RTK-based multi-legged robot collaborative mapping method and system

Abstract

The invention provides a multi-legged robot collaborative mapping method and system based on RTK, and relates to multi-robot collaborative mapping, and the method comprises the steps: constructing a hybrid processing architecture; various sensor data of the foot type robot are obtained, and timestamp alignment is carried out on the various sensor data; calculating a rotation matrix in an initial pose homogeneous transformation matrix between the foot robots and X and Y axis values in a translation vector; the height of a robot body is obtained, and a complete homogeneous transformation matrix between the foot type robots is determined; laser SLAM real-time mapping is executed through the distributed sensing layer; transmitting the local point cloud map of each foot type robot to a data center processing host; performing coarse splicing on the local point cloud map; in the centralized decision-making layer, error compensation is carried out on drifting of the foot-type robot, then fine correction is carried out on the spliced point cloud map, and a global consistency point cloud map is output. According to the method, the accuracy of map splicing is effectively improved, and the calculation amount of mapping is reduced.

Description

Technical Field

[0001] The present invention relates to multi-robot collaborative mapping, and in particular to an RTK-based multi-legged robot collaborative mapping method and system. Background Art

[0002] The RTK-based multi-legged robot collaborative mapping method is a technology that uses RTK (real-time dynamic positioning) technology to achieve high-precision collaborative mapping of multi-legged robots in complex environments. RTK (real-time dynamic positioning) technology provides high-precision positioning services to help robots accurately obtain their position and posture in the environment, thereby accurately building maps. A legged robot refers to a robot that uses multiple legs to move, which can move stably and perform tasks in complex terrain.

[0003] With the rapid development of robotics technology, the perception and mapping of unknown environments have become the primary link in robotic operations. The operating effect and application scope of a single robot in a complex and changing environment have obvious limitations, while a multi-robot system can quickly establish a comprehensive understanding of the unknown environment through collaborative search. Its overall performance significantly exceeds that of a single robot system, and it can greatly improve operating efficiency by processing tasks in parallel.

[0004] However, traditional collaborative mapping methods rely on the overlapping areas between local maps to achieve map stitching, which is computationally difficult, burdensome, and has low mapping efficiency. In addition, when the overlapping areas between local maps are small, the accuracy of map matching will be greatly reduced, and effective stitching cannot be performed. In a multi-robot system, due to the relative motion and inertial errors between robots, posture drift will occur, affecting the consistency of the global map, resulting in reduced mapping accuracy and efficiency. Summary of the invention

[0005] In order to solve the technical problems that traditional collaborative mapping methods rely on overlapping areas between local maps to achieve map stitching, which has high computational difficulty, heavy burden, and low mapping efficiency, and when there are fewer overlapping areas between local maps, the accuracy of map matching will be greatly reduced, and effective stitching cannot be performed; in a multi-robot system, due to relative motion and inertial errors between robots, posture drift will occur, affecting the consistency of the global map, resulting in reduced mapping accuracy and efficiency, the present invention provides a multi-legged robot collaborative mapping method and system based on RTK.

[0006] The technical solution provided by the embodiment of the present invention is as follows:

[0007] First aspect:

[0008] An embodiment of the present invention provides a multi-legged robot collaborative mapping method based on RTK, comprising:

[0009] S1: Building a hybrid processing architecture, the hybrid processing architecture including a distributed perception layer and a centralized decision layer;

[0010] The distributed perception layer is composed of a plurality of legged robots, each of which is equipped with a laser radar, an RTK positioning module, a wireless communication module and a CPU computing unit;

[0011] The centralized decision-making layer includes a data center processing host;

[0012] S2: Acquire various sensor data of the legged robot, and align timestamps of various sensor data;

[0013] S3: Based on the RTK master-slave antenna posture data, calculate the rotation matrix in the initial posture homogeneous transformation matrix between the legged robots and the X and Y axis values ​​in the translation vector;

[0014] S4: by deriving the forward kinematics of the legged robot, obtaining the height of the body, and optimizing the foot-end touchdown judgment, obtaining the Z-axis value of the translation vector in the initial posture homogeneous transformation matrix between the robots, and combining the rotation matrix and the X-axis and Y-axis values ​​in the translation vector, determining the complete homogeneous transformation matrix between the legged robots;

[0015] S5: Execute laser SLAM real-time mapping through the distributed perception layer, and each of the legged robots constructs a local point cloud map based on its own computing unit;

[0016] S6: Transmitting the local point cloud map of each of the legged robots to the processing host in the data center through a gigabit wireless communication link constructed by an AP gateway; and roughly splicing the local point cloud map based on the complete homogeneous transformation matrix;

[0017] S7: In the centralized decision-making layer, the drift of the legged robot is compensated for by the RTK path trajectory in each of the local point cloud maps, and then the spliced ​​point cloud map is precisely corrected to output a globally consistent point cloud map.

[0018] Second aspect:

[0019] An embodiment of the present invention provides a multi-legged robot collaborative mapping system based on RTK, comprising:

[0020] processor;

[0021] A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the RTK-based multi-legged robot collaborative mapping method as described in the first aspect is implemented.

[0022] The third aspect:

[0023] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the RTK-based multi-legged robot collaborative mapping method as described in the first aspect is implemented.

[0024] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0025] In the embodiment of the present invention, the combination of the distributed perception layer and the centralized decision-making layer can realize the allocation of tasks and efficient processing of data, share the computing burden and reduce the computing resource bottleneck, and improve the collaborative efficiency of the entire system. By synchronizing the time of different sensor data, the stitching error and time delay problems caused by the asynchrony of sensor data in the traditional method are avoided. The initial posture transformation matrix between the robots is calculated based on the data of the RTK master and slave antennas, which eliminates the dependence on the overlapping area of ​​the local map and avoids the difficulty of stitching due to the lack of overlapping areas or scarcity of feature points. The adaptability and accuracy in open environments are enhanced. The robot's forward kinematics model is combined with the foot-end touchdown judgment to optimize the calculation of the Z axis, which can accurately determine the relative posture between robots and effectively avoid the errors caused by the robot's gait changes. The local point cloud map is constructed in real time through laser SLAM, which can accurately perceive the environment and generate local maps, providing high-quality data support for the stitching of the global map. The RTK path trajectory is used to compensate for the drift of the robot during operation, ensuring that the posture differences between each local map are effectively corrected, ensuring the consistency and accuracy of the global map, improving the accuracy and stability of the global map, and reducing the drift errors commonly seen in multi-robot systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 A schematic diagram of a process flow of a multi-legged robot collaborative mapping method based on RTK provided in an embodiment of the present invention;

[0028] Figure 2 A schematic diagram of the hardware framework of a multi-legged robot collaborative mapping system based on RTK provided in an embodiment of the present invention;

[0029] Figure 3 A schematic structural diagram of an RTK-based multi-legged robot collaborative mapping system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0031] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0032] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0033] In the embodiments of the present invention, sometimes the subscripts such as W 1 It may be written in non-subscript form such as W1. When the difference is not emphasized, the meaning is the same.

[0034] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0035] Reference Manual Attached Figure 1 , shows a flow chart of a multi-legged robot collaborative mapping method based on RTK provided in an embodiment of the present invention.

[0036] The embodiment of the present invention provides a multi-legged robot collaborative mapping method based on RTK, which can be implemented by a multi-legged robot collaborative mapping device based on RTK, and the multi-legged robot collaborative mapping device based on RTK can be a terminal or a server. The processing flow of the multi-legged robot collaborative mapping method based on RTK may include the following steps:

[0037] S1: Build a hybrid processing architecture, which includes a distributed perception layer and a centralized decision-making layer.

[0038] It should be noted that the hybrid architecture reduces the burden of a single processing center by distributing data processing to each robot, thereby improving the processing efficiency and robustness of the system. At the same time, the centralized decision-making layer can globally optimize the data collected by each robot to ensure consistency and accuracy when the system works together, effectively improving the performance of multi-robot collaborative mapping.

[0039] The distributed perception layer is composed of multiple legged robots, each of which is equipped with a laser radar, an RTK positioning module, a wireless communication module and a CPU computing unit.

[0040] The centralized decision-making layer includes data center processing hosts.

[0041] Among them, the distributed perception layer refers to a perception system composed of multiple robots. The distributed perception layer obtains data through sensors carried by each robot to achieve local environmental perception and data processing. A legged robot is a robot that uses multiple legs to move. LiDAR is a sensor that obtains information such as the distance, shape and position of the environment by emitting a laser beam and analyzing the reflected laser signal. RTK is a high-precision positioning technology that usually uses satellite signals for differential positioning to achieve centimeter-level positioning accuracy. The RTK positioning module provides high-precision position and attitude data by receiving and processing satellite signals in real time. The CPU computing unit is the core hardware device inside the robot for processing data, executing algorithms and making decisions. The centralized decision-making layer refers to a system layer that is centrally managed and optimized, usually composed of a data center processing host. The data center processing host is the core computing unit of the centralized decision-making layer.

[0042] S2: Acquire various sensor data of the legged robot and align the timestamps of the various sensor data.

[0043] It should be noted that by synchronizing the data of each sensor according to the timestamp, it can be ensured that the data of all sensors can be synchronously processed under the same time reference, thereby avoiding calculation errors or splicing errors caused by inconsistent data.

[0044] In a possible implementation manner, S2 is specifically: The data of different sensors are adjusted to the same timestamp by interpolation. Based on the timestamp of the lidar sensor data, the timestamps of the inertial unit IMU, joint motor, RTK sensor and lidar are aligned to complete the time synchronization: Among them, Date s Indicates the synchronization data with the laser radar data frame acquisition time, Date j and Date j+1 Indicates the jth data and j+1th data of the aligned object on the timeline. and Indicates the timestamps of the jth data and the j+1th data of the aligned object on the timeline. Represents the timestamp of the i-th lidar data, Indicates the timestamp of the j-th data aligned with the i-th lidar data.

[0048] S3: Based on the RTK master-slave antenna pose data, calculate the rotation matrix in the initial pose homogeneous transformation matrix between the legged robots and the X and Y axis values ​​in the translation vector.

[0049] Among them, RTK master-slave antenna pose data refers to the high-precision positioning data provided by RTK technology through the master-slave antenna system. The master antenna is fixed to the base station, and the slave antenna is installed on the robot. The precise pose of the robot is obtained by differential calculation between the two. The initial pose homogeneous transformation matrix is ​​a mathematical tool that describes the transformation relationship from one coordinate system to another, including rotation matrix and displacement vector.

[0050] Specifically, by using the RTK master-slave antenna pose data to calculate the rotation matrix and the translation vectors of the X and Y axes, the relative pose between robots can be determined with high precision, and an accurate initial pose homogeneous transformation matrix can be constructed. This method does not rely on overlapping map areas in traditional methods, and can achieve high-precision pose calculation in environments with scarce feature points. This improves the positioning accuracy in environments with scarce feature points and ensures the efficiency and accuracy of multi-robot collaborative mapping.

[0051] In a possible implementation, the calculation method of the rotation matrix specifically includes:

[0052] By calculating the coordinate difference between the master and slave antennas of the RTK mobile station on the robot and the fixed master base station, the horizontal angle and pitch angle between the legged robots are obtained:

[0053]

[0054] Among them, θ represents the horizontal angle, γ represents the pitch angle, and aractan represents the inverse tangent function. represents the main antenna coordinates, Represents the coordinates from the antenna.

[0055] Specifically, by calculating the master-slave antenna coordinate difference between the RTK mobile station and the fixed master base station, the horizontal angle and pitch angle between the robots are obtained, which can accurately describe the posture changes of the robots in space.

[0056] According to the horizontal angle and pitch angle, calculate the rotation matrix:

[0057]

[0058] Where R represents the rotation matrix, The rotation matrix representing the pitch angle, A rotation matrix representing the horizontal angle.

[0059] S4: By deriving the forward kinematics of the legged robot, obtaining the body height, and optimizing the foot-end touchdown judgment, the Z-axis value in the translation vector in the initial posture homogeneous transformation matrix between the robots is obtained, and the complete homogeneous transformation matrix between the legged robots is determined by combining the rotation matrix and the X and Y axis values ​​in the translation vector.

[0060] It should be noted that by deriving the forward kinematics of the legged robot to obtain the body height, and combining it with the optimization of the foot-touch judgment, the robot's displacement in the vertical direction can be accurately determined, which improves the accuracy of the posture solution and makes the robot's position in a complex environment more accurate, thereby ensuring the accurate stitching of the global map. By combining the X, Y, and Z axis values ​​of the rotation matrix and the translation vector, a complete homogeneous transformation matrix can be constructed, which enhances the stability and consistency of the robot's collaborative mapping.

[0061] In a possible implementation, the X-axis and Y-axis values ​​in the translation vector are calculated as follows:

[0062] The longitude and latitude coordinates obtained by RTK are converted into X and Y values ​​of the plane coordinate system through UTM projection. The conversion formula is:

[0063]

[0064] Among them, X represents the X-axis value in the translation vector, Y represents the Y-axis value in the translation vector, and k 0 represents the scale factor, a represents the semi-major axis of the earth ellipsoid, e represents the first eccentricity of the ellipsoid, λ represents the longitude, Indicates latitude, represents the difference between the longitude and the central longitude, Indicates latitude The radius of curvature at From the equator to the latitude The arc length.

[0065] Specifically, converting the longitude and latitude coordinates obtained by RTK into the X and Y values ​​of the plane coordinate system through the UTM (Universal Transverse Mercator) projection can effectively avoid the errors caused by the curvature of the earth, make the coordinate calculation more accurate, improve the accuracy and stability of the calculation, and enhance the accuracy of multi-robot collaborative mapping.

[0066] In a possible implementation, the Z-axis value in the translation vector is calculated as follows:

[0067]

[0068] Among them, Z represents the Z-axis value in the translation vector, L 1 is the hip link length, L2 Indicates the length of the thigh link, L 3 represents the length of the shank link, θ 1 represents the hip joint pitch angle, θ 2 represents the pitch angle of the thigh joint, θ 3 Indicates the calf joint pitch angle.

[0069] In a possible implementation manner, the foot contact determination optimization in S4 is specifically as follows:

[0070] The force on the foot end is determined by calculation. When the force on the foot end is greater than the threshold, it is determined that the foot end is in contact with the ground. The data when the foot end is in contact is used as the basis for calculating the Z-axis value. The specific calculation method of the force on the foot end is:

[0071]

[0072] Among them, F iz Indicates the force on the z-axis of the i-th leg, J i represents the Jacobian matrix of the ith leg, T represents the matrix transpose operation, F iq Represents the joint force vector of the i-th leg, including the forces of the hip joint, thigh joint and calf joint, [] z Represents the component on the z-axis.

[0073] Specifically, by calculating the force on the foot end and judging whether it is in contact with the ground, the robot's vertical displacement (Z-axis value) can be accurately determined. This judgment optimizes the calculation of the Z-axis and avoids posture deviations caused by unstable ground contact or sensor errors, thereby improving the accuracy and stability of the robot's posture solution and ensuring the accuracy and reliability of the multi-legged robot in complex environments.

[0074] S5: Real-time laser SLAM mapping is performed through the distributed perception layer, and each legged robot builds a local point cloud map based on its own computing unit.

[0075] It should be noted that performing real-time laser SLAM mapping through a distributed perception layer can significantly improve the efficiency and flexibility of map construction. Each robot processes data independently, reducing the computational burden of a single system while avoiding delays in centralized processing. This enhances the real-time and robustness of multi-robot collaborative mapping, making it more accurate, especially in complex environments.

[0076] S6: The local point cloud map of each legged robot is transmitted to the processing host in the data center through the gigabit wireless communication link built by the AP gateway; the local point cloud map is roughly spliced ​​based on the complete homogeneous transformation matrix.

[0077] It should be noted that the gigabit wireless communication link built through the AP gateway can efficiently and quickly transmit the local point cloud map of each legged robot to the data center, and combine it with the complete homogeneous transformation matrix for coarse splicing, which not only ensures low latency and high bandwidth for data transmission, but also enables accurate map integration in the data center, significantly improving the efficiency and accuracy of multi-robot collaborative mapping.

[0078] S7: In the centralized decision-making layer, the drift of the legged robot is compensated by the RTK path trajectory in each local point cloud map, and then the spliced ​​point cloud map is fine-calibrated to output a globally consistent point cloud map.

[0079] Among them, error compensation refers to correcting or reducing errors caused by various factors through certain algorithms or methods during data processing or system operation, thereby improving the precision and accuracy of the system.

[0080] It should be noted that by using the RTK path trajectory through the centralized decision-making layer to compensate for the drift of the legged robot, the deviation caused by motion error, sensor error or environmental change can be effectively corrected, the accuracy of the local map of each robot is improved, and the consistency and accuracy of the spliced ​​global point cloud map are ensured, making the results of multi-robot collaborative mapping more reliable and avoiding map distortion caused by error accumulation.

[0081] In a possible implementation, S7 specifically includes:

[0082] At preset intervals, the drift of the legged robot during operation is corrected through the RTK path trajectory in each local point cloud map:

[0083]

[0084] Among them, P corr (t) represents the corrected position at time t, P r (t) represents the actual pose at time t, which is given by the state estimation provided by the IMU and encoder, P RTK (t) represents the precise position provided by RTK at time t, and α(t) represents the weighting of the correction at time t.

[0085] It should be noted that by correcting the drift of the robot during operation at preset intervals, the accumulated errors during long-term operation can be effectively reduced. The RTK path trajectory is used for real-time correction to ensure the accuracy of each local map, making the final spliced ​​global map more accurate and stable, thereby improving the accuracy and reliability of multi-robot collaborative mapping.

[0086] In a possible implementation, the preset duration ranges from [1, 10] seconds.

[0087] In a possible implementation, the weighted value range of the correction amount is between [0, 1], where 1 represents full use of RTK information, and 0 represents full use of the IMU encoder estimation of the legged robot itself.

[0088] In the present invention, the system obtains the position information of the robot through the data provided by RTK and sensors, derives the body height of the robot through forward kinematics, and optimizes the foot-end touchdown judgment, then converts the longitude and latitude coordinates to ensure the accuracy of the data, and then uses the positioning information of the master and slave antennas to calculate the rotation matrix to obtain the relative position of the robot, and constructs a pose homogeneous transformation matrix to perform map splicing between robots, and finally, combines the RTK path trajectory to perform drift correction, optimize the local map, and output a global consistency map.

[0089] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0090] In the embodiment of the present invention, the combination of the distributed perception layer and the centralized decision-making layer can realize the allocation of tasks and efficient processing of data, share the computing burden and reduce the computing resource bottleneck, and improve the collaborative efficiency of the entire system. By synchronizing the time of different sensor data, the stitching error and time delay problems caused by the asynchrony of sensor data in the traditional method are avoided. The initial posture transformation matrix between the robots is calculated based on the data of the RTK master and slave antennas, which eliminates the dependence on the overlapping area of ​​the local map and avoids the difficulty of stitching due to the lack of overlapping areas or scarcity of feature points. The adaptability and accuracy in open environments are enhanced. The robot's forward kinematics model is combined with the foot-end touchdown judgment to optimize the calculation of the Z axis, which can accurately determine the relative posture between robots and effectively avoid the errors caused by the robot's gait changes. The local point cloud map is constructed in real time through laser SLAM, which can accurately perceive the environment and generate local maps, providing high-quality data support for the stitching of the global map. The RTK path trajectory is used to compensate for the drift of the robot during operation, ensuring that the posture differences between each local map are effectively corrected, ensuring the consistency and accuracy of the global map, improving the accuracy and stability of the global map, and reducing the drift errors commonly seen in multi-robot systems.

[0091] Reference Manual Attached Figure 2 , showing a schematic diagram of the hardware framework of a RTK-based multi-legged robot collaborative mapping system provided by an embodiment of the present invention.

[0092] like Figure 2As shown in the figure, the system is divided into two main levels: a centralized decision-making layer and a distributed perception layer. In the centralized decision-making layer, there is a data center processing host, which is responsible for data processing and decision-making of the entire system, and communicates with the distributed perception layer through the AP gateway device. The distributed perception layer includes multiple robots, each of which is equipped with an AP gateway device and connected to other robots and data centers through wireless communication. Each robot consists of a robot body and a readable storage medium, which stores related programs such as positioning and mapping. In addition, each robot is also equipped with sensor components (such as RTK modules, processors, etc.) to achieve precise positioning and data processing. The system also includes a main radio station to provide overall power support. The entire architecture realizes collaboration and data sharing between robots through wireless network connections.

[0093] Reference Manual Attached Figure 3 , showing a structural schematic diagram of a multi-legged robot collaborative mapping system based on RTK provided by the present invention.

[0094] The present invention further provides a multi-legged robot collaborative mapping system 20 based on RTK, which is applied to the above-mentioned multi-legged robot collaborative mapping method based on RTK, comprising:

[0095] Processor 201.

[0096] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201, the RTK-based multi-legged robot collaborative mapping method of the method embodiment is implemented.

[0097] The RTK-based multi-legged robot collaborative mapping system 20 provided in the present invention can execute the above-mentioned RTK-based multi-legged robot collaborative mapping method and achieve the same or similar technical effects. To avoid repetition, the present invention will not go into details.

[0098] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0099] In the embodiment of the present invention, the combination of the distributed perception layer and the centralized decision-making layer can realize the allocation of tasks and efficient processing of data, share the computing burden and reduce the computing resource bottleneck, and improve the collaborative efficiency of the entire system. By synchronizing the time of different sensor data, the stitching error and time delay problems caused by the asynchrony of sensor data in the traditional method are avoided. The initial posture transformation matrix between the robots is calculated based on the data of the RTK master and slave antennas, which eliminates the dependence on the overlapping area of ​​the local map and avoids the difficulty of stitching due to the lack of overlapping areas or scarcity of feature points. The adaptability and accuracy in open environments are enhanced. The robot's forward kinematics model is combined with the foot-end touchdown judgment to optimize the calculation of the Z axis, which can accurately determine the relative posture between robots and effectively avoid the errors caused by the robot's gait changes. The local point cloud map is constructed in real time through laser SLAM, which can accurately perceive the environment and generate local maps, providing high-quality data support for the stitching of the global map. The RTK path trajectory is used to compensate for the drift of the robot during operation, ensuring that the posture differences between each local map are effectively corrected, ensuring the consistency and accuracy of the global map, improving the accuracy and stability of the global map, and reducing the drift errors commonly seen in multi-robot systems.

[0100] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0101] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0102] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0103] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0104] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0105] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0106] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0107] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0108] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0109] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0110] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0111] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0112] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the RTK-based multi-legged robot collaborative mapping method as described in the method embodiment is implemented.

[0113] A computer-readable storage medium provided by the present invention can implement the steps and effects of the RTK-based multi-legged robot collaborative mapping method of the above method embodiment. To avoid repetition, the present invention will not go into details.

[0114] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0115] In the embodiment of the present invention, the combination of the distributed perception layer and the centralized decision-making layer can realize the allocation of tasks and efficient processing of data, share the computing burden and reduce the computing resource bottleneck, and improve the collaborative efficiency of the entire system. By synchronizing the time of different sensor data, the stitching error and time delay problems caused by the asynchrony of sensor data in the traditional method are avoided. The initial posture transformation matrix between the robots is calculated based on the data of the RTK master and slave antennas, which eliminates the dependence on the overlapping area of ​​the local map and avoids the difficulty of stitching due to the lack of overlapping areas or scarcity of feature points. The adaptability and accuracy in open environments are enhanced. The robot's forward kinematics model is combined with the foot-end touchdown judgment to optimize the calculation of the Z axis, which can accurately determine the relative posture between robots and effectively avoid the errors caused by the robot's gait changes. The local point cloud map is constructed in real time through laser SLAM, which can accurately perceive the environment and generate local maps, providing high-quality data support for the stitching of the global map. The RTK path trajectory is used to compensate for the drift of the robot during operation, ensuring that the posture differences between each local map are effectively corrected, ensuring the consistency and accuracy of the global map, improving the accuracy and stability of the global map, and reducing the drift errors commonly seen in multi-robot systems.

[0116] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0117] There are a few points to note:

[0118] (1) The drawings of the embodiments of the present invention only involve structures related to the embodiments of the present invention. Other structures may refer to conventional designs.

[0119] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0120] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0121] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A multi-legged robot collaborative mapping method based on RTK, characterized in that: include: S1: Building a hybrid processing architecture, the hybrid processing architecture including a distributed perception layer and a centralized decision layer; The distributed perception layer is composed of a plurality of legged robots, each of which is equipped with a laser radar, an RTK positioning module, a wireless communication module and a CPU computing unit; The centralized decision-making layer includes a data center processing host; S2: Acquire various sensor data of the legged robot, and align timestamps of various sensor data; S3: Based on the RTK master-slave antenna posture data, calculate the rotation matrix in the initial posture homogeneous transformation matrix between the legged robots and the X and Y axis values ​​in the translation vector; S4: by deriving the forward kinematics of the legged robot, obtaining the height of the body, and optimizing the foot-end touchdown judgment, obtaining the Z-axis value of the translation vector in the initial posture homogeneous transformation matrix between the robots, and combining the rotation matrix and the X-axis and Y-axis values ​​in the translation vector, determining the complete homogeneous transformation matrix between the legged robots; S5: Execute laser SLAM real-time mapping through the distributed perception layer, and each of the legged robots constructs a local point cloud map based on its own computing unit; S6: Transmitting the local point cloud map of each of the legged robots to the processing host in the data center through a gigabit wireless communication link constructed by an AP gateway; and roughly splicing the local point cloud map based on the complete homogeneous transformation matrix; S7: In the centralized decision-making layer, the drift of the legged robot is compensated for by the RTK path trajectory in each of the local point cloud maps, and then the spliced ​​point cloud map is precisely corrected to output a globally consistent point cloud map.

2. The RTK-based multi-legged robot collaborative mapping method according to claim 1, characterized in that: The S2 is specifically: The data of different sensors are adjusted to the same timestamp by interpolation. Based on the timestamp of the lidar sensor data, the timestamps of the inertial unit IMU, joint motor, RTK sensor and lidar are aligned to complete the time synchronization: ； Among them, Date s Indicates the synchronization data with the laser radar data frame acquisition time, Date j and Date j+1 Indicates the jth data and j+1th data of the aligned object on the timeline. and Indicates the timestamps of the jth data and the j+1th data of the aligned object on the timeline. Represents the timestamp of the i-th lidar data, Indicates the timestamp of the j-th data aligned with the i-th lidar data.

3. The RTK-based multi-legged robot collaborative mapping method according to claim 1, characterized in that: The calculation method of the rotation matrix specifically includes: The horizontal angle and pitch angle between the legged robots are obtained by calculating the master-slave antenna coordinate difference between the RTK mobile station on the robot and the fixed main base station: ； Among them, θ represents the horizontal angle, γ represents the pitch angle, and aractan represents the inverse tangent function. represents the main antenna coordinates, represents the coordinates from the antenna; According to the horizontal angle and the pitch angle, the rotation matrix is ​​calculated: ； Where R represents the rotation matrix, The rotation matrix representing the pitch angle, A rotation matrix representing the horizontal angle.

4. The RTK-based multi-legged robot collaborative mapping method according to claim 1, characterized in that: The calculation method of the X and Y axis values ​​in the translation vector is specifically as follows: The longitude and latitude coordinates obtained by RTK are converted into X and Y values ​​of the plane coordinate system through UTM projection. The conversion formula is: The RTK-based multi-legged robot collaborative mapping method according to claim 1 is characterized in that the X-axis and Y-axis values ​​in the translation vector are calculated as follows: The longitude and latitude coordinates obtained by RTK are converted into X and Y values ​​of the plane coordinate system through UTM projection. The conversion formula is: ； Where X represents the X-axis value in the translation vector, Y represents the Y-axis value in the translation vector, k0 represents the scale factor, a represents the semi-major axis of the earth ellipsoid, e represents the first eccentricity of the ellipsoid, and λ represents the longitude. Indicates latitude, represents the difference between the longitude and the central longitude, Indicates latitude The radius of curvature at From the equator to the latitude Arc length ; Where X represents the X-axis value in the translation vector, Y represents the Y-axis value in the translation vector, k0 represents the scale factor, a represents the semi-major axis of the earth ellipsoid, e represents the first eccentricity of the ellipsoid, and λ represents the longitude. Indicates latitude, represents the difference between the longitude and the central longitude, Indicates latitude The radius of curvature at From the equator to the latitude The arc length.

5. The RTK-based multi-legged robot collaborative mapping method according to claim 1, characterized in that: The Z-axis value in the translation vector is calculated as follows: ； Among them, Z represents the Z-axis value in the translation vector, L1 represents the length of the hip link, L2 represents the length of the thigh link, L3 represents the length of the calf link, θ1 represents the pitch angle of the hip joint, θ2 represents the pitch angle of the thigh joint, and θ3 represents the pitch angle of the calf joint.

6. The RTK-based multi-legged robot collaborative mapping method according to claim 1, characterized in that: The optimization of foot contact determination in S4 is specifically as follows: The force on the foot end is determined by calculation. When the force on the foot end is greater than the threshold, it is determined that the foot end is in contact with the ground. The data when the foot end is in contact is used as the basis for calculating the Z-axis value. The specific calculation method of the force on the foot end is: ； Among them, F iz Indicates the force on the z-axis of the i-th leg, J i represents the Jacobian matrix of the ith leg, T represents the matrix transpose operation, F iq Represents the joint force vector of the i-th leg, including the forces of the hip joint, thigh joint and calf joint, [] z Represents the component on the z-axis.

7. The RTK-based multi-legged robot collaborative mapping method according to claim 1, characterized in that: The S7 specifically includes: At each preset time interval, the drift generated during the operation of the legged robot is corrected through the RTK path track in each of the local point cloud maps: ； Among them, P corr (t) represents the corrected position at time t, P r (t) represents the actual pose at time t, which is given by the state estimation provided by the IMU and encoder, P RTK (t) represents the precise position provided by RTK at time t, and α(t) represents the weighting of the correction at time t.

8. The RTK-based multi-legged robot collaborative mapping method according to claim 7, characterized in that: The preset duration ranges from [1, 10] seconds.

9. The RTK-based multi-legged robot collaborative mapping method according to claim 7, characterized in that: The weighted value range of the correction amount is between [0, 1], where 1 means that RTK information is fully adopted, and 0 means that the IMU encoder of the legged robot itself is fully adopted for estimation.

10. A multi-legged robot collaborative mapping system based on RTK, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the RTK-based multi-legged robot collaborative mapping method according to any one of claims 1 to 9 is implemented.

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