Autonomous Hierarchical Exploration and Mapping Method, Device and System for Ground Mobile Robots

Abstract

The present invention provides a method, device and system for autonomous hierarchical exploration and mapping of a ground mobile robot. First, an autonomous exploration boundary is set, and the system parameters corresponding to the autonomous hierarchical exploration and mapping of the corresponding environment are loaded. Environmental data is obtained through sensors, and an initial environmental map is established using the SLAM algorithm. The autonomous mapping is divided into a two-layer structure: global exploration mapping and local exploration mapping. The global exploration mapping updates and divides the exploration subspace in real time and plans the shortest global exploration path. Based on an incremental cache topological-lattice hybrid map, the local exploration mapping plans the shortest local exploration path and realizes real-time obstacle avoidance. The robot constructs and saves the overall three-dimensional and two-dimensional environmental maps and autonomously returns to the starting point after the autonomous mapping is completed. The purpose of realizing autonomous mapping and safe return of a ground mobile autonomous robot in an unknown complex environment is achieved, and while ensuring the quality and efficiency of map construction, the autonomy of the ground mobile robot is enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of robotics, and particularly relates to a method, device, and system for autonomous hierarchical exploration and mapping of a ground mobile robot. Background Art

[0002] The application scenarios of ground mobile robots are extensive and complex, including but not limited to harsh urban search and rescue (USAR) environments, urban combat environments, and cave system disasters. In these environments, the robot needs to explore unknown and potentially dangerous environments.

[0003] In such scenarios, conventional positioning systems, such as the Global Navigation Satellite System (GNSS) and wireless positioning systems, may not function properly due to environmental limitations and interference. Therefore, the ground mobile robot needs to build a map of the environment and perform positioning while executing tasks to ensure that it can complete the tasks safely and effectively.

[0004] This technology is generally referred to as SLAM (Simultaneous Localization And Mapping), and its main goal is to solve the problems of map building and positioning navigation of mobile robots in unknown environments. However, traditional SLAM methods often rely on manual operation of the robot's motion trajectory, which not only reduces the autonomy of the robot but also makes it difficult to implement in the above scenarios with limited communication conditions.

[0005] A mapping method known to the applicant uses sensors to collect environmental data, and then uses the Simultaneous Localization And Mapping (SLAM) algorithm to determine the areas to be visited on a pre-estimated initial SLAM environmental map. For the identified areas to be visited, this patent adopts an active exploration strategy for path planning, selects an exploration path from the planned paths according to the principle of the highest utility, and executes the path exploration task. According to the results of the path exploration, this patent can build an environmental map corresponding to the autonomous exploration. However, this method has the following deficiencies:

[0006] Insufficient path planning: In this technical solution, although the principle of the highest utility is proposed to select the exploration path, there is no clear path planning algorithm and real-time obstacle avoidance strategy, which may affect the exploration efficiency and safety of the robot.

[0007] Autonomy of the robot: Although this technical solution emphasizes improving the autonomy of the robot, in the actual exploration process, if the environment changes or unknown obstacles are encountered, the robot may not be able to respond autonomously.

[0008] Limitations of a single SLAM method: This technical solution mainly relies on the SLAM algorithm to construct an environmental map. However, the SLAM algorithm has problems with positioning uncertainty in complex environments, and the maps created by traditional SLAM algorithms are not conducive to the autonomous exploration of robots.

[0009] Therefore, the current challenge is to develop a new method that can enhance the autonomy of ground mobile robots while ensuring the quality and efficiency of map construction, enabling them to autonomously explore and map the environment without human intervention. Summary of the Invention

[0010] To solve the above technical problems, the present invention proposes an autonomous hierarchical exploration and mapping method, device, and system for ground mobile robots.

[0011] In the first aspect of the present invention, an autonomous hierarchical exploration and mapping method for ground mobile robots is disclosed. The method is implemented by an autonomous hierarchical exploration and mapping device for ground mobile robots. Sensors are provided on the ground mobile robot to collect relevant information about the exploration environment and acquire surrounding environment data.

[0012] The method includes:

[0013] S1, Set the exploration area boundary, load the autonomous mapping configuration parameters for the corresponding environment, and use the SLAM algorithm based on sensor data to establish a preliminary environmental map.

[0014] S2, According to the exploration area boundary, divide the exploration area into multiple subspaces, and globally plan the optimal path through all subspaces.

[0015] S3, According to the subspace boundary points globally planned and the data input by the sensor, in each subspace, generate the optimal exploration target points based on the incremental cache topological-grid hybrid map (TGHM), and use the improved TEB algorithm to plan the best exploration path between the target points, and avoid dynamic and static obstacles in real time.

[0016] S4, The ground mobile robot completes the exploration according to the best exploration path and completes the map construction of the entire exploration area according to the exploration results.

[0017] In step S3, the specific process of generating the optimal exploration target points based on the incremental cache topological-grid hybrid map (TGHM) in each subspace according to the subspace boundary points globally planned and the data input by the sensor includes:

[0018] S31, Apply a candidate target point generation method based on geometric rules to quickly extract candidate target points.

[0019] S32. Construct an incremental cache topology-lattice hybrid map and evaluate the information gain of each candidate topology node thereon;

[0020] Among them, the incremental cache topology-lattice hybrid map is a fusion map of a topology map and a grid map; candidate target points serve as candidate topology nodes;

[0021] S33. Select the node with the best evaluation value as the next target point, and update the topology map after the ground mobile robot moves towards it;

[0022] Among them, when the topology map is updated, each candidate node performs grid traversal within a certain range to calculate the information gain around it.

[0023] In step S32, the information gain of each candidate topology node is evaluated using a utility function, where the utility function is defined as:

[0024] U = N unknown exp(-λL(T))

[0025] Among them, λ is a positive constant, L(T) is the topological distance between the target node T and the current node, and the information gain can be calculated according to the number of unknown grids, represented by N unknown The candidate topology node with the maximum U value is selected as the next target point.

[0026] In step S3, the steps of using the improved TEB algorithm to plan the best exploration path between target points and avoiding dynamic and static obstacles in real time include:

[0027] S34. Use the A* algorithm to plan the global path of the subspace in local exploration. In the initialization stage, select a part of the global path as the initial local path, and convert this initial path into a trajectory composed of a pose sequence and a time sequence;

[0028] S35. Aim at minimizing the total cost function of the TEB algorithm and perform iterative calculations to obtain the best exploration path between target points;

[0029] The total cost function f(B) is defined as:

[0030]

[0031] Among them, B is the path, γ k is the weight of each target point, and f k (B) is the cost function of each target.

[0032] In step S35, in each iteration, a new pose is inserted or a previously processed pose is removed to ensure that the length of the optimized trajectory remains unchanged, and at the same time, the obstacle information in the cost function is updated in real time.

[0033] In step S1, the step of establishing a preliminary environment map using the SLAM algorithm includes:

[0034] Perform motion de-skewing on each point in the point cloud data obtained by the sensor, and extract key features from the de-skewed point cloud along lines and edges;

[0035] Compare and scan-match the key features with a part of the local key frames in the sliding window filter;

[0036] Determine the loop closure factor through the Euclidean distance metric, use the iSAM2 solver to add a new loop closure factor to the graph, and optimize the odometry of the graph through GTSAM;

[0037] According to the optimized odometry, merge the point cloud key frames into the initial environment map.

[0038] Step S2 specifically includes:

[0039] Construct a sparse random roadmap within the subspace covered by the previously explored trajectory;

[0040] Use the A* search algorithm to search on the random roadmap to find the shortest path between each subspace;

[0041] Use the TSP solver to determine the optimal order of visiting subspaces, and thus obtain the optimal path between all subspaces.

[0042] Step S4 specifically includes:

[0043] The ground mobile robot reaches the boundary point of the subspace, completes the exploration of the subspace and the construction of the subspace map, and moves to the next unexplored subspace;

[0044] The ground mobile robot reaches the edge of the exploration area, completes the exploration of the entire area, and completes the construction of the three-dimensional map of the entire exploration area;

[0045] The ground mobile robot returns to the exploration starting point, generates a two-dimensional map based on the three-dimensional map, and saves the map with a named number.

[0046] The second aspect of the present invention provides a ground mobile robot autonomous hierarchical exploration and mapping device. A sensor is provided on the ground mobile robot to collect relevant information about the exploration environment and collect surrounding environment data;

[0047] The device includes:

[0048] The global exploration module is configured to divide the exploration area into multiple sub-spaces according to the exploration area boundary and plan the optimal path through all sub-spaces;

[0049] The local path and obstacle avoidance module is configured to generate the optimal exploration target points based on the incremental cached topological-grid hybrid map (TGHM) according to the boundary points of the sub-spaces globally planned and the data input by the sensors, plan the best exploration path between the target points by using the improved TEB algorithm, and avoid dynamic and static obstacles in real time;

[0050] The map construction and management module is configured to receive the data input by the sensors, construct the three-dimensional and two-dimensional environmental maps of the exploration area based on the SLAM algorithm, save the constructed maps with named numbers, and output the positioning and trajectory information of the ground mobile robot in real time; at the same time, output the environmental map feature information to the global exploration module and the local path and obstacle avoidance module for corresponding path planning;

[0051] The mobile robot driving module is configured to receive the motion instructions of the ground mobile robot output by the local path and obstacle avoidance module and control the movement of the ground mobile robot chassis;

[0052] The user display and communication module is configured to display the constructed environmental map in real time and display the position and trajectory of the ground mobile robot in the environmental map; realize the communication between the ground mobile robot and the user terminal and monitor the status of the ground mobile robot in real time.

[0053] The third aspect of the present invention discloses a ground mobile robot autonomous hierarchical exploration and mapping system, the system includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, it realizes the steps in any one of the ground mobile robot autonomous hierarchical exploration and mapping methods in the first aspect of the present disclosure.

[0054] The fourth aspect of the present invention discloses a computer-readable storage medium. A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, it realizes the steps in any one of the ground mobile robot autonomous hierarchical exploration and mapping methods in the first aspect of the present disclosure.

[0055] In summary, the scheme proposed in the present invention has the following technical effects: the present invention first sets the autonomous exploration boundary, loads the system parameters corresponding to the autonomous hierarchical exploration mapping of the corresponding environment, obtains environmental data through sensors, and uses the SLAM algorithm to establish an initial environmental map; for large-scene autonomous mapping, the autonomous mapping is divided into a two-layer structure: global exploration mapping and local exploration mapping; the global exploration mapping is updated and divided into exploration subspaces in real time, and the global exploration shortest path is planned; based on the incremental cache topology-grid hybrid map, the local exploration mapping will have both the shortest time and the maximum area coverage principles, plan the local exploration shortest path and achieve real-time obstacle avoidance; the robot will construct and save the overall three-dimensional and two-dimensional environmental maps, and autonomously return to the starting point after the autonomous mapping is completed; the purpose of realizing the autonomous mapping and safe return of the ground mobile autonomous robot in an unknown complex environment is achieved, which makes up for the instability of the traditional SLAM method in constructing maps in complex environments, and can enhance the autonomy of the ground mobile robot while ensuring the quality and efficiency of map construction.

[0056] The main protection points of the present invention are:

[0057] 1. Hierarchical mapping: This technology divides autonomous mapping of large scenes into two levels: global exploration mapping and local exploration mapping. Global exploration mapping is used to update and divide exploration subspaces in real time, and plan the shortest path for global exploration. Local exploration mapping is based on the incremental cache topology-grid hybrid map, plans the shortest path for local exploration, and achieves real-time obstacle avoidance. This hierarchical structure can improve the efficiency and quality of map construction.

[0058] 2. An exploration method based on the incremental cache topology-grid hybrid map (TGHM) can plan the shortest local exploration path and achieve real-time obstacle avoidance while taking into account the principles of shortest time and maximum area coverage, thereby improving the robot's autonomous exploration capability.

[0059] 3. Real-time obstacle avoidance and autonomous return to the starting point. During autonomous exploration, the robot can achieve real-time obstacle avoidance. After completing global exploration and map construction, the robot can autonomously return to the starting point, which enhances the robot's autonomy and improves its survivability in unknown and complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0061] Figure 1Schematic flowchart of a method for autonomous hierarchical exploration and mapping of a ground mobile robot based on an incremental cache topological-lattice hybrid map according to an embodiment of the present invention;

[0062] Figure 2 Flowchart of a method for autonomous hierarchical exploration and mapping of a ground mobile robot based on an incremental cache topological-lattice hybrid map according to an embodiment of the present invention;

[0063] Figure 3 Schematic internal structure diagram of a device for autonomous hierarchical exploration and mapping of a ground mobile robot based on an incremental cache topological-lattice hybrid map according to an embodiment of the present invention;

[0064] Figure 4 Schematic functional module structure diagram of a system for autonomous hierarchical exploration and mapping of a ground mobile robot based on an incremental cache topological-lattice hybrid map according to an embodiment of the present invention.

[0065] Figure 5 Schematic functional module structure diagram of another system for autonomous hierarchical exploration and mapping of a ground mobile robot based on an incremental cache topological-lattice hybrid map according to an embodiment of the present invention. Detailed implementation manners

[0066] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are only a part rather than all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0067] It can be understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of this application, a first image may be referred to as a second image, and similarly, a second image may be referred to as a first image. Both the first image and the second image are images, but they are not the same image.

[0068] In order to solve the problems of low mapping efficiency and quality and uncertain positioning of existing SLAM mapping methods for ground mobile robots in unknown complex environments, this embodiment provides a method, device and system for autonomous mapping and positioning of ground mobile autonomous robots. In addition, the autonomous mapping and positioning of the robot are divided into two levels: global exploration mapping and local exploration mapping, where the local map is based on an incremental cache topological-lattice hybrid map, thereby improving the efficiency of the robot's autonomous mapping and the accuracy of positioning, and greatly enhancing the robot's autonomous ability in unknown complex scenarios.

[0069] In order to solve the problems of low mapping efficiency and quality and uncertain positioning of existing SLAM mapping methods for ground mobile robots in unknown complex environments, this embodiment provides a method, device and system for autonomous mapping and positioning of ground mobile autonomous robots. In addition, the autonomous mapping and positioning of the robot are divided into two levels: global exploration mapping and local exploration mapping, where the local map is based on an incremental cache topological-lattice hybrid map, thereby improving the efficiency of the robot's autonomous mapping and the accuracy of positioning, and greatly enhancing the robot's autonomous ability in unknown complex scenarios.

[0070] As Figure 1 shown, Figure 1 FIG. is a schematic flowchart of an implementation manner of the autonomous hierarchical exploration mapping method for a ground mobile robot based on an incremental cache topological-lattice hybrid map according to the present invention. The autonomous hierarchical exploration mapping method for a ground mobile robot based on an incremental cache topological-lattice hybrid map according to the present invention can be implemented as the following steps S1 to S4:

[0071] The method is implemented by an autonomous hierarchical exploration mapping device for a ground mobile robot; a sensor is provided on the ground mobile robot for collecting relevant information of the exploration environment and collecting surrounding environment data;

[0072] S1, set the exploration area boundary, load the autonomous mapping configuration parameters of the corresponding environment, and establish a preliminary environment map based on the sensor data using the SLAM algorithm;

[0073] In the embodiment of the present invention, the user can set the autonomous mapping exploration boundary of the ground mobile robot through the operation interface. The exploration area is composed of polygons, and the user sets the values of the polygon vertices relative to the starting point of the robot. And the set polygon area values are saved in the PLY file format.

[0074] Next, the user selects the environment type of the current autonomous mapping, including: open park, wild forest, indoor room, garage tunnel, etc.; the autonomous mapping program will load the autonomous mapping configuration parameters of the corresponding environment, including: sensor detection distance, map resolution, static obstacle judgment threshold, etc.

[0075] In the embodiments of the present invention, the robot is equipped with appropriate sensors to collect relevant information about the explored environment and acquire the surrounding environment data. These sensors include, but are not limited to, an inertial measurement unit (IMU), lidar, and wheel odometer. According to different environmental and scenario requirements, the sensors equipped on this mobile robot can be extended.

[0076] With the help of the SLAM (Simultaneous Localization and Mapping) algorithm, we can establish an initial environmental map and achieve the initial positioning of the robot. The steps of establishing the initial environmental map by the SLAM algorithm can be divided into:

[0077] Remove the motion skew for each point in the point cloud data obtained by the sensor, and extract key features along lines and edges from the motion-skew-removed point cloud;

[0078] Compare and scan-match the key features with a part of the local key frames in the sliding window filter;

[0079] Determine the loop closure factor through the Euclidean distance metric, use the iSAM2 solver to add new loop closure factors to the graph, and optimize the odometry of the graph through GTSAM (Georgia Tech Smoothing and Mapping, a C++ optimization library based on factor graphs);

[0080] According to the optimized odometry, merge the point cloud key frames into the initial environmental map.

[0081] S2. According to the boundary of the exploration area, divide the exploration area into multiple subspaces and globally plan the optimal path through all subspaces;

[0082] Step S2 specifically includes:

[0083] Construct a sparse random roadmap within the subspaces covered by the past exploration trajectory;

[0084] Use the A* search algorithm to search on the random roadmap to find the shortest path between each subspace;

[0085] Use the TSP solver to determine the optimal order of visiting subspaces, and then obtain the optimal path between all subspaces.

[0086] In the example of the present invention, first, all spaces in the initial environmental map are divided into cube sub-spaces of equal size. Each sub-space records the information of the covered and uncovered surfaces therein. During the exploration process, each sub-space has a corresponding state, and these states include "unexplored", "being explored", and "explored". If a sub-space does not contain any covered or uncovered surfaces, its state is "unexplored". If a sub-space only contains covered surfaces, its state is "explored". If a sub-space contains any uncovered surfaces, its state is "being explored". In the global planning, only the sub-spaces with the state of "being explored" will be considered.

[0087] S3. According to the boundary points of the sub-spaces in the global planning and the data input by the sensors, in each sub-space, an optimal exploration target point is generated based on the incremental cached topological-grid hybrid map (TGHM), and the best exploration path between the target points is planned by using the improved TEB algorithm, and dynamic and static obstacles are avoided in real time;

[0088] In step S3, the specific steps of generating an optimal exploration target point based on the incremental cached topological-grid hybrid map (TGHM) in each sub-space according to the boundary points of the sub-spaces in the global planning and the data input by the sensors are as follows:

[0089] S31. Apply a candidate target point generation method based on geometric rules to quickly extract candidate target points;

[0090] S32. Construct an incremental cached topological-grid hybrid map, and evaluate the information gain of each candidate topological node thereon;

[0091] Among them, the incremental cached topological-grid hybrid map is a fusion map of a topological map and a grid map; the candidate target points serve as candidate topological nodes;

[0092] In step S32, the utility function is used to evaluate the information gain of each candidate topological node. Among them, the utility function is defined as:

[0093] U = N unknown exp(-θL(T))

[0094] Among them, λ is a positive constant, L(T) is the topological distance between the target node T and the current node, and the information gain can be calculated according to the number of unknown grids, which is represented by N unknown The candidate topological node with the maximum U value is selected as the next target point.

[0095] S33. Select the node with the best evaluation value as the next target point, and update the topological map after the ground mobile robot moves towards it;

[0096] Among them, when the topological map is updated, each candidate node performs grid traversal within a certain range to calculate the information gain around it.

[0097] In the step of generating the optimal exploration target point, the robot first obtains the exploration target point from the global path, and then the local path planning module calculates the best path from the current position to the target point.

[0098] In the exploration method based on the incremental cache topological-grid hybrid map (TGHM), the mobile robot continuously moves along the frontier boundary, and the representation of the unknown environment is provided to the robot based on the incremental cache topological-grid hybrid map (TGHM). TGHM is the fusion of a topological map and a grid map. The former contains the information gain and motion cost of exploration, and the latter represents the established map for navigation and positioning.

[0099] In the incremental cache topological-grid hybrid map, the newly generated candidate target points are added to the topological map as candidate topological nodes. When the topological map is updated, each candidate node performs grid traversal within a certain range to calculate the information gain around it. This information gain is used to evaluate the exploration value of the candidate topological nodes.

[0100] The topological map records each visited and unvisited node. Based on the topological map, the topological distance between any two topological nodes can be quickly calculated.

[0101] Among them, the topological node can be expressed as:

[0102] T = {Parents, Children}

[0103] Among them, Parents and Children are the parent node and child node of the topological node respectively. The candidate topological node has only one parent node, which we define as:

[0104] T_c = {Parents, Children, U}

[0105] Among them, Pose is the pose of the robot at this node. If T_c is selected as the next target point. U is the value of the utility function explored at this position.

[0106] In step S3, the step of using the improved TEB algorithm to plan the best exploration path between target points and avoid dynamic and static obstacles in real time includes:

[0107] S34, Use the A* algorithm to plan the global path of the subspace in local exploration. In the initialization stage, select a part of the global path as the initial local path, and convert this initial path into a trajectory composed of a pose sequence and a time sequence;

[0108] In S35, iterative calculations are performed with the goal of minimizing the total cost function of the TEB algorithm to obtain the best exploration path between target points;

[0109] In step S35, in subsequent iterative solution cycles, each iteration is performed according to the number of sampling points n. During each iteration, new poses are inserted or previously processed poses are removed to ensure that the length of the optimized trajectory remains unchanged, and at the same time, the obstacle information in the cost function is updated in real time.

[0110] Shortest Euclidean distance penalty function f dis Connected to the pose vertices S i and S i+n The shortest distance constraint function can be expressed as follows:

[0111] f dis (S i+n , S i ) = (x i+n - x i ) 2 + (y i+n - y i )

[0112] The total cost function of the improved TEB algorithm is composed of the weighted sum of the penalty function and the objective function in the formula, and the corresponding optimization problem is to select the shortest path B to minimize the cost function. The total cost function f(B) is defined as:

[0113]

[0114] B * = min f(B)

[0115] where B is the path, B * is the optimal path, γ k is the weight of each target point, and f k (B) is the cost function of each target point.

[0116] In the step of using the improved TEB algorithm to plan the best exploration path between target points, the target points will be used as input, and the TEB (Timed Elastic Band) algorithm will search for the optimal path to reach the target points in real time. The TEB method will consider the dynamic constraints of the robot. This method not only considers the static optimal path problem, but also considers the optimization of both the path and time. This enables the robot to better adapt to the dynamic changes of the environment and avoid dynamic and static obstacles in real time.

[0117] At the same time, the heuristic idea of the A* algorithm is introduced into the TEB algorithm to improve the efficiency of the local path. The specific algorithm description is as follows.

[0118] The A* algorithm is a heuristic algorithm that takes into account the actual cost and the estimated cost. The evaluation function is used to estimate the total length of the path. The general cost estimation function is as follows:

[0119] f(n) = g(n) + h(n)

[0120] Where g(n) represents the actual cost from the starting point to the current node, and h(n) represents the estimated cost from the current node to the target node. The calculation formula of h(n) is as follows:

[0121]

[0122] Where (x n , y n ) represents the horizontal and vertical coordinates of the current point on the map, and (x G , y G ) represents the horizontal and vertical coordinates of the target point on the map.

[0123] When solving the local detour problem that may occur during the TEB path planning turn, the algorithm, inspired by the above formula, applies the shortest distance constraint to the local path to make it as close to the path edge as possible, thereby improving the local path planning efficiency of the unmanned vehicle.

[0124] S4. The ground mobile robot completes the exploration according to the optimal exploration path and constructs the map of the entire exploration area according to the exploration results.

[0125] Step S4 specifically includes:

[0126] The ground mobile robot reaches the boundary point of the subspace, completes the exploration of the subspace and the construction of the subspace map, and goes to the next unexplored subspace;

[0127] The ground mobile robot reaches the edge of the exploration area, completes the exploration of the entire area, and completes the construction of the three-dimensional map of the entire exploration area;

[0128] The ground mobile robot returns to the exploration starting point, generates a two-dimensional map according to the three-dimensional map, and saves the map with a named number.

[0129] As Figure 2 shown, Figure 2 is the flowchart of an implementation method of the autonomous hierarchical exploration and mapping method of the ground mobile robot based on the incremental cache topology-lattice hybrid map in the present invention. In Figure 2In the described embodiments, first, the ground mobile robot system completes initialization, including sensor initialization, loading of autonomous mapping parameters, setting of autonomous exploration boundaries, etc.; based on sensor data, it completes the perception of the surrounding environment to form an initial environmental map; in global path planning, the exploration area is divided into multiple sub-spaces, and the optimal paths between each sub-space are planned, and the paths are continuously updated according to the real-time updated environmental map; in local path planning, in the exploration method based on the incremental cached topological-grid hybrid map (TGHM), the incremental cached topological-grid hybrid map (TGHM) is used to generate and select the optimal exploration target point, and the optimal path for the robot to reach the target point is dynamically generated based on the improved TEB method of A*; when reaching the boundary point of the sub-space, the exploration of the sub-space and the construction of the sub-space map are completed, and then it goes to the next unexplored sub-space; when reaching the edge of the exploration area, the exploration of the entire area is completed, and the three-dimensional map of the entire exploration area is constructed; it returns to the exploration starting point, and based on the three-dimensional map, a two-dimensional map is generated, and the map is named and numbered for storage.

[0130] Corresponding to Figure 1 and Figure 2 An embodiment provides a method for autonomous hierarchical exploration and mapping of a ground mobile robot based on an incremental cached topological-grid hybrid map. The second aspect of the present invention provides a device for autonomous hierarchical exploration and mapping of a ground mobile robot, and this device can implement Figure 1 and Figure 2 the steps of the method for autonomous hierarchical exploration and mapping of a ground mobile robot based on an incremental cached topological-grid hybrid map described in the embodiment, and this device can be configured on the ground mobile robot to complete the operation of autonomous hierarchical exploration and mapping. Sensors are provided on the ground mobile robot to collect relevant information about the exploration environment and acquire data of the surrounding environment;

[0131] Based on the description of the above embodiments, as Figure 3 shown, Figure 3 is a schematic diagram of the functional modules of an embodiment of the device for autonomous hierarchical exploration and mapping of a ground mobile robot based on an incremental cached topological-grid hybrid map of the present invention; in the embodiments of the present invention, in terms of function, the device for autonomous hierarchical exploration and mapping of a ground mobile robot includes:

[0132] A global exploration module, configured to divide the exploration area into multiple sub-spaces according to the exploration area boundary and plan the optimal paths through all sub-spaces;

[0133] The local path and obstacle avoidance module is configured to generate an optimal exploration target point based on the subspace boundary points planned globally and the data input by the sensors, based on an incremental cache topological-grid hybrid map (TGHM), and use an improved TEB algorithm to plan the best exploration path between the target points, and avoid dynamic and static obstacles in real time;

[0134] The map construction and management module is configured to receive the data input by the sensors, construct a three-dimensional and two-dimensional environmental map of the exploration area based on the SLAM algorithm, save the constructed map with a named number, and output the positioning and trajectory information of the ground mobile robot in real time; at the same time, output the environmental map feature information to the global exploration module and the local path and obstacle avoidance module for corresponding path planning;

[0135] The mobile robot driving module is configured to receive the motion instructions of the ground mobile robot output by the local path and obstacle avoidance module and control the movement of the ground mobile robot chassis;

[0136] The user display and communication module is configured to display the constructed environmental map in real time and display the position and trajectory of the ground mobile robot in the environmental map; realize the communication between the ground mobile robot and the user terminal and monitor the status of the ground mobile robot in real time.

[0137] The ground mobile robot autonomous hierarchical exploration and mapping device based on an incremental cache topological-grid hybrid map of the present invention sets an autonomous exploration boundary, loads the system parameters corresponding to autonomous mapping and positioning of the corresponding environment, obtains environmental data through sensors, and uses the SLAM algorithm to establish an initial environmental map; for large-scale scene autonomous mapping, the autonomous mapping is divided into a two-layer structure: global exploration mapping and local exploration mapping; the global exploration mapping updates and divides the exploration subspace in real time and plans the shortest global exploration path; based on the incremental cache topological-grid hybrid map, the local exploration mapping combines the principles of the shortest time and the largest area coverage, uses an improved TEB algorithm to plan the shortest local exploration path and realizes real-time obstacle avoidance; the robot will construct and save the overall three-dimensional and two-dimensional environmental map and autonomously return to the starting point after the autonomous mapping is completed; it achieves the purpose of realizing autonomous mapping and safe return of the ground mobile autonomous robot in an unknown complex environment, makes up for the instability of the traditional SLAM method in constructing maps in complex environments, and can enhance the autonomy of the ground mobile robot while ensuring the quality and efficiency of map construction.

[0138] The third aspect of the present invention discloses a ground mobile robot autonomous hierarchical exploration and mapping system. The ground mobile robot autonomous hierarchical exploration and mapping system includes a memory and a processor. When the processor executes the computer program, the steps in any one of the ground mobile robot autonomous hierarchical exploration and mapping methods in the first aspect of the present disclosure are implemented.

[0139] As shown in Figure 5 , the system includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, near field communication (NFC), or other technologies. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the electronic device, or an external keyboard, touchpad, or mouse, etc.

[0140] Those skilled in the art can understand that Figure 5 the structure shown in

[0141] is only a structural diagram of a part related to the technical solution of the present disclosure, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific system may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements. Figure 4 , as another embodiment, Figure 4 shows the internal structure of an implementation manner of the system.

[0142] In this instance, this system includes at least a memory, a data processor, a wireless communication interface, a network and serial interface, a vehicle driver, and sensors. The memory includes at least one type of readable storage medium, such as flash memory, a hard disk, a multimedia card, a card-type memory (such as an SD or DX memory), a magnetic memory, a disk, an optical disc, etc. The memory can be an internal storage unit of the device, an external storage device, or a combination of both. The memory can be used not only to store application software and various types of data on the device, such as the code of the autonomous exploration mapping program, but also to temporarily store data that has been output or will be output.

[0143] The data processor may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips, and is used to run the program code in the memory or process data, such as executing an autonomous multi-layer exploration mapping program, etc.

[0144] The network and serial interface are used to achieve the connection and communication between components. The network interface may include a wired network interface, and the serial interface includes serial interfaces such as 485 / 232, etc., which are used to establish communication connections between the data processor, memory and other devices, including sensors.

[0145] In addition, the electronic device may further include a user interface, such as a display, an input unit (such as a keyboard), a wired interface, a wireless interface, etc. In some instances, the display may be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (organic light emitting diode) toucher, etc.

[0146] The robot driver is used to receive the speed control instruction of the program and control the mobile robot to move towards the target point.

[0147] Sensors generally include lidar, binocular cameras, inertial measurement units (IMUs), wheel odometers, etc., which are used to output environmental perception data and hand it over to the autonomous multi-layer exploration and mapping program running in the data processor for autonomous exploration and mapping.

[0148] According to Figure 1 、 Figure 2 the embodiment of, the autonomous multi-layer exploration and mapping program is stored in the memory and can run on the data processor.

[0149] The embodiment of the present invention provides a system that sets an autonomous exploration boundary, loads the system parameters corresponding to the autonomous mapping and positioning of the corresponding environment, obtains environmental data through sensors, and uses the SLAM algorithm to establish an initial environmental map; for large-scale scene autonomous mapping, the autonomous mapping is divided into a two-layer structure: global exploration mapping and local exploration mapping; the global exploration mapping updates and divides the exploration subspace in real time and plans the shortest global exploration path; the local exploration mapping will be based on an incremental cache topology-lattice hybrid map, taking into account both the shortest time and the largest area coverage principles, planning the shortest local exploration path and realizing real-time obstacle avoidance; the robot will construct and save the overall three-dimensional and two-dimensional environmental maps and autonomously return to the starting point after the autonomous exploration and mapping is completed; it achieves the purpose of realizing autonomous exploration and mapping and safe return of the ground mobile autonomous robot in an unknown complex environment, makes up for the instability of the traditional SLAM method in constructing maps in complex environments, and can enhance the autonomy of the ground mobile robot while ensuring the quality and efficiency of map construction. In addition, a device and a system for autonomous hierarchical exploration and mapping of a ground mobile robot based on an incremental cache topology-lattice hybrid map provided by the present invention can be configured on the robot described in the embodiment of the present invention, so as to achieve the purpose of the present invention. Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects.

[0150] A fourth aspect of the present invention discloses a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps in any one of the methods for autonomous hierarchical exploration and mapping of a ground mobile robot in the first aspect of the present disclosure are implemented.

[0151] In summary, the solution proposed by the present invention has the following technical effects: The present invention first sets an autonomous exploration boundary, loads the system parameters corresponding to the autonomous hierarchical exploration and mapping of the corresponding environment, obtains environmental data through sensors, and uses the SLAM algorithm to establish an initial environmental map; for large-scale autonomous mapping, the autonomous mapping is divided into a two-layer structure: global exploration mapping and local exploration mapping; the global exploration mapping updates and divides the exploration subspace in real time, and plans the shortest global exploration path; based on the incremental cache topological-lattice hybrid map, the local exploration mapping will follow the principles of the shortest time and the largest area coverage, plan the shortest local exploration path, and achieve real-time obstacle avoidance; the robot will construct and save the overall three-dimensional and two-dimensional environmental maps, and autonomously return to the starting point after the autonomous mapping is completed; it achieves the purpose of enabling the ground mobile autonomous robot to autonomously map in an unknown complex environment and safely return, compensates for the instability of the traditional SLAM method in constructing maps in complex environments, and can enhance the autonomy of the ground mobile robot while ensuring the quality and efficiency of map construction.

[0152] The main protection points of the present invention:

[0153] 1. Hierarchical mapping. This technology divides large-scale autonomous mapping into two levels: global exploration mapping and local exploration mapping. The global exploration mapping is used to update and divide the exploration subspace in real time and plan the shortest global exploration path. The local exploration mapping is based on the incremental cache topological-lattice hybrid map, plans the shortest local exploration path, and realizes real-time obstacle avoidance. This hierarchical structure can improve the efficiency and quality of map construction.

[0154] 2. Exploration method based on the incremental cache topological-lattice hybrid map (TGHM). This method can plan the shortest local exploration path and achieve real-time obstacle avoidance under the principles of the shortest time and the largest area coverage, improving the autonomous exploration ability of the robot.

[0155] 3. Real-time obstacle avoidance and autonomous return to the starting point. During autonomous exploration, the robot can achieve real-time obstacle avoidance. After completing global exploration and map construction, the robot can autonomously return to the starting point, which enhances the autonomy of the robot and improves its survival ability in an unknown complex environment.

[0156] Please note that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification. The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the scope of the invention patent. It should be pointed out that for those of ordinary skill in the art, without departing from the concept of the present application, several deformations and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. An autonomous hierarchical exploration and mapping method for a ground mobile robot, characterized in that, The method is implemented by an autonomous hierarchical exploration and mapping device for a ground mobile robot; sensors are arranged on the ground mobile robot for collecting relevant information of the exploration environment and acquiring surrounding environment data; The method includes: S1, setting the exploration area boundary, loading the autonomous mapping configuration parameters of the corresponding environment, and establishing a preliminary environment map based on the sensor data using the SLAM algorithm; in step S1, the step of establishing the preliminary environment map using the SLAM algorithm includes: Removing motion skew for each point in the point cloud data obtained by the sensor, and extracting key features from the motion-skew-removed point cloud along lines and edges; Comparing and scanning and matching the key features with a part of the local key frames in the sliding window filter; Determining the loop closure factor through the Euclidean distance metric, using the iSAM2 solver to add a new loop closure factor to the graph, and optimizing the odometer for the graph through GTSAM; Merging the point cloud key frames into the preliminary environment map according to the optimized odometer; S2, dividing the exploration area into multiple subspaces according to the exploration area boundary, and globally planning the optimal path through all subspaces; S3, based on the globally planned subspace boundary points and the data input by the sensor, in each subspace, generating an optimal exploration target point based on an incremental cache topological-lattice hybrid map, using an improved TEB algorithm to plan the best exploration path between the target points, and avoiding dynamic and static obstacles in real time; in step S3, the step of generating an optimal exploration target point based on the globally planned subspace boundary points and the data input by the sensor in each subspace specifically includes: S31, quickly extracting candidate target points by applying a candidate target point generation method based on geometric rules; S32, constructing an incremental cache topological-lattice hybrid map, and evaluating the information gain of each candidate topological node thereon; Wherein, the incremental cache topological-lattice hybrid map is a fusion map of a topological map and a grid map; the candidate target points are used as candidate topological nodes; In step S32, the information gain of each candidate topology node is evaluated using a utility function, where the utility function is defined as: , Among them, is a positive constant, is the topological distance between the target node T and the current node. The information gain can be calculated based on the number of unknown grids and is represented by Select the candidate topological node with the maximum U value as the next target point; S33, selecting the node with the best evaluation value as the next target point, and updating the topological map after the ground mobile robot moves towards it; Wherein, when the topological map is updated, each candidate node performs grid traversal within a certain range to calculate the information gain around it; S4, the ground mobile robot completes the exploration according to the best exploration path, and completes the map construction of the entire exploration area according to the exploration results.

2. The autonomous hierarchical exploration and mapping method for a ground mobile robot according to claim 1, characterized in that In step S3, the step of using the improved TEB algorithm to plan the best exploration path between the target points and avoiding dynamic and static obstacles in real time includes: S34, using the A* algorithm to plan the global path of the subspace in the local exploration. In the initialization stage, a part of the global path is selected as the initial local path, and this initial path is converted into a trajectory composed of a pose sequence and a time sequence; S35, aiming at minimizing the total cost function of the TEB algorithm, performing iterative calculation to obtain the best exploration path between the target points; The total cost function is defined as: , Among them, B is the path, is the weight of each target point, is the cost function of each target point.

3. The autonomous hierarchical exploration and mapping method for a ground mobile robot according to claim 2, characterized in that In step S35, during each iteration, new poses are inserted or previously processed poses are removed to ensure that the length of the optimized trajectory remains unchanged, and at the same time, the obstacle information in the cost function is updated in real time.

4. The autonomous hierarchical exploration and mapping method for a ground mobile robot according to claim 1, characterized in that Step S2 specifically includes: A sparse random roadmap is constructed within the subspace covered by the previously explored trajectories; Utilize Perform a search on the random roadmap using a search algorithm to find the shortest path between each subspace; The TSP solver is used to determine the optimal order to visit the subspaces, and thus obtain the optimal path between all subspaces.

5. The autonomous hierarchical exploration and mapping method for a ground mobile robot according to claim 1, wherein Step S4 specifically includes: The ground mobile robot reaches the boundary point of the subspace, completes the exploration of the subspace and the construction of the subspace map, and then moves to the next unexplored subspace; The ground mobile robot reaches the edge of the exploration area, completes the exploration of the entire area, and completes the construction of the three-dimensional map of the entire exploration area; The ground mobile robot returns to the exploration starting point, generates a two-dimensional map based on the three-dimensional map, and saves the map with a named number.

6. An autonomous hierarchical exploration and mapping device for a ground mobile robot, characterized in that, Sensors are installed on the ground mobile robot to collect relevant information about the exploration environment and acquire surrounding environment data; The device includes: A global exploration module, configured to divide the exploration area into multiple subspaces according to the exploration area boundary and plan the optimal path through all subspaces; The steps of establishing a preliminary environment map using the SLAM algorithm include: Remove the motion skew from each point in the point cloud data obtained by the sensor, and extract key features from the motion-skew-removed point cloud along lines and edges; Compare and scan-match the key features with a part of the local key frames in the sliding window filter; Determine the loop closure factor through the Euclidean distance metric, use the iSAM2 solver to add a new loop closure factor to the graph, and optimize the odometry of the graph through GTSAM; Merge the point cloud key frames into the preliminary environment map according to the optimized odometry; A local path and obstacle avoidance module, configured to generate an optimal exploration target point based on the incrementally cached topological-lattice hybrid map according to the subspace boundary points globally planned and the data input by the sensor, and use the improved TEB algorithm to plan the best exploration path between the target points and avoid dynamic and static obstacles in real time; The specific steps of generating an optimal exploration target point based on the incrementally cached topological-lattice hybrid map in each subspace according to the subspace boundary points globally planned and the data input by the sensor include: Quickly extract candidate target points by applying a geometric rule-based candidate target point generation method; Construct an incrementally cached topological-lattice hybrid map and evaluate the information gain of each candidate topological node on it; Among them, the incrementally cached topological-lattice hybrid map is a fusion map of a topological map and a grid map; The candidate target points serve as candidate topological nodes; Evaluate the information gain of each candidate topology node using a utility function, where the utility function is defined as: , wherein, is a positive constant, is the topological distance between the target node T and the current node, and the information gain can be calculated according to the number of unknown grids, which is represented by ; select the candidate topological node with the maximum U value as the next target point; Select the node with the best evaluation value as the next target point, and update the topological map after the ground mobile robot moves towards it; Among them, when the topological map is updated, each candidate node performs grid traversal within a certain range to calculate the information gain around it; The map construction and management module is configured to receive the data input by the sensor, construct a three-dimensional and two-dimensional environmental map of the exploration area based on the SLAM algorithm, save the constructed map with a named number, and output the positioning and trajectory information of the ground mobile robot in real time; at the same time, output the environmental map feature information to the global exploration module and the local path and obstacle avoidance module for corresponding path planning; The mobile robot driving module is configured to receive the motion instructions of the ground mobile robot output by the local path and obstacle avoidance module and control the movement of the ground mobile robot chassis; The user display and communication module is configured to display the constructed environmental map in real time and display the position and trajectory of the ground mobile robot in the environmental map; realize the communication between the ground mobile robot and the user terminal and monitor the state of the ground mobile robot in real time.

7. An autonomous hierarchical exploration and mapping system for a ground mobile robot, characterized in that, The system includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps in the autonomous hierarchical exploration and mapping method of the ground mobile robot according to any one of claims 1 to 5 are implemented.

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