Multi-robot mapping method, device, electronic device and storage medium

Abstract

The present disclosure proposes a multi-robot mapping method, device, electronic device, and storage medium, relating to the field of robot mapping technology. The method includes: obtaining first path information of a master robot and second path information of slave robots under the master robot, where there are at least two master robots; performing position correction on each master robot based on the first path information, and obtaining corrected third path information; performing position correction on the slave robots based on the third path information and the second path information; obtaining data information collected by the master robot and the slave robots, and constructing a map based on the data information. By performing position correction on each master robot using the master robot path information and performing position correction on the slave robots under the master robot, the efficiency of path planning for the master and slave robots can be improved, thereby improving the efficiency and accuracy of data acquisition by the master and slave robots, as well as the quality of the ultimately generated map, and reducing mapping costs.

Description

Technical Field

[0001] The present disclosure relates to the field of robot mapping technology, and in particular to a multi-robot mapping method, device, electronic device, and storage medium. Background Art

[0002] 90% of the coal industry is underground mining, making perception and decision-making difficult for traditional mining robots. Underground mines are a harsh, unstructured environment with no GNSS global positioning signals. Challenges include cramped working space, high dust concentrations, high humidity, extremely low light levels, fires, floods, gas outbursts, rock bursts, and complex distribution of personnel and equipment. Consequently, underground mapping is challenging, costly, and time-consuming. Summary of the Invention

[0003] The present disclosure aims to solve one of the technical problems in the related art at least to a certain extent.

[0004] To this end, one objective of the present disclosure is to propose a multi-robot mapping method.

[0005] The second objective of the present disclosure is to provide a multi-robot mapping device.

[0006] A third objective of the present disclosure is to provide an electronic device.

[0007] A fourth object of the present disclosure is to provide a non-transitory computer-readable storage medium.

[0008] A fifth object of the present disclosure is to provide a computer program product.

[0009] To achieve the above-mentioned objectives, the first embodiment of the present disclosure proposes a multi-robot mapping method, comprising: obtaining first path information of a master robot and second path information of slave robots subordinate to the master robot, where there are at least two master robots; based on the first path information, performing position correction on each of the master robots, and obtaining corrected third path information; based on the third path information and the second path information, performing position correction on the slave robots; obtaining data information collected by the master robot and the slave robots, and constructing a map based on the data information.

[0010] According to one embodiment of the present disclosure, the position correction of each of the master robots based on the first path information includes: for any target master robot, sorting the other master robots according to the distance from the target master robot; obtaining the correction weight of each of the other master robots based on the sorting; and determining the third path information of the target master robot based on the correction weight, the first path information of the other master robots, and the first path information of the target master robot.

[0011] According to one embodiment of the present disclosure, determining the third path information of the target master robot based on the correction weight, the first path information of the other master robots and the first path information of the target master robot includes: Heuristic Search An algorithm is provided to calculate the first path information of the target master robot and the first path information of the other master robots and obtain a calculation result; in response to the calculation result, it is determined that there is a conflict between the paths of the target master robot and the other master robots, and based on the first path information of the target master robot and the first path information of the conflicting master robots and the correction weights corresponding to the conflicting master robots, conflict adjustment is performed to determine the third path information of the target master robot.

[0012] According to one embodiment of the present disclosure, the first path information includes time and position constraints, and the method further includes: for any master robot, based on the first path information of the master robot, establishing a binary tree of time and position constraints.

[0013] According to one embodiment of the present disclosure, the conflict adjustment is performed based on the first path information of the target master robot, the first path information of the conflicting master robot, and the correction weight corresponding to the conflicting master robot, including: determining the conflict node based on the binary tree of the target master robot and the binary tree of the conflicting master robot; correcting the first path information of the target master robot based on the time and position constraints of the conflicting node and the correction weight corresponding to the conflicting master robot until there is no conflict between the path of the target master robot and the other master robots.

[0014] According to one embodiment of the present disclosure, the data information includes inertial navigation data, radar data and image data, and the method further includes: for any robot, fusing the inertial navigation data with the radar data to obtain first sub-map data; fusing the inertial navigation data with the image data to obtain second sub-map data; and determining the sub-map data of the robot based on the first sub-map data and the second sub-map data.

[0015] According to one embodiment of the present disclosure, the map construction based on the data information includes: fusing all sub-map data to obtain a candidate map; extracting key frames from the candidate map, and fusing and optimizing repeated key frames to obtain a target map.

[0016] To achieve the above-mentioned objectives, the second aspect of the present disclosure proposes a multi-robot mapping device, including: an acquisition module for acquiring first path information of a master robot and second path information of slave robots subordinate to the master robot, where there are at least two master robots; a first correction module for performing position correction on each of the master robots based on the first path information, and acquiring corrected third path information; a second correction module for performing position correction on the slave robots based on the third path information and the second path information; and a mapping module for acquiring data information collected by the master robot and the slave robots, and constructing a map based on the data information.

[0017] To achieve the above-mentioned objectives, the third aspect embodiment of the present disclosure proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the multi-robot mapping method as described in the first aspect embodiment of the present disclosure.

[0018] To achieve the above-mentioned objectives, the fourth embodiment of the present disclosure proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to implement the multi-robot mapping method as described in the first embodiment of the present disclosure.

[0019] To achieve the above-mentioned objectives, the fifth embodiment of the present disclosure proposes a computer program product, including a computer program, which, when executed by a processor, is used to implement the multi-robot mapping method as described in the first embodiment of the present disclosure.

[0020] By using the master robot's path information to correct the position of each master robot and the position of the slave robots under the master robot, the efficiency of path planning for the master and slave robots can be improved, thereby improving the efficiency and accuracy of data acquisition for the master and slave robots, as well as the quality of the final generated map, and reducing mapping costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a schematic diagram of a multi-robot mapping method according to one embodiment of the present disclosure;

[0022] Figure 2 This is a schematic diagram of robot grouping in a multi-robot mapping method according to one embodiment of the present disclosure;

[0023] Figure 3 is a schematic diagram of a robot mapping strategy of a multi-robot mapping method according to one embodiment of the present disclosure;

[0024] Figure 4is a schematic diagram of another multi-robot mapping method according to an embodiment of the present disclosure;

[0025] Figure 5 is a schematic diagram of another multi-robot mapping method according to an embodiment of the present disclosure;

[0026] Figure 6 is a schematic diagram of another multi-robot mapping method according to an embodiment of the present disclosure;

[0027] Figure 7 is a schematic diagram of a multi-robot mapping device according to one embodiment of the present disclosure;

[0028] Figure 8 is a schematic diagram of an electronic device according to one embodiment of the present disclosure. DETAILED DESCRIPTION

[0029] The following describes in detail embodiments of the present disclosure, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0030] Figure 1 This is a schematic diagram of an exemplary embodiment of a multi-robot mapping method proposed in the present disclosure, such as Figure 1 As shown, the multi-robot mapping method includes the following steps:

[0031] S101, obtaining first path information of a master robot and second path information of slave robots subordinate to the master robot, where there are at least two master robots.

[0032] The execution subject of the embodiment of the present disclosure is a server, electronic device, etc. that controls the master robot and the slave robot. A wireless connection is set between the execution subject and the robot to realize the control of the robot and receive the data fed back by the robot.

[0033] It should be noted that the master robot can have multiple slave robots under it, and this is not limited here. The specific limitation needs to be determined according to actual design requirements. In the disclosed embodiment, the master robot and its slave robots can form a formation to facilitate the classification and separate storage of collected data, thereby improving the efficiency of subsequent data processing.

[0034] Different main robots correspond to different formations. For example, Figure 2 As shown, it may include formation 1, formation 2, formation 3... formation n, etc.

[0035] In the disclosed embodiments, the first path information may include various types, such as the navigation route, target location, and location constraints of the master robot. The second path information may also include various types, such as the navigation route, target location, and location constraints of the slave robot. This is not intended to limit the scope of this disclosure.

[0036] It should be noted that the first path information and the second path information are pre-set and can be changed according to actual detection needs, and are not limited here.

[0037] S102: Based on the first path information, perform position correction on each master robot and obtain corrected third path information.

[0038] The master robots in different formations operate in parallel, using parallel collaborative positioning. Equipped with the same or similar sensors, they integrate the positioning data of each robot to achieve mutual correction during positioning. This robustness ensures the positioning of the remaining robots even if a single robot fails. Within the same formation, the master robot, equipped with a high-precision navigation sensor, serves as the master node within the formation, providing accurate position reference information for all robots in the formation. Slave robots, using low-cost sensors, receive data from the master node and integrate it with their own sensor position information to accurately position and correct themselves, thus achieving self-positioning for all robots in the entire robotic system.

[0039] In the embodiment of the present disclosure, there may be multiple methods for performing position correction on each master robot based on the first path information, which are not limited herein.

[0040] Optionally, all the main robots can be used as parallel targets, and each main robot can be adjusted according to the first path information of all the main robots to achieve the final detection target between the robots and ensure that the data collected jointly by the main robots do not overlap as much as possible, thereby improving the efficiency of data collection.

[0041] Optionally, for any master robot, the master robot can also be adjusted based on the first path information of the master robots around the master robot to prevent collisions between the master robots during data collection due to path overlap, etc., which may cause losses.

[0042] S103 , performing position correction on the slave robot based on the third path information and the second path information.

[0043] In the embodiment of the present disclosure, after obtaining the third path information, the position of the slave robot can be corrected based on the third path information of the master robot and the second path information of the subordinate slave robot. There can be many correction methods, which are not limited here.

[0044] Optionally, the slave robot may be adjusted based on preset rules, which may include the distance between the slave robot and the master robot, the acquisition angle, etc.

[0045] Optionally, the second path information and the third path information may be used to determine whether there is a collision point between the master robot and the slave robot. If so, the third path information may be adjusted based on the collision point.

[0046] S104: Acquire data information collected by the master robot and the slave robots, and construct a map based on the data information.

[0047] In the embodiments of the present disclosure, there are various methods for constructing a map based on the data information collected by the master robot and the slave robots.

[0048] Optionally, the data information collected by the master robot and its slave robots can be fused through the server, and then the fused data can be processed with the fused data of other master robots to generate a map.

[0049] Alternatively, as Figure 3 As shown in the figure, in the process of multi-robot collaborative mapping, a single robot can have the ability to build maps independently. Different robots can build maps independently, and then all the mapping data can be integrated to generate a map.

[0050] In the disclosed embodiment, first, first path information of a master robot and second path information of its subordinate slave robots are obtained. There are at least two master robots. Then, based on the first path information, position correction is performed on each master robot, and corrected third path information is obtained. Then, based on the third and second path information, position correction is performed on the slave robots. Finally, data collected by the master and slave robots is obtained, and a map is constructed based on the data information. By performing position correction on each master robot and its subordinate slave robots using the master robot's path information, the efficiency of path planning for the master and slave robots can be improved, thereby improving the efficiency and accuracy of data acquisition by the master and slave robots, as well as the quality of the resulting map, and reducing mapping costs.

[0051] In the disclosed embodiment, after the map is built, loop closure detection can be used to improve accuracy. Finally, the map is globally optimized based on graph optimization theory to obtain high-precision environmental map information.

[0052] In the disclosed embodiments, multi-robot collaborative navigation technology implements functions such as robot path planning and obstacle avoidance. Path planning for robots in the same formation uses a coupled path planning method, while path planning between different teams uses a decoupled path planning method. The coupled path planning method first allocates tasks through a market mechanism, which can quickly solve the multi-objective task allocation problem. A collaborative non-dominated sorting genetic algorithm is then used to simultaneously construct multi-robot path search from a global optimization perspective. The path search is modeled as multiple subpopulations, and global path search is achieved through subpopulation procurement and population cooperation.

[0053] In the above embodiment, the position of each master robot is corrected based on the first path information, and the position of each master robot can also be corrected by Figure 4 Explaining further, the method includes:

[0054] S401 , for any target master robot, sorting other master robots according to their distance from the target master robot.

[0055] In the disclosed embodiment, the target master robot is any one of the master robots. It is understood that the closer the master robot is to the target master robot, the greater its influence on the target master robot's path planning. Therefore, the master robots can be sorted based on distance.

[0056] S402: Based on the ranking, obtain the correction weight of each other master robot.

[0057] In the embodiment of the present disclosure, the correction weight is the influence weight of the master robot on the target master robot. The larger the correction weight, the greater its influence on the path correction of the target master robot.

[0058] It should be noted that the higher the ranking, that is, the closer the master robot is to the target master robot, the greater the correction weight. It should be noted that the correction weight is pre-set and can be changed according to actual design needs, and is not limited here.

[0059] S403 : Determine third path information of the target master robot based on the correction weight, the first path information of the other master robots, and the first path information of the target master robot.

[0060] In the embodiment of the present disclosure, after obtaining the correction weight of each other master robot, the first path information of the target master robot and the first path information of the other master robots can be calculated based on a heuristic search algorithm, and the calculation results can be obtained. In response to the calculation results, it is determined that there is a conflict between the paths of the target master robot and the other master robots. Based on the first path information of the target master robot and the first path information of the conflicting master robot and the correction weight corresponding to the conflicting master robot, conflict adjustment is performed to determine the third path information of the target master robot.

[0061] It's important to note that the A-star algorithm is a highly effective direct search method for finding the shortest path in static road networks and is also a powerful algorithm for solving many search problems. The closer the distance estimate in the algorithm is to the actual value, the faster the search will ultimately be.

[0062] In the embodiment of the present disclosure, firstly, based on a heuristic search algorithm, the first path information of the target master robot and the first path information of the other master robots are calculated and the calculation results are obtained. Then, in response to the calculation results, it is determined that there is a conflict between the paths of the target master robot and the other master robots. Based on the first path information of the target master robot and the first path information of the conflicting master robot and the correction weight corresponding to the conflicting master robot, conflict adjustment is performed to determine the third path information of the target master robot.

[0063] In the above embodiment, conflict adjustment is performed based on the first path information of the target master robot, the first path information of the conflicting master robot, and the correction weight corresponding to the conflicting master robot. Figure 5 Explaining further, the method includes:

[0064] S501: Determine the conflicting node based on the binary tree of the target master robot and the binary tree of the conflicting master robot.

[0065] In the embodiment of the present disclosure, for any master robot, a binary tree of time and position constraints is established based on the first path information of the master robot.

[0066] It's important to note that binary trees are an important type of tree structure. Many practical problems often result in data structures that take the form of binary trees. Even ordinary trees can be easily converted to binary trees, and their storage structure and algorithms are relatively simple. Therefore, binary trees are particularly important. A binary tree is characterized by the fact that each node can have at most two subtrees, one on the left and one on the right.

[0067] If the binary tree of the target master robot and the binary tree of other master robots have intersecting nodes, it can be considered that there is a conflict in the paths between the two.

[0068] S502 , based on the time and position constraints of the conflicting nodes and the correction weights corresponding to the conflicting master robots, correct the first path information of the target master robot until there is no conflict between the paths of the target master robot and the other master robots.

[0069] In the disclosed embodiment, the first path information of the target robot and the first path information of the other master robots are first calculated based on a heuristic search algorithm, and the calculation results are obtained. In response to the calculation results, it is determined that there is a conflict between the paths of the target robot and the other master robots. Based on the first path information of the target robot and the first path information of the conflicting master robots, as well as the correction weights corresponding to the conflicting master robots, conflict adjustment is performed to determine the third path information of the target master robot. Thus, by establishing a binary tree for the master robots, it is possible to accurately determine whether there is a conflict between the paths of the master robots, accurately locate the conflicting nodes, and thus quickly perform path correction on the target master robot, improving the correction rate and accuracy.

[0070] In the above embodiment, the data information includes inertial navigation data, radar data and image data, and the map can be constructed by Figure 6 Explaining further, the method includes:

[0071] S601: For any robot, fuse inertial navigation data with radar data to obtain first sub-map data.

[0072] In the multi-robot collaborative mapping process, individual robots also need to have mapping capabilities. To address the problem of weak perception capabilities of single sensors, the robot adopts a mapping method based on multi-sensor data fusion during the mapping process. Therefore, the present disclosure provides a mapping algorithm based on the fusion of inertial navigation data, radar data, and image data to improve the positioning accuracy, mapping accuracy, and robustness of the system. The federated filter can implement block estimation and two-step cascade decentralized filtering, and can hierarchically and parallelize data. When a single sub-filter fails, the system can still output the filtering result, which is beneficial to improving the robustness of the system.

[0073] In the disclosed embodiment, inertial navigation data may be collected through inertial navigation, radar data may be collected through a radar device provided on the robot, and image data may be collected through an image acquisition device provided on the robot.

[0074] S602: Fusing the inertial navigation data with the image data to obtain second sub-map data.

[0075] In the disclosed embodiments, inertial navigation data and radar data may be fused using a fusion algorithm to obtain first sub-map data, or inertial navigation data and image data may be fused to obtain second sub-map data. This fusion algorithm may be pre-defined and may be modified based on actual design needs, and is not limited herein.

[0076] Optionally, the inertial navigation data can be fused with the radar data using a fusion model to obtain the first sub-map data, or the inertial navigation data can be fused with the image data to obtain the second sub-map data. The fusion model can be pre-trained and stored in the storage space of the electronic device for easy access when needed.

[0077] S603: Determine the sub-map data of the robot based on the first sub-map data and the second sub-map data.

[0078] After obtaining the first sub-map data and the second sub-map data, a weighted least squares method is used to assign different weights according to the importance of the information, thereby obtaining a global optimal estimate.

[0079] In this disclosed embodiment, for any robot, inertial navigation data is first fused with radar data to generate the first sub-map data. The inertial navigation data is then fused with image data to generate the second sub-map data. Finally, the robot's sub-map data is determined based on the first and second sub-map data. Generating map data through multi-data fusion prevents the system from outputting filtered results even if a single sub-filter fails, improving system robustness.

[0080] In the embodiment of the present disclosure, all sub-map data may be fused to obtain a candidate map, and then key frames may be extracted from the candidate map, and repeated key frames may be fused and optimized to obtain a target map.

[0081] Corresponding to the multi-robot mapping methods provided in the above-mentioned embodiments, an embodiment of the present disclosure further provides a multi-robot mapping device. Since the multi-robot mapping device provided in the embodiment of the present disclosure corresponds to the multi-robot mapping methods provided in the above-mentioned embodiments, the implementation methods of the above-mentioned multi-robot mapping methods are also applicable to the multi-robot mapping device provided in the embodiment of the present disclosure and will not be described in detail in the following embodiments.

[0082] Figure 7 This is a schematic diagram of a multi-robot mapping device proposed in this disclosure, such as Figure 7 As shown, the multi-robot mapping device 700 includes: an acquisition module 710 , a first correction module 720 , a second correction module 730 , and a mapping module 740 .

[0083] The acquisition module 710 is configured to acquire first path information of a master robot and second path information of slave robots subordinate to the master robot, where there are at least two master robots.

[0084] The first correction module 720 is configured to perform position correction on each of the master robots based on the first path information, and obtain corrected third path information.

[0085] The second correction module 730 is configured to perform position correction on the slave robot based on the third path information and the second path information.

[0086] The mapping module 740 is used to obtain the data information collected by the master robot and the slave robots, and construct a map based on the data information.

[0087] In one embodiment of the present disclosure, the first correction module 720 is further used to: for any target master robot, sort the other master robots according to their distance from the target master robot; based on the sorting, obtain the correction weight of each of the other master robots; and determine the third path information of the target master robot based on the correction weight, the first path information of the other master robots and the first path information of the target master robot.

[0088] In one embodiment of the present disclosure, the first correction module 720 is further configured to: Heuristic Search An algorithm is provided to calculate the first path information of the target master robot and the first path information of the other master robots and obtain a calculation result; in response to the calculation result, it is determined that there is a conflict between the paths of the target master robot and the other master robots, and based on the first path information of the target master robot and the first path information of the conflicting master robots and the correction weights corresponding to the conflicting master robots, conflict adjustment is performed to determine the third path information of the target master robot.

[0089] In one embodiment of the present disclosure, the first correction module 720 is further configured to: for any master robot, establish a binary tree of time and position constraints based on the first path information of the master robot.

[0090] In one embodiment of the present disclosure, the first correction module 720 is further used to: determine the conflict node based on the binary tree of the target master robot and the binary tree of the conflicting master robot; correct the first path information of the target master robot based on the time and position constraints of the conflicting node and the correction weight corresponding to the conflicting master robot until there is no conflict between the path of the target master robot and the other master robots.

[0091] In one embodiment of the present disclosure, the mapping module 740 is further used to: for any robot, fuse the inertial navigation data with the radar data to obtain first sub-map data; fuse the inertial navigation data with the image data to obtain second sub-map data; and determine the sub-map data of the robot based on the first sub-map data and the second sub-map data.

[0092] In one embodiment of the present disclosure, the mapping module 740 is further configured to: fuse all sub-map data to obtain a candidate map; extract key frames from the candidate map, and perform fusion optimization on repeated key frames to obtain a target map.

[0093] In order to implement the above embodiment, the present disclosure also provides an electronic device 800, such as Figure 8 As shown, the electronic device 800 includes: a processor 801 and a memory 802 communicatively connected to the processor, the memory 802 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 801 to implement the multi-robot mapping method as an embodiment of the first aspect of the present disclosure.

[0094] In order to implement the above embodiments, the embodiments of the present disclosure further propose a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to implement the multi-robot embodiment of the first aspect of the present disclosure.

[0095] In order to implement the above embodiments, the embodiments of the present disclosure further provide a computer program product, including a computer program. When the computer program is executed by a processor, it implements the multi-robot mapping method of the first embodiment of the present disclosure.

[0096] In the description of the present disclosure, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present disclosure and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation to the present disclosure.

[0097] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. Throughout the present disclosure, "plurality" means two or more, unless otherwise specifically defined.

[0098] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and features of different embodiments or examples, unless they are mutually inconsistent.

[0099] Although the embodiments of the present disclosure have been shown and described above, it is understood that the above embodiments are illustrative and are not to be construed as limitations on the present disclosure. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present disclosure.

Claims

1. A multi-robot mapping method, characterized in that: include: Obtaining first path information of a master robot and second path information of slave robots subordinate to the master robot, wherein there are at least two master robots; Based on the first path information, performing position correction on each of the master robots, and obtaining corrected third path information; performing position correction on the slave robot based on the third path information and the second path information; Acquiring data information collected by the master robot and the slave robot after position correction, and constructing a map based on the data information; The performing position correction on each of the master robots based on the first path information includes: For any target master robot, sort the other master robots according to their distance from the target master robot; Based on the ranking, obtaining a correction weight for each of the other master robots; determining third path information of the target master robot based on the correction weight, the first path information of the other master robot, and the first path information of the target master robot; The data information includes inertial navigation data, radar data and image data, and the method further includes: For any robot, fusing the inertial navigation data with the radar data to obtain first sub-map data; fusing the inertial navigation data with the image data to obtain second sub-map data; Sub-map data of the robot is determined based on the first sub-map data and the second sub-map data.

2. The method according to claim 1, characterized in that The determining, based on the correction weight, the first path information of the other master robots, and the first path information of the target master robot, the third path information of the target master robot includes: Calculating the first path information of the target master robot and the first path information of the other master robots based on a heuristic search algorithm, and obtaining calculation results; In response to the calculation result, it is determined that there is a conflict between the paths of the target master robot and the other master robots. Based on the first path information of the target master robot and the first path information of the conflicting master robot and the correction weight corresponding to the conflicting master robot, conflict adjustment is performed to determine the third path information of the target master robot.

3. The method according to any one of claims 1 to 2, characterized in that The first path information includes time and location constraints, and the method further includes: For any master robot, a binary tree of time and position constraints is established based on the first path information of the master robot.

4. The method according to claim 3, characterized in that The conflict adjustment based on the first path information of the target master robot, the first path information of the conflicting master robot, and the correction weight corresponding to the conflicting master robot includes: Determining a conflicting node based on the binary tree of the target master robot and the binary tree of the conflicting master robot; Based on the time and position constraints of the conflicting nodes and the correction weight corresponding to the conflicting master robot, the first path information of the target master robot is corrected until there is no conflict between the paths of the target master robot and the other master robots.

5. The method according to claim 1, wherein The map construction based on the data information includes: All sub-map data are fused to obtain candidate maps; Key frames are extracted from the candidate maps, and repeated key frames are fused and optimized to obtain the target map.

6. A multi-robot mapping device, characterized in that: include: an acquisition module, configured to acquire first path information of a master robot and second path information of slave robots subordinate to the master robot, wherein there are at least two master robots; a first correction module, configured to perform position correction on each of the master robots based on the first path information, and obtain corrected third path information; a second correction module, configured to perform position correction on the slave robot based on the third path information and the second path information; a mapping module, configured to obtain data information collected by the master robot and the slave robots after position correction, and construct a map based on the data information; The first correction module is further configured to: For any target master robot, sort the other master robots according to their distance from the target master robot; Based on the ranking, obtaining a correction weight for each of the other master robots; determining third path information of the target master robot based on the correction weight, the first path information of the other master robot, and the first path information of the target master robot; The data information includes inertial navigation data, radar data and image data. The mapping module is further used to: For any robot, fusing the inertial navigation data with the radar data to obtain first sub-map data; fusing the inertial navigation data with the image data to obtain second sub-map data; Sub-map data of the robot is determined based on the first sub-map data and the second sub-map data.

7. An electronic device, characterized in that: Including memory and processor; The processor reads the executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 5 when executed by a processor.

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