CN112000754B - Map construction method, 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 map construction 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; running along the path planning towards the target point. The application realizes efficient full-autonomous exploration and mapping without human intervention, avoids difficult-to-pass areas which are easy to trigger collision through autonomous planning paths in the exploration and mapping process, ensures the safety and feasibility of the areas passed by the robot, and improves the efficiency of autonomous exploration and mapping.
Description
Technical Field
The present application relates to the field of artificial intelligence technologies, and in particular, to a map construction method, a map construction device, a storage medium, and a computer device.
Background
With the rapid development of synchronous map construction and localization technology (i.e., SLAM), autonomous mobile robots are receiving increasing attention from the engineering and academia. The SLAM technology mainly realizes the position location of the mobile robot in an unknown environment so as to realize autonomous movement. However, the traditional mobile robot map building mainly relies on a manual intervention mode, and a plurality of navigation target points are manually set or a keyboard is used for directly controlling the mobile robot to move, so that map building is realized, and time, manpower and material resources are wasted when the map building faces a large and complex indoor environment; the sweeping robot does not need manual intervention, but builds a map in a bow-shaped global sweeping mode, so that a great deal of time is wasted, environmental attributes cannot be set before a sweeping task is executed, and local sweeping and other tasks cannot be executed.
In some autonomous detection mapping methods, manual intervention is needed in most of the existing autonomous detection mapping methods, and some problems exist in some of the full autonomous detection mapping methods. For example, the problem of safety and feasibility of the area through which the robot passes from the current position to the boundary point is not considered. Generally, the global planner will preferentially select the shortest path, but the shortest path is not optimal in complex areas, so the global planner can plan areas of long and thin obstacles such as stool feet and desk feet passing through indoor environments, or narrow channels, but when a mobile robot actually walks in the areas, the mobile robot is easy to trigger collision and is trapped in the areas for a long time, which seriously affects the efficiency of drawing and navigation, and the aim of high-efficiency autonomous detection drawing cannot be achieved.
Disclosure of Invention
In order to solve the technical problems that in the prior art, most of detection mapping needs manual intervention and mapping time is long and safety and feasibility of an area through which a robot passes from a current position to a target point are not considered, the embodiment of the application provides a map construction method, a map construction device, a storage medium and computer equipment.
In a first aspect, an embodiment of the present application provides a map construction method, applied to a robot, where 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;
Running along the path planning 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 boundary points of a current search area;
And acquiring the target point according to the boundary point of the current search area.
Optionally, the method further comprises:
if the target point is not acquired according to the grid map, constructing a global map according to the grid map corresponding to the current position and the grid map acquired before the current position;
the current position is the position of the robot at the current task allocation moment;
after traveling along the path planning towards the target point, the method further comprises:
acquiring a next position corresponding to a next task allocation moment in the operation process;
And taking the next position as the current position, and executing the construction of the grid map corresponding to the current position.
Optionally, acquiring the current search area according to the grid map includes:
and acquiring the circumscribed rectangle of the grid map to determine the current search area corresponding to the grid map.
Optionally, searching for a boundary point of the current search area includes:
The boundary points of the current search area are searched by the global boundary point detector and the local boundary point detector, respectively.
Optionally, before acquiring the current search area according to the grid map, the method further comprises:
preprocessing the grid map;
Wherein, the pretreatment comprises an expansion treatment and a corrosion treatment in sequence.
Optionally, acquiring the target point according to the boundary point of the current search area includes:
Obtaining 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 a benefit value of each effective boundary point clustering center;
and taking the effective boundary point cluster center with the maximum profit value as a target point.
Optionally, filtering the boundary point cluster center to obtain an effective boundary point cluster center, including:
acquiring information gain of each boundary point clustering center;
acquiring global paths of the robot from the current position to the clustering center of each boundary point 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 judging result;
And filtering the boundary point clustering center according to the judging result and the information gain of the boundary point clustering center to obtain an effective boundary point clustering center.
Optionally, the state of each map grid in the grid map is one of occupied, idle, unknown;
The method further comprises the steps of:
the state of the map grid corresponding to the area which is difficult to pass is set to be occupied.
Optionally, 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;
the preset radius is larger than the radius of the robot and does not exceed the range which can be measured by the sensing sensor;
Judging whether a map grid with an occupied state exists in the map grids corresponding to the first target circle or not;
if the map grid with the occupied state does not exist, a second target circle is acquired by taking the clustering center of the boundary points as the circle center and the radius of the information gain as the radius,
Wherein the radius of the information gain does not exceed the range which can be measured by the sensing sensor,
Taking the difference between the area of the map grid with unknown state in the second target circle and the area of the map grid with occupied state in the second target circle as the information gain of the boundary point clustering center;
And if the map grid with the occupied state exists, setting the information gain of the boundary point clustering center as a preset value.
Optionally, filtering the boundary point cluster center according to the judgment result and the information gain of the boundary point cluster center to obtain an effective boundary point cluster center, including:
Removing boundary point clustering centers with information gain smaller than a threshold value in the boundary point clustering centers, and/or removing boundary point clustering centers corresponding to the areas which are difficult to pass as a judgment result;
and taking the reserved boundary point cluster center as an effective boundary point cluster center.
Optionally, acquiring a global path of the robot from the current position to each boundary point cluster center includes:
And planning global paths of the robot from the current position to the clustering center of each boundary point respectively through an A-algorithm.
In a second aspect, an embodiment of the present application provides a map construction 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 making a path plan from the current position to the target point;
and the movement control module is used for running towards the target point along the path planning.
In a third aspect, embodiments of the present application provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, causes the processor to perform the steps of a method as described in any of the preceding claims.
In a fourth aspect, an embodiment of the application provides 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 performs the steps of the method as any one of the preceding claims when executing the program.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages:
According to the embodiment of the application, the grid map corresponding to the current position is autonomously constructed, the target point is determined according to the grid map, the path planning from the current position to the target point is formulated, and the autonomous planning operation path is realized by running towards the target point along the path planning, so that manual intervention and traction are not needed; the safety and feasibility of the area where the robot passes through from the current position to the boundary point are considered, so that the robot is preferentially pulled to a simple environment without an unvented area through autonomous path planning to carry out detection and mapping, and the aim of efficient autonomous detection and mapping under a complex environment is fulfilled; after the robot is no longer required to complete map construction through a fixed and low-efficiency bow-shaped global cleaning mode, 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 invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a flow diagram of a map construction method in one embodiment;
FIG. 2 is a circumscribed rectangular schematic of a grid map in one embodiment;
FIG. 3 is a grid map prior to 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 of a map construction apparatus in one embodiment;
fig. 7 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
SLAM scales Simultaneous Localization AND MAPPING, i.e., positioning and mapping simultaneously.
Electronic maps are widely used in the field of artificial intelligence for querying, locating, navigating and other scenes. In particular, artificial intelligence oriented robot-specific maps, which may be used for e.g. autopilot, robot navigation and positioning, etc.
The robot obtains sensing data through a visual sensor carried by the robot, obtains a grid map of an environment space according to an SLAM mode, realizes grid conversion of the SLAM map, and performs map fusion on a local grid map to obtain a global map. And 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.
Grid maps are a form of high-precision map commonly used in the robotic arts that divide an environment into a series of grids, where each grid is marked with a value that indicates whether the grid is occupied or free or unknown. That is, the grid map is a product of digitally rasterizing a real environment, which identifies obstacles in the environment by whether the grid is occupied. The grid map is widely applied to navigation and positioning scenes in various fields such as intelligent robots and the like due to the requirements of robot navigation and positioning.
In one embodiment, an application environment for a map construction method is disclosed. The executing subject in this example application environment is a robot (e.g., a sweeping robot). The robot has an environmental information collection device fixed thereto. The environmental information collection device may include, but is not limited to, a lidar sensor, an odometer, and the like. The environment information acquisition device is used for acquiring the space data or the environment information of the surrounding environment.
The environmental information acquisition device has a laser scanner or laser radar that can acquire data of the surrounding environment during operation of the robot to form a laser point cloud. In the embodiments described herein, a laser point cloud refers to a set of massive points expressing a target spatial distribution obtained by acquiring the spatial coordinates of each sampling point of the object surface in the 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 robot does not know the information of the obstacle in the new environment, so that the robot needs to traverse the whole environment, detect the position of the obstacle, the position without the obstacle and the position of an unknown area, and obtain a corresponding grid map according to the position information. The grid map includes a plurality of map grids, each of which is marked as a state that is one of occupied, idle, and unknown.
It should be understood by those skilled in the art that the map construction method of the present application may be applied to various scenes, for example, collecting indoor laser point cloud data, in which the collected laser point cloud may be used for navigation or positioning of an indoor robot (e.g., an indoor sweeping robot), etc. 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 chart of a map construction method 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, in the map construction in the embodiment of the application, a local map is circularly acquired at a preset frequency, and then the acquired local map is spliced or fused to obtain a global map.
And when the current position is reached according to the preset frequency, acquiring current environment information acquired at the current position, 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 obtained according to the preset frequency and the previous position. The grid map includes a plurality of map grids, and the state of each map grid is one of occupied, free and unknown.
Acquiring a current map to be rasterized obtained by the robot at a current position machine; environmental information is acquired in real time, the environmental information is converted into point cloud data and mapped into a grid map corresponding to the current position, and the grid map corresponding to the current position is a current local grid map.
S400: and acquiring the target point according to the grid map.
S600: a path plan is formulated from the current location to the target point.
Specifically, the target point is used as a navigation mark point for path planning, and the path planning is used for guiding the running direction, speed, angular speed and the like of the robot. That is, the robot is directed to travel from the current position at what speed or angular velocity in which direction.
Preferably, the robot may autonomously plan a path by an a-algorithm based on the current position and the position of the target point. Of course, the path may also be planned using the D-x algorithm or Dijkstra algorithm.
S800: running along the path planning towards the target point.
Specifically, after the path planning is finished, the robot autonomously runs to the target point according to the path planning, if the robot reaches the next position in the running process, a grid map corresponding to the next position is built 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 planning from the next position to the target point corresponding to the next position is formulated, and the robot runs towards the target point corresponding to the next position along the path planning. And (3) circulating until the target point cannot be found according to the grid map corresponding to a certain position, ending the construction of the grid map, namely, acquiring global environment information of the whole environment or the global map.
According to the method, the path is planned autonomously, the environment information is acquired in the running process according to the planned path, and when a target point cannot be acquired through a grid map at a certain position, the global map is constructed according to the environment information acquired currently and before.
By simplifying the grid map complex region, the robot is prevented from being pulled to the complex region in the autonomous exploration map building, and is trapped in the complex region, so that the robot is better pulled to a path channel without an obstacle or an unperforatable region to walk, and the autonomous exploration map building efficiency is improved.
The grid map corresponding to the current position is built through the main structure, the target point is determined according to the grid map, the path planning from the current position to the target point is formulated, the autonomous planning operation path is realized by running towards the target point along the path planning, and manual intervention traction is not needed; the safety and feasibility of the area where the robot passes through from the current position to the boundary point are considered, so that the robot is preferentially pulled to a simple environment without an unvented area through autonomous path planning to carry out detection and mapping, and the aim of efficient autonomous detection and mapping under a complex environment is fulfilled; after the robot is no longer required to complete map construction through a fixed and low-efficiency bow-shaped global cleaning mode, 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 boundary points of a current search area;
And acquiring the 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 the current search area corresponding to the grid map by acquiring the circumscribed rectangle of the grid map. Referring to fig. 2, a schematic diagram of a rectangle circumscribed by 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 obtaining boundary points of the current search area through an RRT algorithm. In one embodiment, the boundary points of the current search area are searched by global boundary point detectors and local boundary point detectors, respectively.
The local boundary point detector takes the current position of the robot as a root node, a sampling point is randomly selected in a current search area as a detection direction through an RRT algorithm, and if an RRT tree generated from the root node to the sampling point does not encounter an obstacle and can reach an unknown area, the node of the RRT tree which reaches the unknown area first 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 the current search area as an exploration direction through an RRT algorithm, and if an RRT tree generated from the root node to the sampling points does not encounter an obstacle and can reach an unknown area, the node of the RRT tree, which reaches the unknown area, is the 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 acquire a first boundary point.
The global boundary point detector randomly selects sampling points in the search area by using the initial pose as a root node through a global RRT boundary point detection method to obtain a second boundary point.
The first boundary point and the second boundary point are used 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; it is also possible to acquire the second boundary point as the boundary point of the current search area using only the global boundary point detector.
The boundary point detector can be added with an Opencv-based boundary point detector to improve the boundary point detection efficiency.
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 is not searched, judging that the robot does not have a new reachable path, namely, the whole environment area is traversed by the robot.
Dynamically determining a search area through real-time map data to improve detection efficiency and ensure complete map construction; through setting up the grid that the area that is difficult to pass of robot corresponds as the occupation state, avoid planning out the global route through such area to improve navigation efficiency, avoid the robot to stranded in the area that is difficult to pass for a long time, make to pull the robot to walk on the big passageway better, with the efficiency that improves the autonomous detection and construct the drawing.
In a specific embodiment, if the target point is not acquired according to the grid map, constructing a global map according to the grid map corresponding to the current position and the grid map acquired before the current position, wherein the current position of the global map is the position of the robot at the current task allocation moment;
After traveling along the path planning towards the target point, the method further comprises:
acquiring a next position corresponding to a next task allocation moment in the operation process;
And taking the next position as the current position, and executing the construction of the 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 grid map is constructed, the global map is obtained according to the grid map corresponding to the current position and the grid map acquired before the current position, and the map construction is completed.
If the target point is acquired according to the grid map or the boundary point of the current search area is searched, acquiring the next position corresponding to the next task allocation moment when the next task allocation moment arrives in the process that the robot runs along the path planning towards the target point, wherein the next position is the beginning of a new cycle.
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, and 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 running 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 an N+2 position in the running process, and circulating until no boundary point is searched at a certain position, so that the traversing is completed.
That is, the method includes: constructing a grid map corresponding to the current position; judging whether the target point can be acquired according to the grid map; if the target point is acquired according to the grid map, a path plan from the current position to the target point is formulated; running along the path plan towards the target point. If the target point is not acquired according to the grid map, a global map is constructed according to the grid map corresponding to the current position and the grid map acquired before the current position.
In one embodiment, a map construction method is disclosed, the method is applied to a robot, the method comprises:
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;
running along the path plan towards the target point.
In another embodiment, the method further comprises:
If the target point is not acquired according to the grid map, constructing a global map according to the environment information acquired by the current position and the environment information acquired before the current position.
The environmental information is data acquired according to the environmental information acquisition device. For example, from point cloud data acquired by lidar.
That is, 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 acquired according to the grid map; if the target point is acquired according to the grid map, a path plan from the current position to the target point is formulated; running along the path plan towards the target point. If the target point is not acquired according to the grid map, constructing a global map according to the environment information acquired by the current position and the environment information acquired before the current position.
In one embodiment, before acquiring the current search area from the grid map, the method further comprises:
preprocessing the grid map;
Wherein, the pretreatment comprises an expansion treatment and a corrosion treatment in sequence.
Specifically, the grid map is subjected to expansion treatment, and then the expanded grid map is subjected to corrosion treatment, so that the simplification of a complex area of the map is realized.
Swelling and corrosion are morphological methods in image processing. Image erosion refines the highlight or white portions of the image. The image expansion is the reverse operation of the corrosion operation, the highlight region or the white part in the image is expanded, the operation result diagram is larger than the highlight region of the original image, and the lines are thickened, so that the method is mainly used for denoising. The purpose of expansion is to find 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:
Obtaining 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 a benefit value of each effective boundary point clustering center;
and taking the effective boundary point cluster center with the maximum profit value as a target point.
Specifically, at least one boundary point of the current search area can be clustered by adopting a density clustering algorithm, so as to obtain at least one boundary point clustering center. Boundary points densely clustered together are classified into the same class by a density clustering algorithm. The centroid of boundary points 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 clustering algorithm based on SNN density.
Points which obviously do not accord with the path planning may exist in the boundary point clustering centers, and unnecessary operation expenditure can be reduced by filtering out the invalid boundary point clustering centers to obtain valid boundary point clustering centers.
The benefit value is used for measuring which boundary point cluster center is used as a target point of path planning, and the larger the benefit value is, the more likely the benefit value becomes the target point.
In a specific embodiment, filtering the boundary point cluster center to obtain an effective boundary point cluster center includes:
acquiring information gain of each boundary point clustering center;
acquiring global paths of the robot from the current position to the clustering center of each boundary point 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 judging result;
And filtering the boundary point clustering center according to the judging result and the information gain of the boundary point clustering center to obtain an effective boundary point clustering center.
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 with an occupied state exists in the map grids corresponding to the first target circle or not;
if the map grid with the occupied state does not exist, a second target circle is acquired by taking the clustering center of the boundary points as the circle center and the radius of the information gain as the radius,
Taking the difference between the area of the map grid with unknown state in the second target circle and the area of the map grid with occupied state in the second target circle as the information gain of the boundary point clustering center;
And if the map grid with 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 around 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 is not larger than the range which can be measured by the environment information acquisition equipment or the perception sensor (such as a laser radar).
The radius of the information gain does not exceed the range that can be measured by the environmental information acquisition device or a perception sensor (e.g., a lidar).
Preferably, the information gain radius is greater than the preset radius.
The preset value may be set to 0.
The application reasonably quantizes the environment complexity to restrain the information gain based on the improved information gain calculation algorithm, and filters the boundary point clustering center with smaller information gain.
Namely, the application removes the boundary point clustering centers with the information gain smaller than the threshold value in the boundary point clustering centers, thereby carrying out first filtering on the boundary point clustering centers.
The global path of the robot from the current position to the clustering center of each boundary point can be calculated by an A-type algorithm, a D-type algorithm or a Dijkstra algorithm.
Judging whether an area which is difficult to pass exists in the global path: and acquiring 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 judging the area as an area difficult to pass if the robot cannot pass through a narrow area exists in the path channel. And then removing the boundary point clustering centers corresponding to the global paths with the difficult-to-pass areas, thereby performing second filtering on the boundary point clustering centers.
According to the application, the invalid boundary point clustering center is removed through twice filtering, so that redundant boundary points are removed, and the subsequent calculation cost is reduced.
Of course, the valid boundary point cluster center may be obtained by removing the invalid boundary point cluster center only by the first filtering or only by the second filtering.
In a specific embodiment, obtaining the benefit value of each valid boundary point cluster center includes: and obtaining the gain value of the corresponding 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 clustering 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. Setting a constant larger than 1 in the hysteresis radius range, wherein the meaning is that the robot preferentially searches the boundary points nearby; outside the hysteresis radius, a constant 1 is set. Wherein the hysteresis radius should be set to a value within a range that can be measured by the environmental information acquisition device or a perception sensor (e.g., lidar).
I adj is an improved information gain, I adj is a preset value (the preset value may be set to 0), or I adj is a difference between the area of the map grid whose state is unknown in the second target circle and the area of the map grid whose state is occupied in the second target circle, as shown in the foregoing calculation method.
N adj is an improved navigation cost, where N cost is a cost value of a global path of the robot from the current position to the active boundary point cluster center, and the global path planning algorithm may use an 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.
N obst is the map grid area occupied by the state in the path channel, and the path channel is a channel which is slightly larger than the robot body and takes the global path as the center.
Referring to the path channel schematic of fig. 5, where 10 is a robot, 20 is a global path, and 30 is a path channel.
In one particular embodiment, the status of each map grid in the grid map is one of occupied, idle, unknown;
The method further comprises the steps of:
the state of the map grid corresponding to the area which is difficult to pass is set to be occupied.
Specifically, global paths of the robot from the current position to each effective boundary point clustering center are obtained; judging whether a difficult-to-pass area exists in a path channel corresponding to each global path according to the size of the robot body to obtain a judging result; setting the state of the map grid corresponding to the area which is difficult to pass as occupied according to the judging result; and obtaining the information gain of each boundary point clustering center.
In a specific embodiment, filtering the boundary point cluster center according to the judgment result and the information gain of the boundary point cluster center to obtain an effective boundary point cluster center includes:
Removing boundary point clustering centers with information gain smaller than a threshold value in the boundary point clustering centers, and/or removing boundary point clustering centers corresponding to the areas which are difficult to pass as a judgment result;
and taking the reserved boundary point cluster center as an effective boundary point cluster center.
In a specific embodiment, obtaining the global path of the robot from the current position to each valid boundary point cluster center includes:
And planning global paths of the robot from the current position to the clustering center of each effective boundary point respectively through an A-algorithm.
Removing boundary point clustering centers which are small in unknown areas to be detected and complex in environment from a filter through an improved information gain calculation algorithm, and avoiding the influence of the boundary point clustering centers on the efficiency of autonomous detection and mapping; through an improved profit value calculation algorithm, the robot can preferentially select the boundary point clustering centers of the large unknown area which are pulled to the simple environment, so that the autonomous detection and mapping efficiency is improved.
In a specific embodiment, running along a path plan towards a target point comprises:
Acquiring a speed instruction according to path planning;
and running towards the target point according to the speed command.
Specifically, an optimal speed command is calculated based on a dynamic window method and is sent to a robot controller, so that the robot operates according to the speed command. The speed command may include, but is not limited to, a speed of operation, an angular speed or direction, and the like.
The present application creates grid maps of local environments, which are local maps expressed in the form of grids, at different times (different positions) by one robot. According to the method, the environment information of each position is acquired by controlling the movement of the self-planning path, the corresponding grid map is obtained according to the environment information, a plurality of grid maps are obtained by traversing the whole environment, and then the plurality of grid maps are spliced or fused to obtain the global map.
The grid map is an environment description obtained by processing the internal and external sensor data of the robot by the SLAM algorithm.
The application realizes the full-automatic path planning of the robot so as to realize full-automatic map building, does not need human intervention traction, excludes the global path in the area which is difficult to pass, avoids the robot from running to a complicated narrow area with barriers, ensures the safety and feasibility of the robot in the running process, and accelerates the efficient automatic exploration map building. In addition, by dynamically acquiring different grid maps at different positions, the detection efficiency is improved and the complete map construction is ensured; and removing the boundary point clustering centers which are small in unknown area to be explored and complex in environment through an improved gain calculation method and a benefit calculation method, avoiding the influence of the boundary point clustering centers on the efficiency of autonomous exploration and mapping, and improving the efficiency of autonomous exploration and mapping.
Fig. 6 is a block diagram of a map construction apparatus in one embodiment. Referring to fig. 6, the apparatus includes:
The grid module 200 is used for constructing a grid map corresponding to the current position;
the detection module 400 is used for acquiring a target point according to the grid map;
A path planning module 600 for making a path plan from the current location to the target point;
the movement control module 800 is configured to run toward the target point along the path plan.
In one 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 searching module is used for searching boundary points of the current searching area;
And the target point positioning module is used for acquiring the 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 apparatus further comprises:
And the circulation module is used for acquiring a next position corresponding to the next task allocation moment in the operation process, taking the next position as the current position, and jumping to the grid module 200.
In one embodiment, a map construction apparatus is disclosed, the apparatus being applied to a robot, the apparatus comprising:
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 environmental information acquired by 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 making a path plan from the current position to the target point;
and the movement control module is used for running towards the target point along the path planning.
In another embodiment, the apparatus further comprises:
and the ending module is used for constructing a global map according to the environment information acquired by the current position and the environment information acquired before the current position if the target point is not acquired according to the grid map.
In a specific embodiment, the first computing module is specifically configured to obtain an 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 an expansion treatment and a corrosion treatment in sequence.
In one embodiment, the target point positioning module specifically includes:
the clustering unit is used for obtaining 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 computing unit is used for obtaining the benefit value of each effective boundary point clustering center;
and the comparison unit is used for taking the effective boundary point clustering center with the largest profit value as a target point.
In a specific embodiment, the filter 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 global paths of the robot from the current position to the clustering center of each boundary point respectively;
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 center according to the judging result and the information gain of the effective boundary point clustering center so as to obtain the effective boundary point clustering center.
In one particular embodiment, the status of each map grid in the grid map is one of occupied, idle, unknown; the apparatus further comprises:
And the setting module is used for setting the state of the map grid corresponding to the area which is difficult to pass as occupation.
In a specific embodiment, the second computing unit specifically comprises:
The first dividing 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 sensing sensor;
The sub-judging unit is used for judging whether the map grids corresponding to the first target circle exist in the map grids with occupied states or not;
A second dividing unit for acquiring a second target circle by taking the clustering center of the boundary points as the circle center and the information gain radius as the radius if the map grid with the occupied state does not exist,
Wherein the radius of the information gain does not exceed the range which can be measured by the sensing sensor,
A first sub-calculation unit configured to set a difference between an area of the map grid in which the state in the second target circle is unknown and an area of the map grid in which the state in the second target circle is occupied as an information gain of a boundary point cluster center;
And the second sub-calculation unit is used for setting the information gain of the boundary point clustering center to be a preset value if the map grid with the occupied state exists.
In a specific embodiment, the sub-filtration unit is specifically for:
Removing boundary point clustering centers with information gain smaller than a threshold value in the boundary point clustering centers, and/or removing boundary point clustering centers corresponding to the areas which are difficult to pass as a judgment result;
and taking the reserved boundary point cluster center as an effective boundary point cluster center.
And the path planning unit is used for planning global paths of the robot from the current position to the clustering center of each boundary point respectively through an A-algorithm.
Or, planning global paths of the robot from the current position to the clustering center of each boundary point respectively through a D-algorithm.
Or planning global paths of the robot from the current position to the clustering center of each boundary point respectively through Dijkstra algorithm.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 1 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of other steps or sub-steps of other steps.
Fig. 7 is an internal structural diagram of a computer device in one embodiment. As shown in fig. 7, the computer device is connected with a processor, a memory, a network interface, an input device, an acquisition device and a display screen through a system bus. The memory includes a nonvolatile 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 a processor, causes the processor to implement a map construction method. The internal memory may also have stored therein a computer program which, when executed by the processor, causes the processor to perform the process map construction method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, the input device of the computer 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 computer equipment, and can also be 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.
It will be appreciated by those skilled in the art that the structure shown in FIG. 7 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the 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 steps of 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; running along the path planning towards the target point.
In one embodiment, the processor, when executing the computer program, performs the steps of:
if the target point is not acquired according to the grid map, a global map is constructed according to the grid map corresponding to the current position and the grid map acquired before the current position.
The processor when executing the computer program also implements the steps of the map construction method of any one of the above.
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; running along the path planning towards the target point.
In one embodiment, the computer program when executed by the processor further performs the steps of:
if the target point is not acquired according to the grid map, a global map is constructed according to the grid map corresponding to the current position and the grid map acquired before the current position.
The computer program when executed by a processor implements the steps of the map construction method of any one of the above.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile 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 (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It should be noted that in this document, relational terms such as "first" and "second" and the like are 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. Moreover, 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 one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the 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 (9)
1. A map construction method applied to a robot, the method comprising:
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;
running along the path plan towards the target point;
determining a target point according to the grid map, including: acquiring a current search area according to the grid map; searching boundary points of the current search area; acquiring a target point according to the boundary point of the current search area;
acquiring a target point according to the boundary point of the current search area, wherein the method comprises the following steps: obtaining a boundary point clustering center of the boundary point through clustering; filtering the boundary point clustering centers to obtain effective boundary point clustering centers; obtaining a benefit value of each effective boundary point clustering center; taking the effective boundary point cluster center with the biggest profit value as a target point;
Filtering the boundary point clustering center to obtain an effective boundary point clustering center, including: acquiring information gain of each boundary point clustering center; acquiring global paths 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 judging result; and filtering the boundary point clustering center according to the judging result and the information gain of the boundary point clustering center to obtain an effective boundary point clustering center.
2. The method according to claim 1, wherein the method further comprises:
if the target point is not acquired according to the grid map, constructing a global map according to the grid map corresponding to the current position and the grid map acquired 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:
the next position corresponding to the next task allocation moment is obtained in the running process,
And taking the next position as the current position, and executing the grid map corresponding to the current position.
3. The method of claim 1, wherein searching for boundary points of the current search area comprises:
and searching the boundary points of the current search area by detecting through a global boundary point detector and a local boundary point detector respectively.
4. The method of claim 1, wherein the status of each map grid in the grid map is one of occupied, idle, unknown;
the method further comprises the steps of:
the state of the map grid corresponding to the area which is difficult to pass is set to be occupied.
5. The method of claim 1, wherein obtaining the information gain for 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 with an occupied state exists in the map grids corresponding to the first target circle or not;
if the map grid with the occupied state does not exist, a second target circle is acquired by taking the clustering center of the boundary points as the circle center and the radius of the information gain as the radius,
Taking the difference between the area of the map grid with the unknown state in the second target circle and the area of the map grid with the occupied state in the second target circle as the information gain of the boundary point clustering center;
and if the map grid with the occupied state exists, setting the information gain of the boundary point clustering center as a preset value.
6. The method according to claim 1, wherein filtering the boundary point cluster center to obtain an effective boundary point cluster center according to the determination result and the information gain of the boundary point cluster center, comprises:
removing boundary point clustering centers with information gain smaller than a threshold value in the boundary point clustering centers, and/or removing boundary point clustering centers corresponding to the areas which are difficult to pass as the judgment result;
and taking the reserved boundary point cluster center as an effective boundary point cluster center.
7. A map construction 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 making a path plan from the current position to the target point;
A movement control module for planning to run towards the target point along the path;
The detection module specifically comprises: the first calculation module is used for acquiring a current search area according to the grid map; the searching module is used for searching boundary points of the current searching area; the target point positioning module is used for acquiring a target point according to the boundary point of the current search area;
the target point positioning module specifically comprises: the clustering unit is used for obtaining 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 computing unit is used for obtaining the benefit value of each effective boundary point clustering center; the comparison unit is used for taking the effective boundary point clustering center with the largest profit value as a target point;
The filter unit includes: 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 global paths of the robot from the current position to the clustering center of each boundary point respectively; 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 center according to the judging result and the information gain of the effective boundary point clustering center so as to obtain the effective boundary point clustering center.
8. A computer readable storage medium having stored thereon a computer program, which, when executed by a processor, causes the processor to perform the steps of the method according to any of claims 1-6.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor performs the steps of the method according to any of claims 1-6 when the program is executed.
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