CN109855645A - Agv trolley hybrid vision navigation method - Google Patents
Agv trolley hybrid vision navigation method Download PDFInfo
- Publication number
- CN109855645A CN109855645A CN201910235937.XA CN201910235937A CN109855645A CN 109855645 A CN109855645 A CN 109855645A CN 201910235937 A CN201910235937 A CN 201910235937A CN 109855645 A CN109855645 A CN 109855645A
- Authority
- CN
- China
- Prior art keywords
- agv trolley
- agv
- trolley
- point
- navigation method
- 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
- 238000000034 method Methods 0.000 title claims abstract description 41
- 239000000203 mixture Substances 0.000 claims abstract description 5
- 230000000007 visual effect Effects 0.000 claims abstract description 5
- 239000002245 particle Substances 0.000 claims description 23
- 238000001514 detection method Methods 0.000 claims description 7
- 230000008569 process Effects 0.000 claims description 6
- 230000003936 working memory Effects 0.000 claims description 6
- 230000004888 barrier function Effects 0.000 claims description 4
- 241000283074 Equus asinus Species 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 230000007787 long-term memory Effects 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 3
- 238000004804 winding Methods 0.000 claims description 3
- 238000004088 simulation Methods 0.000 claims 1
- 238000005516 engineering process Methods 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000003550 marker Substances 0.000 description 1
- 239000000463 material Substances 0.000 description 1
Landscapes
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses an agv trolley hybrid vision navigation method, which comprises the following steps: step 1: detecting obstacles and constructing an environment three-dimensional map; step 2: planning a navigation path based on a three-dimensional space; and step 3: navigating the trolley along the path by using the characteristic point navigation; and 4, step 4: agv, the invention provides a mixed visual navigation method of agv trolley, aiming at providing a mixed visual navigation method of no-mark and no-fixed line working environment agv trolley, providing a positioning and composition solution based on appearance and irrelevant to time and scale, replacing navigation methods of two-dimensional codes with marks or ground laying magnets and the like which need external markers, reducing cost and improving efficiency.
Description
Technical field:
The invention belongs to the field of navigation technology of agv trolley, in particular to a kind of agv trolley mixing vision navigation method.
Background technique:
Existing agv trolley at work, is laid with outside the needs such as magnet using tagged two dimensional code or ground
Tagging object navigates, higher cost, and efficiency is lower.
Summary of the invention:
The object of the invention is that a kind of agv trolley mixing vision navigation method is provided to solve the above-mentioned problems,
It solves existing agv trolley at work, is mostly external using the needs such as tagged two dimensional code or ground laying magnet
What marker navigated, higher cost, the lower disadvantage of efficiency.
To solve the above-mentioned problems, the present invention provides a kind of technical solutions:
A kind of agv trolley mixing vision navigation method, comprising the following steps:
Step 1: detection of obstacles, constructing environment three-dimensional map；
Step 2: the navigation path planning based on three-dimensional space；
Step 3: being navigated using characteristic point, carry out trolley along path navigation；
Step 4:agv trolley detects characteristic point, is accurately positioned, and preplanned mission is completed.
The step 1 includes:
Step 11: control AGV moving of car surrounds place one week；
Step 12: data being acquired by infrared sensor, then three-dimensional based on the building of collected camera image data
Map；
Step 13: surrounding three-dimensional map is imported into AGV navigation map data library.
The step 12 provides the positioning based on appearance unrelated with time and scale and composition solution
Donkey, comprising:
Step 12-1: feature extraction and matching, optimization PnP is as a result, generate the i.e. opposite global map of local map；
Step 12-2: anchor point is extracted by algorithm, anchor point is used to measure to form closed loop by the number of connected reference
Weight, when needing from WorkingMemory transfer anchor point into Long-TermMemory, preferential selection has minimum
The anchor point of weight, if having multiple again with the anchor points of lowest weightings, by algorithm it is longest to storage time that
A carry out state estimation extracts pose data；
Step 12-3: winding detection, using the signature of bag of words method creation image, the signature of piece image is by visual dictionary
Word set expression, be that online increment type creates, estimated to form the probability of closed loop with discrete bayes filter, will be new
Anchor point be compared with the anchor point being stored in WorkingMemory, when finding there is high probability between new and old anchor point
When forming closed loop, as soon as a closed loop is just detected, new and old anchor point is also joined together.
The step 2 includes:
Step 21: importing surrounding three-dimensional map；
Step 22: since starting point, as unit of certain step-length, carrying out point spread, it is the smallest to choose cost value
Node needs to consider some constraints as expanding node in expansion process, for example, turning radius limitation and to risk barrier
Evade etc., so extend step by step, until turning backtracking around when some expanding node reaches target endpoint, then from terminal
To starting point, in this way, each node in the process is stringed together, the path of a planning is formed.Then in surrounding three-dimensional map
Describe the preliminary guidance path of AGV trolley；
Step 23: according to the feature of preliminary guidance path and the foundation motion mode of AGV trolley, being mentioned using ORB method
Take the characteristic point on preliminary guidance path；
Step 24: by AGV trolley actual motion, observing and recording its method of operation between two neighboring collection point
And situation generates guidance path by bearing calibration correction feature point position in algorithm and posture information.
The step 3 includes:
Step 31:AGV trolley loading environment three-dimensional map, obtains AGV trolley in surrounding three-dimensional map by the way of PnP
In current pose；
Step 32: being loaded into final guidance path；
Step 33: camera real-time scan data and map match, real-time update AGV trolley pose；
Step 34: current time AGV trolley pose being transmitted in the master controller of AGV car interior setting by algorithm
The input of rate control module, rate control module is required current AGV trolley pose and navigation route information, is exported as AGV
Current time velocity information is transmitted to the small car of AGV by the master controller of trolley current time velocity information, the setting of AGV car interior
The slave controller of portion's setting, control AGV trolley are walked by set speed and guidance path；
Step 35: repeating step 34 and 35, agv trolley is made to reach target point along guidance path.
The step 4 includes:
Step 41: trolley carries out positional deviation correction and pose adjustment after reaching predetermined position；
Step 42: being accurately positioned using the method for particle filter, spread one map space is well-proportioned at the beginning
Particle, then by obtaining the motion of robot come improved, such as 90 degree of revolute, all particles
Just to 90 degree of rotation, regardless of the position of this present particle is to or not, each particle present position is used to simulate a sensor
Information is compared with the sensor information observed, to be assigned to one probability of each particle, later according to the probability of generation come
Particle is regenerated, the probability of the higher generation of probability is bigger, and after such iteration, all particles can converge at leisure
Together, the accurate location of robot also just is calculated out, and matches to characteristic point, realize final accurate positioning and
Pose matching.
The cost value can be anything, comprising being not limited only to obstacle, virtual wall, region and semanteme etc..
The camera be placed on camera sensing device placement position, data be by camera obtain data, pose by
Algorithm is obtained and is updated.
The beneficial effects obtained by the present invention are as follows being: the present invention is a kind of agv trolley mixing vision navigation method, it is therefore an objective to be mentioned
For it is a kind of without mark, without fixed route working environment agv trolley mixing vision navigation method, provide one and time and scale without
The positioning and composition solution based on appearance closed replace tagged two dimensional code or ground to be laid with magnet etc. and need external mark
The air navigation aid for remembering object, reduces costs, improves efficiency.
Detailed description of the invention:
Detailed description will be given by the following detailed implementation and drawings by the present invention for ease of explanation,.
Fig. 1 is the principle of the present invention block diagram；
Fig. 2 is flow chart of the invention.
Specific embodiment:
As shown in Figs. 1-2, present embodiment uses a kind of following technical scheme: agv trolley mixing vision guided navigation side
Method, comprising the following steps:
Step 1: detection of obstacles, constructing environment three-dimensional map；
Step 2: the navigation path planning based on three-dimensional space；
Step 3: being navigated using characteristic point, carry out trolley along path navigation；
Step 4:agv trolley detects characteristic point, is accurately positioned, and preplanned mission is completed.
Step 1 includes:
Step 11: control AGV moving of car surrounds place one week；
Step 12: data being acquired by infrared sensor, then three-dimensional based on the building of collected camera image data
Map；
Step 13: surrounding three-dimensional map is imported into AGV navigation map data library.
Step 12 provides the positioning based on appearance unrelated with time and scale and composition solution Donkey, packet
It includes:
Step 12-1: feature extraction and matching, optimization PnP is as a result, generate the i.e. opposite global map of local map；
Step 12-2: anchor point is extracted by algorithm, anchor point is used to measure to form closed loop by the number of connected reference
Weight, when needing from WorkingMemory transfer anchor point into Long-TermMemory, preferential selection has minimum
The anchor point of weight, if having multiple again with the anchor points of lowest weightings, by algorithm it is longest to storage time that
A carry out state estimation extracts pose data；
Step 12-3: winding detection, using the signature of bag of words method creation image, the signature of piece image is by visual dictionary
Word set expression, be that online increment type creates, estimated to form the probability of closed loop with discrete bayes filter, will be new
Anchor point be compared with the anchor point being stored in WorkingMemory, when finding there is high probability between new and old anchor point
When forming closed loop, as soon as a closed loop is just detected, new and old anchor point is also joined together.
Step 2 includes:
Step 21: importing surrounding three-dimensional map；
Step 22: since starting point, as unit of certain step-length, carrying out point spread, it is the smallest to choose cost value
Node needs to consider some constraints as expanding node in expansion process, for example, turning radius limitation and to risk barrier
Evade etc., so extend step by step, until turning backtracking around when some expanding node reaches target endpoint, then from terminal
To starting point, in this way, each node in the process is stringed together, the path of a planning is formed.Then in surrounding three-dimensional map
Describe the preliminary guidance path of AGV trolley；
Step 23: according to the feature of preliminary guidance path and the foundation motion mode of AGV trolley, being mentioned using ORB method
Take the characteristic point on preliminary guidance path；
Step 24: by AGV trolley actual motion, observing and recording its method of operation between two neighboring collection point
And situation generates guidance path by bearing calibration correction feature point position in algorithm and posture information.
Step 3 includes:
Step 31:AGV trolley loading environment three-dimensional map, obtains AGV trolley in surrounding three-dimensional map by the way of PnP
In current pose；
Step 32: being loaded into final guidance path；
Step 33: camera real-time scan data and map match, real-time update AGV trolley pose；
Step 34: current time AGV trolley pose being transmitted in the master controller of AGV car interior setting by algorithm
The input of rate control module, rate control module is required current AGV trolley pose and navigation route information, is exported as AGV
Current time velocity information is transmitted to the small car of AGV by the master controller of trolley current time velocity information, the setting of AGV car interior
The slave controller of portion's setting, control AGV trolley are walked by set speed and guidance path；
Step 35: repeating step 34 and 35, agv trolley is made to reach target point along guidance path.
Step 4 includes:
Step 41: trolley carries out positional deviation correction and pose adjustment after reaching predetermined position；
Step 42: being accurately positioned using the method for particle filter, spread one map space is well-proportioned at the beginning
Particle, then by obtaining the motion of robot come improved, such as 90 degree of revolute, all particles
Just to 90 degree of rotation, regardless of the position of this present particle is to or not, each particle present position is used to simulate a sensor
Information is compared with the sensor information observed, to be assigned to one probability of each particle, later according to the probability of generation come
Particle is regenerated, the probability of the higher generation of probability is bigger, and after such iteration, all particles can converge at leisure
Together, the accurate location of robot also just is calculated out, and matches to characteristic point, realize final accurate positioning and
Pose matching.
Cost value can be anything, comprising being not limited only to obstacle, virtual wall, region and semanteme etc..
Camera is placed on the position of camera sensing device placement, and data are the data obtained by camera, and pose is by algorithm
It obtains and updates.
It is specific: a kind of agv trolley mixing vision navigation method, step 1: detection of obstacles, utilizes infrared sensor
Detect barrier, constructing environment three-dimensional map；Step 2: the navigation path planning based on three-dimensional space；Step 3: utilizing feature
Point navigation, master controller and from controller control agv trolley along path navigation；Step 4: agva trolley detects characteristic point, into
Row accurate positioning, completes preplanned mission, and this method can be reliable, stablizes, and the completion with certain precision is stored in a warehouse and transported, object
The tasks such as material carrying, the requirement to environment is low, and adaptability is extensive.
The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention, 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, these changes
Change and improvement all fall within the protetion scope of the claimed invention, the claimed scope of the invention by appended claims and its
Equivalent thereof.
Claims (8)
1. a kind of agv trolley mixing vision navigation method, which comprises the following steps:
Step 1: detection of obstacles, constructing environment three-dimensional map；
Step 2: the navigation path planning based on three-dimensional space；
Step 3: being navigated using characteristic point, carry out trolley along path navigation；
Step 4:agv trolley detects characteristic point, is accurately positioned, and preplanned mission is completed.
2. a kind of agv trolley mixing vision navigation method according to claim 1, it is characterised in that: step 1 packet
It includes:
Step 11: control AGV moving of car surrounds place one week；
Step 12: data being acquired by infrared sensor, then construct three-dimensional map based on collected camera image data；
Step 13: surrounding three-dimensional map is imported into AGV navigation map data library.
3. a kind of agv trolley mixing vision navigation method according to claim 2, it is characterised in that: the step 12 mentions
For a positioning based on appearance unrelated with time and scale and composition solution Donkey, comprising:
Step 12-1: feature extraction and matching, optimization PnP is as a result, generate the i.e. opposite global map of local map；
Step 12-2: anchor point is extracted by algorithm, anchor point is used to measure the power to form closed loop by the number of connected reference
Weight, when needing from Working Memory transfer anchor point into Long-Term Memory, preferential selection has minimum power
The anchor point of weight, if having multiple again with the anchor points of lowest weightings, by algorithm it is longest to storage time that
State estimation is carried out, pose data are extracted；
Step 12-3: winding detection, using the signature of bag of words method creation image, the signature of piece image is by the word in visual dictionary
Set expression, be that online increment type creates, estimated to form the probability of closed loop with discrete bayes filter, new is determined
Site is compared with the anchor point being stored in Working Memory, when finding have high probability to be formed between new and old anchor point
When closed loop, as soon as a closed loop is just detected, new and old anchor point is also joined together.
4. a kind of agv trolley mixing vision navigation method according to claim 1, it is characterised in that: step 2 packet
It includes:
Step 21: importing surrounding three-dimensional map；
Step 22: since starting point, as unit of certain step-length, carrying out point spread, choose the smallest node of cost value
As expanding node, need to consider some constraints in expansion process, for example, turning radius limitation and rule to risk barrier
It keeps away etc., so extends step by step, until when some expanding node reaches target endpoint, then from terminal is turned around and traced back to
Point forms the path of a planning in this way, each node in the process is stringed together.Then describe in surrounding three-dimensional map
The preliminary guidance path of AGV trolley；
Step 23: according to the feature of preliminary guidance path and the foundation motion mode of AGV trolley, being extracted just using ORB method
Walk the characteristic point on guidance path；
Step 24: by AGV trolley actual motion, observing and recording its method of operation and shape between two neighboring collection point
Condition generates guidance path by bearing calibration correction feature point position in algorithm and posture information.
5. a kind of agv trolley mixing vision navigation method according to claim 1, it is characterised in that: step 3 packet
It includes:
Step 31:AGV trolley loading environment three-dimensional map, obtains AGV trolley in surrounding three-dimensional map by the way of PnP
Current pose；
Step 32: being loaded into final guidance path；
Step 33: camera real-time scan data and map match, real-time update AGV trolley pose；
Step 34: by algorithm by current time AGV trolley pose be transmitted to AGV car interior setting master controller in speed
The input of control module, rate control module is required current AGV trolley pose and navigation route information, is exported as AGV trolley
Current time velocity information is transmitted to AGV car interior and set by current time velocity information, the master controller of AGV car interior setting
The slave controller set, control AGV trolley are walked by set speed and guidance path；
Step 35: repeating step 34 and 35, agv trolley is made to reach target point along guidance path.
6. a kind of agv trolley mixing vision navigation method according to claim 1, it is characterised in that: step 4 packet
It includes:
Step 41: trolley carries out positional deviation correction and pose adjustment after reaching predetermined position；
Step 42: being accurately positioned using the method for particle filter, spread a grain map space is well-proportioned at the beginning
Son, then by obtaining the motion of robot come improved, such as 90 degree of revolute, all particles also just to
90 degree of rotation uses one sensor information of each particle present position simulation regardless of the position of this present particle is to or not
It is compared with the sensor information observed, so that one probability of each particle is assigned to, later according to the probability of generation come again
Particle is generated, the probability of the higher generation of probability is bigger, and after such iteration, all particles can converge to one at leisure
It rises, the accurate location of robot also just is calculated out, and matches to characteristic point, realizes final accurate positioning and position
Appearance matching.
7. a kind of agv trolley mixing vision navigation method according to claim 4, it is characterised in that: the cost value can
To be anything, comprising being not limited only to obstacle, virtual wall, region and semanteme etc..
8. a kind of agv trolley mixing vision navigation method according to claim 5, it is characterised in that: the camera is put
In the position that camera sensing device is placed, data are the data obtained by camera, and pose is obtained and updated by algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910235937.XA CN109855645A (en) | 2019-03-27 | 2019-03-27 | Agv trolley hybrid vision navigation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910235937.XA CN109855645A (en) | 2019-03-27 | 2019-03-27 | Agv trolley hybrid vision navigation method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109855645A true CN109855645A (en) | 2019-06-07 |
Family
ID=66902008
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910235937.XA Pending CN109855645A (en) | 2019-03-27 | 2019-03-27 | Agv trolley hybrid vision navigation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109855645A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110568447A (en) * | 2019-07-29 | 2019-12-13 | 广东星舆科技有限公司 | Visual positioning method, device and computer readable medium |
| CN111679677A (en) * | 2020-06-24 | 2020-09-18 | 浙江大华技术股份有限公司 | AGV pose adjusting method and device, storage medium and electronic device |
| CN112346446A (en) * | 2019-08-08 | 2021-02-09 | 阿里巴巴集团控股有限公司 | Code-shedding recovery method and device for automatic guided transport vehicle and electronic equipment |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101552934A (en) * | 2009-05-07 | 2009-10-07 | 电子科技大学 | Optical internet cross-domain reliable route calculating method based on PCE backtracking recursion |
| WO2015017691A1 (en) * | 2013-08-02 | 2015-02-05 | Irobot Corporation | Time-dependent navigation of telepresence robots |
| CN105045268A (en) * | 2015-08-25 | 2015-11-11 | 深圳力子机器人有限公司 | AGV laser tape hybrid navigation system |
| CN105867389A (en) * | 2016-06-14 | 2016-08-17 | 深圳力子机器人有限公司 | Blended laser navigation method of AGV (Automated Guided Vehicle) |
| CN106153048A (en) * | 2016-08-11 | 2016-11-23 | 广东技术师范学院 | A kind of robot chamber inner position based on multisensor and Mapping System |
| CN106840148A (en) * | 2017-01-24 | 2017-06-13 | 东南大学 | Wearable positioning and path guide method based on binocular camera under outdoor work environment |
| CN106970648A (en) * | 2017-04-19 | 2017-07-21 | 北京航空航天大学 | Unmanned plane multi-goal path plans combined method for searching under the environment of city low latitude |
| CN107063258A (en) * | 2017-03-07 | 2017-08-18 | 重庆邮电大学 | A kind of mobile robot indoor navigation method based on semantic information |
| CN107194984A (en) * | 2016-03-14 | 2017-09-22 | 武汉小狮科技有限公司 | Mobile terminal real-time high-precision three-dimensional modeling method |
| CN108415445A (en) * | 2018-02-09 | 2018-08-17 | 西北工业大学 | A kind of submarine navigation device positioning robust Optimal methods based on switch constraint |
| CN109146144A (en) * | 2018-07-26 | 2019-01-04 | 西安工程大学 | Based on the landslide disaster prediction technique for improving Bayesian network |
| CN109165680A (en) * | 2018-08-01 | 2019-01-08 | 东南大学 | Single target object dictionary model refinement method under the indoor scene of view-based access control model SLAM |
| CN109426832A (en) * | 2017-08-30 | 2019-03-05 | 湖南拓视觉信息技术有限公司 | Closed loop detection method, storage medium and electronic equipment in scene three-dimensional modeling |
-
2019
- 2019-03-27 CN CN201910235937.XA patent/CN109855645A/en active Pending
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101552934A (en) * | 2009-05-07 | 2009-10-07 | 电子科技大学 | Optical internet cross-domain reliable route calculating method based on PCE backtracking recursion |
| WO2015017691A1 (en) * | 2013-08-02 | 2015-02-05 | Irobot Corporation | Time-dependent navigation of telepresence robots |
| CN105045268A (en) * | 2015-08-25 | 2015-11-11 | 深圳力子机器人有限公司 | AGV laser tape hybrid navigation system |
| CN107194984A (en) * | 2016-03-14 | 2017-09-22 | 武汉小狮科技有限公司 | Mobile terminal real-time high-precision three-dimensional modeling method |
| CN105867389A (en) * | 2016-06-14 | 2016-08-17 | 深圳力子机器人有限公司 | Blended laser navigation method of AGV (Automated Guided Vehicle) |
| CN106153048A (en) * | 2016-08-11 | 2016-11-23 | 广东技术师范学院 | A kind of robot chamber inner position based on multisensor and Mapping System |
| CN106840148A (en) * | 2017-01-24 | 2017-06-13 | 东南大学 | Wearable positioning and path guide method based on binocular camera under outdoor work environment |
| CN107063258A (en) * | 2017-03-07 | 2017-08-18 | 重庆邮电大学 | A kind of mobile robot indoor navigation method based on semantic information |
| CN106970648A (en) * | 2017-04-19 | 2017-07-21 | 北京航空航天大学 | Unmanned plane multi-goal path plans combined method for searching under the environment of city low latitude |
| CN109426832A (en) * | 2017-08-30 | 2019-03-05 | 湖南拓视觉信息技术有限公司 | Closed loop detection method, storage medium and electronic equipment in scene three-dimensional modeling |
| CN108415445A (en) * | 2018-02-09 | 2018-08-17 | 西北工业大学 | A kind of submarine navigation device positioning robust Optimal methods based on switch constraint |
| CN109146144A (en) * | 2018-07-26 | 2019-01-04 | 西安工程大学 | Based on the landslide disaster prediction technique for improving Bayesian network |
| CN109165680A (en) * | 2018-08-01 | 2019-01-08 | 东南大学 | Single target object dictionary model refinement method under the indoor scene of view-based access control model SLAM |
Non-Patent Citations (2)
| Title |
|---|
| 张赫: "《公路智能配货系统理论模型和技术方案研究》", 30 June 2012, 辽宁科学技术出版社 * |
| 高晓光: "《无人机协同路径规划》", 31 January 2012, 国防工业出版社 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110568447A (en) * | 2019-07-29 | 2019-12-13 | 广东星舆科技有限公司 | Visual positioning method, device and computer readable medium |
| CN112346446A (en) * | 2019-08-08 | 2021-02-09 | 阿里巴巴集团控股有限公司 | Code-shedding recovery method and device for automatic guided transport vehicle and electronic equipment |
| CN111679677A (en) * | 2020-06-24 | 2020-09-18 | 浙江大华技术股份有限公司 | AGV pose adjusting method and device, storage medium and electronic device |
| CN111679677B (en) * | 2020-06-24 | 2023-10-03 | 浙江华睿科技股份有限公司 | AGV pose adjustment method and device, storage medium and electronic device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Liu et al. | Large-scale autonomous flight with real-time semantic slam under dense forest canopy | |
| CN104914865B (en) | Intelligent Mobile Robot Position Fixing Navigation System and method | |
| KR101761313B1 (en) | Robot and method for planning path of the same | |
| Wang et al. | A simple and parallel algorithm for real-time robot localization by fusing monocular vision and odometry/AHRS sensors | |
| CN112129297B (en) | Multi-sensor information fusion self-adaptive correction indoor positioning method | |
| CN115200588B (en) | SLAM autonomous navigation method and device for mobile robot | |
| CN111457929A (en) | Logistics vehicle autonomous path planning and navigation method based on geographic information system | |
| CN108290294A (en) | Mobile robot and its control method | |
| US8369606B2 (en) | System and method for aligning maps using polyline matching | |
| CN109855645A (en) | Agv trolley hybrid vision navigation method | |
| CN112797985B (en) | Indoor positioning method and indoor positioning system based on weighted extension Kalman filtering | |
| Kuswadi et al. | Application SLAM and path planning using A-star algorithm for mobile robot in indoor disaster area | |
| Chronis et al. | Sketch-based navigation for mobile robots | |
| Taylor et al. | Exploration strategies for mobile robots | |
| Bai et al. | A novel plug-and-play factor graph method for asynchronous absolute/relative measurements fusion in multisensor positioning | |
| CN114879660A (en) | Robot environment sensing method based on target driving | |
| Li et al. | Research on Robot Path Planning Based on Point Cloud Map in Orchard Environment | |
| US12105519B2 (en) | Method for estimating positioning of moving object by using big cell grid map, recording medium in which program for implementing same is stored, and computer program stored in medium in order to implement same | |
| Tang et al. | IC-GVINS: A robust, real-time, INS-centric GNSS-visual-inertial navigation system for wheeled robot | |
| Etzion et al. | MoRPI: Mobile Robot Pure Inertial Navigation | |
| Kozák et al. | Robust visual teach and repeat navigation for unmanned aerial vehicles | |
| Wang et al. | A new algorithm for robot localization using monocular vision and inertia/odometry sensors | |
| Mukherjee et al. | A novel IPSO technique for path navigation and obstacle avoidance | |
| Davis et al. | Motion planning under uncertainty: application to an unmanned helicopter | |
| Diamantas et al. | Localisation and mapping using a laser range finder: A goal-seeking approach |
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 |
Application publication date: 20190607 |
|
| RJ01 | Rejection of invention patent application after publication |