CN114911882A

CN114911882A - Map construction and navigation method and device, electronic equipment and readable storage medium

Info

Publication number: CN114911882A
Application number: CN202110179020.XA
Authority: CN
Inventors: 刘烨航; 成鹏; 张广鹏; 王旭
Original assignee: Lingdong Technology Beijing Co Ltd
Current assignee: Lingdong Technology Beijing Co Ltd
Priority date: 2021-02-09
Filing date: 2021-02-09
Publication date: 2022-08-16
Also published as: WO2022170806A1

Abstract

The embodiment of the disclosure discloses a map construction and navigation method, a map construction and navigation device, electronic equipment and a readable storage medium. The map construction method comprises the following steps: a node dividing step in which the first area is divided into nodes; acquiring node states and positions, wherein the states and the positions of the nodes are acquired; a semantic tag obtaining step, wherein a semantic tag is obtained according to a node in the first area; and a semantic tag state obtaining step, wherein a first state and/or a second state of the semantic tag are obtained, and the first region is a region which is possibly occupied by the mobile equipment in a specific region, so that the region is accurately divided, the state is completely marked, detailed semantic analysis is performed, the scene change is adapted, and flexible scheduling of the mobile equipment is facilitated.

Description

Map construction and navigation method and device, electronic equipment and readable storage medium

Technical Field

The disclosure relates to the technical field of navigation, and in particular relates to a map construction method, a map construction device, a map navigation device, an electronic device and a readable storage medium.

Background

In the field of navigation, how to design a map representing an environment and navigate a mobile device in the map is an important and critical issue. Meanwhile, positioning And Mapping (SLAM) is the most common map construction method, And positioning And scene map information of the position And posture of a robot or other carriers is usually generated by collecting And calculating data through various sensors on the robot or other carriers.

In patent document CN110807782A, "a map representation system of a visual robot and a construction method thereof", a scene map is constructed in a geometric and topological manner, but semantic analysis on the scene is not sufficient, and cannot cope with a complex scene, and a method for navigating in combination with map semantics is not proposed, which results in high subsequent scheduling overhead and no scheduling flexibility for a changing scene.

Disclosure of Invention

In order to solve the problems in the related art, embodiments of the present disclosure provide a map construction and navigation method, apparatus, electronic device, and readable storage medium.

In a first aspect, an embodiment of the present disclosure provides a map building method, including: a node dividing step in which the first area is divided into nodes;

acquiring node states and positions, wherein the states and the positions of the nodes are acquired;

a semantic label obtaining step, wherein a semantic label is obtained according to the node in the first area;

a semantic tag state acquisition step, wherein a first state and/or a second state of the semantic tag is acquired,

the first area is an area that can be occupied by a mobile device in a specific area.

With reference to the first aspect, in a first implementation manner of the first aspect, the node dividing step includes:

and dividing the first area into the nodes according to the area perceived by the mobile equipment and/or the area occupied by the mobile equipment.

With reference to the first aspect, the present disclosure provides, in a second implementation form of the first aspect,

acquiring the position of the node by using a synchronous positioning and mapping method; and/or

And acquiring the state of the node by using a visual method.

With reference to the first aspect, in a third implementation manner of the first aspect, the state of the node includes:

occupied state, unoccupied state.

With reference to the third implementation manner of the first aspect, in a fourth implementation manner of the first aspect of the present disclosure, the semantic tag obtaining step includes:

and acquiring the semantic label by using a clustering method according to the state and the position of the node in the first area.

With reference to the first aspect, in a fifth implementation manner of the first aspect, the first state of the semantic tag includes:

unblocked state, crowded state, blocked state; and/or

The second state of the semantic tag comprises:

locked state, unlocked state.

With reference to the fifth implementation manner of the first aspect, in a sixth implementation manner of the first aspect, the first state of the semantic label is the blocking state under the condition that continuous nodes connecting two sides of the semantic label are both in the occupied state;

and under the condition that the number of the nodes in the occupied state in the semantic tag is greater than a first threshold value, or the proportion of the nodes in the occupied state in the semantic tag is greater than a second threshold value, the first state of the semantic tag is the congestion state.

With reference to the fifth implementation manner of the first aspect, in a seventh implementation manner of the first aspect, under the condition that the first state of the semantic tag is a clear state, or the first state of the semantic tag is a congestion state and a congestion index is less than or equal to a third threshold, the second state of the semantic tag is an unlocked state;

and under the condition that the first state of the semantic tag is a jam state or the first state of the semantic tag is a congestion state and the congestion index is greater than the third threshold value, the second state of the semantic tag is a locking state.

In a second aspect, an embodiment of the present disclosure provides a method for navigating a mobile device according to the mapping method in any one of the first to seventh implementation manners of the first aspect, where the method includes:

a navigation starting and stopping position obtaining step, wherein a navigation starting position and a navigation stopping position of the mobile equipment are obtained;

a navigation path planning step, wherein a navigation path is calculated according to the navigation starting position, the navigation ending position, the first state of the semantic tag and/or the second state of the semantic tag, and the state and the position of the node;

a moving step of controlling the mobile device to move along the navigation path;

a state updating step, wherein the state of the node, the first state of the semantic tag and/or the second state of the semantic tag is updated according to the position of the mobile equipment and/or the detection result of the mobile equipment;

and a navigation path updating step, wherein the navigation path is updated according to the updated state of the node, the first state of the semantic label and/or the second state of the semantic label, and the state and the position of the node.

With reference to the second aspect, in a first implementation manner of the second aspect, the navigation path planning step includes:

under the condition that the navigation starting position and the navigation ending position are within the same semantic label, calculating the navigation path by using a topological graph-level path planning algorithm;

and under the condition that the navigation starting position and the navigation ending position are not in the same semantic label, calculating the navigation path by using a semantic label-level path planning algorithm.

In a third aspect, an embodiment of the present disclosure provides a map building apparatus, including:

a node dividing module configured to divide the first area into nodes;

a node state and location acquisition module configured to acquire a state and location of the node;

a semantic tag obtaining module configured to obtain a semantic tag according to the node in the first region;

a semantic tag state acquisition module configured to acquire a first state and/or a second state of the semantic tag,

With reference to the third aspect, in a first implementation manner of the third aspect, the node dividing module is further configured to:

and dividing the area possibly occupied by the mobile equipment in the first area into the nodes according to the area perceived by the mobile equipment and/or the area occupied by the mobile equipment.

With reference to the third aspect, in a second implementation manner of the third aspect, the present disclosure obtains the position of the node by using a synchronous positioning and mapping method; and/or

And acquiring the state of the node by using a visual method.

With reference to the third aspect, in a third implementation manner of the third aspect, the state of the node includes:

occupied state, unoccupied state.

With reference to the third implementation manner of the third aspect, in a fourth implementation manner of the third aspect, the semantic tag obtaining module is further configured to:

With reference to the third aspect, in a fifth implementation manner of the third aspect, the first state of the semantic tag includes:

a smooth state, a crowded state, a blocked state; and/or

The second state of the semantic tag comprises:

locked state, unlocked state.

With reference to the fifth implementation manner of the third aspect, in a sixth implementation manner of the third aspect,

under the condition that continuous nodes connected with the two sides of the semantic label are in the occupied state, the first state of the semantic label is the blocking state;

and under the condition that the number of the nodes in the occupied state in the semantic tag is greater than a first threshold value or the proportion of the nodes in the occupied state in the semantic tag is greater than a second threshold value, the first state of the semantic tag is the congestion state.

With reference to the fifth implementation manner of the third aspect, in a seventh implementation manner of the third aspect,

under the condition that the first state of the semantic tag is a clear state or the first state of the semantic tag is a congestion state and the congestion index is less than or equal to a third threshold value, the second state of the semantic tag is an unlocked state;

In a fourth aspect, an embodiment of the present disclosure provides a mobile device navigation apparatus that is the map building apparatus according to any one of the seventh implementation manners of the third aspect, including:

a navigation start and stop position acquisition module configured to acquire a navigation start position and a navigation end position of the mobile device;

a navigation path planning module configured to calculate a navigation path according to the navigation start position, the navigation end position, the first state of the semantic tag and/or the second state of the semantic tag, and the state and position of the node;

a movement module configured to control the mobile device to move along the navigation path;

a state update module configured to update a state of the node, a first state of the semantic tag and/or a second state of the semantic tag according to a location of the mobile device and/or a detection result of the mobile device;

a navigation path update module configured to update the navigation path according to the updated state of the node, the first state of the semantic label and/or the second state of the semantic label, and the state and the position of the node.

With reference to the fourth aspect, in a first implementation manner of the fourth aspect, the navigation path planning module is further configured to:

In a fifth aspect, an embodiment of the present disclosure provides an electronic device, including a memory and a processor; wherein the content of the first and second substances,

the memory is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method according to any one of the first aspect, the first implementation manner to the ninth implementation manner of the first aspect.

In a sixth aspect, an embodiment of the present disclosure provides a readable storage medium, on which computer instructions are stored, and the computer instructions, when executed by a processor, implement the method according to any one of the first aspect, the first implementation manner to the ninth implementation manner of the first aspect.

The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:

according to an embodiment of the present disclosure, by the node dividing step, wherein the first area is divided into nodes; a node state and position acquisition step, wherein the state and position of the node are acquired; a semantic label obtaining step, wherein a semantic label is obtained according to the nodes in the first area; and a semantic tag state obtaining step, wherein a first state and/or a second state of the semantic tag are obtained, and the first region is a region which is possibly occupied by the mobile equipment in a specific region, so that the region is accurately divided, the state is completely marked, detailed semantic analysis is performed, the scene change is adapted, and flexible scheduling of the mobile equipment is facilitated.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

Other features, objects, and advantages of the present disclosure will become more apparent from the following detailed description of non-limiting embodiments when taken in conjunction with the accompanying drawings. In the drawings:

FIG. 1a shows an exemplary schematic diagram of an implementation scenario of a mapping method according to an embodiment of the present disclosure;

FIG. 1b shows an exemplary diagram of an implementation scenario of a mapping method according to an embodiment of the present disclosure;

fig. 1c shows an exemplary schematic diagram of an implementation scenario of a navigation method according to an embodiment of the present disclosure;

fig. 1d shows an exemplary schematic diagram of an implementation scenario of a navigation method according to an embodiment of the present disclosure;

FIG. 2 illustrates a flow diagram of a mapping method according to an embodiment of the present disclosure;

FIG. 3 illustrates a flow chart of a navigation method according to an embodiment of the present disclosure;

FIG. 4 shows a block diagram of a map construction apparatus according to an embodiment of the present disclosure;

fig. 5 shows a block diagram of a navigation device according to an embodiment of the present disclosure;

FIG. 6 shows a block diagram of an electronic device according to an embodiment of the present disclosure;

FIG. 7 is a block diagram of a computer system suitable for use in implementing a mapping method and a navigation method according to an embodiment of the present disclosure.

Detailed Description

Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. Also, for the sake of clarity, parts not relevant to the description of the exemplary embodiments are omitted in the drawings.

In the present disclosure, it is to be understood that terms such as "including" or "having," etc., are intended to indicate the presence of labels, numbers, steps, actions, components, parts, or combinations thereof disclosed in the present specification, and are not intended to exclude the possibility that one or more other labels, numbers, steps, actions, components, parts, or combinations thereof are present or added.

It should be further noted that the embodiments and labels in the embodiments of the present disclosure may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

In order to solve the above problems, the present disclosure provides a map construction and navigation method, apparatus, electronic device, and readable storage medium.

Fig. 1a shows an exemplary schematic diagram of an implementation scenario of a mapping method according to an embodiment of the present disclosure.

It will be understood by those of ordinary skill in the art that fig. 1a exemplarily illustrates an implementation scenario of the map construction method, and does not constitute a limitation of the present disclosure.

As shown in fig. 1a, in a specific area 100 such as a warehouse surrounded by the end ABCD, there are a shelf 1, a shelf 2, a shelf 3, a shelf 4, a shelf 5, and a shelf 6. A moving apparatus such as an automatic transfer robot can move in an area other than the rack in the warehouse, that is, a first area, and perform an access operation to the goods on the rack.

In the embodiment of the present disclosure, the first area may be divided into nodes according to an area that can be perceived by one automated transfer robot or an area occupied by one automated transfer robot. For example node 101 in fig. 1 a. The node 101 may be an empty upper position 1, an empty upper position 2, or a left 4 of a parking space 1 occupied by an automatic transfer robot. In fig. 1a and fig. 1b to 1d described below, nodes occupied by the automatic transfer robot are indicated in gray. By the above manner of dividing the nodes, a plurality of nodes are divided in the first area, for example, the upper side position 1.. times 9, the left side position 1-1.. times 1-16, the trunk road 1-2.. times 310, and the like. The sensing of the automatic handling robot may be mechanical arm touch sensing, ultrasonic sensing, or other sensing methods, which is not limited by this disclosure.

In fig. 1a, the position of a node may be acquired using a positioning method such as a synchronous positioning and Mapping (SLAM) method, and the state of the node, for example, the state of whether the node is occupied or not, may be acquired using a vision method such as machine vision of an automatic transfer robot. The automatic transfer robot may identify that a node is in an occupied state by being in the node.

In the embodiment of the present disclosure, the state information of the node may further include state information of adjacent nodes, thereby facilitating overall analysis of the plurality of nodes and facilitating movement of the automatic transfer robot.

It will be understood by those skilled in the art that the specific area 100 may be other areas such as a manufacturing shop, a warehouse, etc., and the first area that the automatic transfer robot may occupy may vary according to the variation of the position of the rack in the warehouse or the adjustment of the manufacturing line in the manufacturing shop, etc., and the present disclosure is not limited thereto. The mobile device may be an automatic transfer robot, an automatic forklift, an automatic detection robot, or other mobile devices, which is not limited in this disclosure. Besides the SLAM method, the position of the node may be acquired by using ultrasonic, wireless positioning, and the like, which is not limited in this disclosure.

Fig. 1b shows an exemplary schematic diagram of an implementation scenario of a mapping method according to an embodiment of the present disclosure.

It will be understood by those of ordinary skill in the art that fig. 1b exemplarily illustrates an implementation scenario of the map construction method, and does not constitute a limitation of the present disclosure.

As shown in fig. 1b, the nodes in fig. 1a may be clustered by using a clustering manner such as K-means clustering, so as to obtain semantic tags. The resulting semantic tags may be the top lane 111, the bottom lane 112, the left lane 11131, the left lane 21132, the lane 11141, the lane 21142, the lane 31143, the lane 41144, the right lane 11151, and the right lane 21152.

In an embodiment of the present disclosure, the first state of the semantic tag may include: clear status, crowded status and blocked status. For example, none of the nodes in the left lane 11131 is in an occupied state, and the first state of the left lane 11131 is a clear state; lane 41144 has both of the

consecutive nodes

1171, 1172 on either side connected in an occupied state, and the first state of lane 41144 is a blocked state. 5

nodes

1161, 1162, 1163, 1164, and 1165 in the lane 31143 are in an occupied state, the number of nodes in the occupied state is greater than a first threshold value, for example, 3, and the first state of the lane 31143 is a congestion state. The first state of the semantic tag is used to identify a congestion level of the semantic tag.

In the embodiment of the present disclosure, it may also be adopted that the node ratio of the occupied state in the semantic tag is greater than the second threshold, for example, greater than 1/4, so as to determine that the first state of the semantic tag is the congestion state.

In embodiments of the present disclosure, the transfer robot may be scheduled to pass semantic tags of a clear state first and then to pass semantic tags of a crowded state second. The semantic tags of the jam state may not be passed through the transfer robot.

In an embodiment of the present disclosure, the second state of the semantic tag may include: a locked state and an unlocked state. Semantic tags in the unlocked state can be scheduled and semantic tags in the locked state cannot be scheduled.

In an embodiment of the present disclosure, the second state of the semantic tag is a non-locked state on a condition that the first state of the semantic tag is a clear state. The second state of the semantic tag is a locked state on condition that the first state of the semantic tag is a jam state. On a condition that the first state of the semantic tags is a congestion state and the congestion index is less than or equal to a third threshold, e.g., 0.2, the second state of the semantic tags is a non-locked state; the second state of the semantic tags is a locked state on the condition that the first state of the semantic tags is a congestion state and the congestion index is greater than a third threshold, e.g., 0.2.

In an embodiment of the present disclosure, the second state of the left lane 11131 is a non-locked state, which may be navigated to schedule; the second state of lane 41144 is a locked state, which cannot be scheduled by navigation. Although the lane 31143 is not completely blocked, there are many nodes in the occupied state, the congestion index is greater than the third threshold, the subsequent transfer robot is difficult to pass through, and the second state is the locked state and cannot be navigated and scheduled.

It will be understood by those of ordinary skill in the art that instead of K-means clustering, semantic tags may be obtained from nodes using mean shift clustering, density-based clustering, or other clustering methods, which are not limited in the present disclosure. The third threshold may be other values, and the disclosure is not limited thereto.

It will be appreciated by those skilled in the art that the node status and semantic tag status may change as the location of the shelf in the warehouse changes or as the production line in the production plant changes, as well as the movement of the mobile device and/or the movement of the people in the warehouse, production plant, etc.

Fig. 1c shows an exemplary schematic diagram of an implementation scenario of a navigation method according to an embodiment of the present disclosure.

It will be understood by those of ordinary skill in the art that fig. 1c exemplarily illustrates an implementation scenario of the navigation method, and does not constitute a limitation of the present disclosure.

As shown in fig. 1c, the transfer robot a starts from the node 121 and needs to access the goods at the

node

122 or 123. For example, the transfer robot needs to store goods, and the shelf positions corresponding to the

nodes

122 and 123 have appropriate free storage positions; or the transfer robot needs to take out the article a, and the article a is stored in the shelf positions corresponding to the

nodes

122 and 123.

In the embodiment of the present disclosure, in the navigation task, the navigation start position may be the node 121, and the navigation end position may be the

node

122 or 123. Node 122 is selected as the final navigation termination location because semantic label lane 31143, where node 123 is located, is in a congested, locked state and cannot be scheduled. The navigation path 124 is calculated from the first and second states of each semantic tag, and the state and location of each node as: left side channel 21132, main gallery 113, left side channel 11131, upper side channel 111, gallery 21142.

In the embodiment of the present disclosure, under the condition that the navigation start position and the navigation end position are within different semantic tags, for example, the aforementioned navigation start position is node 121, which is located in the lower lane 112, the navigation end position is node 122, which is located in the upper lane 111, the navigation path may be calculated by using a semantic tag level path planning algorithm. The semantic tag level path planning algorithm may compute using, for example, topological join relationships between semantic tags, or other relationships between semantic tags.

Fig. 1d shows an exemplary schematic diagram of an implementation scenario of a navigation method according to an embodiment of the present disclosure.

It will be understood by those of ordinary skill in the art that fig. 1d exemplarily illustrates an implementation scenario of the navigation method, and does not constitute a limitation of the present disclosure.

As shown in fig. 1d, when the transfer robot a moves to the node 131, the node 132 of the left lane 11131 of the semantic label is occupied by the transfer robot B, and is in an occupied state. At this time, the first state of the left lane 11131 of the semantic tags is updated to the jammed state, the second state is updated to the locked state, and the transfer robot a cannot move along the original navigation path 124 in fig. 1 c. Updating the navigation path of the transfer robot a to 133 according to the first state and the second state of each semantic tag, and the state and the position of each node: left channel 21132 > main aisle 113 > roadway 21142. When the transfer robot a is located at the node 131 and the navigation end position is located at the node 122, the node 131 and the node 122 are not located in the same semantic tag, and a semantic tag-level path planning algorithm is used for path planning. When the transfer robot a moves into the semantic tag roadway 21142 along the navigation path 133, and the node where the transfer robot a is located and the navigation end position are within the same semantic tag, the navigation path may be calculated by using a topological graph-level path planning algorithm. The topological graph level path planning algorithm may perform calculations using topological connection relationships between nodes in semantic tags, for example, or other topological relationships.

The transfer robot a finally moves along the updated navigation path 133 to reach the node 122, thereby completing the storage and retrieval of the goods.

It will be understood by those skilled in the art that node 132 of the left lane 11131 of semantic tags in fig. 1d is occupied by transfer robot B, and may be detected by transfer robot a or marked by transfer robot B. The navigation path calculation and updating in fig. 1c and 1d can be automatically calculated by each transfer robot according to the state of the semantic tag, the state of the node and the position, and the movement is automatically controlled; the calculation may be performed by a central control system that controls a plurality of transfer robots, and then each transfer robot may be controlled to move.

Fig. 2 illustrates a flow chart of a mapping method according to an embodiment of the present disclosure.

As shown in fig. 2, the map construction method includes: steps S201, S202, S203, S204.

In step S201, the first area is divided into nodes.

In step S202, the state and position of the node are acquired.

In step S203, semantic tags are acquired from nodes in the first region.

In step S204, a first state and/or a second state of the semantic tag is obtained.

In an embodiment of the present disclosure, the first area may be an area that may be occupied by a mobile device, such as an automated handling robot, other than a rack, in the warehouse area 100 shown in fig. 1 a. The first region may be divided into nodes, such as upper 1.. upper 9, left 1-1.. left 1-16, trunk 1-2.. trunk 310, etc., and the states and positions of the nodes may be obtained. The semantic tags may be obtained from nodes in the first area, for example, by aggregating the nodes. Semantic tags may be the various channels shown in FIG. 1b, such as the top channel 111, the left channel 11131, the backbone 113, and so on. The first state and/or the second state of the semantic tag can be obtained, the first state is used for identifying the congestion degree of the semantic tag, and the second state is used for identifying whether the semantic tag is locked or not and further can be scheduled by navigation or not.

According to an embodiment of the present disclosure, by the node dividing step, wherein the first area is divided into nodes; a node state and position acquisition step, wherein the state and position of the node are acquired; a semantic label obtaining step, wherein a semantic label is obtained according to the nodes in the first area; and a semantic tag state obtaining step, wherein a first state and/or a second state of the semantic tag are obtained, and the first region is a region which can be occupied by the mobile equipment in a specific region, so that the region is accurately divided, the state is completely marked, detailed semantic analysis is performed, the scene change is adapted, and flexible scheduling of the mobile equipment is facilitated.

In embodiments of the present disclosure, the first area may be divided into nodes according to the area perceived by the mobile device, e.g., tactile perception, ultrasonic perception, and/or the area occupied by the mobile device.

According to an embodiment of the present disclosure, the partitioning by nodes includes: according to the region sensed by the mobile equipment and/or the region occupied by the mobile equipment, the first region is divided into nodes, so that the region is accurately divided, the state is completely marked, detailed semantic analysis is performed, the change of a scene is adapted, and the flexible scheduling of the mobile equipment is facilitated.

In the embodiments of the present disclosure, the position of the node may be acquired by using, for example, a simultaneous localization and mapping (SLAM) method, and the state of the node may be acquired by using a visual method.

According to the embodiment of the disclosure, the position of a node is obtained by using a synchronous positioning and mapping method; and/or acquiring the state of the node by using a visual method, thereby accurately dividing the region and completely marking the state, performing detailed semantic analysis, adapting to the change of the scene and facilitating the flexible scheduling of the mobile equipment.

It can be understood by those skilled in the art that other positioning methods such as ultrasonic wave and wireless positioning may also be used to obtain the node position, and the mobile device may also identify the node state according to its own position, which is not limited in this disclosure.

In an embodiment of the present disclosure, the node state may include: occupied state, unoccupied state. The occupied state of the node may be occupied by a mobile device, occupied by a person, or occupied by a temporarily placed cargo or other manners, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the state by the node includes: the method comprises the following steps of occupying the state and not occupying the state, so that accurate division of the region and complete marking of the state are carried out, detailed semantic analysis is carried out, the method is adaptive to change of a scene, and flexible scheduling of the mobile equipment is facilitated.

In the embodiment of the disclosure, a method such as K-means clustering may be adopted, and the semantic tags are obtained by using a clustering method according to the states and positions of the nodes in the first region.

According to the embodiment of the disclosure, the semantic tag obtaining step comprises: according to the state and the position of the node in the first region, the semantic label is obtained by using a clustering method, so that the region is accurately divided, the state is completely marked, detailed semantic analysis is performed, the change of a scene is adapted, and the flexible scheduling of the mobile equipment is facilitated.

It will be understood by those of ordinary skill in the art that instead of K-means clustering, semantic tags may be obtained from nodes using mean shift clustering, density-based clustering, or other clustering methods, which are not limited in the present disclosure.

In an embodiment of the present disclosure, the first state of the semantic tag includes: unblocked state, crowded state, blocked state; the second state of the semantic tags includes: locked state, unlocked state. Semantic tags in a blocked state cannot be scheduled, semantic tags in a unblocked state are scheduled first, and semantic tags in a congested state are scheduled second. Semantic tags in a locked state are not scheduled and semantic tags in an unlocked state can be scheduled.

According to an embodiment of the present disclosure, the first state by the semantic tag comprises: a smooth state, a crowded state, a blocked state; and/or the second state of the semantic label comprises: the locking state and the non-locking state are adopted, so that the region is accurately divided, the state is completely marked, the detailed semantic analysis is carried out, the change of a scene is adapted, and the flexible scheduling of the mobile equipment is facilitated.

In the embodiment of the present disclosure, as shown in fig. 1b, the lane 41144 connects the

continuous nodes

1171, 1172 on both sides to be in an occupied state, and the first state of the lane 41144 is a blocked state. 5

nodes

1161, 1162, 1163, 1164, 1165 in the lane 31143 are in an occupied state, the number of nodes in the occupied state is greater than a first threshold, e.g., 3, and the first state of the lane 31143 is a congested state. The first state of the semantic tag is used to identify a congestion level of the semantic tag.

According to the embodiment of the disclosure, under the condition that continuous nodes connecting two sides of the semantic tag are in an occupied state, the first state of the semantic tag is a blocking state; under the condition that the number of nodes in an occupied state in the semantic tag is greater than a first threshold value or the proportion of the nodes in the occupied state in the semantic tag is greater than a second threshold value, the first state of the semantic tag is in a crowded state, so that the region is accurately divided, the state is completely marked, detailed semantic analysis is performed, scene change is adapted, and flexible scheduling of the mobile device is facilitated.

In an embodiment of the present disclosure, the first state is a congested state, or the second state of semantic tags in a congested state and with a congestion index greater than a third threshold, e.g., 0.2, is a locked state; the first state is in a clear state or a second state of semantic tags in a congested state with a congestion index less than or equal to a third threshold, such as 0.2, is a non-locked state. The third threshold may be other values, which are not limited in this disclosure.

According to the embodiment of the disclosure, under the condition that the first state of the semantic tag is a clear state or the first state of the semantic tag is a congestion state and the congestion index is less than or equal to a third threshold value, the second state of the tag is an unlocked state; and under the condition that the first state of the semantic tag is a blocked state or the first state of the semantic tag is a crowded state and the crowding index is greater than a third threshold value, the second state of the semantic tag is a locked state, so that the region is accurately divided, the state is completely marked, detailed semantic analysis is performed, the change of scenes is adapted, and the flexible scheduling of the mobile equipment is facilitated.

Fig. 3 shows a flow chart of a navigation method according to an embodiment of the present disclosure.

In particular, fig. 3 illustrates a method of navigating a mobile device according to fig. 2 and the aforementioned mapping method.

As shown in fig. 3, the navigation method includes: steps S301, S302, S303, S304, S305.

In step S301, a navigation start position and a navigation end position of the mobile device are acquired.

In step S302, a navigation path is calculated according to the navigation start position, the navigation end position, the first state of the semantic tag and/or the second state of the semantic tag.

In step S303, the mobile device is controlled to move along the navigation path.

In step S304, the state of the node, the first state of the semantic tag and/or the second state of the semantic tag is updated according to the location of the mobile device and/or the detection result of the mobile device.

In step S305, the navigation path is updated according to the updated state of the node, the first state of the semantic tag and/or the second state of the semantic tag.

In the embodiment of the present disclosure, as shown in fig. 1c, the automatic transfer robot a starts from the node 121 and needs to access the goods at the

node

122 or 123. According to the first state and the second state of each semantic tag and the state and the position of each node, the node 122 is selected as a navigation termination position, and the navigation path 124 is calculated as: left side channel 21132, main gallery 113, left side channel 11131, upper side channel 111, gallery 21142. As shown in fig. 1d, when the transfer robot is controlled to move to the node 131, the node 132 is occupied by the automatic transfer robot B, and the left lane 11131 is blocked and locked, thereby updating the navigation path 133 of the automatic transfer robot a. The automatic transfer robot a moves to the node 122 along the new navigation path 133, and the cargo access is completed.

According to the embodiment of the disclosure, a navigation starting and stopping position obtaining step is adopted, wherein a navigation starting position and a navigation stopping position of the mobile equipment are obtained; a navigation path planning step, wherein a navigation path is calculated according to the navigation starting position, the navigation ending position, the first state of the semantic label and/or the second state of the semantic label, and the state and the position of the node; a moving step, wherein the mobile equipment is controlled to move along the navigation path; a state updating step, wherein the state of the node, the first state of the semantic label and/or the second state of the semantic label are updated according to the position of the mobile equipment and/or the detection result of the mobile equipment; and a navigation path updating step, wherein the navigation path is updated according to the updated state of the node, the first state of the semantic tag and/or the second state of the semantic tag, and the state and the position of the node, so that the moving path of the mobile equipment is flexibly scheduled based on the map of accurate region division, state marking and semantic analysis, and can adapt to the dynamic change of the scene.

In the embodiment of the present disclosure, as shown in fig. 1c, under the condition that the navigation start position, the node 121, and the navigation end position of the automatic transfer robot a are located in different semantic tags, the semantic tag level path planning algorithm is used to calculate the navigation path 124. The semantic tag level path planning algorithm may compute using, for example, topological join relationships between semantic tags, or other relationships between semantic tags.

As shown in fig. 1d, after the automatic transfer robot a moves into the semantic tag roadway 21142, the node where the automatic transfer robot a is located, the navigation end position, and the node 122 are in the same semantic tag, and the navigation path is calculated by using a topological graph-level path planning algorithm. The topological graph level path planning algorithm can perform calculation by using topological connection relations among nodes in the same semantic label or other topological relations, for example.

According to an embodiment of the present disclosure, the planning by navigation path step includes: under the condition that the navigation starting position and the navigation ending position are in the same semantic label, calculating a navigation path by using a topological graph-level path planning algorithm; and under the condition that the navigation starting position and the navigation ending position are not in the same semantic tag, calculating the navigation path by using a semantic tag level path planning algorithm, so that the mobile path of the mobile equipment is flexibly scheduled based on the map of accurate regional division, state labeling and semantic analysis, and the dynamic change of the scene can be adapted.

Fig. 4 shows a block diagram of a map construction apparatus according to an embodiment of the present disclosure.

As shown in fig. 4, the map construction apparatus 400 includes: a node dividing module 401, a node state and position obtaining module 402, a semantic tag obtaining module 403, and a semantic tag state obtaining module 404.

The node partitioning module 401 is configured to partition the first area into nodes.

The node status and location acquisition module 402 is configured to acquire the status and location of the node.

Semantic tag acquisition module 403 is configured to acquire semantic tags from the nodes in the first region.

The semantic tag state acquisition module 404 is configured to acquire a first state and/or a second state of the semantic tag.

According to an embodiment of the present disclosure, a first region is configured to be divided into nodes by a node dividing module; a node state and position acquisition module configured to acquire a state and a position of a node; a semantic tag obtaining module configured to obtain a semantic tag according to a node in the first region; the semantic tag state acquisition module is configured to acquire a first state and/or a second state of a semantic tag, wherein the first region is a region which can be occupied by the mobile equipment in a specific region, so that the region is accurately divided, the state is completely marked, detailed semantic analysis is performed, scene change is adapted, and flexible scheduling of the mobile equipment is facilitated.

In an embodiment of the disclosure, the node partitioning module is further configured to: and dividing the area possibly occupied by the mobile equipment in the first area into the nodes according to the area perceived by the mobile equipment and/or the area occupied by the mobile equipment.

According to an embodiment of the present disclosure, the partitioning by nodes module is further configured to: according to the region sensed by the mobile device and/or the region occupied by the mobile device, the region possibly occupied by the mobile device in the first region is divided into the nodes, so that the regions are accurately divided, the states are completely marked, the detailed semantic analysis is performed, the scene change is adapted, and the flexible scheduling of the mobile device is facilitated.

In the embodiment of the present disclosure, for example, a synchronous positioning and mapping (SLAM) method may be used to obtain the position of the node, and a visual method may be used to obtain the state of the node.

In an embodiment of the present disclosure, the node state may include: occupied state, unoccupied state. The occupied state of the node may be occupied by a mobile device, or occupied by a person, or occupied by a temporarily placed cargo or other manners, and the disclosure is not limited to this.

According to an embodiment of the present disclosure, the state by the node includes: and the occupied state and the non-occupied state are adopted, so that the region is accurately divided, the state is completely marked, the detailed semantic analysis is carried out, the scene change is adapted, and the flexible scheduling of the mobile equipment is facilitated.

In the embodiment of the present disclosure, a method such as K-means clustering may be adopted, and a semantic label is obtained by using a clustering method according to the state and the position of the node in the first region.

According to the embodiment of the disclosure, the semantic tag obtaining step includes: according to the states and positions of the nodes in the first region, the semantic labels are obtained by using a clustering method, so that the regions are accurately divided, the states are completely marked, detailed semantic analysis is performed, scene change is adapted, and flexible scheduling of the mobile device is facilitated.

In an embodiment of the present disclosure, the first state of the semantic tag includes: unblocked state, crowded state, blocked state; the second state of the semantic tags includes: locked state, unlocked state. Semantic tags in a blocked state cannot be scheduled, semantic tags in a unblocked state are scheduled first, and semantic tags in a congested state are scheduled second. Semantic tags in a locked state are not scheduled and semantic tags in an unlocked state may be scheduled.

According to an embodiment of the present disclosure, the first state by semantic tags includes: a smooth state, a crowded state, a blocked state; and/or the second state of the semantic tag comprises: and the locking state and the non-locking state are adopted, so that the regions are accurately divided, the states are completely marked, the detailed semantic analysis is carried out, the scene change is adapted, and the flexible scheduling of the mobile equipment is facilitated.

continuous nodes

nodes

In an embodiment of the present disclosure, it may also be adopted that the node ratio of the occupied states in the semantic tag is greater than a second threshold, for example, greater than 1/4, so as to determine that the first state of the semantic tag is a congestion state.

According to the embodiment of the disclosure, under the condition that continuous nodes connecting two sides of the semantic tag are in the occupied state, the first state of the semantic tag is in the blocking state; under the condition that the number of nodes in an occupied state in a semantic tag is greater than a first threshold value or the proportion of the nodes in the occupied state in the semantic tag is greater than a second threshold value, the first state of the semantic tag is a crowded state, so that accurate region division and complete state marking are performed, detailed semantic analysis is performed, scene change is adapted, and flexible scheduling of mobile equipment is facilitated.

In an embodiment of the present disclosure, the first state is a blocked state, or the second state of the semantic tags that are in a congested state and the congestion index is greater than a third threshold, e.g., 0.2, is a locked state; the first state is a clear state or a second state of semantic tags in a congested state with a congestion index less than or equal to a third threshold, e.g., 0.2, is a non-locked state. The third threshold may be other values, and the disclosure is not limited thereto.

According to an embodiment of the present disclosure, the second state of the tag is an unlocked state by a condition that the first state of the semantic tag is a clear state or the first state of the semantic tag is a crowded state and the crowd index is less than or equal to a third threshold; and under the condition that the first state of the semantic tag is a blocked state or the first state of the semantic tag is a crowded state and the crowding index is greater than a third threshold value, the second state of the semantic tag is a locked state, so that the region is accurately divided, the state is completely marked, detailed semantic analysis is performed, the change of scenes is adapted, and the flexible scheduling of the mobile equipment is facilitated.

Fig. 5 shows a block diagram of a navigation device according to an embodiment of the present disclosure.

In particular, fig. 5 shows an apparatus for navigating a mobile device according to fig. 4 and the aforementioned map building apparatus.

As shown in fig. 5, the navigation device 500 includes: a navigation starting and stopping position obtaining module 501, a navigation path planning module 502, a moving module 503, a state updating module 504 and a navigation path updating module 505.

The navigation start and end position obtaining module 501 is configured to obtain a navigation start position and a navigation end position of the mobile device.

The navigation path planning module 502 is configured to calculate a navigation path based on the navigation start position, the navigation end position, the first state of the semantic tag and/or the second state of the semantic tag, and the state and position of the node.

The movement module 503 is configured to control the mobile device to move along the navigation path.

The state update module 504 is configured to update the state of the node, the first state of the semantic tags and/or the second state of the semantic tags according to the location of the mobile device and/or the detection result of the mobile device.

The navigation path update module 505 is configured to update the navigation path according to the updated state of the node, the first state of the semantic tag and/or the second state of the semantic tag, and the state and location of the node.

node

122 or 123. According to the first state and the second state of each semantic label and the state and the position of each node, the node 122 is selected as a navigation termination position, and the navigation path 124 is calculated as: left side channel 21132, main gallery 113, left side channel 11131, upper side channel 111, gallery 21142. As shown in fig. 1d, when the transfer robot is controlled to move to the node 131, the node 132 is occupied by the automatic transfer robot B, and the left lane 11131 is blocked and locked, so that the navigation path 133 of the automatic transfer robot a is updated. The automatic transfer robot a moves to the node 122 along the new navigation path 133, and the cargo access is completed.

According to the embodiment of the disclosure, the navigation starting and stopping position acquisition module is configured to acquire a navigation starting position and a navigation stopping position of the mobile equipment; a navigation path planning module configured to calculate a navigation path according to the navigation start position, the navigation end position, the first state of the semantic tag and/or the second state of the semantic tag, and the state and position of the node; a movement module configured to control the mobile device to move along a navigation path; a state updating module configured to update the state of the node, the first state of the semantic tag and/or the second state of the semantic tag according to a location of a mobile device and/or a detection result of the mobile device; and the navigation path updating module is configured to update the navigation path according to the updated state of the node, the first state of the semantic tag and/or the second state of the semantic tag, and the state and the position of the node, so that the mobile path of the mobile device is flexibly scheduled based on the map of accurate regional division, state marking and semantic analysis, and the dynamic change of the scene can be adapted.

As shown in fig. 1d, after the automatic transfer robot a moves into the semantic label lane 21142, the node where the automatic transfer robot a is located, the navigation end position, and the node 122 are in the same semantic label, and the navigation path is calculated by using the topological graph-level path planning algorithm. The topological graph level path planning algorithm can perform calculation by using topological connection relations among nodes in the same semantic label or other topological relations, for example.

According to an embodiment of the present disclosure, the planning by navigation path step includes: under the condition that the navigation starting position and the navigation ending position are within the same semantic label, calculating a navigation path by using a topological graph-level path planning algorithm; and under the condition that the navigation starting position and the navigation ending position are not in the same semantic tag, calculating the navigation path by using a semantic tag level path planning algorithm, so that the mobile path of the mobile equipment is flexibly scheduled based on the map of accurate regional division, state labeling and semantic analysis, and the dynamic change of the scene can be adapted.

Fig. 6 shows a block diagram of an electronic device according to an embodiment of the present disclosure.

The embodiment of the present disclosure further provides an electronic device, as shown in fig. 6, the electronic device 600 includes a processor 601 and a memory 602; wherein the memory 602 stores instructions executable by the at least one processor 601 to perform the following steps performed by the at least one processor 601: a node dividing step in which the first area is divided into nodes;

a node state and position acquisition step, wherein the state and position of the node are acquired;

In an embodiment of the present disclosure, the node partitioning step includes:

In the embodiment of the disclosure, the positions of the nodes are obtained by using a synchronous positioning and mapping method; and/or

And acquiring the state of the node by using a visual method.

In an embodiment of the present disclosure, the state of the node includes:

occupied state, unoccupied state.

In an embodiment of the present disclosure, the semantic tag obtaining step includes:

In an embodiment of the present disclosure, the first state of the semantic tag includes:

a smooth state, a crowded state, a blocked state; and/or

The second state of the semantic tag comprises:

locked state, unlocked state.

In an embodiment of the present disclosure, under a condition that continuous nodes connecting two sides of the semantic tag in the semantic tag are both in the occupied state, the first state of the semantic tag is the blocking state;

In an embodiment of the present disclosure, under the condition that the first state of the semantic tag is a clear state, or the first state of the semantic tag is a crowded state and a crowding index is less than or equal to a third threshold, the second state of the semantic tag is an unlocked state;

The instructions may also be executable by the at least one processor 601 to implement the steps of:

a navigation path planning step, wherein a navigation path is calculated according to the navigation starting position, the navigation ending position, the first state of the semantic label and/or the second state of the semantic label, and the state and the position of the node;

In an embodiment of the present disclosure, the navigation path planning step includes:

As shown in fig. 7, the computer system 700 includes a processing unit 701 that can execute various processes in the embodiments shown in the above-described figures according to a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM703, various programs and data necessary for the operation of the system 700 are also stored. The processing unit 701, the ROM702, and the RAM703 are connected to each other by a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary. The processing unit 701 may be implemented as a CPU, a GPU, a TPU, an FPGA, an NPU, or other processing units.

In particular, according to embodiments of the present disclosure, the methods described above with reference to the figures may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a medium readable thereby, the computer program comprising program code for performing the methods of the figures. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units or modules described in the embodiments of the present disclosure may be implemented by software or hardware. The units or modules described may also be provided in a processor, and the names of the units or modules do not in some cases constitute a limitation on the units or modules themselves.

As another aspect, the present disclosure also provides a computer-readable storage medium, which may be the computer-readable storage medium included in the node in the above embodiment; or it may be a separate computer readable storage medium not incorporated into the device. The computer readable storage medium stores one or more programs for use by one or more processors in performing the methods described in the present disclosure.

The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept. For example, the above features and the technical features disclosed in the present disclosure (but not limited to) having similar functions are replaced with each other to form the technical solution.

Claims

1. A map building method, comprising:

a node dividing step in which the first area is divided into nodes;

a semantic tag obtaining step, wherein a semantic tag is obtained according to the node in the first area;

a semantic tag state acquisition step in which a first state and/or a second state of the semantic tag is acquired,

2. The method of claim 1, wherein the node partitioning step comprises:

3. The method of claim 1,

And acquiring the state of the node by using a visual method.

4. The method of claim 1, wherein the state of the node comprises:

occupied state, unoccupied state.

5. The method of claim 4, wherein the semantic tag obtaining step comprises:

6. The method of claim 1, wherein the first state of the semantic tag comprises:

a smooth state, a crowded state, a blocked state; and/or

The second state of the semantic tag comprises:

locked state, unlocked state.

7. The method of claim 6,

8. The method of claim 6,

under the condition that the first state of the semantic tag is a clear state or the first state of the semantic tag is a congestion state and the congestion index is less than or equal to a third threshold value, the second state of the semantic tag is a non-locking state;

9. A method for mobile device navigation according to the mapping method of any of claims 1-8, comprising:

10. The method of claim 9, wherein the step of planning the navigation path comprises:

11. A map building apparatus comprising:

a node dividing module configured to divide the first area into nodes;

12. The apparatus of claim 11, wherein the node partitioning module is further configured to:

and according to the area perceived by the mobile equipment and/or the area occupied by the mobile equipment, dividing the area possibly occupied by the mobile equipment in the first area into the nodes.

13. The apparatus of claim 11,

And acquiring the state of the node by using a visual method.

14. The apparatus of claim 11, wherein the state of the node comprises:

occupied state, unoccupied state.

15. The apparatus of claim 14, wherein the semantic tag acquisition module is further configured to:

16. The apparatus of claim 11, wherein the first state of the semantic tag comprises:

a smooth state, a crowded state, a blocked state; and/or

The second state of the semantic tag comprises:

locked state, unlocked state.

17. The apparatus of claim 16,

18. The apparatus of claim 16,

19. A mobile device navigation apparatus of the mapping apparatus of any of claims 11-18, comprising:

a state updating module configured to update the state of the node, the first state of the semantic tag and/or the second state of the semantic tag according to the location of the mobile device and/or the detection result of the mobile device;

a navigation path updating module configured to update the navigation path according to the updated state of the node, the first state of the semantic tag and/or the second state of the semantic tag, and the state and the position of the node.

20. The apparatus of claim 19, wherein the navigation path planning module is further configured to:

21. An electronic device comprising a memory and a processor; wherein the content of the first and second substances,

the memory is to store one or more computer instructions, wherein the one or more computer instructions are to be executed by the processor to implement the method of any one of claims 1-10.

22. A readable storage medium having stored thereon computer instructions, which when executed by a processor, perform the method of any one of claims 1-10.