Optimized deployment method and system for ultra wide band positioning anchor points in gallery environment

Abstract

The invention belongs to the technical field of positioning, and particularly provides an optimized deployment method and system for UWB positioning anchor points in a gallery environment, and the method comprises the steps: arranging initial UWB anchor points in a gallery, and forming an initial deployment scheme; the mobile robot carries a laser radar and a UWB label and moves in the corridor environment; the laser radar obtains gallery point cloud information, and a perception degradation area in the gallery is identified through a perception degradation algorithm; distance measurement information between the UWB tag and the UWB anchor point is obtained, and an area where the received UWB signal is poor in the scene is identified through a signal evaluation algorithm; comparing the sensing degradation area with the area with the poor UWB signal, and identifying an overlapped area of the sensing degradation area and the area with the poor UWB signal; and adding anchor points in the overlapped area to form an anchor point deployment scheme. The method can more accurately and comprehensively determine the area in which the deployment of the exogenous UWB anchor points needs to be enhanced in the corridor environment.

Description

Technical Field

[0001] The present invention belongs to the field of navigation and positioning technology, and in particular relates to a method and system for optimizing the deployment of ultra-wideband positioning anchor points in a corridor environment. Background Art

[0002] When a mobile robot navigates corridors, it can't use satellite positioning and navigation systems for positioning, as it can outdoors. Therefore, in enclosed indoor environments, the robot's onboard sensors can sense the environment and use simultaneous localization and mapping (SLAM) to achieve both robot positioning and environmental mapping. SLAM technology is widely used in robotics, drones, autonomous driving, and other fields.

[0003] However, existing SLAM positioning and mapping methods are prone to performance degradation in accuracy and reliability in corridor environments. This is because positioning and mapping sensors such as laser and vision are affected by the scarcity of environmental features, poor lighting, and structural similarity in corridor environments, resulting in insufficient constraint information for positioning and mapping, leading to positioning failure.

[0004] For example, laser SLAM uses point cloud data collected by lidar to estimate the pose between consecutive frames through registration algorithms such as iterative closest point (ICP), and combines it with a global optimization algorithm to generate a consistent environment map. Compared to visual SLAM, laser SLAM is not restricted by lighting and texture and can operate stably in most indoor and outdoor scenes. However, the performance of laser SLAM depends on the richness of the geometric features of the environment. In corridor environments such as long straight corridors, corners, or areas with sparse geometric features, the characteristics of structural symmetry and repeated geometric features make laser SLAM prone to insufficient constraints or registration drift in these scenarios, which in turn leads to the accumulation of positioning errors. This makes it prone to drift or even positioning failure in the absence of external constraints.

[0005] Therefore, to ensure the robustness and accuracy of SLAM systems, ultra-wideband positioning can introduce additional spatial ranging information, effectively compensating for the lack of global information and enhancing the performance of laser SLAM systems through high-precision ranging capabilities. UWB technology, by measuring the signal's time of flight (ToF), provides centimeter-level distance measurement accuracy under ideal conditions. Unlike lidar, UWB can provide additional global positioning constraints in areas with sparse geometric features, thereby addressing the limitations of laser SLAM in perception-degraded environments such as corridors. In dynamic interference scenarios, UWB signals are less susceptible to dynamic objects and provide stable ranging data, further enhancing positioning robustness. By fusing UWB with sensor data from lidar and inertial measurement units (IMUs), SLAM systems can not only achieve highly accurate pose estimation but also effectively address challenges in complex environments.

[0006] However, ultra-wideband signals face challenges such as multipath and signal obstruction when propagating in corridor environments. Walls, floors, and ceilings within corridors cause multiple signal reflections, resulting in multipath effects that affect ranging accuracy and reliability. Furthermore, signal coverage may be limited along slopes, around corners, or in areas with significant obstruction. Therefore, by rationally deploying ultra-wideband positioning anchor points within corridors, signal coverage can be significantly improved, blind spots can be reduced, and the impact of multipath effects can be mitigated, addressing signal coverage and reliability issues. Furthermore, optimizing the number and placement of anchor points can reduce hardware costs while improving overall system efficiency and performance.

[0007] To properly deploy ultra-wideband positioning anchor points, the existing solutions are: 1. Chinese invention patent with announcement number CN116321198A, this invention discloses a non-line-of-sight base station deployment optimization method based on UWB positioning. First, a reference point is set, and a rough area division is performed according to the characteristics of the terrain scene and experience, and data is collected at each reference point. Secondly, based on the collected data, a probability distribution map of the ranging error and a histogram of errors at different distances are constructed, the error model distribution is fitted, and the parameter values ​​of the reference points in different areas are statistically analyzed. Then, the ranging area is divided according to the clustering algorithm, and the variance expression of the ranging error is obtained in combination with the function fitting relationship. Finally, based on the geometric precision dilution factor evaluation index, an evaluation index for base station deployment under non-line-of-sight conditions is constructed, and a base station deployment optimization model is established around the evaluation index. This invention makes up for the shortcomings of the geometric precision factor evaluation index under non-line-of-sight scenarios, and provides a certain reference value for base station deployment strategies in complex indoor scenes in non-line-of-sight.

[0008] This method does not take into account the performance degradation of the mobile robot's own SLAM in the environment when deploying ultra-wideband anchor points, resulting in the optimization method still being unable to ensure the accuracy and reliability of the robot SLAM when integrating ultra-wideband information. In addition, it is necessary to collect a large amount of data at each reference point and train the algorithm for this data. This process is labor-intensive and consumes a lot of time and resources, making it difficult to quickly deploy and expand in actual applications. In addition, this method only uses a single evaluation indicator to evaluate the effectiveness of base station deployment, and fails to comprehensively consider multiple key factors, resulting in the evaluation results may not be comprehensive, and the optimized deployment plan may not perform well in actual complex scenarios. Third, this method has poor adaptability to environmental changes. In dynamic environments or perception degradation scenarios, it is necessary to re-collect data and train models, which limits its efficiency and flexibility in actual applications.

[0009] 2. Announcement No. CN114245316A provides a base station deployment optimization method and system based on UWB positioning. The method includes: obtaining the deployable area of ​​the base station in the indoor space, determining the spatial constraints of the deployable area, and dividing the deployable area into a grid. A set of base station position combinations is randomly generated in each grid, recorded as the initial base station position combination; all base station populations in the deployable area are divided into two populations, with the initial base station position combination as the initial value. The base station deployment is optimized with the minimum geometric distance precision dilution (GDOP), the minimum positioning root mean square error, and the minimum number of base stations as the optimization objectives. An improved adaptive genetic algorithm and a Levy flight strategy algorithm are used to update the positions of the base stations in the two populations, respectively, to optimize the base station combination and obtain the optimal base station deployment combination. This application can improve the rationality of the base station deployment method.

[0010] This method combines an adaptive genetic algorithm with the Levy flight strategy. This algorithm is complex, resulting in excessive computational overhead, particularly in large-scale spatial or high-dimensional parameter scenarios. Furthermore, the algorithm is sensitive to the initial base station position combination and the algorithm parameter settings. Randomly generated base station position combinations can cause the optimization process to fall into a local optimal solution, resulting in a decrease in the effectiveness of the optimization results in practical applications. This method also fails to consider the performance degradation of the mobile robot's own SLAM in the environment when deploying ultra-wideband anchor points, resulting in the optimization method still being unable to ensure the accuracy and reliability of the robot's SLAM when integrating ultra-wideband information.

[0011] Therefore, ultra-wideband (UWB) positioning technology can provide stable and reliable distance constraint information for vision- or laser-based simultaneous localization and mapping (SLAM) systems, effectively improving the robustness of mobile robot SLAM systems. However, ensuring that mobile robots consistently obtain high-quality UWB ranging signals during dynamic movement remains a major challenge. Summary of the Invention

[0012] The technical problem to be solved by the present invention is to provide a method and system for optimizing the deployment of ultra-wideband positioning anchor points in a corridor environment, so as to solve the problem that the existing positioning anchor point deployment method is difficult to ensure that the mobile robot can continuously receive high-quality UWB ranging signals during movement.

[0013] To solve the above technical problems, the technical solution adopted by the present invention is: a method for optimizing the deployment of ultra-wideband positioning anchor points in a corridor environment, comprising the following steps: Step 1: Arrange initial UWB anchor points in the corridor to form an initial deployment plan; Step 2: The mobile robot is equipped with a lidar and UWB tags and moves in a corridor environment where initial UWB anchor points are evenly distributed; Step 3: The laser radar obtains the corridor point cloud information, extracts the features of the point cloud information, and then uses the perception degradation algorithm to identify the perception degradation area in the corridor; Step 4: The mobile robot obtains the ranging information between the UWB tag and the UWB anchor point, and uses the signal evaluation algorithm to identify areas in the scene where the UWB signal reception is poor; Step 5: Compare the perceived degradation area and the area with poor UWB signals, identify the overlapping area between the two, and calculate the specific location of the overlapping area in the corridor; Step 6: Add anchor points in the overlapping area to form an anchor point deployment plan.

[0014] In a preferred solution, in step 1, initial UWB anchor points are evenly arranged at fixed intervals in the corridor.

[0015] In the preferred solution, in step 2, the lidar is used to acquire point cloud data of the corridor environment in real time; the UWB tag is used to measure the distance with the initial UWB anchor point arranged in the corridor, and the UWB tag maintains the same communication frequency and transmission data frame with the initial UWB anchor point fixed in the environment.

[0016] In a preferred solution, in step 3, identifying the perceptually degraded area in the corridor by using a perceptual degradation algorithm includes the following steps: The lidar-based positioning problem is formulated as solving a set of constraint equations, which can be expressed as: (1); in, represents the robot position and orientation, is the point index in the laser scan, m Indicates the maximum index number, represents three-dimensional Euclidean space, represents a special orthogonal group; , which encodes the normal vector and distance estimated by fitting the local plane of the neighboring points, represents the set of real numbers; , represents the unit odometry vector expressed in the robot body coordinate system; , indicating that the laser radar is moving from the current robot position along the direction The distance value of the scanned environmental point; calculate right and The derivative of is used as a measure of sensitivity, and the derivatives of all constraint calculations are superimposed to obtain two information matrices F and T: (2); (3); Perform eigenvalue decomposition on the information matrix F and T respectively: (4); (5); in, and These two symmetric positive definite matrices represent the overall constraint strength of all lidar scanning points on the robot's position and posture. and They are matrices and The orthogonal matrix composed of the eigenvectors of and It is a diagonal matrix, and its diagonal elements are and The characteristic value of By observation and The eigenvalue size distribution of the corridor is used to determine the degree of degradation of the regional geometric features and obtain the perceived degradation area in the corridor.

[0017] In a preferred solution, the method for determining the degree of degradation of regional geometric features is: if the eigenvalue in the straight line direction is significantly smaller than the eigenvalues ​​in other directions, the region is marked as a perception degradation region for robot SLAM in a corridor environment.

[0018] In a preferred solution, in step 4, the ranging information between the UWB tag and the UWB anchor point includes the first path signal strength, the total signal strength and ranging information, and the timestamp of the ranging data is recorded.

[0019] In a preferred solution, in step 4, the signal evaluation algorithm is used to identify areas in the scene where the UWB signal reception is poor, and the operation is as follows: Combined with the total signal strength RX_RSSI and the first path signal strength FP_RSS, the two indicators are calculated according to the following formula: (6); in: ; ; ; in, C Indicates the signal confidence factor, which represents the evaluation of the UWB signal confidence at the current location. The smaller the value, the more reliable the UWB signal. 、 、 represents the weighting coefficient; 、 They represent the average values ​​of all FP_RSSI and RX_RSSI within a period of time; N represents the number of UWB anchor points that received signals during this period of time.

[0020] In the preferred embodiment, according to the calculated C The size of the threshold is set. C When the threshold is exceeded, the time point at which the signal quality drops and the corresponding spatial position are marked. C Spatial locations where the value exceeds the threshold are identified as areas where the UWB signal reception is poor.

[0021] In the preferred solution, in step 5, the perception degradation area identified by the lidar is compared with the signal poor area identified by the UWB signal evaluation algorithm, and the timestamp information is used to mark the overlapping parts of the two types of areas. The displacement path of the overlapping area is calculated based on the robot's driving speed and timestamp, and the three-dimensional coordinate range of the overlapping area is determined through the mapping relationship between time and space. The overlapping area is mapped to the actual geometric model of the corridor, and the specific area that needs to be optimized is marked.

[0022] The present invention also provides an optimized deployment system for ultra-wideband positioning anchor points in a corridor environment, which is used to execute the above-mentioned optimized deployment method for ultra-wideband positioning anchor points in a corridor environment, including a mobile robot, which is equipped with a laser radar and a UWB tag, and simultaneously realizes laser SLAM positioning and UWB positioning; the mobile robot is equipped with an on-board computer for storing data, and has a built-in perception degradation algorithm and a signal evaluation algorithm. The perception degradation algorithm is used to identify areas with perception degradation in the scene, and the signal evaluation algorithm is used to identify areas in the scene where the UWB signal is poorly received.

[0023] The present invention provides a method and system for optimizing the deployment of ultra-wideband positioning anchor points in a corridor environment, which has the following beneficial effects: 1. The present invention simultaneously utilizes the perception degradation algorithm and the UWB signal quality assessment algorithm, which can be used for robot positioning and navigation tasks to more accurately and comprehensively determine the areas in the corridor environment where the deployment of exogenous UWB anchor points needs to be strengthened. Specifically, the present invention dynamically optimizes the areas in the corridor where the positioning performance is degraded by creating a coupled perception degradation algorithm and a UWB signal quality assessment algorithm. Based on the point cloud data collected by the lidar, the eigenvalue decomposition of the constraint matrix is ​​used to identify the perception degradation areas in the corridor, ensuring that areas with insufficient geometric constraints can be accurately identified. And by analyzing the UWB signal, the signal blind spots, areas with significant multipath effects, or areas with insufficient signal coverage are accurately judged. Ultimately, the precise identification of perception degradation areas and areas with weak signal coverage is achieved, significantly improving the reliability and accuracy of positioning performance in the corridor environment.

[0024] 2. This invention compares the degraded areas identified by the degradation perception algorithm with the areas of poor signal quality identified by the UWB signal quality assessment algorithm. The overlap between the two areas is marked based on the time axis. Combining the robot's movement speed and timestamp information, the spatial location of these overlapping areas is inferred and used as the focus for optimizing UWB anchor point density. This avoids resource waste and over-deployment, further improving the system's optimization efficiency and effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The present invention will be further described below with reference to the accompanying drawings and examples: Figure 1 Flowchart of the present invention; Figure 2 This is the test scenario in the embodiment; Figure 3 is the degradation area obtained by the perceptual degradation algorithm; Figure 4 is the UWB data in Example 2; Figure 5 This is a comparison chart of the signal quality evaluation at various locations in the corridor before and after optimization in Example 2. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0027] Example 1: Most existing methods use the anchor point's own positioning error or geometric distribution factor as a single optimization metric, failing to comprehensively consider multi-dimensional factors such as ranging reliability, corridor environmental degradation, and equipment deployment costs. This single optimization strategy is difficult to meet the actual needs of complex corridor scenarios, resulting in poor performance of optimization results in practical applications. For mobile robot positioning applications, existing technologies fail to effectively identify areas of perception degradation and lack the targeted deployment of ultra-wideband positioning anchors in these areas. This results in a significant decrease in the robustness of existing SLAM systems in corridor environments. Therefore, existing technologies struggle to provide effective external signal source positioning constraints for these perception-degraded areas.

[0028] The present invention provides an optimized deployment method for ultra-wideband positioning anchor points in a corridor environment, which solves the problems of insufficient signal coverage and insufficient accuracy and reliability of robot SLAM positioning in corridor environments in the prior art.

[0029] like Figure 1 As shown, the specific steps include: Step 1: Arrange the initial UWB anchor points evenly at fixed intervals in the corridor to form an initial deployment plan.

[0030] For example, an anchor point is set every 80 meters. This distance interval can be determined by conducting ranging experiments between UWB tags and anchor points in the target corridor environment to ensure the validity of the ranging results between the anchor points and tags. At the same time, the ranging interval is extended as much as possible to reduce the number of anchor point deployments and the cost.

[0031] The evenly spaced initial anchor point scheme forms a basic configuration, ensuring basic coverage and positioning capabilities throughout the corridor. This basic configuration provides an initial reference for subsequent optimization and adjustment. In actual deployment, each anchor point needs to be secured with a dedicated bracket and calibrated with unified coordinates to ensure precise positioning.

[0032] Step 2: The mobile robot is equipped with a lidar and UWB tags and moves in a corridor environment where initial UWB anchor points are evenly distributed.

[0033] The mobile robot is equipped with two types of core sensors: lidar and UWB tags, both of which are connected to the onboard computer of the mobile robot to ensure the real-time data.

[0034] LiDAR is used to acquire real-time point cloud data of the corridor environment, providing high-precision environmental perception capabilities, including running LiDAR-based SLAM algorithms for robot positioning and environmental mapping. Due to the structural similarity and sparse textures of the corridor environment, LiDAR SLAM positioning and mapping performance may degrade, such as accumulated positioning errors and environmental mapping distortion. This point cloud data can be used to run the perception degradation algorithm.

[0035] The UWB tag is used to measure distance with the initial UWB anchor point arranged in the corridor. The UWB tag maintains the same communication frequency and transmission data frame with the initial UWB anchor point fixed in the environment, obtains the ranging data between the two, and provides this data to the signal evaluation algorithm.

[0036] The mobile robot is controlled to move along a corridor route, covering the entire corridor space. The movement path follows the robot's actual navigation route, which includes typical scenarios such as long straight corridors, corners, and uphill and downhill slopes. The mobile robot moves at a steady speed, synchronously collecting LiDAR point cloud data and UWB ranging data. The data is time-aligned on the robot's onboard computer to ensure that the data from both sensors have the same time base.

[0037] Step 3: The laser radar obtains the corridor point cloud information, extracts the features of the point cloud information, and then uses the perception degradation algorithm to identify the perception degradation area in the corridor.

[0038] The laser radar continuously collects point cloud information in the corridor during the movement of the robot. The point cloud is filtered to remove noise points and extract effective geometric features, and then the geometric structure data of the three-dimensional space is generated and the timestamp is retained.

[0039] Specifically, the collected original point cloud data needs to be preprocessed to reduce the computational complexity and retain key features. Therefore, the saved point cloud is preprocessed, and the original point cloud is sparsely processed using voxel grid filtering to reduce the amount of point cloud data while retaining the main geometric features. Subsequently, outliers are removed through statistical filtering to obtain sparse and denoised point cloud data.

[0040] For each point in the preprocessed point cloud data, several neighboring points are selected from its surroundings, and a local plane is fitted using these neighboring points to obtain the normal vector Indicates the surface direction and plane distance of the area where the point is located represents the vertical distance between the plane where the point is located and the origin, thus forming the constraint equation shown in formula (1).

[0041] All data is synchronously stored in the on-board computer of the mobile robot.

[0042] After extracting features from the point cloud information generated by the lidar, the perceptual degradation algorithm is used to analyze the environmental features in the corridor, which includes the following steps: First, the lidar-based positioning problem is formulated as solving a set of constraint equations, which are expressed as: (1); in, represents the robot position and orientation, is the point index in the laser scan, m Indicates the maximum index number, represents three-dimensional Euclidean space, represents a special orthogonal group; , which encodes the normal vector and distance estimated by fitting the local plane of the neighboring points, represents the set of real numbers; , represents the unit odometry vector expressed in the robot body coordinate system; , indicating that the laser radar is moving from the current robot position along the direction The distance value of the scanned environment point.

[0043] In general, when the robot is positioned correctly, the scan points should be aligned with the map, so we can define a map coordinate system that is aligned with the robot body coordinate system, so that The strength of the constraint can be evaluated by comparing the sensitivity of the measurement value to the robot's posture. If the robot's posture is slightly disturbed, but the measurement result does not change much, then the constraint is weak. Otherwise, the constraint is strong. Therefore, by calculating right and The derivative of is used as a measure of sensitivity, and the derivatives of all constraint calculations are superimposed to obtain two information matrices F and T: (2); (3); Perform eigenvalue decomposition on the information matrix F and T respectively: (4); (5); in, and These two symmetric positive definite matrices represent the overall constraint strength of all lidar scanning points on the robot's position and posture. and They are matrices and The orthogonal matrix composed of the eigenvectors of and It is a diagonal matrix, and its diagonal elements are and The characteristic value of The eigenvalue decomposition of the constraint matrix is ​​used to determine the degree of degradation of the regional geometric features and obtain the perceived degradation area in the corridor. and The eigenvalue size distribution of the corridor is used to determine the degree of degradation of the regional geometric features and obtain the perceived degradation area in the corridor.

[0044] The method for determining the degree of degradation of regional geometric features is: if the eigenvalue in the straight line direction is significantly smaller than the eigenvalues ​​in other directions, the region is marked as a perception-degraded region for robot SLAM in the corridor environment.

[0045] Step 4: The mobile robot obtains the ranging information between the UWB tag and the UWB anchor point, and uses the signal evaluation algorithm to identify the area in the scene where the UWB signal reception is poor.

[0046] The UWB ranging transmission frame includes the first path signal strength (First Path Received Signal Strength Indicator, FP_RSSI), the total signal strength (Received Signal Strength Indicator, RX_RSSI) and ranging information and records the timestamp of the ranging data.

[0047] The signal evaluation algorithm is used to process the ranging data, combining the total signal strength RX_RSSI and the first path signal strength FP_RSS, and calculating according to the following formula: (6); in: When it is less than 1.15, it means that the proportion of direct paths is high and the overall signal credibility is high; ,When it is less than 10, it means that the multipath reflection signal intensity in the ,scene is low, and the signal credibility is higher; , represents the difference in standard deviation. The smaller it is, the more stable the signal is.

[0048] 、 、 Represents the weighting coefficient, which is used to adjust the impact of various indicators on the final confidence level. In actual use, it needs to be adjusted accordingly according to changes in the scenario. 、 Respectively, they represent the average values ​​of all FP_RSSI and RX_RSSI values ​​within a period of time. N represents the number of UWB anchor points that received signals during this period of time.

[0049] According to the above formula, a smaller C indicates a more reliable UWB signal. A threshold is set, which can be adjusted based on the specific target corridor environment. When C exceeds the threshold, the time point and corresponding spatial location at which signal quality degrades are marked.

[0050] Step 5: Compare the perceived degradation area and the area with poor UWB signals, identify the overlapping area between the two, and calculate the specific location of the overlapping area in the corridor.

[0051] By comparing the perception degradation areas identified by the lidar with the poor signal areas identified by the UWB signal evaluation algorithm, the overlapping parts of the two types of areas are marked using timestamp information. The displacement path of the overlapping areas is calculated based on the robot's driving speed and timestamp. The three-dimensional coordinate range of the overlapping areas is determined through the mapping relationship between time and space. The overlapping areas are mapped to the actual geometric model of the corridor, and the specific areas that need to be optimized are marked.

[0052] By comparing the areas of sensor degradation identified by the lidar with the areas of poor signal quality identified by the UWB signal evaluation algorithm, we identify areas of overlap. These overlapping points on the timeline are marked as potential optimization areas, indicating that these areas experience both environmental geometry degradation and insufficient signal coverage. The two types of data are aligned on the timeline, using the robot's motion timestamps as a reference. Spatial mapping is performed on these overlapping areas, marking them as areas requiring key optimization.

[0053] The specific location of the overlapping area in the corridor is calculated by combining the robot's movement speed and the timestamp information of the point cloud data. The displacement path of the overlapping area is inferred based on the robot's movement speed and timestamp. The three-dimensional coordinate range of the overlapping area is determined by mapping the time and space relationship. The overlapping area is then mapped to the actual geometric model of the corridor, marking the specific areas that need optimization.

[0054] Step 6: Add anchor points in the overlapping area to form an anchor point deployment plan.

[0055] Within the overlapping areas of the markers, the density of UWB anchor points is increased to improve signal coverage and ranging accuracy. Anchor points are prioritized for deployment at key locations with high impact within the overlapping areas. This approach comprehensively considers signal coverage, ranging performance, and deployment cost, while also adding positioning constraints for robot SLAM degraded areas. This ensures that while improving system positioning accuracy and reliability, resources are not wasted, achieving a balance between cost and accuracy.

[0056] The optimized deployment of external signal sources in the corridor needs to be combined with the type, structural characteristics and system requirements of the corridor to determine the deployment density, location and number of signal sources. The present invention uses a signal evaluation algorithm to evaluate the effectiveness of the signal. In order to further improve the effectiveness of the deployment of external signal sources, a perceptual degradation recognition technology is developed. By constructing a point cloud feature degradation model, the areas in the corridor where degradation may occur are identified, and the density and location of the external signal sources are optimized based on the distribution characteristics of the degradation area. The density of external signal sources is reasonably increased in areas where signal degradation is severe and UWB signals are poor to compensate for signal loss and maintain consistency in global signal coverage.

[0057] By closely integrating the robot's own SLAM sensor's perception degradation detection algorithm in corridor environments with its UWB signal evaluation algorithm, the system accurately identifies areas of perception degradation within corridor environments, evaluates the credibility of UWB signals, and locates areas with low signal credibility. Based on a comprehensive analysis of the environmental characteristics of these degraded areas, the system further optimizes the deployment and density of anchor points, building on the existing uniform anchor point layout, thereby improving the system's overall positioning performance.

[0058] This invention breaks away from the traditional method's reliance on prior maps. It uses the robot's onboard laser radar to achieve real-time environmental scanning and perception-degraded area detection, dynamically evaluates the location distribution of signal coverage weak points, and quickly completes anchor point deployment, significantly improving the signal coverage capability within the corridor, thereby further enhancing the adaptability and stability of the SLAM system in complex environments.

[0059] This invention, through the in-depth integration of robotic SLAM sensor degradation detection and UWB signal credibility assessment, not only demonstrates significant innovation in optimizing anchor point deployment but also demonstrates significant practicality in improving SLAM system performance. Its application in complex corridor environments will provide strong technical support for robotic autonomous navigation systems and lay a solid theoretical and practical foundation for high-precision positioning using multi-sensor fusion.

[0060] Example 2: This embodiment has been verified through actual experiments and computer simulations to prove its feasibility and effectiveness in optimizing the deployment of UWB anchor points in a corridor environment. The corridor photos are as follows: Figure 2 The specific results are as follows: This test first evenly arranges fixed anchor points in the scene, measuring each anchor point with a tape measure. A mobile robot is controlled to move through the corridor with the anchor points. A host computer records the distance and signal strength between the mobile robot's anchor points and each fixed anchor point, and transmits point cloud data to the LiDAR via wired transmission.

[0061] Figure 3This is the degradation area obtained by the perception degradation algorithm, where the red vertical line indicates that the quality of the radar point cloud data at the current time point is poor.

[0062] Figure 4 This is the UWB data of the mobile robot, where the horizontal axis is time and the vertical axis is distance. It can be seen that the UWB data is very stable during movement.

[0063] According to the experimental results, Figure 5 As shown in the figure, the threshold of the signal confidence factor C is set to 1, and areas exceeding 1 are considered weak signal areas. With the original UWB deployment, the proportion of weak signal areas was approximately 15%. After the optimized deployment, this proportion dropped to 2%, significantly improving signal coverage. The optimized deployment significantly reduces the overlap between areas with degraded perception and areas with poor signal quality, effectively mitigating signal interference and multipath effects.

[0064] In summary, the present invention has been verified through experiments and simulations. The optimized UWB signal coverage is significantly improved, and the system shows strong robustness and adaptability in dynamic environments. The technical solution is feasible and can significantly improve the performance of the positioning system in the corridor environment, providing reliable technical support for practical applications.

[0065] Example 3: The present invention also provides an optimized deployment system for ultra-wideband positioning anchor points in a corridor environment, which is used for the optimized deployment method for ultra-wideband positioning anchor points in a corridor environment of Example 1, including a mobile robot, which is equipped with a laser radar and a UWB tag, and simultaneously realizes laser SLAM positioning and UWB positioning; the mobile robot is equipped with an on-board computer for storing data, and has a built-in perception degradation algorithm and a signal evaluation algorithm, the perception degradation algorithm is used to identify areas in the scene with perception degradation, and the signal evaluation algorithm is used to identify areas in the scene with poor UWB signal reception.

[0066] The lidar and UWB tag respectively collect lidar point cloud information and information such as the distance to the fixed UWB anchor point, and transmit the collected information to the on-board computer through a wired connection for subsequent processing.

[0067] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing the deployment of ultra-wideband positioning anchor points in a corridor environment, characterized in that: The following steps are involved: Step 1: Arrange initial UWB anchor points in the corridor to form an initial deployment plan; Step 2: The mobile robot is equipped with a lidar and UWB tags and moves in a corridor environment where initial UWB anchor points are evenly distributed; Step 3: The laser radar obtains the corridor point cloud information, extracts the features of the point cloud information, and then uses the perception degradation algorithm to identify the perception degradation area in the corridor; Step 4: The mobile robot obtains the ranging information between the UWB tag and the UWB anchor point, and uses the signal evaluation algorithm to identify areas in the scene where the UWB signal reception is poor; Step 5: Compare the perceived degradation area and the area with poor UWB signals, identify the overlapping area between the two, and calculate the specific location of the overlapping area in the corridor; Step 6: Add anchor points in the overlapping area to form an anchor point deployment plan.

2. The method for optimizing deployment of ultra-wideband positioning anchor points in a corridor environment according to claim 1, characterized in that: In step 1, initial UWB anchor points are evenly arranged at fixed intervals in the corridor.

3. The method for optimizing deployment of ultra-wideband positioning anchor points in a corridor environment according to claim 1, characterized in that: In step 2, the laser radar is used to obtain point cloud data of the corridor environment in real time; the UWB tag is used to measure the distance with the initial UWB anchor point arranged in the corridor, and the UWB tag maintains the same communication frequency and transmission data frame with the initial UWB anchor point fixed in the environment.

4. The method for optimizing deployment of ultra-wideband positioning anchor points in a corridor environment according to claim 1, characterized in that: In step 3, the perceptual degradation area in the corridor is identified by using a perceptual degradation algorithm, including the following steps: The lidar-based positioning problem is formulated as solving a set of constraint equations, which can be expressed as: （1）； in, represents the robot position and orientation, is the point index in the laser scan, m Indicates the maximum index number, represents three-dimensional Euclidean space, represents a special orthogonal group; , which encodes the normal vector and distance estimated by fitting the local plane of the neighboring points, represents the set of real numbers; , represents the unit odometry vector expressed in the robot body coordinate system; , indicating that the laser radar is moving from the current robot position along the direction The distance value of the scanned environmental point; calculate right and The derivative of is used as a measure of sensitivity, and the derivatives of all constraint calculations are superimposed to obtain two information matrices F and T: （2）； （3）； Perform eigenvalue decomposition on the information matrix F and T respectively: （4）； （5）； in, and These two symmetric positive definite matrices represent the overall constraint strength of all lidar scanning points on the robot's position and posture. and They are matrices and The orthogonal matrix composed of the eigenvectors of and It is a diagonal matrix, and its diagonal elements are and The characteristic value of By observation and The eigenvalue size distribution of the corridor is used to determine the degree of degradation of the regional geometric features and obtain the perceived degradation area in the corridor.

5. The method for optimizing deployment of ultra-wideband positioning anchor points in a corridor environment according to claim 4, characterized in that: The method for determining the degree of degradation of regional geometric features is: if the eigenvalue in the straight line direction is significantly smaller than the eigenvalues ​​in other directions, the region is marked as a perception-degraded region for robot SLAM in the corridor environment.

6. The method for optimizing deployment of ultra-wideband positioning anchor points in a corridor environment according to claim 1, characterized in that: In step 4, the ranging information between the UWB tag and the UWB anchor point includes the first path signal strength, the total signal strength and the ranging information, and the timestamp of the ranging data is recorded.

7. The method for optimizing deployment of ultra-wideband positioning anchor points in a corridor environment according to claim 6, characterized in that: In step 4, the signal evaluation algorithm is used to identify areas in the scene where the UWB signal reception is poor. The operation is as follows: Combined with the total signal strength RX_RSSI and the first path signal strength FP_RSS, the two indicators are calculated according to the following formula: （6）； in: ； ； ； in, C Indicates the signal confidence factor, which represents the evaluation of the UWB signal confidence at the current location. The smaller the value, the more reliable the UWB signal. 、 、 represents the weighting coefficient; 、 They represent the average values ​​of all FP_RSSI and RX_RSSI within a period of time; N represents the number of UWB anchor points that received signals during this period of time.

8. The method for optimizing deployment of ultra-wideband positioning anchor points in a corridor environment according to claim 7, characterized in that: According to the calculation C The size of the threshold is set. C When the threshold is exceeded, the time point at which the signal quality drops and the corresponding spatial position are marked. C Spatial locations where the value exceeds the threshold are identified as areas where the UWB signal reception is poor.

9. The method for optimizing deployment of ultra-wideband positioning anchor points in a corridor environment according to claim 1, characterized in that: In step 5, the perception degradation area identified by the lidar is compared with the signal poor area identified by the UWB signal evaluation algorithm, and the overlapping parts of the two types of areas are marked using the timestamp information. The displacement path of the overlapping area is calculated based on the robot's driving speed and timestamp. The three-dimensional coordinate range of the overlapping area is determined through the mapping relationship between time and space, and the overlapping area is mapped to the actual geometric model of the corridor, marking the specific area that needs to be optimized.

10. An optimized deployment system for ultra-wideband positioning anchor points in corridor environments, characterized in that: A method for optimizing the deployment of ultra-wideband positioning anchor points in a corridor environment for executing any one of claims 1 to 9 includes a mobile robot equipped with a laser radar and a UWB tag, and simultaneously realizing laser SLAM positioning and UWB positioning; the mobile robot is equipped with an on-board computer for storing data, and has a built-in perception degradation algorithm and a signal evaluation algorithm, the perception degradation algorithm is used to identify areas in the scene with perception degradation, and the signal evaluation algorithm is used to identify areas in the scene where UWB signals are poorly received.

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