A multi-legged robot cooperative mapping method and system based on RTK

Abstract

The application provides a kind of multi-legged robot cooperative mapping method and system based on RTK, it is related to multi-robot cooperative mapping, method includes: constructing mixed processing architecture;Obtain the sensor data of each foot type robot, and each sensor data is timestamped alignment;Calculate the rotation matrix in the initial pose homogeneous transformation matrix between foot type robots and the X, Y axis value in the translation vector;Get body height, determine the complete homogeneous transformation matrix between foot type robots;Through distributed perception layer, laser SLAM real-time mapping is executed;The local point cloud map of each foot type robot is transmitted to data center processing host;Local point cloud map is roughly spliced;In centralized decision-making layer, the drift of foot type robot is compensated for error, and then the spliced point cloud map is corrected, and the global consistent point cloud map is output.The application effectively improves the accuracy of spliced map, and reduces the calculation amount of mapping.

Description

Technical Field

[0001] This invention relates to multi-robot collaborative mapping, and in particular to a multi-legged robot collaborative mapping method and system based on RTK. Background Technology

[0002] The RTK-based multi-legged robot collaborative mapping method is a technique that uses RTK (Real-Time Kinematics) technology to achieve high-precision collaborative mapping of multi-legged robots in complex environments. RTK technology provides high-precision positioning services, helping robots accurately obtain their position and posture in the environment, thereby accurately constructing maps. Legged robots refer to robots that use multiple legs to move and can move stably in complex terrain and perform tasks.

[0003] With the rapid development of robotics technology, the perception and mapping of unknown environments has become a primary aspect of robotic operations. The effectiveness and application scope of a single robot in complex and ever-changing environments are significantly limited. In contrast, multi-robot systems, through collaborative searching, can quickly establish a comprehensive understanding of unknown environments, significantly outperforming single-robot systems in overall efficiency. Furthermore, parallel task processing can greatly improve operational efficiency.

[0004] However, traditional collaborative mapping methods rely on overlapping areas between local maps to stitch the maps together. This is computationally difficult, burdensome, and inefficient. Furthermore, when there is little overlap between local maps, the accuracy of map matching is greatly reduced, making effective stitching impossible. In multi-robot systems, pose drift occurs due to relative motion and inertial errors between robots, affecting the consistency of the global map and reducing the accuracy and efficiency of mapping. Summary of the Invention

[0005] To address the technical problems of traditional collaborative mapping methods that rely on overlapping areas between local maps for map stitching, resulting in high computational difficulty, heavy workload, and low mapping efficiency, and where map matching accuracy is greatly reduced when the overlapping areas between local maps are small, making effective stitching impossible, and the pose drift that occurs in multi-robot systems due to relative motion and inertial errors between robots, affecting the consistency of the global map and reducing mapping accuracy and efficiency, this invention provides a multi-legged robot collaborative mapping method and system based on RTK.

[0006] The technical solutions provided by the embodiments of the present invention are as follows:

[0007] First aspect:

[0008] This invention provides a cooperative mapping method for multi-legged robots based on RTK, comprising:

[0009] S1: Construct a hybrid processing architecture, which includes a distributed perception layer and a centralized decision-making layer;

[0010] The distributed perception layer consists of multiple legged robots, each of which is equipped with a lidar, 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 the timestamps of each sensor data.

[0013] 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;

[0014] S4: By deriving the forward kinematics of the legged robot, the body height is obtained, and the foot touch-to-ground judgment is optimized. The Z-axis value in the translation vector in the initial homogeneous transformation matrix between the robots is obtained. Combined with the rotation matrix and the X and Y axis values ​​in the translation vector, the complete homogeneous transformation matrix between the legged robots is determined.

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

[0016] S6: Through the gigabit-level wireless communication link constructed by the AP gateway, the local point cloud map of each of the legged robots is transmitted to the data center processing host; based on the complete homogeneous transformation matrix, the local point cloud map is coarsely stitched together.

[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 stitched point cloud map is finely corrected to output a globally consistent point cloud map.

[0018] The second aspect:

[0019] This invention provides an RTK-based multi-legged robot cooperative mapping system, comprising:

[0020] processor;

[0021] A memory storing computer-readable instructions, which, when executed by the processor, implement the RTK-based multi-legged robot cooperative mapping method as described in the first aspect.

[0022] Third aspect:

[0023] The present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the RTK-based multi-legged robot cooperative mapping method as described in the first aspect.

[0024] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0025] In this embodiment of the invention, the combination of a distributed perception layer and a centralized decision-making layer enables task allocation and efficient data processing, reduces computational burden and computational resource bottlenecks, and improves the overall system's collaborative efficiency. By synchronizing data from different sensors, it avoids the stitching errors and time delays caused by asynchronous sensor data in traditional methods. Based on RTK master-slave antenna data, it calculates the initial pose transformation matrix between robots, eliminating dependence on overlapping local map areas and avoiding stitching difficulties caused by a lack of overlapping areas or scarce feature points. This enhances adaptability and accuracy in open environments. By guiding the robot's forward kinematics model and combining foot contact judgment to optimize Z-axis calculation, the relative posture between robots can be accurately determined, effectively avoiding errors caused by changes in robot gait. Through real-time construction of local point cloud maps using laser SLAM, the environment can be accurately perceived and local maps can be generated, providing high-quality data support for global map stitching. RTK path trajectory is used to compensate for robot drift during operation, ensuring that pose differences between each local map are effectively corrected, guaranteeing the consistency and accuracy of the global map, improving the accuracy and stability of the global map, and reducing drift errors common in multi-robot systems. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 A flowchart illustrating a collaborative mapping method for multi-legged robots based on RTK, provided in an embodiment of the present invention;

[0028] Figure 2 This is 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 This is a schematic diagram of a multi-legged robot collaborative mapping system based on RTK, provided in an embodiment of the present invention. Detailed Implementation

[0030] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0031] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0032] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0033] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0034] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0035] Reference manual attached Figure 1 The diagram illustrates a flowchart of a collaborative mapping method for multi-legged robots based on RTK, provided by an embodiment of the present invention.

[0036] This invention provides a method for cooperative mapping of multi-legged robots based on RTK (Real-Time Kinematics). This method can be implemented by an RTK-based cooperative mapping device, which can be a terminal or a server. The processing flow of the RTK-based cooperative mapping method for multi-legged robots may include the following steps:

[0037] S1: Construct 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 on a single processing center by distributing data processing across various robots, thereby improving the system's processing efficiency and robustness. At the same time, the centralized decision-making layer can perform global optimization on the data collected by each robot, ensuring consistency and accuracy when the system works collaboratively, and effectively improving the performance of multi-robot collaborative mapping.

[0039] The distributed perception layer consists of multiple legged robots, each equipped with a lidar, RTK positioning module, wireless communication module, and CPU computing unit.

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

[0041] The distributed perception layer refers to a perception system composed of multiple robots. This layer acquires data through sensors on each robot to achieve local environmental perception and data processing. Legged robots are robots that move using multiple legs. LiDAR (Light Detection and Ranging) is a sensor that acquires information such as distance, shape, and position of the environment by emitting laser beams and analyzing the reflected laser signals. RTK (Real-Time Kinematics) is a high-precision positioning technology that typically uses satellite signals for differential positioning to achieve centimeter-level accuracy. RTK positioning modules provide 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 used for data processing, algorithm execution, and decision-making. The centralized decision-making layer refers to a centrally managed and optimized system layer, typically 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 each sensor data.

[0043] It should be noted that by synchronizing and aligning the data from each sensor according to timestamps, it can be ensured that the data from all sensors can be processed synchronously under the same time base, thereby avoiding calculation errors or splicing errors caused by data inconsistency.

[0044] In one possible implementation, S2 specifically refers to:

[0045] Interpolation is used to adjust data from different sensors to the same timestamp. Using the timestamp of the LiDAR sensor data as a reference, the timestamps of the inertial unit (IMU), articulated motors, RTK sensors, and LiDAR are aligned to achieve time synchronization.

[0046]

[0047] Among them, Date s Date represents the synchronization data with the acquisition time of the lidar data frame. j and Date j+1 This indicates the alignment of the j-th and (j+1)-th data points on the timeline. and This represents the timestamps of the j-th and (j+1)-th data points on the timeline for the aligned object. This represents the timestamp of the i-th LiDAR data. This represents the timestamp of the j-th data point that is aligned with the i-th LiDAR data point.

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

[0049] In RTK master-slave antenna pose data, high-precision positioning data is provided by RTK technology through a master-slave antenna system. The master antenna is fixed to the base station, while the slave antenna is mounted on the robot. The robot's precise pose is obtained through differential calculation between the two. The initial pose homogeneous transformation matrix is ​​a mathematical tool describing the transformation relationship between one coordinate system and another, including rotation matrices and displacement vectors.

[0050] Specifically, by using RTK master-slave antenna pose data to calculate rotation matrices and translation vectors of the X and Y axes, the relative poses between robots can be determined with high precision, and an accurate initial pose homogeneous transformation matrix can be constructed. This eliminates the need to rely on overlapping map regions in traditional methods, enabling high-precision pose calculation in environments with scarce feature points. This improves the positioning accuracy in environments with few feature points and ensures the efficiency and accuracy of multi-robot collaborative mapping.

[0051] In one possible implementation, the rotation matrix is ​​calculated in the following ways:

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

[0053]

[0054] Where θ represents the horizontal angle, γ represents the pitch angle, and aractan represents the arctangent function. Indicates the coordinates of the main antenna. This indicates 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 and pitch angles between the robots can be obtained, which can accurately describe the robot's attitude changes in space.

[0056] Calculate the rotation matrix based on the horizontal and vertical angles:

[0057]

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

[0059] S4: By deriving the forward kinematics of the legged robot, the body height is obtained, and the foot touch-to-ground judgment is optimized. The Z-axis value in the translation vector in the initial homogeneous transformation matrix between robots is obtained. Combined with the rotation matrix and the X and Y axis values ​​in the translation vector, the complete homogeneous transformation matrix between the legged robots is determined.

[0060] It should be noted that by deriving the forward kinematics of the legged robot to obtain the robot's height and combining it with the foot-touching judgment optimization, the robot's vertical displacement can be accurately determined, improving the accuracy of pose calculation and making the robot's position more accurate in complex environments. This ensures the accurate stitching of the global map. By combining the X, Y, and Z axis values ​​of the rotation matrix and translation vector, a complete homogeneous transformation matrix can be constructed, enhancing the stability and consistency of robot collaborative mapping.

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

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

[0063]

[0064] Where X represents the X-axis value of the translation vector, Y represents the Y-axis value of 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 longitude. Indicates latitude, This represents the difference between longitude and central longitude. Indicate latitude The radius of curvature at that point From the equator to latitude The arc length.

[0065] Specifically, by converting the latitude and longitude coordinates obtained by RTK into X and Y values ​​in a plane coordinate system through UTM (Universal Transverse Mercator) projection, errors caused by the curvature of the Earth can be effectively avoided, making coordinate calculations more accurate, improving the accuracy and stability of calculations, and enhancing the accuracy of multi-robot collaborative mapping.

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

[0067]

[0068] Where Z represents the Z-axis value in the translation vector, L1 represents the hip link length, L2 represents the thigh link length, L3 represents the lower leg link length, θ1 represents the hip joint pitch angle, θ2 represents the thigh joint pitch angle, and θ3 represents the lower leg joint pitch angle.

[0069] In one possible implementation, the optimization of foot contact determination in S4 specifically involves:

[0070] The force applied to the foot is calculated and determined. When the force exceeds a threshold, the foot is considered to be in contact with the ground. The data at the point of contact is used as the basis for calculating the Z-axis value. The specific calculation method for the force applied to the foot is as follows:

[0071]

[0072] Among them, F iz J represents the force exerted on the i-th leg along the z-axis. i Let the Jacobian matrix of the i-th leg be represented. T F represents the matrix transpose operation. iq Let represent the joint force vector of the i-th leg, including the forces acting on the hip joint, thigh joint, and calf joint. z This represents the component along the z-axis.

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

[0074] S5: Real-time laser SLAM mapping is performed through a 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 building. Each robot processes data independently, reducing the computational burden of a single system and avoiding the latency issues in centralized processing. This enhances the real-time performance and robustness of multi-robot collaborative mapping, making it more accurate, especially in complex environments.

[0076] S6: Through the gigabit-level wireless communication link built by the AP gateway, the local point cloud map of each legged robot is transmitted to the data center processing host; based on the complete homogeneous transformation matrix, the local point cloud map is coarsely stitched together.

[0077] It should be noted that the gigabit-level 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. Combined with the complete homogeneous transformation matrix for coarse stitching, it not only ensures low latency and high bandwidth of 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 error of the legged robot is compensated by the RTK path trajectory in each local point cloud map, and then the stitched point cloud map is finely corrected to output a globally consistent point cloud map.

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

[0080] It should be noted that by using RTK path trajectories to compensate for the drift of legged robots through a centralized decision-making layer, deviations caused by motion errors, sensor errors, or environmental changes can be effectively corrected. This improves the accuracy of the local map of each robot and ensures the consistency and accuracy of the stitched global point cloud map, making the results of multi-robot collaborative mapping more reliable and avoiding map distortion caused by error accumulation.

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

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

[0083]

[0084] Among them, P corr (t) represents the corrected pose at time t, P r (t) represents the actual pose at time t, given by the state estimate provided by the IMU and encoder, P RTK (t) represents the precise pose provided by RTK at time t, and α(t) represents the weighted adjustment 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. Real-time correction using RTK path trajectory ensures the accuracy of each local map, making the final stitched global map more accurate and stable, thereby improving the accuracy and reliability of multi-robot collaborative mapping.

[0086] In one possible implementation, the preset duration is between [1, 10] seconds.

[0087] In one possible implementation, the weighted value of the correction amount is between [0, 1], where 1 indicates that the RTK information is used completely and 0 indicates that the estimation is made entirely by the IMU encoder of the legged robot itself.

[0088] In this invention, the system obtains the robot's position information through data provided by RTK and sensors, derives the robot's body height through forward kinematics, optimizes the foot-to-ground contact judgment, and then performs latitude and longitude coordinate conversion to ensure data accuracy. Subsequently, it calculates the rotation matrix using the positioning information from the master and slave antennas to obtain the robot's relative position, and performs map stitching between robots by constructing a homogeneous pose transformation matrix. Finally, it combines the RTK path trajectory for drift correction, optimizes the local map, and outputs a globally consistent map.

[0089] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0090] In this embodiment of the invention, the combination of a distributed perception layer and a centralized decision-making layer enables task allocation and efficient data processing, reduces computational burden and computational resource bottlenecks, and improves the overall system's collaborative efficiency. By synchronizing data from different sensors, it avoids the stitching errors and time delays caused by asynchronous sensor data in traditional methods. Based on RTK master-slave antenna data, it calculates the initial pose transformation matrix between robots, eliminating dependence on overlapping local map areas and avoiding stitching difficulties caused by a lack of overlapping areas or scarce feature points. This enhances adaptability and accuracy in open environments. By guiding the robot's forward kinematics model and combining foot contact judgment to optimize Z-axis calculation, the relative posture between robots can be accurately determined, effectively avoiding errors caused by changes in robot gait. Through real-time construction of local point cloud maps using laser SLAM, the environment can be accurately perceived and local maps can be generated, providing high-quality data support for global map stitching. RTK path trajectory is used to compensate for robot drift during operation, ensuring that pose differences between each local map are effectively corrected, guaranteeing the consistency and accuracy of the global map, improving the accuracy and stability of the global map, and reducing drift errors common in multi-robot systems.

[0091] Reference manual attached Figure 2 The diagram shows a hardware framework schematic of a multi-legged robot collaborative mapping system based on RTK provided in an embodiment of the present invention.

[0092] like Figure 2As shown, the system is divided into two main layers: a centralized decision-making layer and a distributed perception layer. In the centralized decision-making layer, there is a data center processing host responsible for data processing and decision-making for the entire system, while also communicating with the distributed perception layer through AP gateway devices. The distributed perception layer includes multiple robots, each equipped with an AP gateway device, connecting to other robots and the data center wirelessly. Each robot consists of a robot body and a readable storage medium, the latter storing localization, mapping, and other related programs. In addition, each robot is equipped with sensor components (such as RTK modules and processors) to achieve precise localization and data processing. The system also includes a main radio station to provide overall power support. The entire architecture achieves collaboration and data sharing between robots through wireless network connections.

[0093] Reference manual attached Figure 3 The diagram shows a structural schematic of a multi-legged robot collaborative mapping system based on RTK provided by the present invention.

[0094] The present invention also provides an RTK-based multi-legged robot cooperative mapping system 20, applied to the above-mentioned RTK-based multi-legged robot cooperative mapping method, comprising:

[0095] Processor 201.

[0096] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201, they implement the RTK-based multi-legged robot cooperative mapping method as described in the method embodiment.

[0097] The RTK-based multi-legged robot collaborative mapping system 20 provided by this invention can execute the above-mentioned RTK-based multi-legged robot collaborative mapping method and achieve the same or similar technical effects. To avoid duplication, this invention will not elaborate further.

[0098] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0099] In this embodiment of the invention, the combination of a distributed perception layer and a centralized decision-making layer enables task allocation and efficient data processing, reduces computational burden and computational resource bottlenecks, and improves the overall system's collaborative efficiency. By synchronizing data from different sensors, it avoids the stitching errors and time delays caused by asynchronous sensor data in traditional methods. Based on RTK master-slave antenna data, it calculates the initial pose transformation matrix between robots, eliminating dependence on overlapping local map areas and avoiding stitching difficulties caused by a lack of overlapping areas or scarce feature points. This enhances adaptability and accuracy in open environments. By guiding the robot's forward kinematics model and combining foot contact judgment to optimize Z-axis calculation, the relative posture between robots can be accurately determined, effectively avoiding errors caused by changes in robot gait. Through real-time construction of local point cloud maps using laser SLAM, the environment can be accurately perceived and local maps can be generated, providing high-quality data support for global map stitching. RTK path trajectory is used to compensate for robot drift during operation, ensuring that pose differences between each local map are effectively corrected, guaranteeing the consistency and accuracy of the global map, improving the accuracy and stability of the global map, and reducing drift errors common in multi-robot systems.

[0100] It should be understood that the processor in the embodiments of the present invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0101] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked 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 thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. 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. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0103] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

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

[0105] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply 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 recognize that the units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0107] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0108] In the embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

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

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

[0111] If a function is implemented as 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 this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0112] This invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the RTK-based multi-legged robot cooperative mapping method as described in the method embodiment.

[0113] The present invention provides a computer-readable storage medium that can implement the steps and effects of the RTK-based multi-legged robot cooperative mapping method in the above-described method embodiments. To avoid repetition, the present invention will not elaborate further.

[0114] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0115] In this embodiment of the invention, the combination of a distributed perception layer and a centralized decision-making layer enables task allocation and efficient data processing, reduces computational burden and computational resource bottlenecks, and improves the overall system's collaborative efficiency. By synchronizing data from different sensors, it avoids the stitching errors and time delays caused by asynchronous sensor data in traditional methods. Based on RTK master-slave antenna data, it calculates the initial pose transformation matrix between robots, eliminating dependence on overlapping local map areas and avoiding stitching difficulties caused by a lack of overlapping areas or scarce feature points. This enhances adaptability and accuracy in open environments. By guiding the robot's forward kinematics model and combining foot contact judgment to optimize Z-axis calculation, the relative posture between robots can be accurately determined, effectively avoiding errors caused by changes in robot gait. Through real-time construction of local point cloud maps using laser SLAM, the environment can be accurately perceived and local maps can be generated, providing high-quality data support for global map stitching. RTK path trajectory is used to compensate for robot drift during operation, ensuring that pose differences between each local map are effectively corrected, guaranteeing the consistency and accuracy of the global map, improving the accuracy and stability of the global map, and reducing drift errors common in multi-robot systems.

[0116] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0117] The following points need to be explained:

[0118] (1) The accompanying drawings of the embodiments of the present invention only involve the structures involved in the embodiments of the present invention. Other structures can refer to the general design.

[0119] (2) For clarity, the thickness of layers or regions is enlarged or reduced in the drawings used to describe embodiments of the invention, i.e., these drawings are not drawn to scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being “above” or “below” another element, the element may be “directly” located “above” or “below” the other element or there may be intermediate elements.

[0120] (3) Where there is no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0121] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A cooperative mapping method for multi-legged robots based on RTK, characterized in that, include: S1: Construct a hybrid processing architecture, which includes a distributed perception layer and a centralized decision-making layer; The distributed perception layer consists of multiple legged robots, each of which is equipped with a lidar, 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 the timestamps of each sensor data. 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; S4: By deriving the forward kinematics of the legged robot, the body height is obtained, and the foot touch-to-ground judgment is optimized. The Z-axis value in the translation vector in the initial homogeneous transformation matrix between the robots is obtained. Combined with the rotation matrix and the X and Y axis values ​​in the translation vector, the complete homogeneous transformation matrix between the legged robots is determined. S5: Real-time laser SLAM mapping is performed through the distributed perception layer, and each legged robot constructs a local point cloud map based on its own computing unit; S6: Through the gigabit-level wireless communication link constructed by the AP gateway, the local point cloud map of each of the legged robots is transmitted to the data center processing host; based on the complete homogeneous transformation matrix, the local point cloud map is coarsely stitched together. 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 stitched point cloud map is finely corrected to output a globally consistent point cloud map.

2. The RTK-based multi-legged robot cooperative mapping method according to claim 1, characterized in that, Specifically, S2 is: Interpolation is used to adjust data from different sensors to the same timestamp. Using the timestamp of the LiDAR sensor data as a reference, the timestamps of the inertial unit (IMU), articulated motors, RTK sensors, and LiDAR are aligned to achieve time synchronization. in, Date s This represents the synchronization data with the acquisition time of the lidar data frame. Date j and Date j+1 This indicates the alignment object's position on the timeline. j The data and the first j +1 data point, and This indicates the alignment object on the timeline. j The data and the first j +1 timestamp of the data Indicates the first i Timestamps of LiDAR data Indicates the first data aligned with the i-th lidar data. j The timestamp of each data item.

3. The RTK-based multi-legged robot cooperative mapping method according to claim 1, characterized in that, The calculation method of the rotation matrix specifically includes: By calculating the master-slave antenna coordinate difference between the RTK mobile station on the robot and the fixed master base station, the horizontal and pitch angles between the legged robots are obtained: in, θ Indicates a horizontal angle. γ The pitch angle is represented by , and aractan represents the arctangent function. Indicates the coordinates of the main antenna. Indicates the coordinates from the antenna; Calculate the rotation matrix based on the horizontal angle and the pitch angle: in, R Represents the rotation matrix. The rotation matrix representing the pitch angle. The rotation matrix represents the horizontal angle.

4. The RTK-based multi-legged robot cooperative mapping method according to claim 1, characterized in that, The X and Y axis values ​​in the translation vector are calculated as follows: The latitude and longitude coordinates obtained by RTK are converted into X and Y values ​​in a plane coordinate system by UTM projection. The conversion formula is as follows: in, X This represents the X-axis value in the translation vector. Y This represents the Y-axis value in the translation vector. k 0 represents the scaling factor. a Represents the semi-major axis of the Earth's ellipsoid. e This represents the first eccentricity of the ellipsoid. λ Indicates longitude. Indicates latitude, This represents the difference between longitude and central longitude. Indicate latitude The radius of curvature at that point From the equator to latitude The arc length.

5. The RTK-based multi-legged robot cooperative mapping method according to claim 1, characterized in that, The Z-axis value in the translation vector is calculated as follows: in, Z This represents the Z-axis value in the translation vector. L 1 indicates the length of the hip link. L 2 indicates the length of the thigh link. L 3 indicates the length of the lower leg connecting rod. θ 1 represents the hip pitch angle. θ 2 indicates the pitch angle of the thigh joint. θ 3 indicates the pitch angle of the lower leg joint.

6. The RTK-based multi-legged robot cooperative mapping method according to claim 1, characterized in that, The optimization of foot contact determination in S4 is as follows: The force applied to the foot is calculated and determined. When the force exceeds a threshold, the foot is considered to be in contact with the ground. The data at the point of contact is used as the basis for calculating the Z-axis value. The specific calculation method for the force applied to the foot is as follows: in, F iz Indicates the first i One-legged z Force in the axial direction J i Indicates the first i A Jacobian matrix with one leg. T This represents the matrix transpose operation. F iq Indicates the first i The joint force vector of a leg includes the forces exerted by the hip joint, thigh joint, and calf joint. z Indicates in z The components of the axis.

7. The RTK-based multi-legged robot cooperative mapping method according to claim 1, characterized in that, Specifically, S7 includes: At preset time intervals, the drift generated during the operation of the legged robot is corrected using the RTK path trajectory in each of the local point cloud maps: in, P corr ( t )express t The pose after constant adjustments. P r ( t )express t The actual pose at any given time is given by the state estimate provided by the IMU and encoder. P RTK ( t )express t The precise pose provided by RTK at any given moment. α ( t )express t Weighting of the correction amount at any given time.

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

9. The RTK-based multi-legged robot cooperative mapping method according to claim 7, characterized in that, The weighted value of the correction amount is between [0, 1]. A value of 1 indicates that the RTK information is used completely, and a value of 0 indicates that the estimation is made by the IMU encoder of the legged robot itself.

10. A multi-legged robot cooperative mapping system based on RTK, characterized in that, include: processor; A memory storing computer-readable instructions, which, when executed by the processor, implement the RTK-based multi-legged robot cooperative mapping method as described in any one of claims 1 to 9.

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