WO2022170806A1 - Procédé et appareil de mappage, procédé et appareil de navigation, dispositif électronique et support de stockage lisible - Google Patents

Procédé et appareil de mappage, procédé et appareil de navigation, dispositif électronique et support de stockage lisible Download PDF

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WO2022170806A1
WO2022170806A1 PCT/CN2021/131528 CN2021131528W WO2022170806A1 WO 2022170806 A1 WO2022170806 A1 WO 2022170806A1 CN 2021131528 W CN2021131528 W CN 2021131528W WO 2022170806 A1 WO2022170806 A1 WO 2022170806A1
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Prior art keywords
state
semantic label
node
navigation
semantic
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PCT/CN2021/131528
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English (en)
Chinese (zh)
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刘烨航
成鹏
张广鹏
王旭
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灵动科技（北京）有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem

Definitions

  • the present disclosure relates to the technical field of navigation, and in particular, to a map construction and navigation method, apparatus, electronic device, and readable storage medium.
  • SLAM Simultaneous Localization And Mapping
  • the embodiments of the present disclosure provide a map construction and navigation method, an apparatus, an electronic device, and a readable storage medium.
  • an embodiment of the present disclosure provides a map construction method, including: a node division step, wherein a 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 acquiring step wherein a semantic label is acquired according to the node in the first area
  • the step of obtaining the semantic label state wherein the first state and/or the second state of the semantic label is obtained,
  • the first area is an area in a specific area that can be occupied by a mobile device.
  • the node division step includes:
  • the first area is divided into the nodes according to the area perceived by the mobile device and/or the area occupied by the mobile device.
  • the present disclosure is in a second implementation manner of the first aspect
  • the state of the node is obtained using a visual method.
  • the state of the node includes:
  • the present disclosure is in the fourth implementation manner of the first aspect, and the semantic label obtaining step includes:
  • the semantic labels are obtained using a clustering method according to the states and positions of the nodes in the first region.
  • the first state of the semantic tag includes:
  • the second state of the semantic tag includes:
  • the consecutive nodes connecting both sides of the semantic label are all in the occupied state.
  • the first state of the semantic label is the blocked state;
  • the semantic The first state of the tag is the congestion state.
  • the first state of the semantic label is a smooth state, or the first state of the semantic label is a crowded state and under the condition that the congestion index is less than or equal to the third threshold, the second state of the semantic label is an unlocked state;
  • the second state of the semantic label is locked when the first state of the semantic label is a congestion state, or the first state of the semantic label is a crowded state and the congestion index is greater than the third threshold state.
  • an embodiment of the present disclosure provides a method for navigating a mobile device according to the map construction method of any one of the first aspect to the seventh implementation manner of the first aspect, including:
  • a navigation start and end position obtaining step wherein the navigation start position and the navigation end position of the mobile device are obtained
  • Navigation path planning step wherein based on the navigation start position, the navigation end position, the first state of the semantic label and/or the second state of the semantic label, and the state and position of the node are calculated navigation path;
  • a moving step wherein the mobile device is controlled to move along the navigation path
  • a state update 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 location of the mobile device and/or the detection result of the mobile device ;
  • a navigation path update 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 position of the node .
  • the navigation path planning step includes:
  • the navigation path is calculated using a topology-graph level path planning algorithm
  • the navigation path is calculated using a semantic label-level path planning algorithm.
  • an embodiment of the present disclosure provides a map construction apparatus, including:
  • a node division module configured to divide the first area into nodes
  • a node state and location acquisition module configured to acquire the state and location of the node
  • a semantic label obtaining module configured to obtain a semantic label according to the node in the first area
  • a semantic tag state acquisition module configured to acquire the first state and/or the second state of the semantic tag
  • the first area is an area in a specific area that can be occupied by a mobile device.
  • the node division module is further configured to:
  • an area that may be occupied by the mobile device in the first area is divided into the nodes.
  • the present disclosure uses a simultaneous positioning and mapping method to obtain the location of the node; and/or
  • the state of the node is obtained using a visual method.
  • the state of the node includes:
  • the semantic tag obtaining module is further configured to:
  • the semantic labels are obtained using a clustering method according to the states and positions of the nodes in the first region.
  • the first state of the semantic tag includes:
  • the second state of the semantic tag includes:
  • the first state of the semantic label is the blocked state
  • the semantic The first state of the tag is the congestion state.
  • the present disclosure is in the seventh implementation manner of the third aspect
  • the second state of the semantic label is the unlocked state ;
  • the second state of the semantic label is locked when the first state of the semantic label is a congestion state, or the first state of the semantic label is a crowded state and the congestion index is greater than the third threshold state.
  • an embodiment of the present disclosure provides a mobile device navigation apparatus of the map construction apparatus according to any one of the third aspect to the seventh implementation manner of the third aspect, including:
  • a navigation start and end 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 base on the navigation start position, the navigation end position, the first state of the semantic label and/or the second state of the semantic label, and the state and position of the node Calculate the navigation path;
  • a movement module configured to control the mobile device to move along the navigation path
  • a state update module configured to update the state of the node, the first state of the semantic label and/or the second state of the semantic label according to the location of the mobile device and/or the detection result of the mobile device state;
  • a navigation path update module configured to update the navigation 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 position of the node path.
  • the navigation path planning module is further configured to:
  • the navigation path is calculated using a topology-graph level path planning algorithm
  • the navigation path is calculated using a semantic label-level path planning algorithm.
  • an embodiment of the present disclosure provides an electronic device, including a memory and a processor; wherein,
  • the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the first aspect, the first implementation manner to the ninth implementation of the first aspect The method of any one of the methods.
  • an embodiment of the present disclosure provides a readable storage medium on which computer instructions are stored, and when the computer instructions are executed by a processor, implement the first aspect, the first implementation manner of the first aspect to the sixth aspect. The method described in any one of the nine implementation manners.
  • a node division step in which the first area is divided into nodes; a node state and position acquisition step, in which the state and position of a node are acquired; and a semantic label acquisition step, in which according to the first area
  • the node of the node obtains the semantic label; the step of obtaining the semantic label state, in which the first state and/or the second state of the semantic label is obtained, and the first area is the area that may be occupied by the mobile device in the specific area, so as to accurately divide the area and determine the state.
  • Fig. 1a shows an exemplary schematic diagram of an implementation scenario of a map construction method according to an embodiment of the present disclosure
  • Fig. 1b shows an exemplary schematic diagram of an implementation scenario of a map construction 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 shows a flowchart of a map construction method according to an embodiment of the present disclosure
  • FIG. 3 shows a flowchart of a navigation method according to an embodiment of the present disclosure
  • FIG. 4 shows a structural block diagram of a map construction apparatus according to an embodiment of the present disclosure
  • FIG. 5 shows a structural block diagram of a navigation device according to an embodiment of the present disclosure
  • FIG. 6 shows a structural block diagram of an electronic device according to an embodiment of the present disclosure
  • Fig. 7 is a schematic structural diagram of a computer system suitable for implementing a map construction method and a navigation method according to an embodiment of the present disclosure.
  • SLAM Simultaneous Localization And Mapping
  • the present disclosure proposes 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 map construction method according to an embodiment of the present disclosure.
  • FIG. 1a exemplarily shows an implementation scenario of the map construction method, and does not constitute a limitation to the present disclosure.
  • a specific area 100 such as a warehouse, surrounded by endpoints ABCD, there are rack 1, rack 2, rack 3, rack 4, rack 5, and rack 6.
  • a mobile device such as an automatic handling robot can move in an area other than the shelves in the warehouse, that is, the first area, and complete the access operation to the goods on the shelves.
  • the first area may be divided into nodes according to an area that an automatic handling robot can perceive, or an area occupied by an automatic handling robot.
  • node 101 in Figure 1a The node 101 may be an idle upper space 1 and an upper space 2, or may be a parking space 1 left 4 occupied by an automatic handling robot.
  • the nodes occupied by the automatic handling robot are marked in gray.
  • a number of nodes are divided in the first area, for example, the upper position 1...the upper position 9, the left position 1-1...the left position 1-16, the main road 1-2 ...arterial road 113 etc.
  • the perception of the automatic handling robot may be tactile perception of a robotic arm, or ultrasonic perception, or other perception methods, which are not limited in the present disclosure.
  • the position of the node can be obtained using a positioning method such as a synchronous Location and Mapping (SLAM) method, and the state of the node can be obtained using a visual method such as machine vision of an automatic handling robot, such as a node occupied state.
  • SLAM synchronous Location and Mapping
  • the automatic handling robot can also identify that the node is in an occupied state by being in a certain node.
  • the state information of the nodes may also include the state information of adjacent nodes, so as to facilitate the overall analysis of the multiple nodes and facilitate the movement of the automatic handling robot.
  • the specific area 100 may be other areas such as a production workshop in addition to a warehouse, and the first area that the automatic handling robot can occupy may vary with the position of the shelves in the warehouse or the production line in the production workshop. Changes may occur due to adjustments, etc., which are not limited in the present disclosure.
  • the mobile device may also be other mobile devices such as an automatic forklift, an automatic detection robot, etc., which is not limited in the present disclosure.
  • methods such as ultrasound and wireless positioning can also be used to obtain the location of the node, which is not limited in the present disclosure.
  • Fig. 1b shows an exemplary schematic diagram of an implementation scenario of a map construction method according to an embodiment of the present disclosure.
  • FIG. 1b exemplarily shows an implementation scenario of the map construction method, and does not constitute a limitation to the present disclosure.
  • a clustering method such as K-means clustering can be used to cluster the nodes in Figure 1a to obtain semantic labels.
  • the resulting semantic labels can be upper channel 111, lower channel 112, left channel 1 1131, left channel 2 1132, road 1 1141, road 2 1142, road 3 1143, road 4 1144, right channel 1 1151, right channel 2 1152 .
  • the first state of the semantic label may include: a clear state, a congested state, and a blocked state.
  • no node in the left channel 1 1131 is in the occupied state
  • the first state of the left channel 1 1131 is the unblocked state
  • the continuous nodes 1171 and 1172 on both sides of the roadway 4 1144 are in the occupied state
  • the first state of the roadway 4 1144 is in the occupied state.
  • the 5 nodes 1161, 1162, 1163, 1164, and 1165 in the roadway 3 1143 are in an occupied state, and the number of nodes in the occupied state is greater than the first threshold such as 3, and the first state of the roadway 3 1143 is a crowded state.
  • the first state of the semantic label is used to identify the congestion level of the semantic label.
  • the proportion of nodes in the occupied state in the semantic label may also be greater than the second threshold, for example, greater than 1/4, so as to determine that the first state of the semantic label is the crowded state.
  • the handling robot may be scheduled to pass the semantic label of the unblocked state first, and then the handling robot may be scheduled to pass the semantic label of the crowded state. For the semantic label in the blocked state, the handling robot may not be scheduled to pass through.
  • the second state of the semantic tag may include a locked state and an unlocked state. Semantic tags in unlocked state can be scheduled, but semantic tags in locked state cannot be scheduled.
  • the second state of the semantic label under the condition that the first state of the semantic label is the unblocked state, the second state of the semantic label is the unlocked state. Under the condition that the first state of the semantic label is the blocked state, the second state of the semantic label is the locked state. Under the condition that the first state of the semantic label is a crowded state, and the congestion index is less than or equal to a third threshold such as 0.2, the second state of the semantic label is an unlocked state; the first state of the semantic label is a crowded state, and the crowded state The second state of the semantic label is a locked state under the condition that the index is greater than a third threshold, eg, 0.2.
  • a third threshold eg, 0.2.
  • the second state of the left lane 1 1131 is an unlocked state and can be scheduled by navigation; the second state of the lane 41144 is a locked state and cannot be scheduled by navigation.
  • the roadway 3 1143 is not completely blocked, there are many nodes in the occupied state, and the congestion index is greater than the third threshold. It is difficult for the subsequent handling robot to pass through.
  • the second state is the locked state and cannot be navigated and dispatched.
  • K-means clustering mean shift clustering, density-based clustering, or other clustering methods can also be used to obtain semantic labels from nodes, which are not limited in this disclosure.
  • the third threshold may also be other values, which are not limited in 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. 1c exemplarily shows an implementation scenario of the navigation method, and does not constitute a limitation to the present disclosure.
  • the transfer robot A starts from the node 121 and needs to access the goods at the node 122 or 123 .
  • the handling robot needs to store goods, and the shelf positions corresponding to nodes 122 and 123 have suitable free storage spaces; or the handling robot needs to take out the goods a, and the shelf positions corresponding to the nodes 122 and 123 both store the goods a.
  • the navigation start position may be node 121
  • the navigation end position may be node 122 or 123 . Since the semantic label lane 3 1143 where the node 123 is located is in a congested state and locked state and cannot be scheduled, node 122 is selected as the final navigation termination position. According to the first state and the second state of each semantic label, and the state and position of each node, the navigation path 124 is calculated as: left channel 2 1132--> main road 113--> left channel 11131--> upper channel 111-- > Lane 2 1142.
  • the navigation path can be calculated using a semantic tag-level path planning algorithm.
  • the semantic label-level path planning algorithm may use, for example, the topological connection relationship between semantic labels to perform computation, or other relationships between semantic labels to perform computation.
  • Fig. 1d shows an exemplary schematic diagram of an implementation scenario of a navigation method according to an embodiment of the present disclosure.
  • FIG. 1d exemplarily shows an implementation scenario of the navigation method, and does not constitute a limitation to the present disclosure.
  • the navigation path of the updated handling robot A is 133: left channel 2 1132 --> main road 113 --> roadway 2 1142.
  • a semantic label-level path planning algorithm is used to plan the route.
  • the handling robot A moves to the semantic label lane 2 1142 along the navigation path 133, the node where the handling robot A is located and the navigation termination position are within the same semantic label, and the navigation path can be calculated by using a topology-level path planning algorithm.
  • the topology-graph-level path planning algorithm can perform computation by using, for example, the topological connection relationship between nodes in the semantic label, or other topological relationships.
  • the transfer robot A finally moves along the updated navigation path 133, reaches the node 122, and completes the access to the goods.
  • the node 132 of the channel 1 1131 on the left side of the semantic label in FIG. 1d is occupied by the handling robot B and is in an occupied state, which may be detected by the handling robot A or marked by the handling robot B.
  • the calculation and update of the navigation path in Figure 1c and Figure 1d can be completed by each handling robot according to the state of the semantic label, the state and position of the node, and autonomously control the movement; it can also be controlled by the central control system of multiple handling robots. After the calculation is completed, each transport robot is controlled to move, which is not limited in the present disclosure.
  • FIG. 2 shows a flowchart of a map construction method according to an embodiment of the present disclosure.
  • the map construction method includes steps S201, S202, S203, and S204.
  • step S201 the first area is divided into nodes.
  • step S202 the state and position of the node are acquired.
  • step S203 a semantic label is acquired according to the nodes in the first area.
  • step S204 the first state and/or the second state of the semantic tag is acquired.
  • the first area is an area in a particular area that can be occupied by a mobile device.
  • the first area may be an area in the warehouse area 100 shown in FIG. 1a that may be occupied by mobile devices such as automatic handling robots other than the shelves.
  • the first area can be divided into nodes, for example, the upper position 1...the upper position 9, the left position 1-1...the left position 1-16, the main road 1-2...the main road 113, etc., and get the state and position of the node.
  • the semantic label may be obtained according to the nodes in the first area, for example, by aggregating the nodes to obtain the semantic label.
  • Semantic labels can be various channels shown in Figure 1b, such as upper channel 111, left channel 1 1131, main road 113, etc.
  • the first state and/or the second state of the semantic label can be acquired, the first state is used to identify the congestion level of the semantic label, and the second state is used to identify whether the semantic label is locked, and further whether it can be navigated and scheduled.
  • a node division step in which the first area is divided into nodes; a node state and position acquisition step, in which the state and position of a node are acquired; and a semantic label acquisition step, in which according to the first area
  • the node of the node obtains the semantic label; the step of obtaining the semantic label state, wherein the first state and/or the second state of the semantic label is obtained, and the first area is the area that can be occupied by the mobile device in a specific area, so as to accurately divide the area and determine the state.
  • the first area may be divided into nodes according to the area sensed by the mobile device, eg, tactile perception, ultrasonic perception, and/or the area occupied by the mobile device.
  • the step of dividing the nodes includes: dividing the first area into nodes according to the area perceived by the mobile device and/or the area occupied by the mobile device, so as to accurately divide the area and complete the marking of the state, and carry out detailed Semantic analysis, and adapt to changes in the scene, to facilitate flexible scheduling of mobile devices.
  • the position of the node may be acquired by, for example, a simultaneous localization and mapping (SLAM) method, and the state of the node may be acquired by a visual method.
  • SLAM simultaneous localization and mapping
  • the position of the node is obtained by using a simultaneous localization and mapping method; and/or the state of the node is obtained by using a visual method, so as to accurately divide the area and complete the marking of the state, perform detailed semantic analysis, and Adapt to changes in the scene and facilitate flexible scheduling of mobile devices.
  • the node state may include: an occupied state and a non-occupied state.
  • the occupancy status of a node may be occupied by a mobile device, or occupied by a person, or occupied by temporarily placed goods, etc., which is not limited in the present disclosure.
  • the states of the nodes include: occupied state and non-occupied state, so as to accurately divide the area and complete the state mark, carry out detailed semantic analysis, and adapt to the change of the scene, so as to facilitate the flexibility of the mobile device. schedule.
  • a method such as K-means clustering may be adopted, and a semantic label may be obtained by using the clustering method according to the state and position of the nodes in the first area.
  • the step of obtaining semantic labels includes: according to the states and positions of nodes in the first area, using a clustering method to obtain semantic labels, so as to accurately divide the area and complete the labeling of the state, and perform detailed semantic Analysis, and adapt to changes in the scene, to facilitate flexible scheduling of mobile devices.
  • K-means clustering mean shift clustering, density-based clustering, or other clustering methods can also be used to obtain semantic labels from nodes, which are not limited in this disclosure.
  • the first state of the semantic label includes: a clear state, a congested state, and a blocked state;
  • the second state of the semantic label includes: a locked state and an unlocked state.
  • Semantic tags in congestion state cannot be scheduled, semantic tags in unblocked state are scheduled first, and semantic tags in congested state are scheduled second. Semantic tags in the locked state are not scheduled, and semantic tags in the unlocked state can be scheduled.
  • the first state of the semantic label includes: a clear state, a crowded state, and a blocked state; and/or the second state of the semantic label includes: a locked state and an unlocked state, so that the region can be precisely calibrated.
  • the continuous nodes 1171 and 1172 on both sides of the roadway 4 1144 are in the occupied state, and the first state of the roadway 4 1144 is the blocked state.
  • the 5 nodes 1161, 1162, 1163, 1164, and 1165 in the roadway 3 1143 are in an occupied state, and the number of nodes in the occupied state is greater than the first threshold such as 3, and the first state of the roadway 3 1143 is a crowded state.
  • the first state of the semantic label is used to identify the congestion level of the semantic label.
  • the proportion of nodes in the occupied state in the semantic label may also be greater than the second threshold, for example, greater than 1/4, so as to determine that the first state of the semantic label is the crowded state.
  • the first state of the semantic label is the blocked state by the condition that the continuous nodes connecting both sides of the semantic label in the semantic label are in the occupied state; the number of nodes in the occupied state in the semantic label If it is greater than the first threshold, or the proportion of nodes in the occupied state in the semantic label is greater than the second threshold, the first state of the semantic label is a crowded state, so that the region can be accurately divided and the state is fully marked, and detailed semantics can be carried out. Analysis, and adapt to changes in the scene, to facilitate flexible scheduling of mobile devices.
  • the first state is in the congestion state, or the second state of the semantic label in the congestion state and the congestion index is greater than a third threshold such as 0.2 is the locked state; the first state is in the unblocked state, or in the congestion state
  • a third threshold such as 0.2 is the locked state
  • the third threshold may also be other values, which are not limited in the present disclosure.
  • the second state of the label is unlocked by the condition that the first state of the semantic label is the unblocked state, or the first state of the semantic label is the congested state and the congestion index is less than or equal to the third threshold state; under the condition that the first state of the semantic label is a blocked state, or the first state of the semantic label is a crowded state and the congestion index is greater than the third threshold, the second state of the semantic label is a locked state, so as to accurately divide the area It can perform detailed semantic analysis and adapt to changes in the scene to facilitate flexible scheduling of mobile devices.
  • FIG. 3 shows a flowchart of a navigation method according to an embodiment of the present disclosure.
  • FIG. 3 shows a method for navigating a mobile device according to FIG. 2 and the aforementioned map construction method.
  • the navigation method includes steps S301 , S302 , S303 , S304 and S305 .
  • step S301 a navigation start position and a navigation end position of the mobile device are acquired.
  • step S302 the navigation path is calculated according to the navigation start position, the navigation end position, the first state of the semantic label and/or the second state of the semantic label.
  • step S303 the mobile device is controlled to move along the navigation path.
  • step S304 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 location of the mobile device and/or the detection result of the mobile device.
  • step S305 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.
  • the automatic handling robot A starts from the node 121 and needs to access the goods at the node 122 or 123 .
  • the node 122 is selected as the navigation termination position, and the navigation path 124 is calculated as: left channel 2 1132 --> main road 113 --> left Channel 1 1131-->The upper channel 111--> Lane 2 1142.
  • the navigation path 124 is calculated as: left channel 2 1132 --> main road 113 --> left Channel 1 1131-->The upper channel 111--> Lane 2 1142.
  • the handling robot when the handling robot is controlled to move to the node 131, the node 132 is occupied by the automatic handling robot B, which causes the left channel 1 1131 to be blocked and locked, thereby updating the navigation path of the automatic handling robot A to 133.
  • the automatic handling robot A moves to the node 122 along the new navigation path 133 to complete the access to the goods.
  • the navigation start and end position acquisition steps are performed, wherein the navigation start position and the navigation end position of the mobile device are acquired; the navigation path planning step, wherein, according to the navigation start position, the navigation end position, the first position of the semantic label is obtained.
  • the semantic label-level path planning is adopted.
  • the algorithm calculates the navigation path 124 .
  • the semantic label-level path planning algorithm may use, for example, the topological connection relationship between semantic labels to perform computation, or other relationships between semantic labels to perform computation.
  • the topology graph level path planning algorithm can perform calculation by using, for example, the topological connection relationship between nodes in the same semantic label, or other topological relationships.
  • the step of planning through the navigation path includes: on the condition that the navigation start position and the navigation end position are within the same semantic label, use a topology-graph level path planning algorithm to calculate the navigation path; Under the condition that the navigation termination position is not within the same semantic tag, the navigation path is calculated by using a semantic tag-level path planning algorithm, so that the movement path of the mobile device can be flexibly based on the map of precise area division, state marking and semantic analysis. Scheduling, and can adapt to the dynamic changes of the scene.
  • FIG. 4 shows a structural block diagram of a map construction apparatus according to an embodiment of the present disclosure.
  • the map construction apparatus 400 includes: a node division module 401 , a node state and position acquisition module 402 , a semantic label acquisition module 403 , and a semantic label state acquisition module 404 .
  • the node division module 401 is configured to divide the first area into nodes.
  • the node state and location acquisition module 402 is configured to acquire the state and location of the node.
  • the semantic label obtaining module 403 is configured to obtain semantic labels according to the nodes in the first area.
  • the semantic tag state acquisition module 404 is configured to acquire the first state and/or the second state of the semantic tag.
  • the first area is an area in a particular area that can be occupied by a mobile device.
  • the node dividing module is configured to divide the first area into nodes; the node status and position acquisition module is configured to acquire the status and position of the node; the semantic label acquisition module is configured to A node in an area obtains a semantic label; the semantic label state obtaining module is configured to obtain the first state and/or the second state of the semantic label, and the first area is an area that can be occupied by a mobile device in a specific area, so as to accurately perform the area Complete labeling of partitions and states, detailed semantic analysis, and adaptation to scene changes, facilitating flexible scheduling of mobile devices.
  • the node dividing module is further configured to: divide an area possibly occupied by the mobile device in the first area into the nodes according to the area perceived by the mobile device and/or the area occupied by the mobile device.
  • the node dividing module is further configured to: divide the area possibly occupied by the mobile device in the first area into the nodes according to the area perceived by the mobile device and/or the area occupied by the mobile device, so as to divide the area into the nodes. Perform precise division and complete state labeling, perform detailed semantic analysis, and adapt to changes in scenarios to facilitate flexible scheduling of mobile devices.
  • the position of the node may be acquired by, for example, a simultaneous localization and mapping (SLAM) method, and the state of the node may be acquired by a visual method.
  • SLAM simultaneous localization and mapping
  • the position of the node is obtained by using a simultaneous localization and mapping method; and/or the state of the node is obtained by using a visual method, so as to accurately divide the area and complete the marking of the state, perform detailed semantic analysis, and Adapt to changes in the scene and facilitate flexible scheduling of mobile devices.
  • the node state may include: an occupied state and a non-occupied state.
  • the occupancy status of a node may be occupied by a mobile device, or occupied by a person, or occupied by temporarily placed goods, etc., which is not limited in the present disclosure.
  • the states of the nodes include: occupied state and non-occupied state, so as to accurately divide the area and complete the state mark, carry out detailed semantic analysis, and adapt to the change of the scene, so as to facilitate the flexibility of the mobile device. schedule.
  • a method such as K-means clustering may be adopted, and a semantic label may be obtained by using the clustering method according to the state and position of the nodes in the first area.
  • the step of obtaining semantic labels includes: according to the states and positions of nodes in the first area, using a clustering method to obtain semantic labels, so as to accurately divide the area and complete the labeling of the state, and perform detailed semantic Analysis, and adapt to changes in the scene, to facilitate flexible scheduling of mobile devices.
  • the first state of the semantic label includes: a clear state, a congested state, and a blocked state;
  • the second state of the semantic label includes: a locked state and an unlocked state.
  • Semantic tags in congestion state cannot be scheduled, semantic tags in unblocked state are scheduled first, and semantic tags in congested state are scheduled second. Semantic tags in the locked state are not scheduled, and semantic tags in the unlocked state can be scheduled.
  • the first state of the semantic label includes: a clear state, a crowded state, and a blocked state; and/or the second state of the semantic label includes: a locked state and an unlocked state, so that the region can be precisely calibrated.
  • the continuous nodes 1171 and 1172 on both sides of the roadway 4 1144 are in the occupied state, and the first state of the roadway 4 1144 is the blocked state.
  • the 5 nodes 1161, 1162, 1163, 1164, and 1165 in the roadway 3 1143 are in an occupied state, and the number of nodes in the occupied state is greater than the first threshold such as 3, and the first state of the roadway 3 1143 is a crowded state.
  • the first state of the semantic label is used to identify the congestion level of the semantic label.
  • the proportion of nodes in the occupied state in the semantic label may also be greater than the second threshold, for example, greater than 1/4, so as to determine that the first state of the semantic label is the crowded state.
  • the first state of the semantic label is the blocked state by the condition that the continuous nodes connecting both sides of the semantic label in the semantic label are in the occupied state; the number of nodes in the occupied state in the semantic label If it is greater than the first threshold, or the proportion of nodes in the occupied state in the semantic label is greater than the second threshold, the first state of the semantic label is a crowded state, so that the region can be accurately divided and the state is fully marked, and detailed semantics can be carried out. Analysis, and adapt to changes in the scene, to facilitate flexible scheduling of mobile devices.
  • the first state is in the congestion state, or the second state of the semantic label in the congestion state and the congestion index is greater than a third threshold such as 0.2 is the locked state; the first state is in the unblocked state, or in the congestion state
  • a third threshold such as 0.2 is the locked state
  • the third threshold may also be other values, which are not limited in the present disclosure.
  • the second state of the label is unlocked by the condition that the first state of the semantic label is the unblocked state, or the first state of the semantic label is the congested state and the congestion index is less than or equal to the third threshold state; under the condition that the first state of the semantic label is a blocked state, or the first state of the semantic label is a crowded state and the congestion index is greater than the third threshold, the second state of the semantic label is a locked state, so as to accurately divide the area It can perform detailed semantic analysis and adapt to changes in the scene to facilitate flexible scheduling of mobile devices.
  • FIG. 5 shows a structural block diagram of a navigation device according to an embodiment of the present disclosure.
  • FIG. 5 shows an apparatus for navigating a mobile device according to FIG. 4 and the aforementioned map construction apparatus.
  • the navigation device 500 includes: a navigation start and end position acquisition module 501 , a navigation path planning module 502 , a movement module 503 , a state update module 504 , and a navigation path update module 505 .
  • the navigation start and end position obtaining module 501 is configured to obtain the navigation start position and the navigation end position of the mobile device.
  • the navigation path planning module 502 is configured to use the navigation start position, the navigation end position, the first state of the semantic label and/or the second state of the semantic label, and the state and position of the node Calculate the navigation path.
  • the moving 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 label and/or the second state of the semantic label according to the location of the mobile device and/or the detection result of the mobile device state.
  • the navigation path update module 505 is configured to update the navigation 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 position of the node path.
  • the automatic handling robot A starts from the node 121 and needs to access the goods at the node 122 or 123 .
  • the node 122 is selected as the navigation termination position, and the navigation path 124 is calculated as: left channel 2 1132 --> main road 113 --> left Channel 1 1131-->The upper channel 111--> Lane 2 1142.
  • the navigation path 124 is calculated as: left channel 2 1132 --> main road 113 --> left Channel 1 1131-->The upper channel 111--> Lane 2 1142.
  • the handling robot when the handling robot is controlled to move to the node 131, the node 132 is occupied by the automatic handling robot B, which causes the left channel 1 1131 to be blocked and locked, thereby updating the navigation path of the automatic handling robot A to 133.
  • the automatic handling robot A moves to the node 122 along the new navigation path 133 to complete the access to the goods.
  • the navigation start and end position acquisition module is configured to acquire the navigation start position and the navigation end position of the mobile device;
  • the navigation path planning module is configured to obtain the navigation start position, navigation end position, semantic label according to the navigation start position, navigation end position The first state of the first state and/or the second state of the semantic label, and the state and position of the node calculate the navigation path;
  • the moving module is configured to control the mobile device to move along the navigation path;
  • the state update module is configured to be based on the position of the mobile device. And/or the detection result of the mobile device, update the state of the node, the first state of the semantic label and/or the second state of the semantic label;
  • the navigation path update module is configured to update the node according to the updated state.
  • the state, the first state of the semantic label and/or the second state of the semantic label, and the state and position of the node update the navigation path, so that the movement path of the mobile device is based on the map of accurate division of regions, state marking and semantic analysis. Perform flexible scheduling and adapt to dynamic changes in the scene.
  • the topology graph level path planning algorithm can perform calculation by using, for example, the topological connection relationship between nodes in the same semantic label, or other topological relationships.
  • the step of planning through the navigation path includes: on the condition that the navigation start position and the navigation end position are within the same semantic label, use a topology-graph level path planning algorithm to calculate the navigation path; Under the condition that the navigation termination position is not within the same semantic tag, the navigation path is calculated by using a semantic tag-level path planning algorithm, so that the movement path of the mobile device can be flexibly based on the map of precise area division, state marking and semantic analysis. Scheduling, and can adapt to the dynamic changes of the scene.
  • FIG. 6 shows a structural block diagram of an electronic device according to an embodiment of the present disclosure.
  • Embodiments of the present disclosure further provide an electronic device.
  • the electronic device 600 includes a processor 601 and a memory 602; wherein, the memory 602 stores instructions that can be executed by at least one processor 601, and the instructions are At least one processor 601 executes to implement the following steps: a node division 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 acquiring step wherein a semantic label is acquired according to the node in the first area
  • the step of obtaining the semantic label state wherein the first state and/or the second state of the semantic label is obtained,
  • the first area is an area in a specific area that can be occupied by a mobile device.
  • the node division step includes:
  • the first area is divided into the nodes according to the area perceived by the mobile device and/or the area occupied by the mobile device.
  • the location of the node is obtained using a simultaneous positioning and mapping method.
  • the state of the node is obtained using a visual method.
  • the status of the node includes:
  • the step of acquiring the semantic label includes:
  • the semantic labels are obtained using a clustering method according to the states and positions of the nodes in the first region.
  • the first state of the semantic label includes:
  • the second state of the semantic tag includes:
  • the first state of the semantic label is the blocked state
  • the semantic The first state of the tag is the congestion state.
  • the semantic label under the condition that the first state of the semantic label is an unblocked state, or the first state of the semantic label is a crowded state and the congestion index is less than or equal to a third threshold, the semantic label
  • the second state of is an unlocked state
  • the second state of the semantic label is locked when the first state of the semantic label is a congestion state, or the first state of the semantic label is a crowded state and the congestion index is greater than the third threshold state.
  • the instructions may also be executed by at least one processor 601 to implement the following steps:
  • a navigation start and end position obtaining step wherein the navigation start position and the navigation end position of the mobile device are obtained
  • Navigation path planning step wherein based on the navigation start position, the navigation end position, the first state of the semantic label and/or the second state of the semantic label, and the state and position of the node are calculated navigation path;
  • a moving step wherein the mobile device is controlled to move along the navigation path
  • a state update 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 location of the mobile device and/or the detection result of the mobile device ;
  • a navigation path update 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 position of the node .
  • the navigation path planning step includes:
  • the navigation path is calculated using a topology-graph level path planning algorithm
  • the navigation path is calculated using a semantic label-level path planning algorithm.
  • FIG. 7 is a schematic structural diagram of a computer system suitable for implementing a map construction method and a navigation method according to an embodiment of the present disclosure.
  • a computer system 700 includes a processing unit 701 that can execute the above-mentioned appendixes 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 Various processes in the embodiment shown in the figure. In the RAM 703, various programs and data necessary for the operation of the system 700 are also stored.
  • the processing unit 701 , the ROM 702 and the RAM 703 are connected to each other through 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 section 706 including a keyboard, a mouse, etc.; an output section 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc. ; and a communication section 709 including a network interface card such as a LAN card, a modem, and 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, etc., is mounted on the drive 710 as needed so that a computer program read therefrom is installed into the storage section 708 as needed.
  • the processing unit 701 may be implemented as a processing unit such as a CPU, a GPU, a TPU, an FPGA, and an NPU.
  • embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a readable medium thereof, the computer program containing program code for performing the methods of the accompanying drawings.
  • the computer program may be downloaded and installed from the network via the communication portion 709 and/or installed from the removable medium 711 .
  • each block in the diagram or block diagram may represent a module, segment, or portion of code that contains one or more functions for implementing the specified logical function. executable instructions.
  • the functions noted in the blocks 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.
  • each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations can be implemented in dedicated hardware-based systems that perform the specified functions or operations , or can be implemented in a combination of dedicated hardware and computer instructions.
  • the units or modules involved in the embodiments of the present disclosure can be implemented in software or hardware.
  • the described units or modules may also be provided in the processor, and the names of these units or modules do not constitute a limitation on the units or modules themselves in certain circumstances.
  • the present disclosure also provides a computer-readable storage medium, and the computer-readable storage medium may be a computer-readable storage medium included in the nodes described in the foregoing embodiments; A computer-readable storage medium that fits into a device.
  • the computer-readable storage medium stores one or more programs used by one or more processors to perform the methods described in the present disclosure.

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Abstract

Des modes de réalisation de la présente divulgation concernent un procédé et un appareil de mappage, un procédé et un appareil de navigation, un dispositif électronique et un support de stockage lisible. Le procédé de mappage comprend : une étape de division de nœud, une première région étant divisée en nœuds; une étape d'obtention d'état et de position de nœud, les états et les positions des nœuds étant obtenus; une étape d'obtention d'étiquette sémantique, une étiquette sémantique étant obtenue en fonction des nœuds dans la première région; et une étape d'obtention d'état d'étiquette sémantique, le premier état et/ou le second état de l'étiquette sémantique étant obtenus, la première région étant une région qui peut être occupée par un dispositif mobile dans une région spécifique. Ainsi, une division précise sur une région et un marquage complet des états sont effectués, une analyse sémantique détaillée est effectuée, et l'adaptation à des changements dans une scène est mise en œuvre, ce qui facilite la planification flexible du dispositif mobile.
PCT/CN2021/131528 2021-02-09 2021-11-18 Procédé et appareil de mappage, procédé et appareil de navigation, dispositif électronique et support de stockage lisible WO2022170806A1 (fr)

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