CN115316887B - Robot control method, robot, and computer-readable storage medium - Google Patents

Abstract

The application discloses a robot control method, a robot and a computer-readable storage medium. The method includes: obtaining a grid map converted from the point cloud of the environmental map, extracting wall information from the grid map; generating a planning edge starting point based on the wall information, and generating several local planning path boxes in the grid map; Control the robot to start from the starting point of the planning along the edge, and move along the border of the current local planning path frame where the starting point of the planning along the edge is located, until after traversing all the path points on the border of the current local planning path frame, the current local planning path frame The internal waypoints are fully covered and traversed; the robot is controlled to move from the current local planning path frame to the next local planning path frame until it traverses all the border path points and internal path points of the local planning path frame. This application can control the robot to quickly carry out full-coverage cleaning in the area of the local planning path frame, and improve the cleaning efficiency of the robot.

Description

Robot control method, robot and computer readable storage medium

technical field

The present application relates to the technical field of sweeping robots, in particular to a robot control method, a robot and a computer-readable storage medium.

Background technique

With the improvement of living standards and the rapid development of household service robot technology, sweeping robots have increasingly entered family life. This kind of robot is usually equipped with a bottom drive wheel that can rotate autonomously, a rolling brush, a side brush, a dust suction device, etc., and can clean the ground surface.

Usually, according to the operating status of the robot, it can be divided into random walk without planning and full-coverage planning sweeping. The former random walk has been gradually eliminated due to its low cleaning efficiency, and the latter full-coverage sweeping has become the preferred choice of sweeping robots. Mainstream method. However, with in-depth research, it has been found that full-coverage cleaning is more suitable for relatively open areas, and relatively hidden corners such as corners, table corners and other dead corners are more likely to accumulate dust. So people proposed a specific cleaning mode "sweeping along the edge" before full coverage cleaning.

Usually, cleaning along the edge is to control the sweeping robot at a certain distance from the wall by using the sensor along the wall, and realize the steering of the robot when encountering obstacles through the collision sensor, so as to achieve the effect of cleaning the sweeping robot along the wall all the time. However, when the area to be cleaned is very large, it takes a lot of time for the sweeping robot to complete the cleaning process along the entire area, which is not conducive to the rapid full-coverage cleaning process. Once the area to be cleaned surrounded by the edge area is too large, it is not conducive to reasonable cleaning. full coverage path planning.

Contents of the invention

The present application proposes a robot control method, a robot and a computer-readable storage medium, so as to enable the robot to quickly carry out full-coverage cleaning and improve the cleaning efficiency of the robot.

In order to solve the above technical problems, a technical solution adopted by the present application is to provide a robot control method, the method comprising:

Obtain the grid map converted from the point cloud of the environmental map, and extract the wall information from the grid map; based on the wall information, generate the starting point of the planning edge, and generate several local planning path boxes in the grid map, where the planning edge The starting point is located at the midpoint of a side parallel to the wall surface of one of the local planning path frames; the control robot starts from the starting point of the planned edge, and proceeds along the border of the current local planning path box where the starting point of the planned edge is located. Movement until all the path points on the border of the current local planning path frame are traversed, and the internal path points of the current local planning path frame are fully covered; the robot is controlled to move from the current local planning path frame to the next local planning path frame, To track the path of the next local planning path box until traversing all the border path points and internal path points of the local planning path box.

Wherein, after extracting the wall surface information in the grid map, before generating several local planning path frames in the grid map, the robot control method also includes:

Obstacle positions are extracted from the grid map; several local planning path boxes are generated in the grid map based on the obstacle positions, wherein there are no obstacles in borders and internal areas of the several local planning path boxes.

Among them, the obstacles include charging piles; based on the wall information, the starting point along the planned edge is generated, including:

Based on the wall surface information and the position of the charging pile, the planning edge starting point is generated; wherein, the planning edge starting point is a number of grid points that are at a first preset distance from the wall, and the relative distance from the charging pile is less than or equal to the second preset distance. at least one grid point with a given distance.

Wherein, the edge-to-edge motion is performed along the border of the current local planning path frame where the starting point of the planned edge-to-edge is located, including:

In the process of controlling the robot to move along the border of the current local planning path frame, the obstacle perception distance in the direction of motion is obtained; when the obstacle perception distance is less than the third preset distance, the obstacle-encountering fixed action is triggered; when the robot executes During the obstacle-encounter fixing action, the lateral distance between the robot and the wall is monitored; when the lateral distance is less than the fourth preset distance, the obstacle-encounter fixing action is exited, and the edge-to-edge movement is continued based on the current position.

Among them, the action of fixing an obstacle is: control the robot to rotate in the first direction, and record the first rotation angle; when the first rotation angle reaches the first preset angle, control the robot to rotate in the second direction, and record the second rotation angle ; When the second rotation angle reaches the second preset angle, the robot is controlled to stop rotating; the first direction and the second direction are two opposite directions.

Among them, based on the current position, continue to perform edge motion, including:

Determine whether the current position of the robot is on the border of the current local planning path frame; if so, continue to perform edge-to-edge motion from the current position; if not, obtain the tracking path point on the border of the current local planning path frame with the smallest distance from the current position , and dispatch the robot from the current position to the tracking waypoint, and continue to perform edge-to-edge motion from the tracking waypoint.

Among them, the robot is dispatched from the current position to the tracking waypoint, including:

Obtain the scheduling path of the robot from the current location to the tracking waypoint; dispatch the robot from the current location to the tracking waypoint according to the scheduling path, and mark the waypoints on the scheduling path as traversed points.

Among them, the robot is controlled to move from the current local planning path frame to the next local planning path frame to perform path tracking on the next local planning path frame, including:

Obtain the relative distance between the current local planning path frame and other local planning path frames; use the local planning path frame with the smallest relative distance to the current local planning path frame as the next local planning path frame of the current local planning path frame; obtain the next The local planning path frame is parallel to the two borders of the wall information of the wall, and the relative distance between the two borders and the wall is calculated; at the midpoint of the border with the smaller relative distance among the two borders, a new planning edge is set Starting point; the robot is controlled to start from the new planning edge starting point, and move along the edge along the border of the next local planning path box.

In order to solve the above-mentioned technical problems, a technical solution adopted by the present application is to provide a robot, the robot includes a processor and a memory connected to the processor, program data is stored in the memory, and the processor executes the program data stored in the memory to Execution implementation: Obtain the grid map converted from the point cloud of the environmental map, extract wall information from the grid map; generate planning edge starting points based on the wall information, and generate several local planning path boxes in the grid map, among which , the starting point of the planned edge is located at the midpoint of a side parallel to the wall surface of the wall information of one of the local planning path frames; The border moves along the edge until all the path points on the border of the current local planning path frame are traversed, and the internal path points of the current local planning path frame are fully covered; the robot is controlled to move from the current local planning path frame to the next local planning Path box, to track the path of the next local planning path box until traversing all the border path points and internal path points of the local planning path box.

In order to solve the above technical problems, another technical solution adopted by the present application is to provide a computer-readable storage medium, which stores program instructions inside, and the program instructions are executed to realize: obtaining the grid map converted from the point cloud of the environmental map , extract the wall information in the grid map; based on the wall information, generate the starting point of the planned edge, and generate several local planning path boxes in the grid map, where the starting point of the planned edge is located between one of the local planning path boxes and The midpoint of a side parallel to the wall in the wall information; the control robot starts from the starting point of the planned edge, moves along the border of the current local planning path box where the starting point of the planning edge is located, until it traverses the border of the current local planning path box After all the path points on the above path, the internal path points of the current local planning path box are fully covered and traversed; the robot is controlled to move from the current local planning path box to the next local planning path box, so as to perform path tracking on the next local planning path box , until the border path points and internal path points of all local planning path boxes are traversed.

The beneficial effects of this application are: different from the situation of the prior art, this application extracts the wall information through the grid map converted from the environmental point cloud, and then generates the planning starting point based on the wall information, and generates several local areas on the grid map Plan the path frame, and control the robot to move along the border of the local planning path frame from the starting point of planning, until after traversing all the path points on the border of the current local planning path frame, complete the internal path points of the current local planning path frame Cover and traverse the cleaning, and then control the robot to move to the next local planning path frame to repeat the above actions. In this application, when the area to be cleaned surrounded by the border area is too large, by setting a number of local planning path frames, the robot can quickly clean the area of the current local planning path frame after the end of the current local planning path frame. Compared with the method in the prior art that completes the entire area along the edge to start full-coverage cleaning, the robot control method of the present application is conducive to reasonable full-coverage path planning, improving the cleaning efficiency of the robot, and can be used in more complex environments and open environments. still apply.

Description of drawings

Fig. 1 is a schematic flow chart of the first embodiment of the robot control method of the present application;

Fig. 2 is a schematic diagram of an embodiment of the grid map of the present application;

Fig. 3 is a schematic flow chart of the second embodiment of the robot control method of the present application;

Fig. 4 is a schematic flow chart of a specific embodiment in step S103 in Fig. 1;

Fig. 5 is a schematic flow chart of a specific embodiment in step S304 in Fig. 4;

FIG. 6 is a schematic flow diagram of a specific embodiment in step S403 in FIG. 5;

Fig. 7 is a schematic flow chart of a specific embodiment in step S104 in Fig. 1;

Fig. 8 is a schematic structural view of an embodiment of the robot of the present application;

FIG. 9 is a schematic structural diagram of an embodiment of a computer-readable storage medium of the present application.

Detailed ways

The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

With the rapid development of household service robot technology, more and more sweeping robots have entered family life. The current operating status of robots can be divided into random walk without planning and full-coverage planning cleaning, but there is no planning. The low efficiency of random walking cleaning has been gradually eliminated. In the full-coverage cleaning of the robot, the full-coverage cleaning is more suitable for relatively open areas, and dust is easier to accumulate in relatively hidden corners such as wall corners and table corners. Therefore, before the full-coverage cleaning type, a specific cleaning mode was proposed to clean along the edge. However, when the area to be cleaned is very large, it takes a lot of time for the robot to complete the edge-side cleaning process of the entire area, which is not conducive to the rapid development of the full-coverage cleaning process. Once the area to be cleaned surrounded by the border area is too large, it is not conducive to reasonable full-coverage path planning.

In order to solve the problem of improving the cleaning efficiency of the robot and quickly carry out the full-coverage cleaning process, the present application first proposes a robot control method, please refer to FIG. 1 , which is a schematic flow diagram of the first embodiment of the robot control method of the present application. As shown in Figure 1, the robot control method specifically includes steps S101 to S104:

Step S101: Obtain a grid map converted from the point cloud of the environmental map, and extract wall information from the grid map.

After the robot receives the edge cleaning command, the robot enables its own lidar sensor, which can be a Laser Direct Structuring (LDS) radar sensor, and the robot simultaneously turns on Simultaneous Localization And Mapping (SLAM) module.

Among them, SLAM can be described as: the robot starts to move from an unknown position in an unknown environment, locates itself according to position estimation and maps during the movement process, and builds an incremental map on the basis of its own positioning to realize the autonomous positioning of the robot and navigation. Localization means that the robot must know where it is in the environment. Mapping means that the robot must record the location of features in the environment. SLAM refers to the establishment of a map of the environment by the robot while positioning.

In this embodiment, the robot can collect the point cloud information of its own environment map through the lidar sensor and the SLAM module, and convert the point cloud information into a grid map.

Please refer to FIG. 2 . FIG. 2 is a schematic diagram of an embodiment of a grid map of the present application. as shown in picture 2.

After the robot obtains the grid map, it can extract wall information from the grid map. As shown in Figure 2, in the grid map, A is the wall information in the grid map, and B and C in the figure are obstacles in the grid map.

In the constructed grid map, when there is wall information that can be extracted, in order to make the subsequent planning along the edge path effective, the angle between the wall surface of the wall information and the coordinate system of the constructed grid map can be obtained, and corrected according to the angle Raster map, which enables the raster map to be parallel or perpendicular to the map coordinate system. If there is no wall information that can be extracted, the current angle of the robot will be the initial planning angle by default.

Step S102: Based on the wall information, generate the starting point of the planning along the edge, and generate several local planning path boxes in the grid map, wherein the planning edge starting point is located in one of the local planning path boxes parallel to the wall in the wall information Midpoint of an edge.

If there is extractable wall information in the grid map, after extracting the wall information, the robot generates a planning edge starting point based on the coordinates of the wall information. The planning edge starting point can be obtained based on the position information of the wall information. In this embodiment, the distance between the planned starting point along the edge and the wall may be half the diameter of the robot, or other preset values, which are not limited herein.

When the starting point of planning along the edge is generated based on the wall information, the selection of the starting point of planning along the edge can be as close as possible to the charging pile of the robot. As shown in Figure 2, if obstacle B is a charging pile, the starting point along the edge of the specification can be selected in the grid near obstacle B. Planning the starting point along the edge to be close to the charging pile of the robot can make the robot return to the vicinity of the charging pile after cleaning along the edge, so that the robot can be charged quickly and the cleaning efficiency of the robot can be improved.

After the robot acquires the starting point along the edge, several local planning path frames are generated on the grid map, wherein the local planning path frame can be a rectangular frame. In this embodiment, several locally planned path frames are adjacent to each other to form a networked global planned edge-along path. In this embodiment, the side length of the local planning path frame is 4.5m. If the area of the grid map generated based on the area to be cleaned is less than the area of the preset local planning path frame, at least one local planning path frame will be generated in the grid map. In other embodiments, it can be based on the area of the grid map The area generates a corresponding number of local planning path boxes in the grid map, which is not limited here.

In other embodiments, the side length of the local planning path frame can be set according to actual needs, and the local planning path frame can also be in other shapes, which are not limited here.

When generating a local planning path, it is necessary to ensure that the starting point of the generated planning edge coincides with the midpoint of a side of one of the local planning path boxes, and the side where the starting point of the planning edge is located must be parallel to the wall surface of the wall information . Therefore, when generating several local planning path frames, the local planning path frame where the starting point of the planning edge is located may be generated first, and then other adjacent local planning path frames are generated.

If there is no extractable wall information in the grid map, the current position of the robot is used as the starting point for planning along the edge.

Step S103: Control the robot to start from the starting point of the planned edge, and move along the border of the current local planning path frame where the starting point of the planning edge is located, until after traversing all the path points on the border of the current local planning path frame, the current local planning The internal path points of the path box are traversed with full coverage.

After several local planning path boxes are established in the grid map, the robot is dispatched to the starting point of the planned edge, and the sweeper is switched to the planned edge state State_EW_plan. In the current state, the robot can be controlled to move along the edge along the border of the current local planning path frame.

During this process, the planning module of the robot will issue the local section path of the current local planning path frame in real time to make the robot move along the edge, and will record all the local path points passed by the robot in real time until it traverses the border of the current partial planning path frame. After all the path points, the robot considers the area enclosed by the current local planning path frame as the area that can be cleaned, and then performs a full coverage traversal of the internal path points of the current local planning path frame, so that the robot can fully understand the current local planning path frame. Full coverage cleaning of all areas.

Step S104: Controlling the robot to move from the current local planning path frame to the next local planning path frame, so as to perform path tracking on the next local planning path frame until traversing all border path points and internal path points of the local planning path frame.

The robot performs full-coverage cleaning on all areas within the current local planning path frame at the starting point of the planning edge, and controls the movement from the current local planning path frame to the next local planning path frame to track the path of the next local planning path frame until It traverses the border waypoints and internal waypoints of all local planning path boxes to complete the full-coverage cleaning of the entire area.

The robot control method of this application can still clean along the edge according to several local planning path frames when the wall information cannot be extracted from the grid map in a complex environment or an open scene. At this time, the planned path is based on the current position of the robot and The orientation is determined, and a local planning path frame rectangular frame with a preset fixed side length is generated, which can ensure that the robot can normally clean along the side in any scene. At the same time, after cleaning the edge and area of a certain local planning path frame, the robot can be directly dispatched to the edge point of the next local planning path frame without the need to find the edge, thereby improving the cleaning efficiency of the robot.

Different from the existing technology, this application extracts the wall information through the grid map converted from the environmental point cloud, and then generates the planning starting point based on the wall information, and generates several local planning path frames on the grid map, and controls the robot to start from the planning The starting point moves along the border of the local planning path frame until all path points on the border of the current local planning path frame are traversed, and then the internal path points of the current local planning path frame are fully covered and traversed, and then the robot is controlled to move to Repeat the above actions for the next local planning path frame. In this application, when the area to be cleaned surrounded by the border area is too large, by setting a number of local planning path frames, the robot can quickly clean the area of the current local planning path frame after the end of the current local planning path frame. Compared with the method in the prior art that completes the entire area along the edge to start full-coverage cleaning, the robot control method of the present application is conducive to reasonable full-coverage path planning, improving the cleaning efficiency of the robot, and can be used in more complex environments and open environments. still apply.

Optionally, please refer to FIG. 3 , which is a schematic flowchart of a second embodiment of the robot control method of the present application. As shown in Figure 3, the robot control method specifically includes steps S201 to S206:

Step S201: Obtain a grid map converted from the point cloud of the environmental map, and extract wall information from the grid map.

Step S201 is the same as step S101 and will not be repeated here.

Step S202: Obstacle locations are extracted from the grid map.

After the robot generates the grid map, it needs to extract the position of the obstacle from the grid map. In the grid map, in addition to the walls with wall information, there will be some specific obstacles, and the robot cannot pass through the positions of these specific obstacles, such as obstacle B and obstacle B in Figure 2 c. Therefore, before generating the local planning road frame, it is necessary to extract the obstacle position in the map.

Step S203: Generate several local planning path frames in the grid map based on the obstacle positions, wherein, there are no obstacles in the borders and inner areas of the several partial planning path frames.

After obtaining the position of obstacles, generating several local planning path boxes in the grid map requires planning the borders of several local planning path boxes, so that there are no obstacles in the borders and internal areas of several local planning path boxes. That is, the obstacles B and C shown in FIG. 2 cannot exist in the generated local planning path frames and the inner areas.

The borders of several local planning path frames are generated, and there are no obstacles in the borders and internal areas of the several local planning path frames, which can prevent the robot from colliding during cleaning, or cause the risk of getting stuck, thereby improving the cleaning efficiency of the robot.

Step S204: Based on the wall surface information, generate the planning edge starting point, and generate several local planning path boxes in the grid map, wherein the planning edge starting point is located in one of the local planning path boxes parallel to the wall surface of the wall information Midpoint of an edge.

As mentioned above, the robot can generate planning starting points based on wall information in the grid map and generate several local planning path boxes in the grid map, where the planning edge starting point is located at the edge of one of the local planning path boxes and the wall The information is the midpoint of an edge parallel to the wall.

When the obstacles in the grid map include charging piles, the starting point of the planning edge can be set near the charging piles, so that the robot returns to the vicinity of the charging piles for fast charging at the end of the edge movement, improving the cleaning efficiency of the robot.

In this embodiment, the step of generating and planning the starting point along the edge based on wall information can be realized by the following method, which specifically includes:

Based on the wall surface information and the position of the charging pile, the planning edge starting point is generated; wherein, the planning edge starting point is a number of grid points that are at a first preset distance from the wall, and the relative distance from the charging pile is less than or equal to the second preset distance. at least one grid point with a given distance.

In this embodiment, when generating the planning starting point, the planning edge starting point may be generated based on the wall surface information and the position of the charging pile. The starting point of the planned edge can be a first preset distance from the wall, the first preset distance can be the radius of the robot body, and the second preset distance can be set based on actual needs, which is not limited here.

Step S205: Control the robot to start from the starting point of the planned edge, and move along the border of the current local planning path frame where the starting point of the planning edge is located, until after traversing all the path points on the border of the current local planning path frame, the current local planning The internal path points of the path box are traversed with full coverage.

Step S205 is consistent with step S103 and will not be repeated here.

Step S206: Controlling the robot to move from the current local planning path frame to the next local planning path frame, so as to perform path tracking on the next local planning path frame until traversing all border path points and internal path points of the local planning path frame.

Step S206 is the same as step S104 and will not be repeated here.

Optionally, the method for controlling the robot to move along the border of the local planning path is shown in FIG. 4 , which is a schematic flowchart of a specific embodiment in step S103 in FIG. 1 . In this embodiment, the step of moving along the edge along the border of the current local planning path frame where the starting point of the planned edge is located in step S103 can be realized by the method shown in FIG. 4 , and this embodiment specifically includes steps S301 to S304:

Step S301: In the process of controlling the robot to move along the border of the current local planning path frame, obtain the obstacle perception distance in the moving direction.

In the process of controlling the robot to move along the border of the current local planning path frame, the robot enables the RGB structured light or line laser sensor and the sensor along the wall, turns on the perception module, and sets the third preset distance Forward_th to calculate the distance between the robot and The obstacle perception distance between obstacles, denoted as d_forward.

Step S302: when the obstacle sensing distance is less than the third preset distance, an obstacle-encounter fixing action is triggered.

When the robot is moving along the edge of the current local planning path frame, and suddenly encounters a new obstacle when moving along the edge, calculate the obstacle perception distance d_forward between the robot and the obstacle in real time. If the obstacle perception distance d_forward is less than The third preset distance, Forward_th, is that there is an obstacle ahead. At this time, the robot receives an obstacle avoidance command and triggers a fixed action when encountering an obstacle. Among them, the obstacle-encounter fixed action is an obstacle-avoiding action preset by the robot, which may be a circular movement around the obstacle, or other movements, which are not limited here.

Specifically, the action of fixing an obstacle also includes: controlling the robot to rotate in the first direction, and recording the first rotation angle; when the first rotation angle reaches the first preset angle, controlling the robot to rotate in the second direction, and recording the second rotation angle; Rotation angle; when the second rotation angle reaches the second preset angle, the robot is controlled to stop rotating; the first direction and the second direction are two opposite directions.

In this embodiment, while triggering the action of fixing an obstacle, the robot is controlled to rotate counterclockwise and record the first rotation angle, and when the first preset angle reaches 90 degrees, it starts to rotate clockwise and records the second rotation angle , when the second preset angle reaches 90 degrees, the rotation will stop.

Step S303: During the process of the robot performing the action of fixing an obstacle, monitor the lateral distance between the robot and the wall.

During the process of the robot performing fixed actions when encountering obstacles, that is, during the rotation process of the robot, the sensor along the wall is used to detect the lateral distance between the robot and the wall in real time.

Step S304: When the lateral distance is less than the fourth preset distance, exit the action of fixing an obstacle, and continue to perform edge-to-edge motion based on the current position.

During the obstacle-encountering fixation process, if the lateral distance is less than the fourth preset distance, the robot immediately exits the obstacle-encounter fixation action, and continues to perform edge-to-edge movement based on the current position of the robot.

Different from the prior art, the robot control method of the present application can move along the border of the current local planning path frame for timely obstacle avoidance processing, which can greatly prevent the sweeping robot from being interfered by temporary obstacles, people or pets , and the application can monitor the lateral distance between the robot and the wall in real time, and can exit the fixed obstacle avoidance action in time, thereby improving the cleaning efficiency of the robot.

Optionally, FIG. 5 is a schematic flowchart of a specific embodiment of step S304 in FIG. 4 . In this embodiment, the step of continuing to move along the edge based on the current position in step S304 can be realized through the method shown in FIG. 5 , and this embodiment specifically includes steps S401 to S404:

Step S401: Determine whether the current position of the robot is on the border of the current local planning path frame.

After the robot exits the fixed action when encountering an obstacle, it will judge whether the current position of the robot is on the border of the current local planning path frame, that is, judge whether the robot is in the planning edge state State_EW_plan.

If yes, enter step S402; if not, enter step S403;

Step S402: Continue to perform sideways motion from the current position.

If the robot is in the planned edge state State_EW_plan, the robot will continue to perform edge motion from the current position.

Step S403: Obtain the tracking path point on the border of the current local planning path frame with the smallest distance from the current position, and dispatch the robot from the current position to the tracking path point, and continue to perform edge-to-edge motion from the tracking path point.

After the robot exits the obstacle-encountered fixed action, it judges that the robot is not in the planned edge state, then obtains the tracking path point M with the smallest distance from the current position on the border of the previous local planning path frame, and switches the state of the robot to the entity edge state State_EW_real, and Record the initial angle init_Real at which the robot uses the sensor to start along the edge. When the robot is in the physical edge state State_EW_real, it uses the robot's lateral distance sensor to control the robot to move along the edge. At this time, the robot is dispatched from the current position to the tracking path point M with the smallest distance from the current position, and continues to perform edge-to-edge motion from the tracking path point M with the smallest distance to the current position.

When the robot is in the entity edge state State_EW_real, when the robot is dispatched from the current position to the tracking waypoint, on this running path, if an obstacle is encountered during the dispatching process, the above-mentioned obstacle-encountering fixed action must be performed, and when exiting the obstacle After the fixed action, the current position of the robot needs to be judged twice. The first time is to judge whether the robot is outside the current local planning path frame. If it is outside the current local planning path frame, the robot is scheduled to return to the tracking path point M through two-point scheduling. . It is judged for the second time whether the robot is on the border path of the current local planning path frame. If the distance between the robot’s current position and the border of the current local planning path frame meets the fifth preset threshold, change the robot’s entity edge state State_EW_real to planning The edge state State_EW_plan enables the robot to continue to perform edge motion from the current path tracking point M.

In addition, when the robot is in the entity edge state State_EW_real, the robot is controlled to move along the wall through the distance information of the sensor along the wall or the laser sensor. When the robot is in the solid edge state State_EW_real and performs an obstacle-fixing action, the robot calculates the difference between the current rotation angle and the initial angle init_Real, and judges whether the difference is greater than 360 degrees. If it is greater, it judges that the robot has deviated from the wall and rotated abnormally for one week. At this time, The control robot dispatches from the current position to track the waypoint M.

Optionally, please refer to FIG. 6 , which is a schematic flowchart of a specific embodiment of step S403 in FIG. 5 . In this embodiment, the step of dispatching the robot from the current position to the tracking path point in step S403 can be realized through the method shown in FIG. 6 , and this embodiment specifically includes steps S501 to S502:

Step S501: Obtain the dispatching path of the robot dispatched from the current position to the tracked waypoint.

When the robot changes from the entity edge state State_EW_real to the planned edge state State_EW_plan, and the robot continues to perform edge motion from the current path tracking point M, it is necessary to update the path points of the current local planning path border traversed by the robot, so it is necessary to obtain the robot Dispatch from the current location after the obstacle-fixed action to track the dispatch path of waypoint M.

Step S502: dispatch the robot from the current position to the tracking waypoint according to the dispatching route, and mark the waypoints on the dispatching route as traversed points.

The robot will dispatch the robot from the current position to the tracking path point M according to the scheduling path, and the robot will update the current trajectory tracking point to the tracking path point M, and set the current local planning path border from the starting point of the planning edge to the tracking path point M before All waypoints of are marked as traversed.

Different from the prior art, the robot control method of the present application is abnormal in the process of moving along the edge, and the robot can autonomously return to the border of the current local planning path to continue the previous edge-moving without requiring the robot to start moving along the edge again, thereby improving the robot It improves the cleaning efficiency and avoids repeated cleaning, and also improves the robustness of the robot in this application to move along the border of the local planning path frame.

In addition, the robot control method of the present application adopts a method of combining planning with the judging of the end condition of the edge motion. By making the robot traverse the border path points of the corresponding local planning path frame, it is considered that the edge motion ends. The robot control method of the present application can be more accurate. Control the end conditions of the robot's edge-to-edge movement accurately, avoid ending the robot's edge-to-edge movement too early or too late, so as to improve the coverage and work efficiency of the robot.

Optionally, please refer to FIG. 7 , which is a schematic flowchart of a specific embodiment of step S104 in FIG. 1 . As shown in FIG. 7 , in this embodiment, the method shown in FIG. 7 can be used to control the robot to move from the current local planning path frame to the next local planning path frame in step S104, so as to perform path tracking on the next local planning path frame. The steps, this embodiment specifically includes step S601 to step S605:

Step S601: Obtain the relative distance between the current local planning path frame and other local planning path frames.

When the robot completes the full-coverage cleaning of the current local planning path frame, it obtains the relative distance between the current local planning path frame and other local planning path frames.

Step S602: Use the local planning path frame with the smallest relative distance to the current local planning path frame as the next local planning path frame of the current local planning path frame.

The robot takes the local planning path frame with the smallest relative distance to the current local planning path frame as the next local planning path frame of the current local planning path frame.

Step S603: Obtain two borders parallel to the wall in the next local planning path frame and the wall information, and calculate the relative distance between the two borders and the wall.

The robot obtains the next local planning path frame and the two borders parallel to the wall in the wall information, and calculates the relative distance between the two borders and the wall.

Step S604: Set a new planning edge starting point at the midpoint of a frame with a smaller relative distance among the two frames.

The robot selects the midpoint of the frame with the smaller relative distance between the two frames, and sets it as the new starting point of the planned edge.

Step S605: The robot is controlled to start from the new starting point of the planned edge, and move along the edge along the border of the next local planned path frame.

Control the robot to start from the new planning edge starting point, move along the border of the next local planning path box, repeat the steps in the previous article, until there is no reachable local planning path box in the current grid map, the current area is considered to be fully covered Cleaning has been completed.

Different from the prior art, in the machine control method of the present application, after a certain local planning path frame is cleaned, the robot can be directly dispatched to the planned edge point of the next local planning path frame, and there is no need to perform edge finding operations, which can improve The cleaning efficiency of the robot.

Optionally, the present application further proposes a robot, please refer to FIG. 8, which is a schematic structural diagram of an embodiment of the robot of the present application. The robot 200 includes a processor 201 and a memory 202 connected to the processor 201, wherein the memory Program data is stored in the memory 202, and the processor 201 executes the program data stored in the memory 202 to implement the steps in the foregoing method embodiments.

The processor 201 may also be referred to as a CPU (Central Processing Unit, central processing unit). The processor 201 may be an integrated circuit chip with signal processing capabilities. The processor 201 can also be a general processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components . A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.

The memory 202 is used to store program data required by the processor 201 to run.

The processor 201 is also used to execute the program data stored in the memory 202 to realize the above robot control method.

Optionally, the present application further provides a computer-readable storage medium. Please refer to FIG. 9 . FIG. 9 is a schematic structural diagram of an embodiment of a computer-readable storage medium of the present application.

The computer-readable storage medium 300 of the embodiment of the present application stores program instructions 310 inside, and the program instructions 310 are executed to implement the robot control method described above.

Among them, the program instruction 310 can form a program file and be stored in the above-mentioned storage medium in the form of a software product, so that an electronic device (which can be a personal computer, a server, or a network device, etc.) or a processor (processor) executes various All or part of the steps of the method of implementation. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, and other media that can store program codes. , or terminal devices such as computers, servers, mobile phones, and tablets.

The computer-readable storage medium 300 of this embodiment may be, but not limited to, a USB flash drive, an SD card, a PD optical drive, a mobile hard disk, a large-capacity floppy drive, a flash memory, a multimedia memory card, a server, and the like.

In one embodiment there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the electronic device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the electronic device executes the steps in the foregoing method embodiments.

In addition, if the above-mentioned functions are implemented in the form of software functions and sold or used as independent products, they can be stored in a storage medium that can be read by a mobile terminal, that is, the application also provides a storage device that stores program data, so The above program data can be executed to implement the methods of the above embodiments, and the storage device can be, for example, a U disk, an optical disk, a server, and the like. That is to say, the present application may be embodied in the form of a software product, which includes several instructions for enabling an intelligent terminal to execute all or part of the steps of the method described in each embodiment.

In addition, the terms "first" and "second" are used for descriptive purposes only, and cannot be interpreted as indicating or implying relative importance or implicitly specifying the quantity of indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include at least one of these features. In the description of the present application, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

Any process or method descriptions described in flowcharts or otherwise herein may be understood to represent a mechanism, segment or portion of code comprising one or more executable instructions for implementing specific logical functions or steps of a process , and the scope of preferred embodiments of the present application includes additional implementations in which functions may be performed out of the order shown or discussed, including in substantially simultaneous fashion or in reverse order depending on the functions involved, which shall It should be understood by those skilled in the art to which the embodiments of the present application belong.

The logic and/or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium, For the use of instruction execution systems, devices or equipment (which may be personal computers, servers, network equipment or other systems that can fetch instructions from instruction execution systems, devices or devices and execute instructions), or in combination with these instruction execution systems, devices or devices And use. For the purposes of this specification, a "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use in or in conjunction with an instruction execution system, device or device. More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection with one or more wires (electronic device), portable computer disk case (magnetic device), random access memory (RAM), Read Only Memory (ROM), Erasable and Editable Read Only Memory (EPROM or Flash Memory), Fiber Optic Devices, and Portable Compact Disc Read Only Memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program can be printed, since the program can be read, for example, by optically scanning the paper or other medium, followed by editing, interpretation or other suitable processing if necessary. The program is processed electronically and stored in computer memory.

The above is only an embodiment of the application, and does not limit the patent scope of the application. Any equivalent structure or equivalent process conversion made by using the specification and drawings of the application, or directly or indirectly used in other related technologies fields, are all included in the scope of patent protection of this application in the same way.

Claims (9)

1. A robot control method, characterized in that, the robot control method comprises: Obtain a grid map converted from the point cloud of the environmental map, and extract wall information from the grid map; extracting obstacle positions in the grid map; generating several local planning path frames in the grid map based on the obstacle positions, wherein there are no obstacles in borders and internal areas of the several partial planning path frames; Based on the wall information, generate a planning edge starting point, and generate several local planning path boxes in the grid map, wherein the planning edge starting point is located in one of the local planning path boxes and the wall information The midpoint of a parallel side of the wall; Controlling the robot to start from the starting point of the planned edge, and move along the border of the current local planning path frame where the starting point of the planning edge is located, until after traversing all the path points on the border of the current local planning path frame , performing a full-coverage traversal on the internal path points of the current local planning path frame; Wherein, said moving along the edge along the frame of the current local planning path frame where the starting point of the planned edge is located includes: After the robot exits the action of fixing an obstacle, it is judged whether the current position of the robot is in the border of the current local planning path frame; The tracking path point with the smallest position distance, and dispatching the robot from the current position to the tracking path point, and continuing to perform sideways motion from the tracking path point; Wherein, the dispatching of the robot from the current position to the tracking waypoint includes: obtaining a dispatching path for dispatching the robot from the current position to the tracking waypoint; The robot is dispatched from the current position to the tracking path point, and the path point on the dispatch path is marked as a traversed point; Controlling the robot to move from the current local planning path frame to the next local planning path frame, so as to perform path tracking on the next local planning path frame until traversing the border path points and internal path points of all local planning path frames . 2. The robot control method according to claim 1, wherein: The obstacle includes a charging pile; The generating planning edge starting point based on the wall surface information includes: Generate the planned starting point along the edge based on the wall information and the position of the charging pile; wherein, the planned starting point along the edge is a number of grid points at a first preset distance from the wall, and The relative distance of the charging pile is less than or equal to at least one grid point of the second preset distance. 3. The robot control method according to claim 1, wherein: The edge-to-edge movement along the border of the current local planning path frame where the starting point of the planned edge-to-edge is located includes: In the process of controlling the robot to move along the border of the current local planning path frame, obtaining the obstacle perception distance in the moving direction; When the obstacle sensing distance is less than a third preset distance, triggering the obstacle-encounter fixing action; monitoring the lateral distance between the robot and the wall during the process of the robot performing the action of fixing the obstacle; When the lateral distance is less than the fourth preset distance, exit the action of fixing an obstacle, and continue to perform sideways motion based on the current position. 4. The robot control method according to claim 3, wherein: The action of fixing an obstacle is: controlling the robot to rotate in a first direction, and recording a first rotation angle; when the first rotation angle reaches a first preset angle, controlling the robot to rotate in a second direction, And record the second rotation angle; when the second rotation angle reaches a second preset angle, control the robot to stop rotating; The first direction and the second direction are two opposite directions. 5. The robot control method according to claim 3, wherein: The step of continuing to move along the edge based on the current position includes: judging whether the current position of the robot is on the border of the current local planning path frame; If so, continue to perform edge-to-edge motion from the current position; If not, then obtain the tracking path point on the border of the current local planning path frame with the smallest distance from the current position, and dispatch the robot from the current position to the tracking path point, from the tracking path Point to continue to perform edge motion. 6. The robot control method according to claim 5, wherein: Said dispatching said robot from said current location to said tracking waypoint comprises: Acquiring a dispatch path of the robot dispatched from the current position to the tracking waypoint; dispatching the robot from the current position to the tracking waypoint according to the dispatching path, and marking the waypoints on the dispatching path as traversed points. 7. The robot control method according to claim 1, wherein: The controlling the robot to move from the current local planning path frame to the next local planning path frame to perform path tracking on the next local planning path frame includes: Obtain the relative distance between the current local planning path frame and other local planning path frames; taking the local planning path frame with the smallest relative distance to the current local planning path frame as the next local planning path frame of the current local planning path frame; Obtaining two borders parallel to the wall in the next local planning path frame and the wall information, and calculating the relative distance between the two borders and the wall; At the midpoint of a frame with a smaller relative distance among the two frames, set a new planning starting point along the edge; The robot is controlled to start from the new planned edge-along starting point, and move along the edge along the frame of the next local planned path frame. 8. A robot, characterized in that the robot comprises a processor and a memory connected to the processor, wherein program data is stored in the memory, and the processor executes the program stored in the memory data, to implement: Obtain a grid map converted from the point cloud of the environmental map, and extract wall information from the grid map; extracting obstacle positions in the grid map; generating several local planning path frames in the grid map based on the obstacle positions, wherein there are no obstacles in borders and internal areas of the several partial planning path frames; Based on the wall information, generate a planning edge starting point, and generate several local planning path boxes in the grid map, wherein the planning edge starting point is located in one of the local planning path boxes and the wall information The midpoint of a parallel side of the wall; Controlling the robot to start from the starting point of the planned edge, and move along the border of the current local planning path frame where the starting point of the planning edge is located, until after traversing all the path points on the border of the current local planning path frame , performing a full-coverage traversal on the internal path points of the current local planning path frame; Wherein, said moving along the edge along the frame of the current local planning path frame where the starting point of the planned edge is located includes: After the robot exits the action of fixing an obstacle, it is judged whether the current position of the robot is in the border of the current local planning path frame; The tracking path point with the smallest position distance, and dispatching the robot from the current position to the tracking path point, and continuing to perform sideways motion from the tracking path point; Wherein, the dispatching of the robot from the current position to the tracking waypoint includes: obtaining a dispatching path for dispatching the robot from the current position to the tracking waypoint; The robot is dispatched from the current position to the tracking path point, and the path point on the dispatch path is marked as a traversed point; Controlling the robot to move from the current local planning path frame to the next local planning path frame, so as to perform path tracking on the next local planning path frame until traversing the border path points and internal path points of all local planning path frames . 9. A computer-readable storage medium, characterized in that it stores program instructions inside, and the program instructions are executed to realize: Obtain a grid map converted from the point cloud of the environmental map, and extract wall information from the grid map; extracting obstacle positions in the grid map; generating several local planning path frames in the grid map based on the obstacle positions, wherein there are no obstacles in borders and internal areas of the several partial planning path frames; Based on the wall information, generate a planning edge starting point, and generate several local planning path boxes in the grid map, wherein the planning edge starting point is located in one of the local planning path boxes and the wall information The midpoint of a parallel side of the wall; Control the robot to start from the starting point of the planned edge, and move along the border of the current local planning path frame where the starting point of the planning edge is located, until after traversing all the path points on the border of the current local planning path frame, to Perform full coverage traversal of the internal path points of the current local planning path frame; Wherein, said moving along the edge along the frame of the current local planning path frame where the starting point of the planned edge is located includes: After the robot exits the action of fixing an obstacle, it is judged whether the current position of the robot is in the border of the current local planning path frame; The tracking path point with the smallest position distance, and dispatching the robot from the current position to the tracking path point, and continuing to perform sideways motion from the tracking path point; Wherein, the dispatching of the robot from the current position to the tracking waypoint includes: obtaining a dispatching path for dispatching the robot from the current position to the tracking waypoint; The robot is dispatched from the current position to the tracking path point, and the path point on the dispatch path is marked as a traversed point; Controlling the robot to move from the current local planning path frame to the next local planning path frame, so as to perform path tracking on the next local planning path frame until traversing the border path points and internal path points of all local planning path frames .

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