots in real time, and if the robots realize communication with the adjacent robots in the form of an adjacency matrix, acquiring the average weight of the particles of the adjacent robots.
7. The particle filter-based multi-robot formation positioning method of claim 1, wherein the step 5 comprises: using weighted fusion or difference correction, such as a method of correcting the average weight using the difference:
wherein, aijThe value of (a) indicates whether or not communication is between robots numbered i and j in the robot formation,
is the average weight of the particles at time k for robot j,
corrected particle weight, r, for robot j at time kijRepresenting parameters related to the relative positions of robot i and robot j in the formation.
8. The particle filter-based multi-robot formation positioning method of claim 1, wherein the iteration termination condition at least includes a number of iterations reached or a variance of particle distribution less than a certain threshold.
9. A particle filter based multi-robot formation positioning robot device, comprising: the device comprises a grid map building module, a formation communication module, a formation control module, a particle filter module and a correction module.
10. The particle-filter-based multi-robot-team-positioned robotic device of claim 9, wherein said grid map building module is configured to build said grid map; the formation communication module is used for communicating with adjacent robots or communicating with a centralized processor; the formation control module is used for realizing the cooperation of the multiple robots and moving according to a fixed formation; the particle filter module is used for iteratively updating the particle filter, and comprises the sampling particle generation process and iterative updating of the important sampling process; the correction module is used for correcting the particle average weight of the robot by using the particle average weight of the adjacent robot.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010128966.9ACN111207754A (en) | 2020-02-28 | 2020-02-28 | Multi-robot formation positioning method and robot equipment based on particle filter |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010128966.9ACN111207754A (en) | 2020-02-28 | 2020-02-28 | Multi-robot formation positioning method and robot equipment based on particle filter |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111207754Atrue CN111207754A (en) | 2020-05-29 |
Family
ID=70785592
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010128966.9APendingCN111207754A (en) | 2020-02-28 | 2020-02-28 | Multi-robot formation positioning method and robot equipment based on particle filter |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111207754A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111930116A (en)* | 2020-07-24 | 2020-11-13 | 哈尔滨工程大学 | Large-scale UUV cluster formation method based on grid method |
| CN112742028A (en)* | 2021-01-22 | 2021-05-04 | 中国人民解放军国防科技大学 | Formation decision method, system, medium and equipment for fighting game |
| CN113984053A (en)* | 2021-09-10 | 2022-01-28 | 福建（泉州）哈工大工程技术研究院 | A ground robot swarm cooperative positioning technology |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107992035A (en)* | 2017-11-15 | 2018-05-04 | 西北工业大学 | A kind of Multi Mobile Robots Formation's control method based on indoor Global localization |
| CN108897315A (en)* | 2018-06-12 | 2018-11-27 | 南京理工大学 | A kind of Multi-agent Team Formation |
| CN109241228A (en)* | 2018-09-04 | 2019-01-18 | 山东理工大学 | A kind of multiple mobile robot's cooperation synchronous superposition strategy |
| CN110007670A (en)* | 2019-02-14 | 2019-07-12 | 四川阿泰因机器人智能装备有限公司 | Localization for Mobile Robot builds drawing method |
| CN110068330A (en)* | 2019-01-16 | 2019-07-30 | 上海交通大学 | Autonomic positioning method based on the robot that arma modeling is realized |
| CN110207707A (en)* | 2019-05-30 | 2019-09-06 | 四川长虹电器股份有限公司 | Quick initial alignment method and robot device based on particle filter |
| CN110333720A (en)* | 2019-07-10 | 2019-10-15 | 国网四川省电力公司电力科学研究院 | A SLAM Optimization Method Based on Particle Filter |
- 2020
- 2020-02-28CNCN202010128966.9Apatent/CN111207754A/enactivePending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107992035A (en)* | 2017-11-15 | 2018-05-04 | 西北工业大学 | A kind of Multi Mobile Robots Formation's control method based on indoor Global localization |
| CN108897315A (en)* | 2018-06-12 | 2018-11-27 | 南京理工大学 | A kind of Multi-agent Team Formation |
| CN109241228A (en)* | 2018-09-04 | 2019-01-18 | 山东理工大学 | A kind of multiple mobile robot's cooperation synchronous superposition strategy |
| CN110068330A (en)* | 2019-01-16 | 2019-07-30 | 上海交通大学 | Autonomic positioning method based on the robot that arma modeling is realized |
| CN110007670A (en)* | 2019-02-14 | 2019-07-12 | 四川阿泰因机器人智能装备有限公司 | Localization for Mobile Robot builds drawing method |
| CN110207707A (en)* | 2019-05-30 | 2019-09-06 | 四川长虹电器股份有限公司 | Quick initial alignment method and robot device based on particle filter |
| CN110333720A (en)* | 2019-07-10 | 2019-10-15 | 国网四川省电力公司电力科学研究院 | A SLAM Optimization Method Based on Particle Filter |
Non-Patent Citations (1)
| Title |
|---|
| 蒋荣欣 等: "《一种多机器人编队协同定位的方法》"* |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111930116A (en)* | 2020-07-24 | 2020-11-13 | 哈尔滨工程大学 | Large-scale UUV cluster formation method based on grid method |
| CN112742028A (en)* | 2021-01-22 | 2021-05-04 | 中国人民解放军国防科技大学 | Formation decision method, system, medium and equipment for fighting game |
| CN112742028B (en)* | 2021-01-22 | 2024-05-21 | 中国人民解放军国防科技大学 | Formation decision method, system, medium and equipment for countermeasure game |
| CN113984053A (en)* | 2021-09-10 | 2022-01-28 | 福建（泉州）哈工大工程技术研究院 | A ground robot swarm cooperative positioning technology |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110927740B (en) | Mobile robot positioning method | |
| CN111442722B (en) | Positioning method, positioning device, storage medium and electronic equipment | |
| CN109579849B (en) | Robot positioning method, robot positioning device, robot and computer storage medium | |
| EP3660618B1 (en) | Map building and positioning of robot | |
| EP3612906B1 (en) | Method and system for environment map generation and alignment | |
| CN111209978B (en) | Three-dimensional visual repositioning method and device, computing equipment and storage medium | |
| Raja et al. | PFIN: An efficient particle filter-based indoor navigation framework for UAVs | |
| Ho et al. | Virtual occupancy grid map for submap-based pose graph SLAM and planning in 3D environments | |
| CN112179330A (en) | Pose determination method and device of mobile equipment | |
| CN108332758B (en) | Corridor identification method and device for mobile robot | |
| Kim et al. | UAV-UGV cooperative 3D environmental mapping | |
| CN111207754A (en) | Multi-robot formation positioning method and robot equipment based on particle filter | |
| CN110749895B (en) | Laser radar point cloud data-based positioning method | |
| CN113093759A (en) | Robot formation construction method and system based on multi-sensor information fusion | |
| CN111435244B (en) | Loop closing method and device and robot | |
| CN117191023A (en) | Unmanned system cluster relative positioning method based on extended Kalman filtering | |
| CN111580530A (en) | Positioning method, positioning device, autonomous mobile equipment and medium | |
| CN118936755A (en) | Gas leakage source determination method, device and equipment | |
| Sobreira et al. | 2D cloud template matching-a comparison between iterative closest point and perfect match | |
| Contreras et al. | Efficient decentralized collaborative mapping for outdoor environments | |
| Thallas et al. | Particle filter—Scan matching hybrid SLAM employing topological information | |
| CN117470239A (en) | Path planning method and device for cleaning robot of underwater end plug of nuclear power station | |
| Prates et al. | Autonomous 3-D aerial navigation system for precision agriculture | |
| CN115661386A (en) | Environment map optimization method and device, robot and readable storage medium | |
| CN109901589B (en) | Mobile robot control method and device |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication | Application publication date:20200529 |