CN111813102B - Distributed autonomous robot environment map construction method - Google Patents
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Abstract
The application discloses a distributed autonomous robot environment map construction method, which comprises the steps of firstly constructing a local map of each robot by searching feature points in the map, then updating map information by using an incremental map updating method, and finally merging subgraphs in each robot into a global map by a distributed map fusion method; the application uses a fully distributed autonomous robot system to realize map fusion between robots; each robot is independently provided with the map construction method, so that the expandability and the robustness of the multi-robot topological network are greatly improved, meanwhile, the calculation load of a single robot is reduced, and the overall stability of the autonomous robot is facilitated.
Description
Technical Field
The application relates to a distributed autonomous robot environment map construction method, and belongs to the field of mobile robots.
Background
With the rise of the field of artificial intelligence, the field of autonomous robots is vigorously developed. The increasing of the labor cost in the society at present, in the fields of warehouse logistics, manufacturing industry and the like, more and more enterprises use automatic guided vehicles to replace high labor cost, and the mobile robot is widely applied in the field of automatic warehouse due to the advantages of high automation degree, good reliability, high production efficiency, strong adaptability, low cost and the like. However, the map construction is always a hotspot problem of autonomous robots, and when the robots pass through feature points of surrounding environments in a strange environment, the information of the map is obtained through information screening, so that the robots can perform task allocation, traffic coordination, acquisition of shared resources and the like in the environment, which is a problem to be solved urgently at present.
Disclosure of Invention
Aiming at the problem that when a mobile robot encounters a new environment in a distributed network environment, the problem of obtaining a local map by the robot is solved under the condition that the robot is not locally provided with the map, the application provides a distributed autonomous robot environment map construction method.
The aim of the application is realized by the following technical scheme:
in one aspect, the application provides a distributed autonomous robot environment map construction method, which comprises the following steps:
step 1, each autonomous mobile robot scans surrounding environments to establish a local map, and stores data in a local database to synchronize the data with other autonomous mobile robots;
step 2, incremental map updating: each autonomous mobile robot deploys an incremental map update module comprising a map incremental parsing component and a map incremental synchronizing component, the incremental map update comprising the sub-steps of:
2.1, constructing an autonomous mobile robot cluster through an autonomous mobile robot network connection module;
2.2 autonomous mobile robots explore new map information: when the environment of the working area changes dynamically, after part of robots explore the change of the environment, writing new map information into a local database; the newly added map data content is map incremental data, written into a binary event and stored in a robot local log file;
2.3 map delta resolution: each robot registers the map increment analysis component to the networking module, and once a certain robot explores new map information, other robots can select whether to keep connection with the map increment analysis component of the robot through the networking module; once a certain robot explores new map information, a binary event can be converted into a map increment message through a map increment analysis component on the local machine; the converted map increment information is stored in an instance, and each map data table corresponds to one instance;
2.4 obtaining map increment message: when a certain robot explores new map information and generates map increment information through a map increment analysis component, other robots in the cluster can subscribe to an instance in the map increment analysis component of the robot through an internet connection module, and the map increment information is acquired from the instance;
2.5 map incremental synchronization: once other robots acquire the map increment information of the robot, the map increment information can be synchronized into an environment map database of the robot through a local map increment synchronization component, and map the map increment data into an environment subgraph.
Step 3, fusing the distributed maps: r= { R 1 ,r 2 ,…,r n The robot set, r 1 ,r 2 ,…,r n Representing each robot; m= { M 1 ,m 2 ,…,m n The local map set of the robot, n= { N }, is represented 1 ,…,N n The feature point set N of any partial map i ＝{p 1 ,p 2 ,…,p t I, N, t.epsilon.N }, where + I is less than or equal to n; the distributed map fusion includes the sub-steps of:
3.1 assume any two robots r k ,r j The respective local map is m k ,m j Wherein k, j E N + ,k≤n,j≤n,N k ＝{p k1 ,p k2 ,…,p kt },p k1 ,p k2 ,…,p kt Representing map m k A series of feature points in (a); n (N) j ＝{p j1 ,p j2 ,…,p js }，p j1 ,p j2 ,…,p js Representing map m j A series of feature points in (a); if the feature point p is satisfied k ,p j The distance between them is smaller than the distance threshold T 1 Then it is regarded as a similar point pair and put into a communication list;
3.2 recording the communication list capacity as M, setting a distance threshold T 2 Randomly searching two pairs of points satisfying the formulas (1) and (2) in a communication list, and calculating the corresponding calibration parameters (t) of the two pairs of points according to the formula (3) x ,t y ,θ)；
|(A 2 +B 2 )-(C 2 +D 2 )|＜T 2 (1)
A 2 +B 2 ≈C 2 +D 2 (2)
Wherein p' k ∈N k ,p′ j ∈N j ,p′ k ＝(x′ k ,y′ k )，p′ j ＝(x′ j ,y′ j ) Is a pair of similar points; a= (x' k -x′ j ),B＝(y′ k -y′ j ),C＝(x k -x j ),D＝(y k -y j )；
3.3 repeating step 3.2 in the communication list to obtain calibration parameters (t x ,t y θ); if a certain calibration parameter (t x ,t y θ) satisfies other T in the communication list 3 * Point of M pair, T 3 And E (0, 1) indicating that the calibration parameter is effective, ending the step 3.2, acquiring the calibration parameter at the moment, and fusing the point pairs meeting the conditions.
Further, in the step 1, the construction of the local map specifically includes the following sub-steps:
1.1 in an occupied grid map, table s=1Indicating that a point is occupied, i.e. that the point has an obstacle, s=0 indicates that the point is free, i.e. free of an obstacle; let the probability of the point being an obstacle-free be p (s=0), the probability of the obstacle being p (s=1), p (s=0) +p (s=1) =1, and introducing an intermediate valueA state indicating a certain point;
1.2 for a point, when a measurement z- {0,1} is newly measured, its state odds(s) is updated by equation (4):
where p (s= 1|z) represents the probability of s=1 when the measured value z occurs, p (s= 0|z) represents the probability of s=0 when the measured value z occurs, and the formula (4) is simplified by a bayesian conditional probability formula and the logarithm is taken to obtain the formula (5):
wherein the method comprises the steps ofFor the measurement model, odds(s) is the state before the measurement;
the state of a point without any measurement is logo dds(s) =0;
1.3 after a certain robot walks through a path randomly, whether the grid around the path has an obstacle or not can be measured, the robot stores the data into a database of the robot, and map data are synchronized to other robots through sharing of the database.
Further, in the step 2, the networking module is distributed and deployed on each robot, and by writing configuration information of the networking module, some independent autonomous mobile robots can form a cluster, and the robots in the cluster can find other robots through the networking module and establish communication connection with the robots.
Further, in the step 2, the map incremental message may support multiple data parsing protocols; the converted map increment information is stored in an instance, each map data table corresponds to one instance, and if a plurality of map data tables change in the robot local area, a plurality of instances are generated; the map delta component can change its internal instances according to the number of environment maps, with instances supporting asynchronous transmission.
Further, in the step 2, the map increment synchronization component interacts with the map increment analysis component in different autonomous mobile robots, and the interaction flow is as follows: the synchronization component is connected with a certain instance of the parsing component; the parsing component starts a handshake connection; the synchronization component subscribes to instances of the specified parsing component; the synchronization component obtains the map increment message from the analysis component; the synchronizing component rolls back the map state; the synchronization component unsubscribes; the synchronization component releases the connection with the parsing component.
Further, in step 2, through incremental map updating, all autonomous mobile robots in the cluster may continuously update, explore and update their own maps and release map increments, and through subscription, obtain map increments of other robots, all autonomous mobile robots may share new maps explored separately, and each robot may construct an entire environment map on its own.
Further, in step 3.3, if (x i ,y i ) And (x' i ,y′ i ) Two other pairs of points in the communication list satisfying the formulas (1) and (2), when (x) i ,y i ) By calibrating the parameter (t x ,t y θ) to obtain (x ") i ,y″ i ) Satisfies the following conditionsIn this case, (t) x ,t y θ) also satisfies (x) i ,y i ) And (x' i ,y′ i ) Point, where T 4 Is a distance threshold.
In another aspect, the application also proposes a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, said processor implementing the steps of the above method when executing said computer program.
The application also proposes a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the above-mentioned method.
The beneficial effects of the application are as follows: the application specifically sets forth a method for constructing a map under a distributed condition through three aspects; firstly, constructing a local map of each robot by searching feature points in the map, then updating map information by using an incremental map updating method, and finally merging sub-images in each robot into a global map by using a distributed map merging method; the application uses a fully distributed autonomous robot map construction system to realize map fusion among robots; the method for independently carrying the map construction on each robot greatly improves the expandability and robustness of the multi-robot topology network, reduces the calculation load of the single robot, and is beneficial to the overall stability of the autonomous robot.
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FIG. 1 is a flowchart of incremental map updating according to an embodiment of the present application.
Detailed Description
The technical scheme of the application is further described in detail below with reference to the accompanying drawings of the embodiment of the application. The advantages and features of the application will become more apparent from the following description and the claims, it being understood that the drawings are in very simplified form and are all made to non-precise proportions, simply for the purpose of conveniently and clearly aiding in the description of embodiments of the application.
The application provides a distributed autonomous robot environment map construction method, which comprises the following steps:
step 1: and (5) constructing a map. Each robot scans the surrounding environment to build a local map, and stores the data in a local database to synchronize with other robots.
The robot acquires the characteristics in the environment by using the sensor, the latest obstacle is represented by using an improved map construction method, and the robot can detect a local map after continuous iteration and then synchronously share the local map with other robots through the database. The method specifically comprises the following substeps:
1.1 in the occupancy grid map, for a point that is occupied, i.e. has an obstacle, s=1 is used, s=0 is used to indicate that it is free, i.e. free of obstacles. Let the probability of the point being an obstacle-free be p (s=0), the probability of the obstacle being p (s=1), p (s=0) +p (s=1) =1, and introducing an intermediate valueIndicating the state of a certain point.
1.2 when a measurement z-0, 1 is newly measured for a point, we need to update its state odds(s), which can be updated by equation (1):
where p (s= 1|z) represents the probability of s=1 when the measured value z occurs, p (s= 0|z) represents the probability of s=0 when the measured value z occurs, and equation (2) can be obtained by taking the logarithm of the degenerated degeneracy of equation (1) by the bayesian conditional probability formula:
wherein the method comprises the steps ofFor the measurement model, a constant value related to the sensor is divided into two cases: occupied and idle (respectively)>And->) Odds(s) is the state before the measurement;
the state of a point without any measurement is logo dds(s) =0 (at initialization).
1.3 after a certain robot walks a path at random, it can be measured which grids around the path are blocked and which are unblocked. The robot will save these data in its own database and synchronize the map data to other robots through the sharing of the database.
Step 2: the application designs an incremental map updating method.
The incremental map updating method mainly depends on an autonomous mobile robot networking module and an incremental map updating module. The incremental map updating module mainly comprises a map incremental analysis component and a map incremental synchronization component, and the map incremental analysis component and the map incremental synchronization component are distributed and deployed in each autonomous mobile robot. As shown in fig. 1, the incremental map updating method specifically includes the following steps:
2.1, constructing an autonomous mobile robot cluster through an autonomous mobile robot net connection module. The autonomous mobile robot system is based on LAN communication, the networking module is distributed on each robot, and by writing networking module configuration information, some independent autonomous mobile robots can form a cluster, and the robots in the cluster can find other robots through the networking module and establish communication connection with the robots.
2.2 autonomous mobile robots explore new map information. When the working area environment changes dynamically, such as generating new barriers, part of robots explore the environment changes and then write new map information into a local database. The content of the map data which is newly added is map increment data, written into a binary event and stored in a robot local log file.
2.3 map delta resolution. Once the autonomous mobile robot explores new map information and stores the map increment data thereof into a local log file, a local map increment analysis component is called to realize binary event analysis. Mainly comprises the following steps:
(1) Each robot registers a map increment analysis component into an autonomous mobile robot networking module, once a certain robot R i Other robots can select whether to keep connection with the robot map increment analysis component through the internet connection module when new map information is explored.
(2) Once a certain robot R i The new map information is explored, the binary event can be converted into the map increment information through the map increment analysis component on the local machine, and the map increment information can support various data analysis protocols. The converted map increment information is stored in the examples, each map data table corresponds to one example, and if a plurality of map data tables change, a plurality of examples can be generated in the robot. The map delta component can change its internal instances according to the number of environment maps, with instances supporting asynchronous transmission.
2.4 autonomous mobile robots acquire map delta messages. When a certain robot R i Searching new map information, generating map increment information through a map increment analysis component, and subscribing other autonomous mobile robots in the cluster to the robot R through the Internet connection module i An instance in the map delta parsing component from which map delta messages are obtained.
2.5 map delta synchronization. Once other robots acquire robot R i The map incremental message can be synchronized into the own environment map database through the local map incremental synchronizing component, and map incremental data into an environment subgraph.
In different autonomous mobile robots, the map increment synchronization component interacts with the map increment parsing component, and the interaction flow is as follows:
(1) The synchronization component is connected with a certain instance of the parsing component;
(2) The parsing component starts a handshake connection;
(3) The synchronization component subscribes to instances of the specified parsing component;
(4) The synchronization component obtains the map increment message from the analysis component;
(5) The synchronizing component rolls back the map state;
(6) The synchronization component unsubscribes;
(7) The synchronization component releases the connection with the parsing component.
Through the incremental map updating step, all autonomous mobile robots in the cluster can continuously update, explore and update own maps and release map increments, map increments of other robots are obtained through subscription, all autonomous mobile robots can share new maps explored respectively, and meanwhile each robot can construct an entire environment map on the own robot.
Step 3: and (5) fusing the maps. The application designs a new map fusion algorithm based on a distributed system. R= { R 1 ,r 2 ,…,r n The robot set, r 1 ,r 2 ,…,r n Representing each robot. When the robot obtains information of the local map, M= { M 1 ,m 2 ,…,m n The feature point set n= { N represents a local map set 1 ,…,N n A feature point set N of any one of the partial maps i ＝{p 1 ,p 2 ,…,p t I, N, t.epsilon.N }, where + ,i≤n。
3.1 assume any two robots r k ,r j The respective local map is m k ,m j Wherein k, j E N + ,k≤n,j≤n,N k ＝{p k1 ,p k2 ,…,p kt },p k1 ,p k2 ,…,p kt Representing map m k A series of feature points in (a); n (N) j ＝{p j1 ,p j2 ,…,p js }，p j＝ ,p j2 ,…,p js Representing map m j Is a series of feature points in the model.
Calculating a communication list between the maps by k ,p j The distance between them is less than T 1 We regard it as a pair of similarity points to put it in the communication list, where p k ∈N k ,p j ∈N j ，T 1 For distance threshold, if p in two-dimensional coordinate system k ＝(x k ,y k ),p j ＝(x j ,y j ) Satisfying the formula 3.1:
3.2 generating a communication list from the points meeting the conditions through the step 3.1, recording the capacity of the communication list as M, and setting a distance threshold T 2 Two pairs of points satisfying the formulas 3.2.1 and 3.2.2 are randomly searched in the communication list, and the calibration parameters (t) are calculated according to the two pairs of points satisfying the conditions x ,t y θ), defined as formula 3.2.3.
|(A 2 +B 2 )-(C 2 +D 2 )|＜T 2 (3.2.1)
A 2 +B 2 ≈C 2 +D 2 (3.2.2)
Wherein p' k ∈N k ,p′ j ∈N j ,p′ k ＝(x′ k ,y′ k )，p′ j ＝(x′ j ,y′ j ) Is a pair of similar points; a= (x' k -x′ j ),B＝(y′ k -y′ j ),C＝(x k -x j ),D＝(y k -y j )。
3.3 repeating step 3.2 in the communication list to obtain calibration parameters (t x ,t y θ). If (x i ,y i ) And (x' i ,y′ i ) Two other pairs of points in the communication list satisfying formulas 3.2.1 and 3.2.2 are shown as (x i ,y i ) By calibrating the parameter (t x ,t y θ) to obtain (x ") i ,y″ i ) When the formula 3.3.1 is satisfied, T 4 For the distance threshold (the distance threshold between the value obtained by calibrating the parameter and the actual value), the value (t x ,t y θ) also satisfies (x) i ,y i ) And (x' i ,y′ i ) And (5) a dot.
If a certain calibration parameter (t x ,y y θ) satisfies other T in the communication list 3 *M(T 3 E (0, 1)) pairs, and ending step 3.2, obtaining the calibration parameters at the moment, fusing the pairs of points meeting the conditions, and obtaining a global map which tends to be consistent after a period of time.
The application also proposes a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, said processor implementing the steps of the above method when executing said computer program.
The application also proposes a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the above-mentioned method.
The foregoing is merely a preferred embodiment of the present application, and the present application has been disclosed in the above description of the preferred embodiment, but is not limited thereto. Any person skilled in the art can make many possible variations and modifications to the technical solution of the present application or modifications to equivalent embodiments using the methods and technical contents disclosed above, without departing from the scope of the technical solution of the present application. Therefore, any simple modification, equivalent variation and modification of the above embodiments according to the technical substance of the present application still fall within the scope of the technical solution of the present application.
Claims (9)
1. The method for constructing the environment map of the distributed autonomous robot is characterized by comprising the following steps of:
step 1, each autonomous mobile robot scans surrounding environments to establish a local map, and stores data in a local database to synchronize the data with other autonomous mobile robots;
step 2, incremental map updating: each autonomous mobile robot is provided with an incremental map updating module, wherein the incremental map updating module comprises a map incremental analysis component and a map incremental synchronization component, and the incremental map updating comprises the following substeps;
step 2.1, constructing an autonomous mobile robot cluster through an autonomous mobile robot network connection module;
step 2.2, the autonomous mobile robot searches for new map information: when the environment of the working area changes dynamically, after part of robots explore the change of the environment, writing new map information into a local database; the newly added map data content is map incremental data, written into a binary event and stored in a robot local log file;
step 2.3, map increment analysis: each robot registers the map increment analysis component to the networking module, and once a certain robot explores new map information, other robots can select whether to keep connection with the map increment analysis component of the robot through the networking module; once a certain robot explores new map information, a binary event can be converted into a map increment message through a map increment analysis component on the local machine; the converted map increment information is stored in an instance, and each map data table corresponds to one instance;
step 2.4, obtaining a map increment message: when a certain robot explores new map information and generates map increment information through a map increment analysis component, other robots in the cluster can subscribe to an instance in the map increment analysis component of the robot through a networking module, and the map increment information is acquired from the instance;
step 2.5, map increment synchronization: once other robots acquire the map increment information of the robot, the map increment information can be synchronized into an environment map database of the robot through a local map increment synchronizing component, and map increment data is built into an environment subgraph;
step 3, fusing the distributed maps: r= { R 1 ，r 2 ，...，r n ' represent robotAggregation, r 1 ，r 2 ，...，r n Representing each robot; m= { M 1 ，m 2 ，...，m n The local map set of the robot, n= { N }, is represented 1 ，...，N n The feature point set N of any partial map i ＝{p 1 ，p 2 ，...，p t I, N, t.epsilon.N }, where + I is less than or equal to n; the distributed map fusion includes the sub-steps of:
step 3.1, assume any two robots r k ，r j The respective local map is m k ，m j Wherein k, j E N + ，k≤n，j≤n，N k ＝{p k1 ，p k2 ，...，p kt }，p k1 ，p k2 ，...，p kt Representing map m k A series of feature points in (a); n (N) j ＝{p j1 ，p j2 ，...，p js }，p j1 ，p j2 ，...，p js Representing map m j A series of feature points in (a); if the feature point p is satisfied k ，p j The distance between them is smaller than the distance threshold T 1 Then it is regarded as a similar point pair and put into a communication list;
step 3.2, the capacity of the communication list is recorded as M, and a distance threshold T is set 2 Randomly searching two pairs of points satisfying the formulas (1) and (2) in a communication list, and calculating the corresponding calibration parameters (t) of the two pairs of points according to the formula (3) x ，t y ，θ)；
|(A 2 +B 2 )-(C 2 +D 2 )|＜T 2 (1)
A 2 +B 2 ≈C 2 +D 2 (2)
Wherein p' k ∈N k ，p′ j ∈N j ，p′ k ＝(x′ k ，y′ k )，p′ j ＝(x′ j ，y′ j ) Is a pair of similar points; a= (x' k -x′ j )，B＝(y′ k -y′ j )，C＝(x k -x j )，D＝(y k -y j )；
Step 3.3, repeating step 3.2 in the communication list to obtain the calibration parameters (t x ，t y θ); if a certain calibration parameter (t x ，t y θ) satisfies other T in the communication list 3 * Point of M pair, T 3 And E (0, 1) indicating that the calibration parameter is effective, ending the step 3.2, acquiring the calibration parameter at the moment, and fusing the point pairs meeting the conditions.
2. The method for constructing a map of a distributed autonomous robot environment according to claim 1, wherein in the step 1, the construction of the local map specifically includes the following sub-steps:
step 1.1, in the occupied grid map, s=1 indicates that a certain point is occupied, namely that the point has an obstacle, and s=0 indicates that the certain point is idle, namely that no obstacle exists; let the probability of the point being an obstacle-free be p (s=0), the probability of the obstacle being p (s=1), p (s=0) +p (s=1) =1, and introducing an intermediate valueA state indicating a certain point;
step 1.2, when a measurement value z to {0,1} is newly measured for a point, its state odds(s) is updated by equation (4):
where p (s= 1|z) represents the probability of s=1 when the measured value z occurs, p (s= 0|z) represents the probability of s=0 when the measured value z occurs, and the formula (4) is simplified by a bayesian conditional probability formula and the logarithm is taken to obtain the formula (5):
wherein the method comprises the steps ofFor the measurement model, odds(s) is the state before the measurement;
the state of a point without any measurement is log odds(s) =0;
step 1.3, after a certain robot randomly walks through a path, whether the grid around the path has an obstacle or not can be measured, the robot stores the data into a database of the robot, and map data are synchronized to other robots through sharing of the database.
3. The method according to claim 1, wherein in the step 2, the networking module is distributed on each robot, and by writing configuration information of the networking module, some independent autonomous mobile robots can build a cluster, and the robots in the cluster can find other robots through the networking module and establish communication connection with the other robots.
4. The method for constructing a map of a distributed autonomous robot environment according to claim 1, wherein in the step 2, the map incremental message can support a plurality of data parsing protocols; the converted map increment information is stored in an instance, each map data table corresponds to one instance, and if a plurality of map data tables change in the robot local area, a plurality of instances are generated; the map delta component can change its internal instances according to the number of environment maps, with instances supporting asynchronous transmission.
5. The method for building the map of the environment of the distributed autonomous robot according to claim 1, wherein in the step 2, the map increment synchronization component interacts with the map increment parsing component in different autonomous mobile robots, and the interaction flow is as follows: the synchronization component is connected with a certain instance of the parsing component; the parsing component starts a handshake connection; the synchronization component subscribes to instances of the specified parsing component; the synchronization component obtains the map increment message from the analysis component; the synchronizing component rolls back the map state; the synchronization component unsubscribes; the synchronization component releases the connection with the parsing component.
6. The method for constructing the environment map of the distributed autonomous robots according to claim 1, wherein in the step 2, all autonomous mobile robots in the cluster can continuously update, explore and update their own maps and release map increments through incremental map updating, and obtain the map increments of other robots through subscription, all autonomous mobile robots can share the new explored maps, and each robot can construct the whole environment map on the own robot.
7. A method of mapping a distributed autonomous robot environment as claimed in claim 1, wherein in step 3.3, if (x i ，y i ) And (x' i ，y′ i ) Two other pairs of points in the communication list satisfying the formulas (1) and (2), when (x) i ，y i ) By calibrating the parameter (t x ，t y θ) to obtain (x ") i ，y″ i ) Satisfies the following conditionsIn this case, (t) x ，t y θ) also satisfies (x) i ，y i ) And (x' i ，y′ i ) Point, where T 4 Is a distance threshold.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of any one of claims 1 to 7 when the computer program is executed.
9. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program implementing the steps of the method of any of claims 1 to 7 when executed by a processor.
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