CN110501907B - Self-adaptive dynamic map grid generation method for robot navigation - Google Patents
Self-adaptive dynamic map grid generation method for robot navigation Download PDFInfo
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Abstract
The invention provides a self-adaptive dynamic map grid generation method for robot navigation, which comprises the following steps: establishing a map grid by taking the robot as a center and generating a navigation path; detecting an obstacle by using a sensor on the robot, determining the size of the obstacle and calibrating the position of the obstacle in a map grid; and calculating the shortest straight-line distance between the obstacle and the navigation path, comparing the relation between the shortest straight-line distance and the preset distance, and adjusting the resolution of the map grid around the obstacle according to the relation between the shortest straight-line distance and the preset distance. The invention not only reduces the data storage and calculation amount of the robot, but also can precisely control the robot.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of electronic robots, in particular to a self-adaptive dynamic map grid generation method for robot navigation.
[ background of the invention ]
An intelligent carrying device (Mobile robot) is a robot system consisting of a sensor, a remote control operator and an automatically controlled moving carrier, is a product developed in recent years and applied by integrating comprehensive subjects, integrates latest research results of multiple subjects such as machinery, electronics, computers, automatic control, artificial intelligence and the like, and represents the highest achievement of electromechanical integration. With the increasing maturity of the intelligent robot technology, more application scenes need the service of the intelligent robot, the work of people is partially or completely replaced, the labor cost is reduced, and the work efficiency is improved. One of the necessary functions of autonomous navigation of the robot is self-positioning, which requires to know the position, direction and destination position of the robot at any time, and the other is obstacle avoidance, which is to detect the obstacle ahead and the distance from the obstacle during movement and effectively avoid the obstacle to finally reach the destination.
In the prior art, an autonomous mobile robot needs to perform digital modeling processing on a sensed external environment in the processes of space positioning, path planning and motion control, and divides a continuous external environment into grids with a certain resolution for storage and calculation. The current mainstream technology uses a dividing method which adopts a global full-time constant grid size. The too small grid density is not beneficial to the fine motion control of the robot, and the increase of the grid density brings great storage and calculation demands, and the hardware cost is increased.
In view of the above, it is actually necessary to provide a new adaptive dynamic map grid generation method for robot navigation to overcome the above-mentioned drawbacks.
[ summary of the invention ]
The invention aims to provide a self-adaptive dynamic map grid generation method for robot navigation, which can reduce the storage data and the calculation amount of a robot and can precisely control the robot.
In order to achieve the above object, the present invention provides an adaptive dynamic map grid generating method for robot navigation, comprising:
establishing a map grid by taking the robot as a center and generating a navigation path;
detecting an obstacle by using a sensor on the robot, determining the size of the obstacle and calibrating the position of the obstacle in a map grid;
and calculating the shortest straight-line distance between the obstacle and the navigation path, comparing the relation between the shortest straight-line distance and the preset distance, and adjusting the resolution of the map grid around the obstacle according to the relation between the shortest straight-line distance and the preset distance.
In a preferred embodiment, the robot-centric building of the map grid and generating the navigation path comprises the steps of:
the robot receives and stores the map data packet, and displays the map data packet in a map grid mode;
the robot positions the position of the robot and the position of the destination in the map grid, and automatically generates a navigation path.
In a preferred embodiment, said detecting obstacles with sensors on the robot and determining the size and position of the obstacles in said map grid comprises the steps of:
detecting whether an obstacle exists by using a sensor on the robot;
and if the obstacles exist, determining the size of the obstacles and calibrating the positions of the obstacles in the map grid.
In a preferred embodiment, the comparing the shortest straight-line distance with a preset distance and adjusting the resolution of the map grid around the obstacle according to the relation of the shortest straight-line distance with the preset distance includes:
the preset distance comprises a first distance, a second distance and a third distance, the first distance is larger than the second distance and larger than the third distance, the resolution of the map grid comprises a first resolution, a second resolution, a third resolution and a fourth resolution, and the first resolution is smaller than the second resolution and smaller than the third resolution and smaller than the fourth resolution;
when the shortest straight-line distance is larger than a first distance, the map grid around the obstacle is at a first resolution, and the grid map does not store the position of the obstacle in the map grid;
when the first distance is larger than or equal to the shortest straight-line distance and larger than the second distance, the grid map stores the position of the obstacle in the map grid, and the map grid around the obstacle is at the second resolution;
when the second distance is larger than or equal to the shortest straight-line distance and is larger than the third distance, the grid map stores the position of the obstacle in the map grid, and the map grid around the obstacle is at the third resolution;
and when the third distance is larger than or equal to the shortest straight line distance, the grid map stores the position of the obstacle in the map grid, and the map grid around the obstacle is at the fourth resolution.
In a preferred embodiment, the resolution of the map grid is inversely proportional to the partitioning accuracy; the dividing precision of the first resolution ratio of the map grid is 8cm, the dividing precision of the second resolution ratio of the map grid is 4cm, the dividing precision of the third resolution ratio of the map grid is 2cm, and the dividing precision of the fourth resolution ratio of the map grid is 1 cm.
In a preferred embodiment, the first distance is 100cm, the second distance is 50cm and the third distance is 20 cm.
In a preferred embodiment, the resolution of the map grid is inversely proportional to the modeled range of the map grid.
In a preferred embodiment, the resolution of the map grid is inversely proportional to the speed of movement of the robot.
According to the self-adaptive dynamic map grid generation method for robot navigation, the positions of the obstacles are marked in the map grid, and the resolution of the map grid around the obstacles is adjusted according to the relation between the shortest straight-line distance and the preset distance, so that the map grid can meet the use precision, the storage space is reduced as much as possible, the calculated amount is reduced, and the robot can be precisely controlled. The self-adaptive dynamic map grid generation method for robot navigation provided by the invention not only reduces the stored data and the calculated amount of the robot, but also can precisely control the robot.
[ description of the drawings ]
Fig. 1 is a flowchart of an adaptive dynamic map grid generation method for robot navigation according to the present invention.
Fig. 2 is a sub-flowchart of the flow chart of the adaptive dynamic map grid generation method for robotic navigation shown in fig. 1.
Fig. 3 is another sub-flowchart of the flow chart of the adaptive dynamic map grid generation method for robotic navigation shown in fig. 1.
Fig. 4 is a schematic diagram of an adaptive dynamic map grid generation method for robot navigation according to the present invention.
[ detailed description ] embodiments
In order to make the objects, technical solutions and advantageous effects of the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and the detailed description. It should be understood that the detailed description and specific examples, while indicating the preferred embodiment of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1 to 3, the present invention provides a method for generating an adaptive dynamic map grid for robot navigation, including the following steps:
step S01, a map grid is created centering on the robot and a navigation path is generated.
Specifically, the step S01 further includes the following steps:
in step S11, the robot receives and stores the map data packet and displays it in the form of a map grid. The robot receives the map data packet of the position through a network or a data line, so that the robot can establish a basic map grid.
In step S12, the robot locates the position of the location and the position of the destination in the map grid, and automatically generates a navigation path. The robot positions the position of the robot and the position of the robot destination in the map grid through a GPS or other positioning systems, and calculates the optimal passing path, namely the navigation path.
And step S02, detecting the obstacle by using the sensor on the robot, determining the size of the obstacle and calibrating the position of the obstacle in the map grid.
Specifically, the step S02 further includes the following steps:
in step S21, the presence or absence of an obstacle is detected by a sensor on the robot.
Step S22, if there is an obstacle, determining the size of the obstacle and calibrating the position of the obstacle in the map grid; if no obstacle exists, the process returns to step S21.
And step S03, calculating the shortest straight-line distance between the obstacle and the navigation path, comparing the relation between the shortest straight-line distance and the preset distance, and adjusting the resolution of the map grid around the obstacle according to the relation between the shortest straight-line distance and the preset distance.
Referring to fig. 4, in this embodiment, the preset distance includes a first distance, a second distance, and a third distance, the first distance > the second distance > the third distance, the resolution of the map grid includes a first resolution, a second resolution, a third resolution, and a fourth resolution, the first resolution < the second resolution < the third resolution < the fourth resolution;
taking an obstacle A as an example, when the shortest straight-line distance is greater than a first distance, the grid map does not store the map grids around the obstacle at the first resolution at the position in the map grids;
taking the obstacle B as an example, when the first distance is larger than or equal to the shortest straight-line distance and is larger than the second distance, the grid map stores the position of the obstacle in the map grid, and the map grid around the obstacle is at the second resolution;
taking the obstacle C as an example, when the second distance is larger than or equal to the shortest straight-line distance and is larger than the third distance, the grid map stores the position of the obstacle in the map grid, and the map grid around the obstacle is at the third resolution;
taking the obstacle D as an example, when the third distance is larger than or equal to the shortest straight-line distance, the grid map stores the position of the obstacle in the map grid, and the map grid around the obstacle is at the fourth resolution.
It can be understood that the robot can directly pass through the area with the obstacle as the center of a circle and the radius larger than the first distance as the safe passing area according to the navigation path.
The area with the first distance being more than or equal to the radius and being more than the second distance is a cautious passing area by taking the obstacle as the circle center, and the robot can pass through the area in a deceleration way according to the navigation path.
And taking the obstacle as the center of a circle, and taking the area with the second distance being more than or equal to the radius and being more than the third distance as a dangerous passing area, wherein the robot can pass through the obstacle in a decelerating way or plan a route again to bypass the obstacle according to the navigation path.
And taking the obstacle as the circle center, and taking the area with the third distance being larger than or equal to the radius as a traffic prohibition area, wherein the robot needs to re-plan the navigation path to bypass the obstacle.
Furthermore, the resolution of the map grid is inversely proportional to the dividing precision; the dividing precision of the first resolution ratio of the map grid is 8cm, the dividing precision of the second resolution ratio of the map grid is 4cm, the dividing precision of the third resolution ratio of the map grid is 2cm, and the dividing precision of the fourth resolution ratio of the map grid is 1 cm.
The first distance is 100cm, the second distance is 50cm, and the third distance is 20 cm. That is, in step S01, the division accuracy of the initial resolution of the map mesh is 8cm, and the smaller the shortest straight-line distance is, the higher the resolution of the map mesh around the obstacle is, and the finer the resolution division accuracy of the map mesh is.
Further, the resolution of the map grid is inversely proportional to the modeled range of the map grid. Specifically, the finer the division accuracy of the map grid, the smaller the modeling range of the map grid. For example, when the map meshing precision is 10 cm, the robot can store environment data within 50 meters; and when the map meshing precision is 1cm, the robot can only store the environmental data within 5 meters.
Furthermore, the resolution of the map grid is inversely proportional to the moving speed of the robot. Namely, the coarser the dividing precision of the map grid, the lower the resolution, the clear navigation path is indicated, and the faster the robot travels; conversely, the finer the division accuracy of the map grid is, the higher the resolution is, which indicates that the possibility that the navigation path meets an obstacle is higher, and the lower the traveling speed of the robot is. For example, when the map grid division accuracy is 1cm, the robot speed is 0.2 meters per second; when the meshing precision is 4cm, the robot speed is 1 meter per second.
In addition, most of the classical navigation path planning and control algorithms rely on a global grid with a constant size for calculation, so when the classical algorithms are used, the resolution of the environment grid is divided into integer multiples. Such as 1cm, 2cm, 4cm, 8cm in this example.
In other embodiments, the preset distance is not limited to the first distance, the second distance, and the third distance; the resolution of the map grid is also not limited to the first resolution, the second resolution, the third resolution, and the fourth resolution.
According to the self-adaptive dynamic map grid generation method for robot navigation, the positions of the obstacles are marked in the map grid, and the resolution of the map grid around the obstacles is adjusted according to the relation between the shortest straight-line distance and the preset distance, so that the map grid can meet the use precision, the storage space is reduced as much as possible, the calculated amount is reduced, and the robot can be precisely controlled. The self-adaptive dynamic map grid generation method for robot navigation provided by the invention not only reduces the stored data and the calculated amount of the robot, but also can precisely control the robot.
The invention is not limited solely to that described in the specification and embodiments, and additional advantages and modifications will readily occur to those skilled in the art, so that the invention is not limited to the specific details, representative apparatus, and illustrative examples shown and described herein, without departing from the spirit and scope of the general concept as defined by the appended claims and their equivalents.
Claims (7)
1. An adaptive dynamic map grid generation method for robotic navigation, the method comprising the steps of:
establishing a map grid by taking the robot as a center and generating a navigation path;
detecting an obstacle by using a sensor on the robot, determining the size of the obstacle and calibrating the position of the obstacle in a map grid;
calculating the shortest straight-line distance between the obstacle and the navigation path, comparing the relation between the shortest straight-line distance and a preset distance, and adjusting the resolution of a map grid around the obstacle according to the relation between the shortest straight-line distance and the preset distance;
the preset distance comprises a first distance, a second distance and a third distance, the first distance is larger than the second distance and larger than the third distance, the resolution of the map grid comprises a first resolution, a second resolution, a third resolution and a fourth resolution, and the first resolution is smaller than the second resolution and smaller than the third resolution and smaller than the fourth resolution; when the shortest straight-line distance is larger than a first distance, the map grid around the obstacle is at a first resolution, and the grid map does not store the position of the obstacle in the map grid; when the first distance is larger than or equal to the shortest straight-line distance and larger than the second distance, the grid map stores the position of the obstacle in the map grid, and the map grid around the obstacle is at the second resolution; when the second distance is larger than or equal to the shortest straight-line distance and is larger than the third distance, the grid map stores the position of the obstacle in the map grid, and the map grid around the obstacle is at the third resolution; when the third distance is larger than or equal to the shortest straight line distance, the grid map stores the position of the obstacle in the map grid, and the map grid around the obstacle is at the fourth resolution;
taking the barrier as the center of a circle, and taking the area with the radius larger than the first distance as a safe passing area, wherein the robot can directly pass through the safe passing area according to the navigation path; the robot can pass through the area with the radius larger than the second distance at a reduced speed according to the navigation path by taking the obstacle as the circle center and taking the area with the radius larger than the first distance as a cautious passing area; the robot can pass through the obstacle in a deceleration way or plan a route again to bypass the obstacle according to the navigation path by taking the obstacle as the center of a circle and taking the area with the second distance more than or equal to the radius and more than the third distance as a dangerous passing area; and taking the obstacle as the circle center, and taking the area with the third distance being larger than or equal to the radius as a traffic prohibition area, wherein the robot needs to re-plan the navigation path to bypass the obstacle.
2. The adaptive dynamic map grid generation method for robotic navigation as defined in claim 1, wherein said robot-centric establishing a map grid and generating a navigation path comprises the steps of:
the robot receives and stores the map data packet, and displays the map data packet in a map grid mode;
the robot positions the position of the robot and the position of the destination in the map grid, and automatically generates a navigation path.
3. An adaptive dynamic map grid generation method for robotic navigation as claimed in claim 1 wherein said detecting obstacles with sensors on the robot and determining the size and location of the obstacles in said map grid comprises the steps of:
detecting whether an obstacle exists by using a sensor on the robot;
and if the obstacles exist, determining the size of the obstacles and calibrating the positions of the obstacles in the map grid.
4. The adaptive dynamic map grid generation method for robotic navigation of claim 1, wherein a resolution of the map grid is inversely proportional to a partitioning accuracy; the dividing precision of the first resolution ratio of the map grid is 8cm, the dividing precision of the second resolution ratio of the map grid is 4cm, the dividing precision of the third resolution ratio of the map grid is 2cm, and the dividing precision of the fourth resolution ratio of the map grid is 1 cm.
5. The adaptive dynamic map grid generation method for robotic navigation of claim 4, wherein said first distance is 100cm, said second distance is 50cm, and said third distance is 20 cm.
6. An adaptive dynamic map grid generation method for robotic navigation as defined in claim 1, wherein a resolution of the map grid is inversely proportional to a modeled range of the map grid.
7. The adaptive dynamic map grid generation method for robot navigation of claim 1, wherein a resolution of the map grid is inversely proportional to a moving speed of the robot.
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