CN109855645A - A kind of agv trolley mixing vision navigation method - Google Patents
A kind of agv trolley mixing vision navigation method Download PDFInfo
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- 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
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Abstract
The invention discloses a kind of agv trolley mixing vision navigation methods, 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, it is accurately positioned, complete preplanned mission, the present invention is a kind of agv trolley mixing vision navigation method, purpose be to provide it is a kind of without mark, without fixed route working environment agv trolley mixing vision navigation method, the positioning based on appearance unrelated with time and scale and composition solution are provided, tagged two dimensional code or ground laying magnet etc. is replaced to need the air navigation aid of external mark object, it reduces costs, improves 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.
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| 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 |
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