An autonomous exploration method and system fusing wireless signal heat maps

Abstract

The application discloses a kind of fusion wireless signal heat map autonomous exploration method and system, it is related to path planning technical field.The present application can evaluate the state of communication in real time in target area by constructing wireless signal heat map, and find the best exploration path in combination with environment map, to improve exploration efficiency and success rate, ensure the real-time and reliability of communication, simultaneously, the present application can quickly obtain wireless signal coverage by robot autonomous exploration and data processing, provide reliable signal feedback and support for mobile robot to execute intended task, avoid the complexity and inaccuracy of test method in the prior art, and then can solve the problems of communication interruption, low exploration efficiency and unreasonable exploration path that robot faces in unknown environment exploration operation.

Description

Technical Field

[0001] This invention relates to the field of path planning technology, and in particular to an autonomous exploration method and system that integrates wireless signal heatmaps. Background Technology

[0002] Autonomous exploration technology for robots in unknown environments has broad application prospects, especially in scenarios such as accident scene rescue and environmental surveying. Currently, the autonomous capabilities of mobile robots are insufficient to independently complete complex tasks, often requiring human intervention. When mobile robots autonomously perform patrol, search, and rescue operations, they often need to transmit detected information to the user in real time via wireless signals. Reliable communication is crucial for ensuring the successful completion of tasks collaboratively by humans and unmanned personnel. The working environments of mobile robots are often complex, and the strength distribution and coverage of radio signals are difficult to ascertain. To ensure that mobile robots can successfully complete their tasks and provide feedback, it is necessary to understand the radio signal strength distribution and coverage of the working environment. Existing exploration algorithms do not consider communication factors, resulting in problems such as low exploration efficiency, unreasonable planned exploration paths, and communication interruptions during the exploration process.

[0003] In addition, to accurately test wireless signal coverage, users usually need to use a handheld wireless tester to select test points for operation and analysis. This testing method requires a high level of expertise and requires repeated test operations. Furthermore, if the test points are not selected scientifically, the generated results will be inaccurate.

[0004] Existing Solution 1: A method for constructing wireless signal heatmaps based on spatial interpolation techniques, generating a continuous signal strength distribution map using limited sampled data. The general steps are as follows:

[0005] Data Acquisition and Preprocessing: First, wireless signal strength data needs to be collected within the target area. Measurements are performed using professional wireless signal testing equipment to ensure sufficient data acquisition density to cover the entire area. The collected data undergoes preprocessing, including removing outliers, noise, and invalid data.

[0006] Creating a grid: To perform interpolation, a regular grid or raster needs to be created within the target area. The size and density of the grid can be adjusted according to the accuracy of the data acquisition and the accuracy requirements of the target heatmap.

[0007] Interpolation calculation: By combining data points and a grid, and using a selected interpolation method, the data is interpolated to obtain the continuous signal intensity distribution over the entire target area. Commonly used spatial interpolation methods include inverse distance weighted interpolation (IDW) and kriging interpolation. Different interpolation methods are suitable for different types of data and spatial distributions.

[0008] Heatmap generation: Convert the interpolated signal strength data into a heatmap.

[0009] It is worth noting that spatial interpolation techniques typically require the collection of large amounts of data when constructing heatmaps, and interpolation based on limited collected data may involve certain uncertainties and limitations. To obtain more accurate results, the density and quality of data collection, the choice of interpolation method, and the interpretation and analysis of the heatmap are all key factors that need to be carefully considered.

[0010] Existing Solution 2: A method for rapidly acquiring WIFI wireless signal coverage based on a track inspection robot (application publication number CN 115314996A), including the following steps:

[0011] Step 1: Install the track inspection robot on the suspended track. Under the guidance of the navigation and positioning module, the track inspection robot runs along the suspended track to several inspection points for inspection. The navigation and positioning module obtains the robot coordinates at the inspection points in real time and sends the robot coordinates to the intelligent processing center.

[0012] Step 2: The storage module in the intelligent processing center stores the received robot coordinates. When the detection unit in the intelligent processing center detects that the track inspection robot has stopped running, it starts to detect the signal strength and signal noise level of the external WIFI antenna and sends the detected signal strength and signal noise level to the processing unit in the intelligent processing center.

[0013] Step 3: The processing unit marks the received signal strength and signal noise level; the processing unit packages the coordinates of the track inspection robot and the signal strength data at the corresponding inspection point, and sends it to the track inspection robot platform terminal, so that the staff can check the track wireless signal coverage.

[0014] Step 4: The guide rail inspection robot moves to the next inspection point and repeats the process from Step 1 to Step 3.

[0015] Existing Solution 3: Radio Map-Assisted Path Planning for UAV Anti-Jamming Communications, the implementation steps are as follows:

[0016] Step 1: Based on real-world terrain data, use the Antenna tool in Matlab to generate signal power and signal-to-noise ratio maps.

[0017] Step 2: Generate a terrain accessibility cost map based on real-world terrain data.

[0018] Step 3: Based on the above signal power diagram and signal-to-noise ratio diagram, calculate the communication feasible region.

[0019] Step 4: Based on the above communication feasible region and terrain accessibility cost map, calculate the comprehensive feasible region.

[0020] Step 5: Use the A* algorithm for path planning.

[0021] Existing Solution 4: A frontier-exploration autonomous exploration algorithm based on boundary regions, where the boundary is defined as the area between known and unknown spaces. It first constructs a grid map by scanning the environment with LiDAR, and then uses a depth-first search (BFS) algorithm on the grid map to search for the boundary from the robot's current position. It then selects the boundary closest to the robot's current position (Euclidean distance) as the first target to move towards. This process is repeated to explore unknown regions until no more unknown regions remain in the environment.

[0022] Based on the above description, the existing technology has the following disadvantages:

[0023] 1) Existing technologies do not consider communication factors: Current exploration algorithms do not take into account the communication factors when mobile robots are exploring in unknown environments. During exploration, the mobile robot needs to transmit the detected information to the user or intelligent processing center in real time via wireless signals. The lack of consideration for communication factors may lead to communication interruptions in complex environments, thus affecting the real-time performance and reliability of the exploration.

[0024] 2) Inappropriate exploration path: Existing technologies may not fully consider the information from the wireless signal heatmap during path planning, leading to inappropriate exploration paths. This may cause the mobile robot to encounter obstacles or unnecessary detours during the exploration process, reducing the efficiency and success rate of the exploration.

[0025] 3) The method of testing wireless signal distribution is cumbersome: In the existing technology, testing wireless signal coverage usually requires the use of a handheld wireless tester, selection of test points for operation and analysis, etc. This method requires a high level of expertise and requires repeated test operations. Furthermore, unscientific selection of test points may lead to inaccurate results. Summary of the Invention

[0026] To address the aforementioned problems in existing technologies, this invention provides an autonomous exploration method and system that integrates wireless signal heatmaps.

[0027] To achieve the above objectives, the present invention provides the following solution:

[0028] An autonomous exploration method integrating wireless signal heatmaps includes:

[0029] A real-time positioning and mapping method is used to map the unknown environment and obtain a topographical feasibility map;

[0030] A logarithmic distance path loss model for wireless access points is constructed using the least squares method based on data from multiple reference points.

[0031] Taking into account RSS loss, RSS noise, and RSS threshold, the RSS and SINR values ​​of each interpolation point are determined based on the constructed logarithmic distance path loss model and the location of the wireless access point.

[0032] By combining the RSS and SINR values ​​of all interpolation points with the sampled data at the reference point, a wireless signal heatmap is obtained; the wireless signal heatmap includes an estimated RSS map and an estimated SINR map.

[0033] The estimated communication feasibility graph is obtained by fusing the estimated RSS graph and the estimated SINR graph.

[0034] A first comprehensive feasible map is obtained based on the estimated communication feasible map and the terrain feasible map;

[0035] Based on the first comprehensive feasible graph, an autonomous exploration algorithm using a fast expanding random tree is used to perform autonomous exploration and mark target points;

[0036] Obtain the real RSS graph and the real SINR graph, and fuse the real RSS graph and the real SINR graph to obtain the real communication feasibility graph;

[0037] A second comprehensive feasible map is obtained based on the actual communication feasible map and the terrain feasible map;

[0038] Based on the second comprehensive feasible graph, the A* algorithm is used for path planning, and the mobile robot is controlled to reach the marked target point along the planned path.

[0039] The robot's position and target point are continuously updated. When the target point changes, the path is replanned so that the robot can explore the updated target point.

[0040] Optionally, the instantaneous localization and mapping method uses the Gmapping algorithm to construct the terrain feasibility map; the terrain feasibility map is a two-dimensional raster map, which divides the environment into raster scales in a two-dimensional plane.

[0041] Optionally, the terrain feasibility map is constructed using the Gmapping algorithm, specifically including:

[0042] Particle filtering is used to determine the robot's pose, transforming the robot trajectory into an incremental estimation problem. A kinematic model is used to propagate the state of each particle to obtain the predicted trajectory for each particle.

[0043] For each particle after state propagation, the observation model is used to calculate weights and normalize them to obtain the robot trajectory at the current moment. Then, based on the estimated trajectory and observation data, a map is constructed to obtain the terrain feasibility map.

[0044] Optionally, a wireless network card module mounted on the robot is used to collect sampling data at the reference point location; the sampling data at the reference point location includes the RSS value and SINR value of the reference point.

[0045] Optionally, the estimated communication feasibility graph is a B×A matrix, where the (i,j)th element of the B×A matrix is ​​represented by y. i,j :

[0046]

[0047] In the formula, The received signal power, p represents the signal-to-noise ratio of the received signal. min The set signal power threshold, s min The signal-to-noise ratio is set.

[0048] Optionally, the first comprehensive feasible graph is a B×A matrix, where the (i,j)th element of the B×A matrix is ​​u. i,j :

[0049]

[0050] In the formula, Represents coordinate x i,j Topographical accessibility data for the location.

[0051] Optionally, the autonomous exploration algorithm includes four operation steps: global boundary detection, local boundary detection, boundary point filtering, and task allocation.

[0052] The global boundary detection is an RRT search method that is continuously executed from the initial position;

[0053] The local boundary detection uses the RRT search method, which expands outward from the robot's current position as the root node. After finding a target point, the RRT tree is destroyed, and after the robot reaches the target point, it expands outward from the robot's current position as the root node.

[0054] The boundary point filtering method uses centroid clustering to cluster the searched target points to form boundary points;

[0055] The task allocation is based on the target point calculated through revenue calculation.

[0056] An autonomous exploration system integrating wireless signal heatmaps is applied to the aforementioned autonomous exploration method integrating wireless signal heatmaps; the system includes:

[0057] The terrain feasibility map construction module is used to construct a map of an unknown environment using real-time positioning and mapping methods to obtain a terrain feasibility map;

[0058] The logarithmic distance path loss model construction module is used to construct a logarithmic distance path loss model for wireless access points based on data from multiple reference points using the least squares method.

[0059] The interpolation point data determination module is used to determine the RSS and SINR values ​​of each interpolation point based on the constructed logarithmic distance path loss model and the location of the wireless access point, taking into account RSS loss, RSS noise and RSS threshold.

[0060] A wireless signal heatmap construction module is used to combine the RSS and SINR values ​​of all interpolation points with the sampling data at the reference point location to obtain a wireless signal heatmap; the wireless signal heatmap includes an estimated RSS map and an estimated SINR map;

[0061] An estimated communication feasibility graph construction module is used to fuse the estimated RSS graph and the estimated SINR graph to obtain an estimated communication feasibility graph;

[0062] The first integrated feasible map determination module is used to obtain a first integrated feasible map based on the estimated communication feasible map and the terrain feasible map;

[0063] The first autonomous exploration module is used to perform autonomous exploration based on the first comprehensive feasible graph using an autonomous exploration algorithm of fast expanding random tree, and to mark target points;

[0064] A real communication feasibility graph construction module is used to obtain real RSS graphs and real SINR graphs, and fuse the real RSS graphs and real SINR graphs to obtain real communication feasibility graphs;

[0065] The second integrated feasible map determination module is used to obtain a second integrated feasible map based on the actual communication feasible map and the terrain feasible map;

[0066] The path planning module is used to perform path planning based on the second comprehensive feasible graph using the A* algorithm, and to control the mobile robot to reach the marked target point along the planned path.

[0067] The second autonomous exploration module is used to continuously update the robot's position and target point. When the target point changes, the path is replanned so that the robot can explore towards the updated target point.

[0068] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0069] This invention constructs a wireless signal heatmap, enabling real-time assessment of communication status within a target area. By combining this with an environmental map, the optimal exploration path can be found, thereby improving exploration efficiency and success rate, and ensuring the real-time nature and reliability of communication. Simultaneously, through autonomous robot exploration and data processing, this invention can quickly obtain wireless signal coverage information, providing reliable signal feedback and support for the mobile robot to perform its predetermined tasks. This avoids the cumbersome and inaccurate testing methods in existing technologies, and thus solves problems such as communication interruptions, low exploration efficiency, and unreasonable exploration paths faced by robots exploring unknown environments. Attached Figure Description

[0070] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the 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.

[0071] Figure 1 A flowchart illustrating the autonomous exploration method for fused wireless signal heatmaps provided in this embodiment of the invention;

[0072] Figure 2 This is an implementation architecture diagram of the autonomous exploration method for fused wireless signal heatmaps provided in the embodiments. Detailed Implementation

[0073] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0074] The purpose of this invention is to provide an autonomous exploration method and system that integrates wireless signal heatmaps, which can solve problems such as communication interruption, low exploration efficiency and unreasonable exploration paths faced by mobile robots in exploring unknown environments.

[0075] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0076] In the specific implementation, the movement area X of the autonomous vehicle is defined as a rectangle, which is divided into B×A square grids with a side length of δ, where A and B represent the number of grids in the longitude and latitude directions, respectively. Using x... i,j ∈{(-180°,180°),(-90°,90°)} represents the coordinates of the center point of the (i,j)th grid cell in region X.

[0077] Define a B×A matrix M (P) (i.e., RSS chart), M (S) (i.e., SINR diagram) and M (C) (i.e., the terrain feasibility map) stores Received Signal Strength (RSS), Signal-to-Interference-plus-Noise Ratio (SINR), and terrain data respectively. Matrix M (P) The (i,j)th element stores the location of the autonomous vehicle at coordinate x. i,j The received signal strength at point M. (S) The (i,j)th element stores the location of the mobile robot at coordinate x. i,j The signal-to-noise ratio at point M. Matrix M (C) The (i,j)th element stores the location of the mobile robot at coordinate x. i,j The terrain at that location has a passability cost.

[0078] Based on the above settings, such as Figure 1 As shown, the autonomous exploration method for fused wireless signal heatmaps provided by this invention includes:

[0079] Step 100: Use real-time positioning and mapping methods to map the unknown environment and obtain a feasible terrain map.

[0080] In practical applications, robots use SLAM (Simultaneous Localization and Mapping) to map the environment in unknown environments, constructing a feasible terrain map M at time t. t (C) Simultaneously, pose estimation is performed, and the pose x of the mobile robot is output.

[0081] Specifically, the SLAM method employs the Gmapping algorithm. The problem that SLAM aims to solve is the problem of processing control data u... 1:t and observation data z 1:t To find the pose x 1:t The joint probability distribution p(x) of map m t ,m|z 1:t ,u 1:t-1Robot pose and map are both state variables, and both need to be estimated simultaneously. The SLAM method involves a conditional joint probability distribution, and as probability theory shows, joint probability can be transformed into conditional probability. For the joint probability distribution p(x... t ,m|z 1:t ,u 1:t-1 By estimating using the conditional Bayes' rule, we can obtain:

[0082] p(x t ,m∣z 1:t ,u 1:t-1 )=p(m∣x 1:t ,z 1:t )p(x 1:t ∣z 1:t ,u 1:t-1 ).

[0083] In the formula, p(x) 1:t ∣z 1:t ,u 1:t-1 p(m|x) represents the estimated robot trajectory. 1:t ,z 1:t This represents a closed-loop calculation for map construction given the robot's trajectory and sensor observation data.

[0084] This decomposition divides the SLAM process into two steps: localization and map building: 1) robot localization, 2) map building based on known robot pose.

[0085] The feasible terrain map M constructed by the Gmapping algorithm at time t t (C) The algorithm uses a two-dimensional grid map to divide the environment into grid-scale segments. It employs particle filtering to estimate the robot's pose, transforming trajectory estimation into an incremental estimation problem. Each particle undergoes state propagation using a kinematic model, yielding a predicted trajectory for each particle. For each propagated particle, a weighted normalization process using an observation model is performed to obtain the robot's trajectory at that moment. Finally, a map is constructed based on the estimated trajectory and observation data.

[0086] For example, assuming each grid cell is independent, for a point in the environment, let p(s=1) represent the probability of being in a free state, and p(s=0) represent the probability of being in an occupied state; the sum of the two is 1. Further, using... As the state of the point, the larger the ratio, the more likely the point is to be an obstacle. After processing, the explored area is marked as 0, the unexplored area is marked as -1, and the obstacle is marked as 1.

[0087] Step 101: Construct a logarithmic distance path loss model for the wireless access point using the least squares method based on data from multiple reference points.

[0088] In practical applications, the robot constructs a logarithmic distance path loss model for the wireless access point (AP) using the least squares method based on data from multiple reference points (RPs) within its surrounding area.

[0089] The logarithmic distance path loss model is as follows:

[0090]

[0091] In the formula, R d Let R be the RSS value at a distance d from AP. d0 X is the RSS value at a distance of d0, where d0 is usually taken as 1 meter, n is the attenuation factor, and X is the distance from d0 to d0. σ It is noise with a mean of 0 and a standard deviation of σ.

[0092] For simplification, we use D to represent the RSS value at a distance d0. Therefore, the logarithmic distance path loss model becomes a function of the variable d, with two unknown parameters D and n.

[0093] R d =D-10nlog 10 (d)+X σ .

[0094] Furthermore, it can be modified to be represented in vector form:

[0095] X σ =D-10nlog 10 (d)-R d

[0096]

[0097] Based on the data from M RP points (M>2), the M equations can be established as follows:

[0098]

[0099] In the formula, R dM X represents the RSS noise received from the AP at the Mth RP. σM Represents RSS noise, d M It is the distance between the Mth RP and AP. Assuming the RSS measurements are independent, the weight matrix P is set as an identity matrix with M×M dimensions.

[0100] Subsequently, the optimal parameters D and n of the logarithmic distance path loss model for wireless access points (APs) are obtained using the least squares method, ultimately constructing the logarithmic distance path loss model for APs within a small area. The least squares method finds the best function match for the data by minimizing the sum of squared errors. Using the least squares method, unknown data can be easily obtained, minimizing the sum of squared errors between the obtained data and the actual data, as follows:

[0101]

[0102] In the formula, To estimate the RSS value at the obtained distance d0, This is the estimated attenuation factor.

[0103] Step 102: Taking into account RSS loss, RSS noise and RSS threshold, determine the RSS value and SINR value of each interpolation point based on the constructed logarithmic distance path loss model and the location of the wireless access point.

[0104] In practical applications, considering RSS loss, RSS noise, and RSS threshold, the RSS and SINR values ​​for each interpolation point are calculated based on the constructed logarithmic distance path loss model and the known AP locations.

[0105] The mobile robot can also use its onboard wireless network card module to collect the RSS and SINR values ​​of reference points.

[0106] Step 103: Combine the RSS and SINR values ​​of all interpolation points with the sampled data at the reference point to obtain the wireless signal heatmap. The wireless signal heatmap includes the estimated RSS map M. e (P) And estimated SINR map M e (S) .

[0107] Estimated RSS plot M e (P) It is constructed using a logarithmic distance path loss model of a wireless access point (AP). The estimated SINR map M... e (S) The signal-to-noise ratio data in the image is calculated using the following formula:

[0108]

[0109] In the formula, This represents the received signal power, which is calculated from the coordinate x using the logarithmic distance path loss model. i,j RSS value. Indicates that it is located at J τThe τth interference source at coordinate x i,j The interference power at the location is calculated using the same method. The signal-to-noise ratio of the received signal.

[0110] Step 104: Fuse the estimated RSS graph and the estimated SINR graph to obtain the estimated communication feasibility graph. The estimated communication feasibility graph is denoted as Y, which is a B×A matrix. The elements of this B×A matrix are represented as y. i,j :

[0111]

[0112] In the formula, p min The set signal power threshold, s min y represents the set signal-to-noise ratio. i,j =1 indicates that both the received signal power and signal-to-noise ratio exceed the coordinate x. i,j The signal power threshold p set at the location min and signal-to-noise ratio s min .

[0113] Step 105: Obtain the first comprehensive feasible map based on the estimated communication feasible map and terrain feasible map. The first comprehensive feasible map is U1, which is also a B×A matrix. The elements of this B×A matrix are represented as u. i,j :

[0114]

[0115] In the formula, u i,j =1 indicates that both communication connection quality requirements and terrain passability requirements are met simultaneously. Represents coordinate x i,j Topographical accessibility data for the location.

[0116] Step 106: Based on the first comprehensive feasible graph, perform autonomous exploration using the fast expanding random tree autonomous exploration algorithm and mark the target point.

[0117] In practical applications, the mobile robot explores autonomously based on the first comprehensive feasible map U1. The autonomous exploration algorithm is an autonomous exploration algorithm based on a fast expanding random tree. The algorithm guides the robot to move by outputting target points. Because the exploration is based on the first comprehensive feasible map, the selection of target points takes into account both wireless signal distribution and terrain information.

[0118] The autonomous exploration algorithm consists of four parts: global boundary detection, local boundary detection, boundary point filtering, and task allocation.

[0119] Global and local boundary detection selects the boundary points to be explored next. Points reached by the rapidly expanding random tree (RRT) are considered boundary points if they are located in unknown areas. Global boundary detection is an RRT search that continues from the initial position. The global RRT tree is not reset but grows and refines over time, primarily to prevent the robot from missing opportunities to explore small corners of the map. Local boundary detection's RRT tree expands outward from the robot's current position, but the entire local random tree is destroyed after finding a target point to ensure the algorithm doesn't consume excessive memory. Then, after the robot reaches the target point, it expands outward again from the robot's current position, ensuring the exploration process is fast.

[0120] The boundary point filtering section clusters the searched points to form boundary points, using centroid clustering.

[0121] The task allocation section obtains the target point through benefit calculation. Since the clustering results are not unique, the robot must select one from multiple clustered boundary points as its target point to plan a path and reach the designated target point. The benefit calculation method selects the robot's next target point from the existing target points based on navigation cost (N) and information gain (I), as shown in the following formula:

[0122] R(x fp )=λh(x fp ,x r )I(x fp )-N(x fp ),

[0123]

[0124] Where, x fp Given boundary points, x r Let I(x) be the robot's current position, λ be a user-defined weight constant, and I(x) be the robot's current position. fp ) represents the information gain, N(x) fp ) represents the path loss, h(x) fp ,x r ) represents the hysteresis gain, h gain This is an intermediate parameter. If the distance between the given boundary point and the current robot position is greater than h... rad (h rad (It is set manually based on experience), then h(x) fp ,x r h(x) = 1. Otherwise, h(x) = 1. fp ,x rThe parameter ) > 1 indicates that the robot is biased towards exploring nearby boundary points to avoid overlap. By finding the boundary point with the largest reward R value as the target point for movement, the robot continuously explores unknown areas and eventually achieves a complete search.

[0125] Step 107: Obtain the real RSS graph and the real SINR graph, and merge the real RSS graph and the real SINR graph to obtain the real communication feasibility graph.

[0126] Step 108: Obtain the second comprehensive feasible map based on the actual communication feasible map and the terrain feasible map.

[0127] In practical applications, steps 107 and 108 can be based on the actual RSS images collected by the robot during its movement. r (P) Real SINR image M r (S) Similar to the methods provided in steps 103-105, the actual RSS image M... r (P) Real SINR image M r (S) And topographical feasibility map M (C) The fusion yields the second comprehensive feasible graph U2.

[0128] Step 109: Based on the second comprehensive feasible graph, use the A* algorithm to perform path planning and control the mobile robot to reach the marked target point along the planned path.

[0129] The specific path planning method is as follows:

[0130] Define the movement path of the mobile robot as {z1,...,z...} K}, where z k Let x be an element in region X, k∈{1,...,K}. Compared to other graph-based and machine learning-based algorithms, the A* algorithm obtains the optimal solution with higher search efficiency. In the A* algorithm, for each iteration, the current coordinate x... i,j Evaluation function f t (x i,j U) is:

[0131] f t (x i,j ,U)=g t (x i,j ,U)+h(x i,j ).

[0132] Evaluation function f t (x i,j U) represents the distance from the starting point z1 via coordinate x, provided that a stable communication connection is maintained.i,j to target point z F The path cost.

[0133] in, This represents the distance from the starting point z1 to x in the comprehensive feasible region U. i,j The shortest path cost. h(x) i,j )=||x i,j -z F || es , indicating from coordinate x i,j To target point z F The path estimation cost. Let h * (x i,j ) indicates from x i,j To z F The true path length can be determined by considering all x coordinates. i,j Satisfy h(x) i,j )≤h * (x i,j The A* algorithm can then converge to the optimal solution, yielding a shortest path.

[0134] Because path planning is based on the second comprehensive feasible map, the movement path takes into account both the distribution of wireless signals and terrain information.

[0135] Step 110: The robot continuously updates its position and optimal target point during its movement. When the optimal target point changes, the planned path is updated to allow the robot to explore the new optimal target point.

[0136] like Figure 2 As shown, in practical applications, the robot's movement can be achieved by using an on-board control unit to drive the motor.

[0137] Based on the above description, the present invention has the following advantages compared to the prior art:

[0138] 1) This invention uses the least squares method based on multiple reference point data, combined with robot position information and environmental terrain data, to construct a wireless signal heat map, which can accurately assess the strength and signal-to-noise ratio distribution of wireless signals in the target area, providing real-time communication signal status assessment for mobile robots.

[0139] 2) This invention integrates RSS maps, SINR maps, and topographic feasibility maps to obtain a comprehensive feasibility map. The comprehensive feasibility map considers communication connection quality requirements and terrain accessibility requirements, thereby effectively assessing communication feasibility within a region.

[0140] 3) The path planning algorithm of this invention adopts the A* algorithm and is based on a comprehensive feasible graph, enabling the mobile robot to select the optimal exploration path according to the wireless signal distribution and terrain information. This path planning method, which comprehensively considers wireless signal and terrain information, improves exploration efficiency and makes the path more reasonable.

[0141] 4) This invention provides an autonomous exploration algorithm based on a fast expanding random tree, enabling mobile robots to explore autonomously in unknown environments. By comprehensively considering the distribution of wireless signals and terrain information, the autonomous exploration algorithm guides the mobile robot to explore unknown areas, ultimately achieving a complete search.

[0142] 5) This invention enables mobile robots to autonomously collect and process data during the exploration process. Specifically, it integrates the actual collected real RSS map, real SINR map and terrain feasibility map to obtain a second comprehensive feasibility map, providing real-time data support for path planning.

[0143] 6) This invention continuously updates the robot's position and optimal target point during movement. When the optimal target point changes, the planned path is updated to allow the robot to explore the new optimal target point. This dynamic adjustment ensures that the robot can adapt to environmental changes at any time and maximize the completion of the exploration task.

[0144] Furthermore, the present invention also provides an autonomous exploration system that integrates wireless signal heatmaps, applicable to the aforementioned autonomous exploration method that integrates wireless signal heatmaps. In this invention, the system includes: a terrain feasibility map construction module, a logarithmic distance path loss model construction module, an interpolation point data determination module, a wireless signal heatmap construction module, an estimated communication feasibility map construction module, a first comprehensive feasibility map determination module, a first autonomous exploration module, a real communication feasibility map construction module, a second comprehensive feasibility map determination module, a path planning module, and a second autonomous exploration module.

[0145] The terrain feasibility map construction module is used to map unknown environments using real-time positioning and mapping methods to obtain terrain feasibility maps.

[0146] The logarithmic distance path loss model building module is used to construct a logarithmic distance path loss model for wireless access points using the least squares method based on data from multiple reference points.

[0147] The interpolation point data determination module is used to determine the RSS and SINR values ​​of each interpolation point based on the constructed logarithmic distance path loss model and the location of the wireless access point, taking into account RSS loss, RSS noise, and RSS threshold.

[0148] The wireless signal heatmap construction module combines the RSS and SINR values ​​of all interpolation points with the sampling data at the reference point to obtain a wireless signal heatmap. The wireless signal heatmap includes an estimated RSS map and an estimated SINR map.

[0149] The estimated communication feasibility graph construction module is used to fuse the estimated RSS graph and the estimated SINR graph to obtain the estimated communication feasibility graph.

[0150] The first comprehensive feasible map determination module is used to obtain the first comprehensive feasible map based on the estimated communication feasible map and the terrain feasible map.

[0151] The first autonomous exploration module is used to perform autonomous exploration based on the first comprehensive feasible graph using an autonomous exploration algorithm of a fast expanding random tree, and to mark the target points.

[0152] The Real Communication Feasibility Graph Construction Module is used to obtain the real RSS graph and the real SINR graph, and to merge the real RSS graph and the real SINR graph to obtain the real communication feasibility graph.

[0153] The second comprehensive feasible map determination module is used to obtain a second comprehensive feasible map based on the actual communication feasible map and terrain feasible map.

[0154] The path planning module is used to perform path planning based on the second comprehensive feasible graph using the A* algorithm, and to control the mobile robot to reach the marked target point along the planned path.

[0155] The second autonomous exploration module is used to continuously update the robot's position and target point. When the target point changes, the path is replanned so that the robot can explore towards the updated target point.

[0156] In summary, the autonomous exploration method of this invention integrates wireless signal heatmaps, enabling real-time assessment of the status of communication facilities. By constructing a wireless signal heatmap, the mobile robot can perceive the strength and coverage of wireless signals within a target area, thereby avoiding communication interruptions. This allows the mobile robot to promptly feed back the detected information to the user or intelligent processing center via wireless signals in real time, ensuring the real-time nature and reliability of communication.

[0157] The path planning method of this invention employs the A* algorithm based on a comprehensive feasible graph, which comprehensively considers the distribution of wireless signals and terrain information. Therefore, the mobile robot can select the optimal path during exploration, avoiding the problem of unreasonable exploration paths in existing technologies. This improves exploration efficiency and success rate, enabling the mobile robot to complete exploration tasks more effectively.

[0158] In this invention, the construction of the wireless signal heatmap does not require the extensive data collection and test point selection using handheld wireless testing instruments as in existing technologies. Instead, wireless signal coverage can be quickly obtained through autonomous robot exploration and data processing. This method is simpler and more accurate, avoiding the cumbersome and inaccurate testing methods of existing technologies, and ensuring that the generated wireless signal heatmap is more reliable.

[0159] This invention provides an autonomous exploration algorithm based on a fast expanding random tree, enabling mobile robots to explore unknown environments autonomously. By comprehensively considering the distribution of wireless signals and terrain information, the autonomous exploration algorithm guides the mobile robot to explore unknown areas, ultimately achieving a complete search. In the path planning stage, the algorithm uses the A* algorithm to plan the path by combining the feasible map and the target point output by the target selection module, ensuring the robot travels to the target point along the optimal path. This autonomous exploration and path planning approach gives the mobile robot greater flexibility and intelligence, allowing it to better adapt to different exploration tasks.

[0160] As can be seen, this invention solves the problems of communication interruption, low exploration efficiency, and unreasonable exploration paths in existing technologies by integrating an autonomous exploration method and system based on wireless signal heatmaps. By considering communication factors, rationally planning exploration paths, conducting simple and accurate wireless signal coverage testing, and combining autonomous exploration with path planning, the mobile robot can perform exploration operations in unknown environments more efficiently, reliably, and intelligently.

[0161] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0162] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for autonomous exploration that integrates wireless signal heatmaps, characterized in that, include: A real-time positioning and mapping method is used to map the unknown environment and obtain a topographical feasibility map; A logarithmic distance path loss model for wireless access points is constructed using the least squares method based on data from multiple reference points. Taking into account RSS loss, RSS noise, and RSS threshold, the RSS and SINR values ​​of each interpolation point are determined based on the constructed logarithmic distance path loss model and the location of the wireless access point. By combining the RSS and SINR values ​​of all interpolation points with the sampled data at the reference point, a wireless signal heatmap is obtained; the wireless signal heatmap includes an estimated RSS map and an estimated SINR map. The estimated communication feasibility graph is obtained by fusing the estimated RSS graph and the estimated SINR graph; the estimated communication feasibility graph is as follows: matrix, The first in the matrix Each element is represented as : ； In the formula, The received signal power, The signal-to-noise ratio of the received signal. The set signal power threshold, The signal-to-noise ratio is set; A first comprehensive feasible map is obtained based on the estimated communication feasible map and the terrain feasible map; the first comprehensive feasible map is... matrix, The first of the matrix Each element is represented as : ； In the formula, Representing coordinates Terrain accessibility data at the location; Based on the first comprehensive feasible graph, an autonomous exploration algorithm using a fast expanding random tree is used to perform autonomous exploration and mark target points; Obtain the real RSS graph and the real SINR graph, and fuse the real RSS graph and the real SINR graph to obtain the real communication feasibility graph; A second comprehensive feasible map is obtained based on the actual communication feasible map and the terrain feasible map; Based on the second comprehensive feasible diagram, using A The algorithm performs path planning and controls the mobile robot to reach the marked target point along the planned path. The robot's position and target point are continuously updated. When the target point changes, the path is replanned so that the robot can explore the updated target point.

2. The autonomous exploration method for fusion wireless signal heatmaps according to claim 1, characterized in that, The instantaneous localization and mapping method uses the Gmapping algorithm to construct the terrain feasibility map; the terrain feasibility map is a two-dimensional raster map, which divides the environment into raster scales in a two-dimensional plane.

3. The autonomous exploration method for fusion wireless signal heatmaps according to claim 2, characterized in that, The terrain feasibility map is constructed using the Gmapping algorithm, specifically including: Particle filtering is used to determine the robot's pose, transforming the robot trajectory into an incremental estimation problem. A kinematic model is used to propagate the state of each particle to obtain the predicted trajectory for each particle. For each particle after state propagation, the observation model is used to calculate weights and normalize them to obtain the robot trajectory at the current moment. Then, based on the estimated trajectory and observation data, a map is constructed to obtain the terrain feasibility map.

4. The autonomous exploration method for fusion of wireless signal heatmaps according to claim 1, characterized in that, The sampling data at the reference point location is collected using a wireless network card module mounted on the robot; the sampling data at the reference point location includes the RSS value and SINR value of the reference point.

5. The autonomous exploration method for fusion wireless signal heatmaps according to claim 1, characterized in that, The autonomous exploration algorithm includes four operation steps: global boundary detection, local boundary detection, boundary point filtering, and task allocation. The global boundary detection is an RRT search method that is continuously executed from the initial position; The local boundary detection uses the RRT search method, which expands outward from the robot's current position as the root node. After finding a target point, the RRT tree is destroyed, and after the robot reaches the target point, it expands outward from the robot's current position as the root node. The boundary point filtering method uses centroid clustering to cluster the searched target points to form boundary points; The task allocation is based on the target point calculated through revenue calculation.

6. An autonomous exploration system integrating wireless signal heatmaps, characterized in that, The autonomous exploration method is applied to the fused wireless signal heatmap as described in any one of claims 1-5; the system comprises: The terrain feasibility map construction module is used to construct a map of an unknown environment using real-time positioning and mapping methods to obtain a terrain feasibility map; The logarithmic distance path loss model construction module is used to construct a logarithmic distance path loss model for wireless access points based on data from multiple reference points using the least squares method. The interpolation point data determination module is used to determine the RSS and SINR values ​​of each interpolation point based on the constructed logarithmic distance path loss model and the location of the wireless access point, taking into account RSS loss, RSS noise and RSS threshold. A wireless signal heatmap construction module is used to combine the RSS and SINR values ​​of all interpolation points with the sampling data at the reference point location to obtain a wireless signal heatmap; the wireless signal heatmap includes an estimated RSS map and an estimated SINR map; An estimated communication feasibility graph construction module is used to fuse the estimated RSS graph and the estimated SINR graph to obtain an estimated communication feasibility graph; The first integrated feasible map determination module is used to obtain a first integrated feasible map based on the estimated communication feasible map and the terrain feasible map; The first autonomous exploration module is used to perform autonomous exploration based on the first comprehensive feasible graph using an autonomous exploration algorithm of fast expanding random tree, and to mark target points; The real communication feasibility graph construction module is used to obtain real RSS graphs and real SINR graphs, and fuse the real RSS graphs and real SINR graphs to obtain real communication feasibility graphs; The second integrated feasible map determination module is used to obtain a second integrated feasible map based on the actual communication feasible map and the terrain feasible map; The path planning module is used to utilize A based on the second comprehensive feasible map. The algorithm performs path planning and controls the mobile robot to reach the marked target point along the planned path. The second autonomous exploration module is used to continuously update the robot's position and target point. When the target point changes, the path is replanned so that the robot can explore towards the updated target point.

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