CN117073697A - Autonomous hierarchical exploration map building method, device and system for ground mobile robot - Google Patents
Autonomous hierarchical exploration map building method, device and system for ground mobile robot Download PDFInfo
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Abstract
The invention provides a method, a device and a system for constructing an autonomous hierarchical exploration map of a ground mobile robot, which are characterized in that an autonomous exploration boundary is firstly set, system parameters corresponding to the autonomous hierarchical exploration map of a corresponding environment are loaded, environment data are acquired through a sensor, and an initial environment map is established by utilizing an SLAM algorithm; dividing the autonomous building map into a double-layer structure: global exploration map construction and local exploration map construction; the global exploration map is updated and divided in real time, and the shortest path of global exploration is planned; based on the increment cache topology-grid point mixed map, the local exploration map is constructed to plan a local exploration shortest path and realize real-time obstacle avoidance; the robot builds and stores the whole three-dimensional and two-dimensional environment map, and returns to the starting point after the autonomous map building is completed; the ground mobile autonomous robot achieves the purposes of autonomous map building and safe return under an unknown complex environment, and enhances the autonomy of the ground mobile robot while guaranteeing the map building quality and efficiency.
Description
Technical Field
The invention belongs to the technical field of robots, and particularly relates to a method, a device and a system for constructing a map by autonomous hierarchical exploration of a ground mobile robot.
Background
The application of the ground mobile robot is widely and complex, including but not limited to severe urban search and rescue (USAR) environments, urban combat environments, and cave system disasters. In these environments, robots need to explore unknown and potentially dangerous environments.
In such scenarios, conventional positioning systems, such as Global Navigation Satellite Systems (GNSS) and wireless positioning systems, may not operate properly due to environmental constraints and interference. Therefore, the ground mobile robot needs to perform environment mapping and positioning while performing tasks to ensure that the ground mobile robot can safely and effectively complete tasks.
This technique is commonly referred to as SLAM (Simultaneous Localization And Mapping, synchronous positioning and mapping), the main objective of which is to solve the mapping and positioning navigation problems of mobile robots in unknown environments. However, the conventional SLAM method often relies on a motion trajectory of a human-operated robot, which reduces the autonomy of the robot and makes implementation difficult in the above-described scene where communication conditions are limited.
One mapping method known to applicant utilizes sensors to collect environmental data and then uses a synchronous localization and mapping (SLAM) algorithm to determine the area to be accessed on the estimated SLAM initial environmental map. For the identified access area, the patent adopts an active exploration strategy to conduct path planning, selects an exploration path from the planned paths according to the principle of highest effectiveness, and executes a path exploration task. According to the result of the route search, the patent can construct an environment map corresponding to the autonomous search. However, this approach suffers from several drawbacks:
the path planning is insufficient: in the technical scheme, although the principle of highest effectiveness is provided for selecting the exploration path, the exploration efficiency and the safety of the robot can be influenced due to the fact that a clear path planning algorithm and a real-time obstacle avoidance strategy are not available.
Autonomy of the robot: although this solution emphasizes improvement of the autonomy of the robot, in the actual exploration process, if the environment changes or an unknown obstacle is encountered, the robot may not be able to cope with autonomously.
Limitations of the single SLAM approach: the technical scheme mainly relies on an SLAM algorithm to construct an environment map, but the SLAM algorithm has the problem of positioning uncertainty in a complex environment, and the map created by the traditional SLAM algorithm is not beneficial to autonomous exploration and realization of a robot.
Therefore, the current challenge is to develop a new method, which can enhance the autonomy of the ground mobile robot while guaranteeing the map construction quality and efficiency, so that the ground mobile robot can autonomously perform environment exploration and map construction without human intervention.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method, a device and a system for constructing a map by autonomous hierarchical exploration of a ground mobile robot.
The invention discloses a ground mobile robot autonomous layered exploration mapping method, which is realized by a ground mobile robot autonomous layered exploration mapping device; the ground mobile robot is provided with a sensor for collecting relevant information of the exploration environment and collecting surrounding environment data;
the method comprises the following steps:
s1, setting an exploration area boundary, loading autonomous mapping configuration parameters of corresponding environments, and establishing a preliminary environment map by utilizing a SLAM algorithm based on sensor data;
s2, dividing the exploration area into a plurality of subspaces according to the exploration area boundary, and globally planning an optimal path passing through all subspaces;
s3, generating optimal exploration target points in each subspace based on an incremental cache topology-grid point hybrid map (TGHM) according to subspace boundary points of global planning and data input by a sensor, planning optimal exploration paths among the target points by utilizing an improved TEB algorithm, and avoiding dynamic and static obstacles in real time;
S4, the ground mobile robot finishes exploration according to the optimal exploration path, and finishes map construction of the whole exploration area according to an exploration result.
In step S3, the generating, in each subspace, the optimal exploration target point based on the incremental cache topology-grid hybrid map (TGHM) according to the subspace boundary points of the global plan and the data input by the sensors specifically includes:
s31, rapidly extracting candidate target points by applying a candidate target point generation method based on geometric rules;
s32, constructing an incremental cache topology-grid point hybrid map, and evaluating the information gain of each candidate topology node on the incremental cache topology-grid point hybrid map;
the topological-grid point hybrid map based on the increment cache is a fusion map of a topological map and a grid map; the candidate target point is used as a candidate topological node;
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 to the node;
wherein, when the topological graph is updated, each candidate node performs grid traversal in a certain range to calculate the information gain around the candidate node.
In step S32, the information gain of each candidate topology node is evaluated using a utility function, where the utility function is defined as:
U＝N unknown exp(-λL(T))
Where 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 from the number of unknown meshes, N unknown And (3) representing that the candidate topological node with the maximum U value is selected as the next target point.
In step S3, the step of planning an optimal exploration path between target points by using the modified TEB algorithm and avoiding dynamic and static obstacles in real time includes:
s34, planning a global path of a local exploration neutron space by using an A-algorithm, selecting a part of the global path as an initial local path in an initialization stage, and converting the initial path into a track formed by a gesture sequence and a time sequence;
s35, carrying out iterative computation by taking the minimum total cost function of the TEB algorithm as a target so as to obtain an optimal exploration path between target points;
the total cost function f (B) is defined as:
wherein B is a path, gamma k Is the weight of each target point, f k (B) Is a cost function for each target.
In step S35, during each iteration, a new gesture or a gesture processed before being removed is inserted to ensure that the optimized track length remains unchanged, and simultaneously, obstacle information in the cost function is updated in real time.
In step S1, the step of creating a preliminary environment map using the SLAM algorithm includes:
performing motion deflection removal on each point in the point cloud data obtained by the sensor, and extracting key features of the point cloud subjected to the motion deflection removal along lines and edges;
comparing and scan matching the key features with a portion of the local key frames in the sliding window filter;
determining a closed-loop factor through Euclidean distance measurement, adding a new closed-loop factor into the graph by utilizing an iSAM2 solver, and optimizing an odometer on the graph through a GTSAM;
and merging the point cloud key frames into the initial environment map according to the optimized odometer.
The step S2 specifically comprises the following steps:
constructing a sparse random roadmap in a subspace covered by the track explored in the past;
searching on the random route map by using an A-search algorithm to find the shortest path between each subspace;
and determining the optimal sequence of accessing the subspaces by using the TSP solver, and further obtaining the optimal paths among all subspaces.
The step S4 specifically comprises the following steps:
the ground mobile robot reaches the boundary point of the subspace, completes the exploration of the subspace and the construction of a subspace map, and goes to the next unexplored subspace;
The ground mobile robot reaches the boundary of the exploration area to finish exploration of the whole area and finish three-dimensional map construction of the whole exploration area;
and returning the ground mobile robot to the exploration starting point, generating a two-dimensional map according to the three-dimensional map, and storing the map naming number.
The second aspect of the invention provides an autonomous hierarchical exploration mapping device of a ground mobile robot, wherein a sensor is arranged on the ground mobile robot and is used for collecting relevant information of an exploration environment and collecting surrounding environment data;
the device comprises:
the global exploration module is configured to divide an exploration area into a plurality of subspaces according to the boundary of the exploration area and plan an optimal path passing through all the subspaces;
the local path and obstacle avoidance module is configured to generate an optimal exploration target point based on an incremental cache topology-grid point hybrid map (TGHM) according to subspace boundary points of global planning and data input by the sensors, plan the optimal exploration path between the target points by utilizing an improved TEB algorithm, and avoid dynamic and static obstacles in real time;
the map construction and management module is configured to receive data input by the sensor, construct three-dimensional and two-dimensional environment maps of the exploration area based on the SLAM algorithm, store the naming numbers of the constructed maps and output the positioning and track information of the ground mobile robot in real time; simultaneously outputting the feature information of the environment map 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 instruction of the ground mobile robot output by the local path and obstacle avoidance module and control the ground mobile robot chassis to move;
the user display and communication module is configured to display the constructed environment map in real time and display the position and track of the ground mobile robot in the environment map; and realizing communication between the ground mobile robot and the user terminal, and monitoring the state of the ground mobile robot in real time.
The third aspect of the invention discloses a system for autonomous hierarchical exploration and mapping of a ground mobile robot, which comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the processor realizes the steps in the autonomous hierarchical exploration and mapping method of the ground mobile robot in any one of the first aspect of the invention.
A fourth aspect of the invention discloses a computer-readable storage medium. A computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps in a ground mobile robot autonomous hierarchical exploration mapping method of any of the first aspects of the present disclosure.
In summary, the scheme provided by the invention has the following technical effects: firstly, setting an autonomous exploration boundary, loading system parameters corresponding to an autonomous hierarchical exploration map of a corresponding environment, acquiring environment data through a sensor, and establishing an initial environment map by utilizing an SLAM algorithm; for large scene autonomous building, the autonomous building is divided into a double-layer structure: global exploration map construction and local exploration map construction; the global exploration map is updated and divided in real time, and the shortest path of global exploration is planned; based on the increment cache topology-grid point mixed map, the local exploration map establishment has the principle of shortest time and maximum area coverage, and the local exploration shortest path is planned and real-time obstacle avoidance is realized; the robot builds and stores the whole three-dimensional and two-dimensional environment map, and returns to the starting point after the autonomous map building is completed; the method achieves the purposes of realizing autonomous map building and safe return of the ground mobile autonomous robot in an unknown complex environment, compensates for instability of map building in the complex environment by the traditional SLAM method, and can enhance the autonomy of the ground mobile robot while guaranteeing the map building quality and efficiency.
The main protection points of the invention are as follows:
1. The technology divides the large-scene autonomous building map into two layers of global exploration building map and local exploration building map. The global exploration map is used for updating and dividing exploration subspaces in real time and planning global exploration shortest paths. The local exploration map is based on an incremental cache topology-grid point hybrid map, a local exploration shortest path is planned, real-time obstacle avoidance is realized, and the layered structure can improve the efficiency and quality of map construction.
2. According to the exploration method based on the incremental cache topology-grid point hybrid map (TGHM), the shortest path can be planned and locally explored and real-time obstacle avoidance can be realized under the principle of taking the shortest time and the maximum area coverage into consideration, so that the autonomous exploration capacity of the robot is improved.
3. The robot can realize real-time obstacle avoidance and return to the starting point automatically after global exploration and map construction are completed, so that the autonomy of the robot is enhanced, and the survivability of the robot in an unknown complex environment is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow diagram of a method for autonomous hierarchical exploration and mapping of a ground mobile robot based on an incremental cache topology-grid point hybrid map according to an embodiment of the present invention;
FIG. 2 is a block flow diagram of a method for autonomous hierarchical exploration mapping of a ground mobile robot based on an incremental cache topology-grid point hybrid map according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an internal structure of a ground mobile robot autonomous hierarchical exploration mapping device based on an incremental cache topology-grid point hybrid map according to an embodiment of the present invention;
fig. 4 is a schematic functional module structure diagram of a system for autonomous hierarchical exploration mapping of a ground mobile robot based on an incremental cache topology-grid point hybrid map according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of a functional module structure of another system for autonomous hierarchical exploration mapping of a ground mobile robot based on an incremental cache topology-grid hybrid map according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of 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, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It will be understood that the terms first, second, etc. as used herein may be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one element from another element. For example, a first image may be referred to as a second image, and similarly, a second image may be referred to as a first image, without departing from the scope of the application. Both the first image and the second image are images, but they are not the same image.
In order to solve the problems of low mapping efficiency and low quality and uncertainty in positioning in an unknown complex environment of the existing SLAM mapping method of the ground mobile robot, the embodiment provides a method, a device and a system for autonomously mapping and positioning the ground mobile autonomous robot. In addition, the autonomous robot map building and positioning are divided into two layers: global exploration map construction and local exploration map construction, wherein the local map is based on an incremental cache topology-grid point hybrid map, so that the efficiency of autonomous map construction of the robot and the accuracy of positioning are improved, and the autonomous capability of the robot in an unknown complex scene is greatly improved.
In order to solve the problems of low mapping efficiency and low quality and uncertainty in positioning in an unknown complex environment of the existing SLAM mapping method of the ground mobile robot, the embodiment provides a method, a device and a system for autonomously mapping and positioning the ground mobile autonomous robot. In addition, the autonomous robot map building and positioning are divided into two layers: global exploration map construction and local exploration map construction, wherein the local map is based on an incremental cache topology-grid point hybrid map, so that the efficiency of autonomous map construction of the robot and the accuracy of positioning are improved, and the autonomous capability of the robot in an unknown complex scene is greatly improved.
As shown in fig. 1, fig. 1 is a flow chart of an embodiment of an autonomous hierarchical exploration mapping method of a ground mobile robot based on an incremental cache topology-grid point hybrid map according to the present invention, and the autonomous hierarchical exploration mapping method of a ground mobile robot based on an incremental cache topology-grid point hybrid map according to the present invention may be implemented as steps S1 to S4 below:
the method is realized by an autonomous layered exploration and mapping device of the ground mobile robot; the ground mobile robot is provided with a sensor for collecting relevant information of the exploration environment and collecting surrounding environment data;
s1, setting an exploration area boundary, loading autonomous mapping configuration parameters of corresponding environments, and establishing a preliminary environment map by utilizing a SLAM algorithm based on sensor data;
in the embodiment of the invention, a user can set the autonomous map building exploration boundary of the ground mobile robot through an operation interface. The exploration area is composed of polygons, and the user sets the value of the polygon vertex relative to the starting point of the robot. And the set polygon area values are stored in PLY file format.
Next, the user selects an environment type of the current autonomous building map, including: open park, field forest, indoor room, garage tunnel, etc.; the autonomous mapping program loads autonomous mapping configuration parameters of the corresponding environment, and comprises the following steps: the sensor detects distance, map resolution, static obstacle judgment threshold, etc.
In the embodiment of the invention, the robot is provided with a proper sensor so as to collect relevant information of the exploration environment and collect surrounding environment data. These sensors include, but are not limited to, inertial Measurement Units (IMUs), lidars, and wheel odometers. According to different environment and scene requirements, the sensor equipped with the mobile robot can be expanded.
By means of SLAM (Simultaneous Localization and Mapping) algorithm, we can build an initial environment map and realize the preliminary positioning of the robot. The step of establishing an initial environment map by the SLAM algorithm can be divided into:
performing motion deflection removal on each point in the point cloud data obtained by the sensor, and extracting key features of the point cloud subjected to the motion deflection removal along lines and edges;
comparing and scan matching the key features with a portion of the local key frames in the sliding window filter;
determining a closed-loop factor through Euclidean distance measurement, adding a new closed-loop factor into the graph by utilizing an iSAM2 solver, and optimizing the odometer for the graph through a GTSAM (Georgia Tech Smoothing and Mapping, based on a C++ optimization library of the factor graph);
and merging the point cloud key frames into the initial environment map according to the optimized odometer.
S2, dividing the exploration area into a plurality of subspaces according to the exploration area boundary, and globally planning an optimal path passing through all subspaces;
the step S2 specifically comprises the following steps:
constructing a sparse random roadmap in a subspace covered by the track explored in the past;
searching on the random route map by using an A-search algorithm to find the shortest path between each subspace;
and determining the optimal sequence of accessing the subspaces by using the TSP solver, and further obtaining the optimal paths among all subspaces.
In the present example, all of the space of the initial environment map is first divided into equally sized cube subspaces. Each subspace will record both covered and uncovered surface information therein. During the exploration, each subspace has a corresponding state, and the states comprise 'unexplored', 'explored' and 'explored'. A subspace is "unexplored" if it does not contain any covered or uncovered surfaces. A subspace is "explored" if it contains only covered surfaces. A subspace is "explored" if it contains any uncovered surfaces. In global planning, only subspaces with states "under exploration" will be considered.
S3, generating optimal exploration target points in each subspace based on an incremental cache topology-grid point hybrid map (TGHM) according to subspace boundary points of global planning and data input by a sensor, planning optimal exploration paths among the target points by utilizing an improved TEB algorithm, and avoiding dynamic and static obstacles in real time;
in step S3, the generating, in each subspace, the optimal exploration target point based on the incremental cache topology-grid hybrid map (TGHM) according to the subspace boundary points of the global plan and the data input by the sensors specifically includes:
s31, rapidly extracting candidate target points by applying a candidate target point generation method based on geometric rules;
s32, constructing an incremental cache topology-grid point hybrid map, and evaluating the information gain of each candidate topology node on the incremental cache topology-grid point hybrid map;
the topological-grid point hybrid map based on the increment cache is a fusion map of a topological map and a grid map; the candidate target point is used as a candidate topological node;
in step S32, the information gain of each candidate topology node is evaluated using a utility function, where the utility function is defined as:
U＝N unknown exp(-θL(T))
where 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 from the number of unknown meshes, N unknown And (3) representing that the candidate topological node with the maximum U value is selected 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 to the node;
wherein, when the topological graph is updated, each candidate node performs grid traversal in a certain range to calculate the information gain around the candidate node.
In the step of generating the optimal exploration target point, the robot first acquires the exploration target point from the global path, and then the local path planning module calculates the optimal path from the current position to the target point.
In the incremental cache topology-grid hybrid map (TGHM) based exploration method, a mobile robot will continue to move towards the leading edge boundary, while a representation of the unknown environment will be provided to the robot based on the incremental cache topology-grid hybrid map (TGHM), which is a fusion of the topology map and the grid map, the former containing the information gain and the cost of movement of the exploration, the latter representing an established map for navigation and localization.
In the incremental cache topology-grid point hybrid map, newly generated candidate target points are added to the topology map as candidate topology nodes. When the topology map is updated, each candidate node performs a grid traversal over a range to calculate the information gain around it. This information gain is used to evaluate the exploratory value of candidate topology nodes.
The topology map records each accessed and non-accessed node. Based on the topological graph, the topological distance between any two topological nodes can be calculated quickly.
Wherein the topology node can be expressed as:
T＝{Parents,Children}
wherein, parts and child are parent node and child node of the topology node, respectively. The candidate topology node has only one parent node, which we define as:
T_c＝{Parents,Children,U}
where 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 that location.
In step S3, the step of planning an optimal exploration path between target points by using the modified TEB algorithm and avoiding dynamic and static obstacles in real time includes:
s34, planning a global path of a local exploration neutron space by using an A-algorithm, selecting a part of the global path as an initial local path in an initialization stage, and converting the initial path into a track formed by a gesture sequence and a time sequence;
s35, carrying out iterative computation by taking the minimum total cost function of the TEB algorithm as a target so as to obtain an optimal exploration path between target points;
in step S35, each iteration is performed in accordance with the number n of sampling points in the subsequent iteration resolution period. In each iteration process, a new gesture is inserted or a previously processed gesture is removed, so that the optimized track length is kept unchanged, and obstacle information in the cost function is updated in real time.
Shortest European distance penalty function f dis Attitude vertex S with two robots i And S is i+n Are connected. The shortest distance constraint function can be expressed as follows:
f dis (S i+n ,S i )＝(x i+n -x i ) 2 +(y i+n -y i )
the total cost function of the modified TEB algorithm consists of a weighted sum of the penalty function and the objective function in the formula, and the corresponding optimization problem is to choose the shortest path B to minimize the cost function, the total cost function f (B) being defined as:
B * ＝minf(B)
wherein B is a path, B * Is the optimal path, gamma k Is the weight of each target point, f k (B) Is a cost function for each target point.
In the step of planning the optimal exploration path between the target points using the modified TEB algorithm, the target point will be taken as input and the TEB (Timed Elastic Band) algorithm will search for the optimal path to reach the target point in real time. The TEB method will take into account the dynamic constraints of the robot. This approach does not only consider the static optimal path problem, but also the optimization of path and time. This enables the robot to better adapt to the dynamic changes of the environment, avoiding dynamic and static obstacles in real time.
Meanwhile, the heuristic idea of the A algorithm is introduced into the TEB algorithm, so that the efficiency of the local path is improved, and the specific algorithm is described as follows.
The algorithm a 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:
f(n)＝g(n)+h(n)
Where g (n) represents the actual cost from the initial point to the current node, h (n) represents the estimated cost from the current node to the target node, and the calculation formula of h (n) is as follows:
wherein, (x) n ,y n ) Representing the horizontal and vertical coordinates of the current point on the map, (x) G ,y G ) Representing the horizontal and vertical coordinates of the target point on the map.
On solving the problem of local detour possibly occurring when the TEB path is planned to turn, the algorithm applies the shortest distance constraint to the local path according to the teaching of the formula so as to enable the shortest distance constraint to be as close to the edge of the path as possible, thereby improving the local path planning efficiency of the unmanned vehicle.
S4, the ground mobile robot finishes exploration according to the optimal exploration path, and finishes map construction of the whole exploration area according to an exploration result.
The step S4 specifically comprises the following steps:
the ground mobile robot reaches the boundary point of the subspace, completes the exploration of the subspace and the construction of a subspace map, and goes to the next unexplored subspace;
the ground mobile robot reaches the boundary of the exploration area to finish exploration of the whole area and finish three-dimensional map construction of the whole exploration area;
and returning the ground mobile robot to the exploration starting point, generating a two-dimensional map according to the three-dimensional map, and storing the map naming number.
As shown in fig. 2, fig. 2 is a flow chart of an embodiment of the autonomous hierarchical exploration mapping method of the ground mobile robot based on the incremental cache topology-grid point hybrid map of the present invention. In the embodiment shown in fig. 2, the ground mobile robot system is initialized, including sensor initialization, autonomous map parameter loading, autonomous exploration boundary setting, etc.; based on the sensor data, sensing the surrounding environment is completed, and an initial environment map is formed; in global path planning, dividing an exploration area into a plurality of subspaces, planning an optimal path reaching each subspace, and continuously updating the path according to an environment map updated in real time; in the local path planning, in a method for exploring a mixed map (TGHM) based on an incremental cache topology-grid point, generating and selecting an optimal exploring target point from the incremental cache topology-grid point mixed map (TGHM), and dynamically generating an optimal path of the robot to the target point based on an A-improved TEB method; reaching boundary points of subspaces, completing exploration of subspaces and subspace map construction, and going to the next unexplored subspace; reaching the edges of the exploration area, completing exploration of the whole area, and completing three-dimensional map construction of the whole exploration area; returning to the searching starting point, generating a two-dimensional map according to the three-dimensional map, and storing the map naming number.
Corresponding to the method for autonomous hierarchical exploration and mapping of a ground mobile robot based on the incremental cache topology-grid point hybrid map provided in the embodiments of fig. 1 and fig. 2, the second aspect of the present invention provides an autonomous hierarchical exploration and mapping device of a ground mobile robot, which can implement the steps of the autonomous hierarchical exploration and mapping method of a ground mobile robot based on the incremental cache topology-grid point hybrid map described in the embodiments of fig. 1 and fig. 2, and the device can be configured on the ground mobile robot, so as to complete the operation of autonomous hierarchical exploration and mapping. The ground mobile robot is provided with a sensor for collecting relevant information of the exploration environment and collecting surrounding environment data;
based on the description of the above embodiment, as shown in fig. 3, fig. 3 is a schematic functional module diagram of an implementation manner of the autonomous hierarchical exploration mapping device of the ground mobile robot based on the incremental cache topology-grid point hybrid map of the present invention; in the embodiment of the invention, functionally, the autonomous hierarchical exploration mapping device of the ground mobile robot comprises:
the global exploration module is configured to divide an exploration area into a plurality of subspaces according to the boundary of the exploration area and plan an optimal path passing through all the subspaces;
The local path and obstacle avoidance module is configured to generate an optimal exploration target point based on an incremental cache topology-grid point hybrid map (TGHM) according to subspace boundary points of global planning and data input by the sensors, plan the optimal exploration path between the target points by utilizing an improved TEB algorithm, and avoid dynamic and static obstacles in real time;
the map construction and management module is configured to receive data input by the sensor, construct three-dimensional and two-dimensional environment maps of the exploration area based on the SLAM algorithm, store the naming numbers of the constructed maps and output the positioning and track information of the ground mobile robot in real time; simultaneously outputting the feature information of the environment map 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 instruction of the ground mobile robot output by the local path and obstacle avoidance module and control the ground mobile robot chassis to move;
the user display and communication module is configured to display the constructed environment map in real time and display the position and track of the ground mobile robot in the environment map; and realizing communication between the ground mobile robot and the user terminal, and monitoring the state of the ground mobile robot in real time.
The invention discloses a ground mobile robot autonomous hierarchical exploration mapping device based on an incremental cache topology-grid point hybrid map, which is used for setting autonomous exploration boundaries, loading autonomous mapping and positioning corresponding system parameters of corresponding environments, acquiring environment data through a sensor, and establishing an initial environment map by utilizing an SLAM algorithm; for large scene autonomous building, the autonomous building is divided into a double-layer structure: global exploration map construction and local exploration map construction; the global exploration map is updated and divided in real time, and the shortest path of global exploration is planned; based on the increment cache topology-grid point mixed map, the local exploration map construction has the principle of shortest time and maximum area coverage, and an improved TEB algorithm is utilized to plan a local exploration shortest path and realize real-time obstacle avoidance; the robot builds and stores the whole three-dimensional and two-dimensional environment map, and returns to the starting point after the autonomous map building is completed; the method achieves the purposes of realizing autonomous map building and safe return of the ground mobile autonomous robot in an unknown complex environment, compensates for instability of map building in the complex environment by the traditional SLAM method, and can enhance the autonomy of the ground mobile robot while guaranteeing the map building quality and efficiency.
The third aspect of the invention discloses an autonomous hierarchical exploration mapping system of a ground mobile robot. The autonomous hierarchical exploration mapping system of the ground mobile robot comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps in the autonomous hierarchical exploration mapping method of the ground mobile robot in any one of the first aspects of the disclosure when executing the computer program.
As shown in fig. 5, the system includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes a nonvolatile 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 computer programs in the non-volatile storage media. The communication interface of the electronic device is used for conducting wired or wireless communication with an external terminal, and the wireless communication can be achieved through WIFI, an operator network, near Field Communication (NFC) or other technologies. The display screen of the electronic equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the electronic equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of a portion related to the technical solution of the present disclosure, and does not constitute a limitation of the electronic device to which the technical solution of the present application is applied, and a specific system may include more or less components than those shown in the drawings, or may combine some components, or have different component arrangements.
Referring to fig. 4, as another embodiment, fig. 4 illustrates the internal structure of one implementation of a system.
In this example, the system includes at least a memory, a data processor, a wireless communication interface, a network and serial interface, a vehicle drive, and a sensor. The memory comprises at least one type of readable storage medium such as flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, and the like. The memory may be an internal storage unit of the device, an external storage device, or a combination of both. The memory may be used not only to store application software on the device and various types of data, such as code of an autonomous exploration diagramming program, but also to temporarily store data that has been output or is to be output.
The data processor may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor or other data processing chip for running program code in memory or processing data, such as executing autonomous multi-layer exploration mapping programs or the like.
The network and serial interfaces are used to enable connections and communications between the components. The network interface may include a wired network interface and the serial interface may include a 485/232 serial interface for establishing communication links between the data processor, memory, and other devices, including sensors.
The electronic device may further comprise a user interface such as a display, an input unit (e.g. a keyboard), a wired interface, a wireless interface, etc. In some examples, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (organic light emitting diode) touch, or the like.
The robot driver is used for receiving a speed control instruction of the program and controlling the mobile robot to move towards the target point.
The sensor generally comprises a laser radar, a binocular camera, an Inertial Measurement Unit (IMU), a wheel type odometer and the like, is used for outputting environment sensing data, and is communicated with an autonomous multi-layer exploration mapping program running in a data processor for autonomous exploration mapping.
According to the embodiment of fig. 1 and 2, an autonomous multi-layer exploration mapping program is stored in the memory and can run on the data processor.
The embodiment of the invention provides a system, which is characterized in that an autonomous exploration boundary is set, autonomous building graphs of corresponding environments and system parameters corresponding to positioning are loaded, environment data are acquired through a sensor, and an initial environment map is built by utilizing an SLAM algorithm; for large scene autonomous building, the autonomous building is divided into a double-layer structure: global exploration map construction and local exploration map construction; the global exploration map is updated and divided in real time, and the shortest path of global exploration is planned; the local exploration map is based on an incremental cache topology-grid point hybrid map, has the principle of shortest time and maximum area coverage, and is used for planning a local exploration shortest path and realizing real-time obstacle avoidance; the robot builds and stores the whole three-dimensional and two-dimensional environment map, and returns to the starting point after the autonomous exploration map building is completed; the method achieves the purposes of realizing autonomous exploration and map building and safe return of the ground mobile autonomous robot in an unknown complex environment, compensates for instability of map building in the complex environment by the traditional SLAM method, and can enhance the autonomy of the ground mobile robot while guaranteeing the map building quality and efficiency. In addition, the device and the system for autonomous hierarchical exploration mapping of the ground mobile robot based on the incremental cache topology-grid point hybrid map provided by the invention can be configured on the robot described in the embodiment of the invention, so that one skilled in the art for achieving the object of the invention can understand that the embodiment of the invention can be provided as a method, a system or a computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.
A fourth aspect of the invention discloses a computer-readable storage medium. A computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements steps of a method for autonomous hierarchical exploration mapping of a ground mobile robot according to any of the first aspects of the present disclosure.
In summary, the scheme provided by the invention has the following technical effects: firstly, setting an autonomous exploration boundary, loading system parameters corresponding to an autonomous hierarchical exploration map of a corresponding environment, acquiring environment data through a sensor, and establishing an initial environment map by utilizing an SLAM algorithm; for large scene autonomous building, the autonomous building is divided into a double-layer structure: global exploration map construction and local exploration map construction; the global exploration map is updated and divided in real time, and the shortest path of global exploration is planned; based on the increment cache topology-grid point mixed map, the local exploration map establishment has the principle of shortest time and maximum area coverage, and the local exploration shortest path is planned and real-time obstacle avoidance is realized; the robot builds and stores the whole three-dimensional and two-dimensional environment map, and returns to the starting point after the autonomous map building is completed; the method achieves the purposes of realizing autonomous map building and safe return of the ground mobile autonomous robot in an unknown complex environment, compensates for instability of map building in the complex environment by the traditional SLAM method, and can enhance the autonomy of the ground mobile robot while guaranteeing the map building quality and efficiency.
The main protection points of the application are as follows:
1. the technology divides the large-scene autonomous building map into two layers of global exploration building map and local exploration building map. The global exploration map is used for updating and dividing exploration subspaces in real time and planning global exploration shortest paths. The local exploration map is based on an incremental cache topology-grid point hybrid map, a local exploration shortest path is planned, real-time obstacle avoidance is realized, and the layered structure can improve the efficiency and quality of map construction.
2. According to the exploration method based on the incremental cache topology-grid point hybrid map (TGHM), the shortest path can be planned and locally explored and real-time obstacle avoidance can be realized under the principle of taking the shortest time and the maximum area coverage into consideration, so that the autonomous exploration capacity of the robot is improved.
3. The robot can realize real-time obstacle avoidance and return to the starting point automatically after global exploration and map construction are completed, so that the autonomy of the robot is enhanced, and the survivability of the robot in an unknown complex environment is improved.
Note that the technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be regarded as the scope of the description. The foregoing examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. The autonomous hierarchical exploration and mapping method of the ground mobile robot is characterized in that the method is realized by an autonomous hierarchical exploration and mapping device of the ground mobile robot; the ground mobile robot is provided with a sensor for collecting relevant information of the exploration environment and collecting surrounding environment data;
the method comprises the following steps:
s1, setting an exploration area boundary, loading autonomous mapping configuration parameters of corresponding environments, and establishing a preliminary environment map by utilizing a SLAM algorithm based on sensor data;
s2, dividing the exploration area into a plurality of subspaces according to the exploration area boundary, and globally planning an optimal path passing through all subspaces;
s3, generating optimal exploration target points in each subspace based on an incremental cache topology-grid point hybrid map (TGHM) according to subspace boundary points of global planning and data input by a sensor, planning optimal exploration paths among the target points by utilizing an improved TEB algorithm, and avoiding dynamic and static obstacles in real time;
s4, the ground mobile robot finishes exploration according to the optimal exploration path, and finishes map construction of the whole exploration area according to an exploration result.
2. The autonomous hierarchical exploration mapping method of a ground mobile robot according to claim 1, wherein in step S3, generating an optimal exploration target point based on an incremental cache topology-grid point hybrid map (TGHM) in each subspace according to the subspace boundary points of the global plan and the sensor input data specifically comprises:
S31, rapidly extracting candidate target points by applying a candidate target point generation method based on geometric rules;
s32, constructing an incremental cache topology-grid point hybrid map, and evaluating the information gain of each candidate topology node on the incremental cache topology-grid point hybrid map;
the topological-grid point hybrid map based on the increment cache is a fusion map of a topological map and a grid map; the candidate target point is used as a candidate topological node;
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 to the node;
wherein, when the topological graph is updated, each candidate node performs grid traversal in a certain range to calculate the information gain around the candidate node.
3. The autonomous hierarchical exploration mapping method of a ground mobile robot of claim 2, wherein in step S32, the information gain of each candidate topology node is evaluated using a utility function, wherein the utility function is defined as:
U＝N unknown exp(-λL(T))
where 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 from the number of unknown meshes, N unknown And (3) representing that the candidate topological node with the maximum U value is selected as the next target point.
4. The autonomous hierarchical exploration mapping method of mobile ground robot of claim 3, wherein in step S3, said planning an optimal exploration path between target points using a modified TEB algorithm and avoiding dynamic and static obstacles in real time comprises:
s34, planning a global path of a local exploration neutron space by using an A-algorithm, selecting a part of the global path as an initial local path in an initialization stage, and converting the initial path into a track formed by a gesture sequence and a time sequence;
s35, carrying out iterative computation by taking the minimum total cost function of the TEB algorithm as a target so as to obtain an optimal exploration path between target points;
the total cost function f (B) is defined as:
wherein B is a path, gamma k Is the weight of each target point, f k (B) Is a cost function for each target point.
5. The autonomous hierarchical exploration mapping method of a ground mobile robot according to claim 4, wherein in step S35, a new gesture is inserted or a previously processed gesture is removed during each iteration to ensure that the optimized track length remains unchanged and simultaneously update the obstacle information in the cost function in real time.
6. The autonomous hierarchical exploration mapping method of a ground mobile robot according to claim 1, wherein in step S1, the step of establishing a preliminary environment map using SLAM algorithm comprises:
performing motion deflection removal on each point in the point cloud data obtained by the sensor, and extracting key features of the point cloud subjected to the motion deflection removal along lines and edges;
comparing and scan matching the key features with a portion of the local key frames in the sliding window filter;
determining a closed-loop factor through Euclidean distance measurement, adding a new closed-loop factor into the graph by utilizing an iSAM2 solver, and optimizing an odometer on the graph through a GTSAM;
and merging the point cloud key frames into the initial environment map according to the optimized odometer.
7. The autonomous hierarchical exploration mapping method of a ground mobile robot according to claim 1, wherein step S2 specifically comprises:
constructing a sparse random roadmap in a subspace covered by the track explored in the past;
searching on the random route map by using an A-search algorithm to find the shortest path between each subspace;
and determining the optimal sequence of accessing the subspaces by using the TSP solver, and further obtaining the optimal paths among all subspaces.
8. The autonomous hierarchical exploration mapping method of a ground mobile robot according to claim 1, wherein step S4 specifically comprises:
the ground mobile robot reaches the boundary point of the subspace, completes the exploration of the subspace and the construction of a subspace map, and goes to the next unexplored subspace;
the ground mobile robot reaches the boundary of the exploration area to finish exploration of the whole area and finish three-dimensional map construction of the whole exploration area;
and returning the ground mobile robot to the exploration starting point, generating a two-dimensional map according to the three-dimensional map, and storing the map naming number.
9. The autonomous hierarchical exploration and mapping device of the ground mobile robot is characterized in that a sensor is arranged on the ground mobile robot and is used for collecting relevant information of an exploration environment and collecting surrounding environment data;
the device comprises:
the global exploration module is configured to divide an exploration area into a plurality of subspaces according to the boundary of the exploration area and plan an optimal path passing through all the subspaces;
the local path and obstacle avoidance module is configured to generate an optimal exploration target point based on an incremental cache topology-grid point hybrid map (TGHM) according to subspace boundary points of global planning and data input by the sensors, plan the optimal exploration path between the target points by utilizing an improved TEB algorithm, and avoid dynamic and static obstacles in real time;
The map construction and management module is configured to receive data input by the sensor, construct three-dimensional and two-dimensional environment maps of the exploration area based on the SLAM algorithm, store the naming numbers of the constructed maps and output the positioning and track information of the ground mobile robot in real time; simultaneously outputting the feature information of the environment map 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 instruction of the ground mobile robot output by the local path and obstacle avoidance module and control the ground mobile robot chassis to move;
the user display and communication module is configured to display the constructed environment map in real time and display the position and track of the ground mobile robot in the environment map; and realizing communication between the ground mobile robot and the user terminal, and monitoring the state of the ground mobile robot in real time.
10. A ground mobile robot autonomous hierarchical exploration mapping system, characterized in that the system comprises a memory and a processor, the memory storing a computer program, the processor implementing the steps in the ground mobile robot autonomous hierarchical exploration mapping method according to any of claims 1 to 8 when executing the computer program.
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