CN112000754A - Map construction method and device, storage medium and computer equipment - Google Patents
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Abstract
The application relates to a map construction method, a map construction device, a storage medium and computer equipment, wherein the method comprises the following steps: constructing a grid map corresponding to the current position; acquiring a target point according to the grid map; formulating a path plan from the current position to the target point; and (5) running along the path plan towards the target point. Through the method and the device, efficient full-autonomous exploration map building is realized, human intervention is not needed, areas which are difficult to pass and easy to trigger collision are avoided through autonomous planning paths in the process of exploring and building the map, the safety and the feasibility of the areas where the robot passes are guaranteed, and the efficiency of autonomous exploration and map building is improved.
Description
Technical Field
The present application relates to the field of artificial intelligence technologies, and in particular, to a map construction method, apparatus, storage medium, and computer device.
Background
With the rapid development of synchronous mapping and positioning technology (i.e., SLAM), autonomous mobile robots are receiving increasing attention from the engineering and academic communities. The SLAM technology mainly realizes the position positioning of the mobile robot in an unknown environment so as to realize autonomous movement. However, the traditional mobile robot construction mainly depends on a manual intervention mode, and the map construction is realized by manually setting some navigation target points or directly controlling the mobile robot to move by using a keyboard, so that time, manpower and material resources are wasted when a large and complex indoor environment is faced; the sweeping robot does not need manual intervention, but builds a map in a bow-shaped global sweeping mode, a large amount of time is wasted, environment attributes cannot be set before a sweeping task is executed, and tasks such as local sweeping cannot be executed.
Most of the autonomous exploration mapping methods proposed at present are semi-autonomous exploration mapping methods requiring manual intervention, and some fully-autonomous exploration mapping methods also have some problems. For example, the problem of safety and feasibility of the area through which the robot reaches the boundary point from the current position is not considered. Generally, the global planner will preferentially select the shortest path, and the shortest path is usually not optimal in a complex area, so the global planner can plan out areas passing through elongated obstacles such as stool feet, desk feet and the like in an indoor environment, or narrow passages, but when a mobile robot actually walks in these areas, collision is easily triggered and the mobile robot is trapped in these areas for a long time, which will seriously affect the efficiency of mapping and navigation, and the purpose of efficient autonomous detection mapping cannot be achieved.
Disclosure of Invention
In order to solve the technical problems that most of the detection maps in the prior art are semi-autonomous detection maps requiring manual intervention, the mapping time is long, and the safety and feasibility of the area through which the robot reaches the target point from the current position are not considered, embodiments of the present application provide a map construction method, apparatus, storage medium, and computer device.
In a first aspect, an embodiment of the present application provides a map building method, which is applied to a robot, and the method includes:
constructing a grid map corresponding to the current position;
acquiring a target point according to the grid map;
formulating a path plan from the current position to the target point;
and (5) running along the path plan towards the target point.
Optionally, determining the target point according to the grid map includes:
acquiring a current search area according to the grid map;
searching the boundary point of the current search area;
and acquiring a target point according to the boundary point of the current search area.
Optionally, the method further comprises:
if the target point is not obtained according to the grid map, constructing a global map according to the grid map corresponding to the current position and the grid map obtained before the current position;
the current position is the position of the robot at the current task allocation moment;
after traveling along the path toward the target point, the method further comprises:
acquiring a next position corresponding to the next task allocation moment in the operation process;
and taking the next position as the current position, and constructing a grid map corresponding to the current position.
Optionally, obtaining the current search area according to the grid map includes:
and acquiring a circumscribed rectangle of the grid map to determine a current search area corresponding to the grid map.
Optionally, searching for a boundary point of the current search area includes:
and respectively detecting by a global boundary point detector and a local boundary point detector to search the boundary point of the current search area.
Optionally, before obtaining the current search area according to the grid map, the method further includes:
preprocessing the grid map;
wherein, the pretreatment comprises expansion treatment and corrosion treatment in sequence.
Optionally, acquiring the target point according to the boundary point of the current search area includes:
acquiring a boundary point clustering center of the boundary points through clustering;
filtering the boundary point clustering centers to obtain effective boundary point clustering centers;
obtaining the income value of each effective boundary point clustering center;
and taking the effective boundary point clustering center with the maximum profit value as a target point.
Optionally, the filtering the boundary point clustering centers to obtain effective boundary point clustering centers includes:
acquiring information gain of each boundary point clustering center;
acquiring a global path of the robot from the current position to each boundary point clustering center respectively;
judging whether an area which is difficult to pass exists in each global path according to the size of the robot body to obtain a judgment result;
and filtering the boundary point clustering centers according to the judgment result and the information gain of the boundary point clustering centers to obtain effective boundary point clustering centers.
Optionally, the status of each map grid in the grid map is one of occupied, free, unknown;
the method further comprises the following steps:
the state of the map grid corresponding to the area difficult to pass through is set as occupied.
Optionally, obtaining an information gain of each boundary point cluster center includes:
acquiring a first target circle by taking the boundary point clustering center as a circle center and a preset radius as a radius;
the preset radius is larger than the radius of the robot and does not exceed the range which can be measured by the perception sensor;
judging whether a map grid in an occupied state exists in the map grids corresponding to the first target circle;
if the map grid occupied by the state does not exist, a second target circle is obtained by taking the center of the boundary point cluster as the center of a circle and the radius of the information gain as the radius,
wherein the information gain radius does not exceed the range which can be measured by the perception sensor,
taking the difference between the area of the map grid in the second target circle, the state of which is unknown, and the area of the map grid in the second target circle, the state of which is occupied, as the information gain of the boundary point clustering center;
and if the map grid in the occupied state exists, setting the information gain of the boundary point clustering center as a preset value.
Optionally, the filtering the boundary point clustering centers according to the judgment result and the information gain of the boundary point clustering centers to obtain effective boundary point clustering centers includes:
removing boundary point clustering centers of which the information gain is smaller than a threshold value in the boundary point clustering centers, and/or removing the boundary point clustering centers corresponding to the areas with the judgment results of difficult passing;
and taking the reserved boundary point clustering center as an effective boundary point clustering center.
Optionally, obtaining a global path of the robot from the current position to each of the boundary point cluster centers respectively includes:
and planning a global path of the robot from the current position to each boundary point clustering center respectively through an A-algorithm.
In a second aspect, an embodiment of the present application provides a map building apparatus, including:
the grid module is used for constructing a grid map corresponding to the current position;
the detection module is used for acquiring a target point according to the grid map;
the path planning module is used for planning a path from the current position to the target point;
and the movement control module is used for planning to move towards the target point along the path.
In a third aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, causes the processor to perform the steps of the method according to any one of the preceding claims.
In a fourth aspect, embodiments of the present application provide a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to perform the steps of the method according to any one of the preceding claims.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages:
the grid map corresponding to the current position is automatically constructed, the target point is determined according to the grid map, the path planning from the current position to the target point is formulated, the operation is performed towards the target point along the path planning, the operation path is automatically planned, and manual intervention traction is not needed; the safety and feasibility of the area where the robot passes from the current position to the boundary point are considered, the robot is preferentially pulled to a simple environment without an impassable area for detection and map building through autonomous path planning, and the purpose of efficient autonomous detection and map building in a complex environment is achieved; the robot does not need to complete map construction in a fixed and low-efficiency arched overall cleaning mode, and then the map construction efficiency is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is a schematic flow chart diagram of a mapping method in one embodiment;
FIG. 2 is a schematic diagram of a circumscribed rectangle of a grid map in one embodiment;
FIG. 3 is a grid map before preprocessing in one embodiment;
FIG. 4 is a grid map after preprocessing in one embodiment;
FIG. 5 is a schematic diagram of a path channel in one embodiment;
FIG. 6 is a block diagram showing the structure of a map building apparatus according to an embodiment;
FIG. 7 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
SLAM is known as Simultaneous Localization and Mapping, i.e., Simultaneous Localization and Mapping.
The electronic map is widely applied to scenes such as inquiry, positioning, navigation and the like in the field of artificial intelligence. In particular, an artificial intelligence oriented robot-specific map, which may be used for e.g. autopilot, robot navigation and positioning, etc.
The robot obtains perception data through a carried visual sensor, obtains a grid map of an environment space according to an SLAM mode, achieves rasterization conversion of the SLAM map, and performs map fusion on a local grid map to obtain a global map. And (3) creating a point cloud map by adopting laser data, and constructing the map according to data acquired by the robot in real time in the traversing process.
A grid map is a form of high-precision map commonly used in the robotic field, which divides the environment into a series of grids, where each grid is marked with a value indicating that the grid is occupied or free or unknown. That is, a grid map is a product of digitally rasterizing a real environment to identify obstacles in the environment by whether the grid is occupied or not. Due to the requirements of robot navigation and positioning, the occupied grid map is widely applied to navigation and positioning scenes in various fields such as intelligent robots.
In one embodiment, an application environment for a mapping method is disclosed. In this example application environment the executing agent is a robot (e.g., a sweeping robot). The robot has an environmental information collection device fixed thereto. The environmental information collecting device may include, but is not limited to, a laser radar sensor, an odometer, and the like. The environment information acquisition equipment is used for acquiring spatial data or environment information of the surrounding environment.
The environmental information collection device has a laser scanner or a laser radar, which collects data of the surrounding environment during the operation of the robot to form a laser point cloud. In the embodiments described herein, the laser point cloud refers to a set of a series of massive points expressing a target spatial distribution obtained by acquiring spatial coordinates of each sampling point of the surface of an object in an environment.
The robot may generate a grid map based on the laser point cloud obtained from the environmental information collection device and store it in a map database, the grid map typically including a plurality of map grids. The map database stores grid maps generated at different times or different positions.
When the robot enters a new environment, the information of the obstacles in the new environment is unknown, so that the robot is required to traverse the whole environment, detect the positions of the obstacles, the positions without the obstacles and the positions of unknown areas, and obtain a corresponding grid map according to the position information. The grid map includes a plurality of map grids, each map grid being labeled as a state, the state being one of occupied, free, unknown.
Those skilled in the art will understand that the mapping method of the present application can be applied to various scenarios, for example, acquiring indoor laser point cloud data, where the acquired laser point cloud can be used for navigation or positioning of an indoor robot (e.g., an indoor sweeping robot), and the like. For example, laser point cloud data on a road is collected, and the collected laser point cloud can be used for autonomous navigation or positioning of a vehicle and the like.
FIG. 1 is a flow diagram of a method for map construction in one embodiment. Referring to fig. 1, the method includes the steps of:
s200: and constructing a grid map corresponding to the current position.
Specifically, the map construction in the embodiment of the present application is to cyclically acquire a local map at a preset frequency, and then splice or fuse the acquired local maps to obtain a global map.
And acquiring current environment information acquired at the current position after the current position is reached according to the preset frequency, wherein the current environment information is acquired point cloud data, and generating a grid map corresponding to the current position according to the current environment information. The current position is acquired according to the preset frequency and the previous position. The grid map comprises a plurality of map grids, and the state of each map grid is one of occupied state, idle state and unknown state.
Acquiring a current map to be rasterized, which is acquired by a robot at a current position; and acquiring environment information in real time, converting the environment information into point cloud data, and mapping the point cloud data into a grid map corresponding to the current position, wherein the grid map corresponding to the current position is a current local grid map.
S400: and acquiring a target point according to the grid map.
S600: and planning a path from the current position to the target point.
Specifically, the target point is used as a navigation landmark point for path planning, and the path planning is used for guiding the direction, speed, angular velocity, and the like of the robot operation. I.e. in which direction the robot is directed to travel at what speed or angular velocity from the current position.
Preferably, the robot may autonomously plan a path through the a-algorithm according to the current position and the position of the target point. Of course, the path may also be planned using the D-star algorithm or Dijkstra algorithm.
S800: and (5) running along the path plan towards the target point.
Specifically, after the path is planned, the robot autonomously moves to the target point according to the path plan, if the next position is reached in the operation process, a grid map corresponding to the next position is constructed at the next position, the target point corresponding to the next position is determined according to the grid map corresponding to the next position, the path plan from the next position to the target point corresponding to the next position is formulated, and the robot moves to the target point corresponding to the next position along the path plan. And circulating in such a way until the target point cannot be found according to the grid map corresponding to a certain position, finishing the construction of the grid map, namely acquiring the global environment information or the global map of the whole environment.
According to the method and the device, the path is planned autonomously, the environment information is obtained in the operation process according to the planned path, and when the target point is not obtained through the raster map at a certain position, the global map is constructed according to the currently and previously obtained environment information.
Through simplifying the complex area of the grid map, the robot is prevented from being dragged to the complex area in the autonomous exploration mapping, and the robot is prevented from being trapped in the complex area, so that the robot is better dragged to a path channel without obstacles or an impassable area to walk, and the efficiency of autonomous exploration mapping is improved.
The grid map corresponding to the current position is constructed mainly, the target point is determined according to the grid map, the path planning from the current position to the target point is formulated, the operation is performed towards the target point along the path planning, the autonomous planning operation path is realized, and manual intervention traction is not needed; the safety and feasibility of the area where the robot passes from the current position to the boundary point are considered, the robot is preferentially pulled to a simple environment without an impassable area for detection and map building through autonomous path planning, and the purpose of efficient autonomous detection and map building in a complex environment is achieved; the robot does not need to complete map construction in a fixed and low-efficiency arched overall cleaning mode, and then the map construction efficiency is improved.
In a specific embodiment, step S400 specifically includes:
acquiring a current search area according to the grid map;
searching the boundary point of the current search area;
and acquiring a target point according to the boundary point of the current search area.
Specifically, if the boundary point exists in the current search area, the target point is acquired according to the boundary point of the current search area. The complex area of the grid map is simplified, and the search area is dynamically determined in real time. And determining a current search area corresponding to the grid map by acquiring a circumscribed rectangle of the grid map. FIG. 2 is a schematic diagram of a grid map; and obtaining the innermost circumscribed rectangle through an algorithm. The area surrounded by the circumscribed rectangle is the current search area.
And acquiring the boundary point of the current search area through an RRT algorithm. In a specific embodiment, the boundary points of the current search area are searched by detecting with a global boundary point detector and a local boundary point detector, respectively.
The local boundary point detector takes the current position of the robot as a root node, randomly selects sampling points in a current search area as detection directions through an RRT algorithm, and if an RRT tree generated between the root node and the sampling points does not encounter an obstacle and can reach an unknown area, the node where the RRT tree first reaches the unknown area is a boundary point.
The global boundary point detector takes the initial position of the robot as a root node, randomly selects sampling points in a current search area as exploration directions through an RRT algorithm, and if an RRT tree generated between the root node and the sampling points does not encounter an obstacle and can reach an unknown area, the node where the RRT tree first reaches the unknown area is a boundary point.
The local boundary point detector randomly selects sampling points in the search area by taking the current pose as a root node through a local RRT boundary point detection method so as to obtain first boundary points.
The global boundary point detector randomly selects sampling points in the search area by taking the initial pose as a root node through a global RRT boundary point detection method so as to obtain second boundary points.
The first boundary point and the second boundary point serve as boundary points of the current search area.
Of course, in another embodiment, only the local boundary point detector may be used to obtain the first boundary point as the boundary point of the current search area; the second boundary point may be acquired by only using the global boundary point detector as the boundary point of the current search area.
The boundary point detector can be added with an Opencv-based boundary point detector to improve the detection efficiency of the boundary point.
If the boundary point of the current search area is searched, judging that a new path exists for the robot to traverse or judging that an area which is not traversed by the robot exists in the whole environment, and acquiring a target point for path planning according to the boundary point of the current search area; if the boundary point of the current search area cannot be searched, the robot is judged to have no new reachable path, namely, the whole environment area is traversed by the robot.
A search area is dynamically determined through real-time map data so as to improve detection efficiency and ensure complete map building; the grid corresponding to the region where the robot is difficult to pass is set to be in an occupied state, a global path passing through the region is prevented from being planned, the navigation efficiency is improved, the robot is prevented from being trapped in the region where the robot is difficult to pass for a long time, the robot is better pulled to a large channel to walk, and the efficiency of autonomous detection and map building is improved.
In a specific embodiment, if the target point is not obtained according to the grid map, constructing a current position of a global map as the position of the robot at the current task distribution moment according to the grid map corresponding to the current position and the grid map obtained before the current position;
after traveling along the path toward the target point, the method further comprises:
acquiring a next position corresponding to the next task allocation moment in the operation process;
and taking the next position as the current position, and constructing a grid map corresponding to the current position.
Specifically, if the target point is not acquired according to the grid map or the boundary point of the current search area is not searched, the whole area is completely traversed, the construction of the grid map is finished, and the global map is obtained according to the grid map corresponding to the current position and the grid map acquired before the current position, so that the map construction is finished.
And if the target point is acquired according to the grid map or the boundary point of the current search area is searched, acquiring a next position corresponding to the next task distribution moment when the next task distribution moment comes in the process that the robot runs towards the target point along the path plan, wherein the next position is the start of a new cycle period.
For example: constructing a grid map corresponding to the N position, determining a corresponding target point according to the grid map corresponding to the N position, making a path plan from the N position to the corresponding target point, and running towards the target point along the path plan; acquiring an N +1 position in the operation process, constructing a grid map corresponding to the N +1 position, determining a corresponding target point according to the grid map corresponding to the N +1 position, and making a path plan from the N +1 position to the corresponding target point; and acquiring the N +2 position in the operation process, and repeating the steps until the boundary point cannot be searched at a certain position, so that the traversal is completed.
Namely, the method includes: constructing a grid map corresponding to the current position; judging whether a target point can be obtained according to the grid map; if a target point is obtained according to the grid map, a path plan from the current position to the target point is formulated; and traveling towards the target point along the path plan. And if the target point is not obtained according to the grid map, constructing a global map according to the grid map corresponding to the current position and the grid map obtained before the current position.
In one embodiment, a map construction method is disclosed, which is applied to a robot, and includes:
acquiring environmental information in real time;
constructing a corresponding grid map according to the environmental information acquired from the current position;
acquiring a target point according to the grid map;
formulating a path plan from the current location to the target point;
and traveling towards the target point along the path plan.
In another embodiment, the method further comprises:
and if the target point is not acquired according to the grid map, constructing a global map according to the environmental information acquired at the current position and the environmental information acquired before the current position.
The environment information is data collected by the environment information collecting device. For example, point cloud data acquired from a lidar.
Namely, the method includes: acquiring environmental information in real time; constructing a corresponding grid map according to the environmental information acquired from the current position; judging whether a target point can be obtained according to the grid map; if a target point is obtained according to the grid map, a path plan from the current position to the target point is formulated; and traveling towards the target point along the path plan. And if the target point is not acquired according to the grid map, constructing a global map according to the environmental information acquired at the current position and the environmental information acquired before the current position.
In one embodiment, before obtaining the current search area from the grid map, the method further comprises:
preprocessing the grid map;
wherein, the pretreatment comprises expansion treatment and corrosion treatment in sequence.
Specifically, the grid map is expanded, and then the expanded grid map is corroded to simplify the complex area of the map.
Dilation and erosion are morphological methods in image processing. The image erosion reduces and refines the highlight area or the white part in the image. The image expansion is the inverse operation of the erosion operation, the highlight area or the white part in the image is expanded, the operation result image is larger than the highlight area of the original image, lines are thickened, and the method is mainly used for denoising. The expansion aims at finding a connected region, namely judging whether the map grids in the grid map have connectivity or not and marking the connected region; the purpose of the corrosion is to eliminate noise. Referring to fig. 3 and 4, fig. 3 is a grid map before preprocessing, and fig. 4 is a grid map after preprocessing.
In a specific embodiment, acquiring the target point according to the boundary point of the current search area includes:
acquiring a boundary point clustering center of the boundary points through clustering;
filtering the boundary point clustering centers to obtain effective boundary point clustering centers;
obtaining the income value of each effective boundary point clustering center;
and taking the effective boundary point clustering center with the maximum profit value as a target point.
Specifically, at least one boundary point of the current search area is obtained, and all the boundary points can be clustered by adopting a density clustering algorithm to obtain at least one boundary point clustering center. The boundary points densely gathered together are classified into the same class by a density clustering algorithm. The centroid of a boundary point belonging to the same class is the boundary point cluster center of such boundary points. The density clustering algorithm can adopt any one of a MeanShift clustering algorithm, a DBSCAN clustering algorithm, an OPTICS clustering algorithm and a SNN density-based clustering algorithm.
Points which are obviously not in accordance with the path planning may be in the boundary point clustering centers, and the invalid boundary point clustering centers are filtered to obtain effective boundary point clustering centers, so that unnecessary operation cost can be reduced.
The profit value is used for measuring which boundary point clustering center is taken as a target point of path planning, and the larger the profit value is, the more possible the profit value is to become the target point.
In a specific embodiment, the filtering the boundary point cluster centers to obtain effective boundary point cluster centers includes:
acquiring information gain of each boundary point clustering center;
acquiring a global path of the robot from the current position to each boundary point clustering center respectively;
judging whether an area which is difficult to pass exists in each global path according to the size of the robot body to obtain a judgment result;
and filtering the boundary point clustering centers according to the judgment result and the information gain of the boundary point clustering centers to obtain effective boundary point clustering centers.
In a specific embodiment, obtaining the information gain of each boundary point cluster center includes:
acquiring a first target circle by taking the boundary point clustering center as a circle center and a preset radius as a radius;
judging whether a map grid in an occupied state exists in the map grids corresponding to the first target circle;
if the map grid occupied by the state does not exist, a second target circle is obtained by taking the center of the boundary point cluster as the center of a circle and the radius of the information gain as the radius,
taking the difference between the area of the map grid in the second target circle, the state of which is unknown, and the area of the map grid in the second target circle, the state of which is occupied, as the information gain of the boundary point clustering center;
and if the map grid in the occupied state exists, setting the information gain of the boundary point clustering center as a preset value.
Specifically, the information gain is quantized by calculating the area of an unknown region surrounding a boundary point within the information gain radius, and the value of the information gain is suppressed based on environmental factors.
The preset radius is slightly larger than the radius of the robot body and does not exceed the range which can be measured by an environmental information acquisition device or a perception sensor (such as a laser radar).
The information gain radius does not exceed the range that can be measured by environmental information collection equipment or a perception sensor (such as a laser radar).
Preferably, the information gain radius is greater than a preset radius.
The preset value may be set to 0.
The method and the device are based on an improved information gain calculation algorithm, reasonably quantize the environment complexity to inhibit information gain, and filter the boundary point clustering center with smaller information gain.
Namely, the method removes the boundary point clustering centers of which the information gain is smaller than the threshold value from the boundary point clustering centers, thereby performing first filtering on the boundary point clustering centers.
The global path of the robot reaching the center of each boundary point cluster from the current position can be calculated by an A-algorithm, a D-algorithm or a Dijkstra algorithm.
Judging whether an area difficult to pass exists in the global path: and obtaining a path channel corresponding to the global path through the global path, comparing the size of the robot body with the width of the path channel corresponding to the global path, and if a narrow area exists in the path channel and the robot cannot pass through the narrow area, judging the narrow area as an area difficult to pass through. And then removing the boundary point clustering centers corresponding to the global paths with the regions difficult to pass through, thereby carrying out second filtering on the boundary point clustering centers.
According to the method and the device, the invalid boundary point clustering centers are removed through twice filtering, so that redundant boundary points are removed, and the subsequent calculation cost is reduced.
Of course, the effective boundary point cluster center may also be obtained by removing the ineffective boundary point cluster center only through the first filtering or only through the second filtering.
In one embodiment, obtaining the profit value of each effective boundary point cluster center includes: and acquiring the corresponding income value of the effective boundary point clustering center according to the information gain of the effective boundary point clustering center.
The calculation formula of the profit value of the effective boundary point cluster center is as follows:
Radj＝λ×h×Iadj-Nadj
Nadj＝Ncost+γ×Nobst
where λ is a weight constant used to highlight the importance of information gain over navigation cost.
h is the hysteresis gain. A constant larger than 1 is set in the hysteresis radius range, so that the robot preferentially explores the nearby boundary points; outside the hysteresis radius range, a constant of 1 is set. Wherein the hysteresis radius should be set to a value within a range that can be measured by an environmental information collecting device or a perception sensor (e.g., a lidar).
IadjFor improved information gain, IadjIs a preset value (the preset value can be set to 0), or, IadjThe difference between the area of the map grid in the second target circle whose state is unknown and the area of the map grid in the second target circle whose state is occupied is shown for the foregoing calculation method.
NadjFor improved navigation costs, wherein NcostThe cost value of the global path from the current position to the effective boundary point clustering center of the robot is obtained, and the global path planning algorithm can use the A-x algorithm.
Gamma is the importance of the map grid occupied by the state in the path channel corresponding to the global path to the navigation cost, and gamma is a constant larger than 1.
NobstThe map grid area occupied by the state in the path channel is the channel which is slightly larger than the robot body and takes the global path as the center.
Referring to fig. 5, a path channel diagram is shown, wherein 10 is a robot, 20 is a global path, and 30 is a path channel.
In a particular embodiment, the status of each map grid in the grid map is one of occupied, free, unknown;
the method further comprises the following steps:
the state of the map grid corresponding to the area difficult to pass through is set as occupied.
Specifically, a global path of the robot from the current position to each effective boundary point clustering center is obtained; judging whether a region which is difficult to pass exists in a path channel corresponding to each global path according to the size of the robot body to obtain a judgment result; setting the state of the map grid corresponding to the area difficult to pass to be occupied according to the judgment result; and then acquiring the information gain of the clustering center of each boundary point.
In a specific embodiment, the filtering the boundary point clustering centers to obtain effective boundary point clustering centers according to the determination result and the information gain of the boundary point clustering centers includes:
removing boundary point clustering centers of which the information gain is smaller than a threshold value in the boundary point clustering centers, and/or removing the boundary point clustering centers corresponding to the areas with the judgment results of difficult passing;
and taking the reserved boundary point clustering center as an effective boundary point clustering center.
In a specific embodiment, obtaining a global path from the current position to each effective boundary point cluster center of the robot includes:
and planning a global path of the robot from the current position to each effective boundary point clustering center respectively through an A-algorithm.
By an improved information gain calculation algorithm, boundary point clustering centers with small unknown regions to be detected and complex environments are removed from a filter, so that the influence of the boundary point clustering centers on the autonomous detection mapping efficiency is avoided; through an improved income value calculation algorithm, the robot can preferentially select the boundary point clustering center of a large unknown region dragged to a simple environment, so that the efficiency of autonomous detection and mapping is improved.
In a particular embodiment, the planning of the trajectory towards the target point comprises:
acquiring a speed instruction according to the path planning;
and moving towards the target point according to the speed command.
Specifically, an optimal speed instruction is calculated based on a dynamic window method and sent to a robot controller, so that the robot operates according to the speed instruction. The speed command may include, but is not limited to, a speed, an angular speed, or a direction of operation.
The method includes the steps that grid maps of local environments are created through one robot at different moments (different positions), and the grid maps are local maps represented in a grid mode. According to the method and the device, the self-movement is controlled through the autonomous planning path to obtain the environment information of each position, the corresponding grid map is obtained according to the environment information, the whole environment is traversed to obtain a plurality of grid maps, and then the grid maps are spliced or fused to obtain the global map.
The grid map is an environmental description obtained by processing the robot's internal and external sensor data by the SLAM algorithm.
Thereby realized the full autonomic path planning of robot through this application and realized full autonomic map, do not need artificial intervention to pull, and will have the global path of the region that is difficult to pass to get rid of outside, avoided the robot to move to the complicacy and have the narrow and small region of barrier, ensured the security and the feasibility of robot in the operation process for high-efficient autonomic exploration is built the map. In addition, different grid maps are dynamically acquired at different positions, so that the detection efficiency is improved, and the complete map construction is ensured; by the improved gain calculation method and the improved profit calculation method, the boundary point clustering centers which are small in unknown region to be explored and complex in environment are removed, the influence of the boundary point clustering centers on the autonomous exploration mapping efficiency is avoided, and the autonomous exploration mapping efficiency is improved.
FIG. 6 is a block diagram showing the structure of a map building apparatus according to an embodiment. Referring to fig. 6, the apparatus includes:
the grid module 200 is used for constructing a grid map corresponding to the current position;
a detection module 400, configured to obtain a target point according to the grid map;
a path planning module 600, configured to formulate a path plan from the current location to the target point;
a motion control module 800 for navigating along the path plan towards the target point.
In a specific embodiment, the detection module 400 specifically includes:
the first calculation module is used for acquiring a current search area according to the grid map;
the search module is used for searching the boundary point of the current search area;
and the target point positioning module is used for acquiring a target point according to the boundary point of the current search area.
In a specific embodiment, the apparatus further comprises:
and the ending module is used for constructing a global map according to the grid map corresponding to the current position and the grid map acquired before the current position if the target point is not acquired according to the grid map.
The current position is the position of the robot at the current task allocation moment;
the device also includes:
and the loop module is configured to obtain a next position corresponding to a next task allocation time in an operation process, take the next position as a current position, and jump to the grid module 200.
In one embodiment, a map building apparatus is disclosed, which is applied to a robot, the apparatus including:
the acquisition module is used for acquiring environmental information in real time;
the grid module is used for constructing a corresponding grid map according to the environment information acquired from the current position;
the detection module is used for acquiring a target point according to the grid map;
the path planning module is used for planning a path from the current position to the target point;
and the movement control module is used for planning to move towards the target point along the path.
In another embodiment, the apparatus further comprises:
and the ending module is used for constructing a global map according to the environmental information acquired at the current position and the environmental information acquired before the current position if the target point is not acquired according to the grid map.
In a specific embodiment, the first calculation module is specifically configured to obtain a circumscribed rectangle of the grid map to determine a current search area corresponding to the grid map.
In a specific embodiment, the search module is specifically configured to search for boundary points of the current search area by detecting with a global boundary point detector and a local boundary point detector, respectively.
In a specific embodiment, the apparatus further comprises:
the preprocessing module is used for preprocessing the grid map;
wherein, the pretreatment comprises expansion treatment and corrosion treatment in sequence.
In one embodiment, the target point location module specifically includes:
the clustering unit is used for acquiring a boundary point clustering center of the boundary points through clustering;
the filtering unit is used for filtering the boundary point clustering centers to obtain effective boundary point clustering centers;
the first calculation unit is used for acquiring the income value of each effective boundary point clustering center;
and the comparison unit is used for taking the effective boundary point clustering center with the maximum profit value as a target point.
In one particular embodiment, the filtration unit comprises:
the second calculation unit is used for acquiring the information gain of each boundary point clustering center;
the path planning unit is used for acquiring a global path from the current position to each boundary point clustering center of the robot;
the judging unit is used for judging whether an area which is difficult to pass exists in each global path according to the size of the robot body to obtain a judging result;
and the sub-filtering unit is used for filtering the boundary point clustering centers according to the judgment result and the information gain of the effective boundary point clustering centers to obtain the effective boundary point clustering centers.
In a particular embodiment, the status of each map grid in the grid map is one of occupied, free, unknown; the device also includes:
and the setting module is used for setting the state of the map grid corresponding to the area difficult to pass to be occupied.
In a specific embodiment, the second calculating unit specifically includes:
the first partitioning unit is used for acquiring a first target circle by taking the boundary point clustering center as a circle center and a preset radius as a radius;
the preset radius is larger than the radius of the robot and does not exceed the range which can be measured by the perception sensor;
the sub-judgment unit is used for judging whether the map grids corresponding to the first target circle exist in the map grids occupied in the state;
a second zoning unit, configured to, if there is no map grid occupied in the state, acquire a second target circle with the center of the boundary point cluster as the center of the circle and the information gain radius as the radius,
wherein the information gain radius does not exceed the range which can be measured by the perception sensor,
a first sub-calculation unit configured to use a difference between an area of the map grid of which the state is unknown in the second target circle and an area of the map grid of which the state is occupied in the second target circle as an information gain of the boundary point cluster center;
and the second sub-calculation unit is used for setting the information gain of the boundary point clustering center as a preset value if the map grid occupied in the state exists.
In one embodiment, the sub-filtration unit is specifically configured to:
removing boundary point clustering centers of which the information gain is smaller than a threshold value in the boundary point clustering centers, and/or removing the boundary point clustering centers corresponding to the areas with the judgment results of difficult passing;
and taking the reserved boundary point clustering center as an effective boundary point clustering center.
And the path planning unit plans a global path of the robot from the current position to each boundary point clustering center respectively through an A-x algorithm.
Or planning a global path of the robot from the current position to the clustering center of each boundary point respectively through a D-algorithm.
Or planning a global path of the robot from the current position to each boundary point clustering center respectively through a Dijkstra algorithm.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
FIG. 7 is a diagram illustrating an internal structure of a computer device according to an embodiment. As shown in fig. 7, the computer device includes a processor, a memory, a network interface, an input device, an acquisition device, and a display screen, which are connected via a system bus. Wherein the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program that, when executed by the processor, causes the processor to implement the mapping method. The internal memory may also have stored therein a computer program that, when executed by the processor, causes the processor to perform a process map construction method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like. The acquisition device is used for acquiring environmental information, and the acquisition device can be a laser radar and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 7 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program: constructing a grid map corresponding to the current position; acquiring a target point according to the grid map; formulating a path plan from the current position to the target point; and (5) running along the path plan towards the target point.
In one embodiment, the processor, when executing the computer program, performs the steps of:
and if the target point is not obtained according to the grid map, constructing a global map according to the grid map corresponding to the current position and the grid map obtained before the current position.
The processor, when executing the computer program, further performs the steps of any of the above-described mapping methods.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: constructing a grid map corresponding to the current position; acquiring a target point according to the grid map; formulating a path plan from the current position to the target point; and (5) running along the path plan towards the target point.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and if the target point is not obtained according to the grid map, constructing a global map according to the grid map corresponding to the current position and the grid map obtained before the current position.
The computer program, when executed by a processor, implements the steps of any of the mapping methods described above.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (12)
1. A map construction method applied to a robot is characterized by comprising the following steps:
constructing a grid map corresponding to the current position;
acquiring a target point according to the grid map;
formulating a path plan from the current location to the target point;
and traveling towards the target point along the path plan.
2. The method of claim 1, further comprising:
if the target point is not obtained according to the grid map, constructing a global map according to the grid map corresponding to the current position and the grid map obtained before the current position;
the current position is the position of the robot at the current task allocation moment;
after traveling along the path plan towards the target point, the method further comprises:
acquiring the next position corresponding to the next task distribution time in the running process,
and taking the next position as the current position, and executing the construction of the grid map corresponding to the current position.
3. The method of claim 1 or 2, wherein determining a target point from the grid map comprises:
acquiring a current search area according to the grid map;
searching the boundary point of the current search area;
and acquiring a target point according to the boundary point of the current search area.
4. The method of claim 3, wherein searching for the boundary point of the current search area comprises:
and respectively detecting by a global boundary point detector and a local boundary point detector to search the boundary point of the current search area.
5. The method of claim 3, wherein obtaining a target point according to a boundary point of the current search area comprises:
acquiring a boundary point clustering center of the boundary points through clustering;
filtering the boundary point clustering centers to obtain effective boundary point clustering centers;
obtaining the income value of each effective boundary point clustering center;
and taking the effective boundary point clustering center with the maximum profit value as a target point.
6. The method of claim 5, wherein filtering the boundary point cluster centers to obtain valid boundary point cluster centers comprises:
acquiring information gain of each boundary point clustering center;
acquiring a global path of the robot from the current position to each boundary point clustering center respectively;
judging whether an area which is difficult to pass exists in each global path according to the size of the robot body to obtain a judgment result;
and filtering the boundary point clustering centers according to the judgment result and the information gain of the boundary point clustering centers to obtain effective boundary point clustering centers.
7. The method of claim 6, wherein the status of each of the grid maps is one of occupied, free, unknown;
the method further comprises the following steps:
the state of the map grid corresponding to the area difficult to pass through is set as occupied.
8. The method of claim 6, wherein obtaining the information gain of each boundary point cluster center comprises:
acquiring a first target circle by taking the boundary point clustering center as a circle center and a preset radius as a radius;
judging whether a map grid in an occupied state exists in the map grids corresponding to the first target circle;
if the map grid occupied by the state does not exist, a second target circle is obtained by taking the center of the boundary point cluster as the center of a circle and the radius of the information gain as the radius,
taking the difference between the area of the map grid in the second target circle, the state of which is unknown, and the area of the map grid in the second target circle, the state of which is occupied, as the information gain of the boundary point clustering center;
and if the map grid in the occupied state exists, setting the information gain of the boundary point clustering center as a preset value.
9. The method of claim 6, wherein filtering the boundary point cluster centers to obtain effective boundary point cluster centers according to the determination result and the information gain of the boundary point cluster centers comprises:
removing the boundary point clustering centers of which the information gain is smaller than a threshold value in the boundary point clustering centers, and/or removing the boundary point clustering centers corresponding to the areas which are difficult to pass in the judgment result;
and taking the reserved boundary point clustering center as an effective boundary point clustering center.
10. A map building apparatus, characterized in that the apparatus comprises:
the grid module is used for constructing a grid map corresponding to the current position;
the detection module is used for acquiring a target point according to the grid map;
a path planning module for planning a path from the current position to the target point;
and the movement control module is used for planning to move towards the target point along the path.
11. A computer-readable storage medium, having a computer program stored thereon, which, when executed by a processor, causes the processor to carry out the steps of the method according to any one of claims 1-9.
12. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor executes the program to perform the steps of the method according to any of claims 1-9.
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