CN112363513A

CN112363513A - Obstacle classification and obstacle avoidance control method based on depth information

Info

Publication number: CN112363513A
Application number: CN202011340692.6A
Authority: CN
Inventors: 戴剑锋; 赖钦伟; 肖刚军
Original assignee: Zhuhai Amicro Semiconductor Co Ltd
Current assignee: Zhuhai Amicro Semiconductor Co Ltd
Priority date: 2020-11-25
Filing date: 2020-11-25
Publication date: 2021-02-12

Abstract

The invention discloses a depth information-based obstacle classification and avoidance control method, which comprises the following steps: step 1, combining depth information of a target obstacle acquired by a TOF camera and internal and external parameters of the TOF camera, calculating to obtain longitudinal height information of the target obstacle, and identifying and classifying the target obstacle based on a data stability statistical algorithm; step 2, deciding a deceleration obstacle avoidance mode or a deceleration obstacle avoidance mode of the robot according to the classification result and the longitudinal height information of the target obstacle in the corresponding type; the execution main body of the obstacle classification obstacle avoidance control method is a robot with a TOF camera and an infrared sensor assembled at the front end of a body, and the target obstacle is in the current view field area of the TOF camera; the robot has the advantages that the current walking modes comprise bow-shaped walking and global edge walking, the robot is suitable for walking in various working modes without collision and collision, and the interference of obstacles is reduced.

Description

Obstacle classification and obstacle avoidance control method based on depth information

Technical Field

The invention relates to the technical field of intelligent robot obstacle avoidance path planning, in particular to an obstacle classification obstacle avoidance control method based on depth information.

Background

At present, SLAM robots based on inertial navigation, vision and laser are more and more popular, a family sweeping cleaning robot is relatively strong in representativeness, the indoor environment is positioned and a map is built in real time by combining the data of the vision, the laser, a gyroscope, acceleration and a wheel odometer, and then positioning navigation is realized according to the built map. However, at present, a robot often has movable obstacles such as toys and electric wires on the ground in a complex obstacle environment, when the robot collides with the obstacle of the type, the robot is either pushed or wound by the obstacle of the electric wire type, and a sofa-type obstacle still exists in a home environment, if the height below the bottom of the sofa is just lower than the height of the top surface of the robot, the robot is possibly blocked when entering the sofa-type obstacle. Chinese patent CN110622085A published in the application of 12/27/2019 relates to acquiring a depth image of an obstacle by using at least one camera, but does not control a robot to effectively avoid or detour an obstacle according to different height traffic conditions of the same type of obstacle.

Disclosure of Invention

In order to solve the technical problem, the invention firstly classifies the obstacles according to the calculated physical size of the obstacles and a data stability statistical algorithm to be classified into the types of walls, toys, doorsills, sofas and electric wires aiming at the obstacles identified by the TOF camera, and avoids obstacles or decelerates to approach or pass through according to the types of the obstacles in the moving process of the robot. The specific technical scheme is as follows:

a depth information-based obstacle classification and avoidance control method comprises the following steps: step 1, combining depth information of a target obstacle acquired by a TOF camera and internal and external parameters of the TOF camera, calculating to obtain longitudinal height information of the target obstacle, and identifying and classifying the target obstacle into a wall type obstacle, a toy type obstacle, a threshold type obstacle, a sofa type obstacle and a wire type obstacle based on a data stability statistical algorithm; step 2, deciding a deceleration obstacle avoidance mode or a deceleration obstacle avoidance mode of the robot according to the classification result and the longitudinal height information of the target obstacle in the corresponding type; the execution main body of the obstacle classification obstacle avoidance control method is a robot with a TOF camera and an infrared sensor assembled at the front end of a body, and the target obstacle is in the current view field area of the TOF camera; the current walking mode of the robot comprises bow-shaped walking and global edgewise walking.

Compared with the prior art, the technical scheme adopts a flexible and effective obstacle avoidance mode according to the type characteristics of the obstacles and the current motion state of the robot, realizes that the robot is controlled by using a deceleration obstacle avoidance or deceleration obstacle avoidance mode to avoid the phenomenon that the robot collides with the obstacles at a high speed, adapts to the fact that the robot walks in front of the obstacles with various heights without collision and with less collision, does not need additional collision warning signals and early warning area prompting, adapts to the fact that the robot walks with less collision in various walking modes, and reduces the interference of the obstacles.

Further, the step 2 comprises: after the target barrier is classified into a toy type barrier and the longitudinal height of the target barrier is calculated to be larger than a first preset toy height, if the robot currently executes bow-shaped walking or the robot currently executes global edgewise walking, the robot is controlled to walk at a reduced speed along the current walking direction, and meanwhile, the barrier detected in the current walking direction is avoided by utilizing the detection information of the infrared sensor; the infrared sensor on the mobile robot detects the obstacles in real time in the process of executing the bow-shaped walking and the process of executing the global edgewise walking. According to the technical scheme, before the robot touches a higher toy obstacle, the robot is controlled to recognize the type of the obstacle in advance and to decelerate and move upwards, and meanwhile, infrared obstacle avoidance is carried out, and the dangerous obstacle, namely the higher toy obstacle, is avoided without collision.

Further, the step 2 further comprises: after the target barrier is classified into a toy type barrier and the longitudinal height of the target barrier is calculated to be smaller than a first preset toy height, if the robot executes bow-shaped walking at present, the robot is controlled to walk at a reduced speed, and when the depth distance between the robot and the target barrier is a first toy safety distance, the robot rotates 90 degrees towards a first preset hour hand direction, then moves forwards for a first preset distance, then rotates 90 degrees towards the first preset hour hand direction, and then moves forwards to realize right-angle turning; after the target obstacle is classified as a toy type obstacle and the longitudinal height of the target obstacle is calculated to be smaller than a first preset toy height, if the robot currently executes global edgewise walking, the robot is controlled to walk at a reduced speed, and when the depth distance between the robot and the target obstacle is the second toy safety distance, the robot rotates 90 degrees in the second preset hour direction and then moves forward by the second preset distance, then rotates 90 degrees in the direction opposite to the second preset hour hand direction, and then advances for a third preset distance, then detecting whether other obstacles exist in the current edge direction by rotating a first observation angle, if so, bypassing the detected obstacles by a first preset movement radian in an obstacle bypassing walking mode and returning to the original global edge path, otherwise, bypassing the target obstacles by a second preset movement radian and returning to the original global edge path; wherein, first preset distance, second preset distance and third preset distance all are relevant with the profile width of the same target obstacle that TOF camera was gathered in real time, and this profile width is: horizontal distances between the leftmost side and the rightmost side of the same target obstacle in a real-time view field region of the TOF camera; wherein the first toy safety distance is related to depth information measured in the process that the robot executes the bow-shaped walking; the second toy safety distance is related to depth information measured during the execution of the global edgewise walking by the robot.

According to the technical scheme, before the robot touches a short and small toy barrier, on the basis of speed reduction walking, the obstacle avoidance path is adjusted in advance according to the real-time walking mode and the depth distance information of the robot, namely, the robot is prevented from colliding with the short toy barrier through right-angle turning and obstacle avoidance walking, the robot is effectively prevented from crossing the short toy barrier, the robot can be guaranteed to return to the originally planned working path after the obstacle avoidance or the obstacle avoidance, and the interference of the obstacle on the work of the robot is reduced.

Further, the first preset toy height is set to 65 mm; wherein the toy type barrier comprises an island type barrier. The height characteristics of the small parts configured under the actual furniture environment are met, so that the contact-forbidden obstacles are effectively detected and identified.

Further, the step 2 further comprises: after the target barrier is classified into a threshold type barrier, if the robot performs zigzag walking at present, controlling the robot to walk at a reduced speed to cross the threshold; after the target obstacle is classified into a threshold type obstacle, if the robot executes global edgewise walking currently, controlling the robot to walk at a reduced speed to cross a threshold; wherein the threshold type obstacle comprises an obstacle that can be crossed by the robot. According to the technical scheme, after the threshold is identified, the robot decelerates to advance to cross the threshold, so that the robot is prevented from impacting the threshold at a high speed, and the function of protecting the threshold is achieved.

Further, the step 2 further comprises: after the target barrier is classified into a wall type barrier, if the robot currently executes the zigzag walking, controlling the robot to keep executing the zigzag walking, and simultaneously avoiding the barrier detected in the current walking direction by using the detection information of the infrared sensor; and after the target barrier is classified as a wall type barrier, if the robot currently executes the global edgewise walking, controlling the robot to keep executing the global edgewise walking so as to realize the wall-wise walking.

According to the technical scheme, the robot is controlled not to execute infrared obstacle avoidance in the process of walking along the wall, and executes infrared obstacle avoidance under the condition of not walking along the wall, so that the interference of the obstacle on the movement and working behavior of the robot in an indoor home environment is reduced. Also plays a role in protecting wall type higher furniture.

Further, the step 2 further comprises: if the walking mode currently executed by the robot is bow-shaped walking, the following deceleration obstacle avoidance modes exist: when the target barrier is classified into a sofa type barrier and the longitudinal height of the target barrier is calculated to be smaller than a first preset sofa height, controlling the robot to keep executing the zigzag walking and avoiding the barrier detected in the current walking direction by utilizing the detection information of the infrared sensor; when the target obstacle is classified into a sofa type obstacle and the longitudinal height of the target obstacle is calculated to be larger than a first preset sofa height and smaller than a second preset sofa height, the vehicle decelerates to walk along the current walking direction, and simultaneously utilizes detection information of an infrared sensor to avoid the obstacle detected in the current walking direction; when the target obstacle is classified as a sofa-type obstacle and the longitudinal height of the target obstacle is calculated to be larger than a second preset sofa height, controlling the robot to enter the bottom of the sofa-type obstacle along the current zigzag path; wherein, the second preset sofa height is larger than the height of the robot body;

if the walking mode currently executed by the robot is global edgewise walking, the following deceleration obstacle avoidance modes exist: when the target obstacle is classified as a sofa type obstacle and the longitudinal height of the target obstacle is calculated to be smaller than a third preset sofa height, controlling the robot to walk along the outline of the target obstacle at a reduced speed so that the robot collides with the target obstacle and is not blocked by the target obstacle; when the target obstacle is classified as a sofa-type obstacle and the longitudinal height of the target obstacle is calculated to be larger than a third preset sofa height, controlling the robot to decelerate and walk along the side, and allowing the robot to collide with the target obstacle in the process of walking along the side, so that the robot determines the specific position of the target obstacle through collision and is not clamped by the sofa-type obstacle after entering the bottom of the sofa-type obstacle along the side; the third preset sofa height is greater than the first preset sofa height, and the second preset sofa height is greater than the third preset sofa height.

Compared with the prior art, the technical scheme is that after the obstacle of the sofa which can pass through is identified in the advancing direction of the robot, whether the robot enters the bottom of the sofa or not and the mode of decelerating and avoiding the obstacle are determined according to the current walking mode of the robot and the longitudinal height value range of the sofa, when the sofa is small in height (the robot cannot go to the bottom of the sofa), the infrared obstacle is directly avoided to avoid touch, when the sofa is moderate in height (one part of the robot can enter the bottom of the sofa), the sofa is decelerated to advance and kept in the infrared obstacle avoiding mode to avoid high-speed collision, when the sofa is large in height (the robot can completely enter the bottom of the sofa), the sofa can directly enter the sofa in the original walking mode without deceleration, and the working efficiency of the robot and the effectiveness of obstacle avoiding are improved.

Further, the third preset sofa height is set to be 110mm, the second preset sofa height is set to be 90mm, and the first preset sofa height is set to be 50 mm; wherein the sofa-type barrier comprises a furniture barrier for the robot to traverse. Thereby recognizing large obstacles that the robot is allowed to touch or even pass through.

Further, the step 2 further comprises: after the target barrier is classified into the electric wire type barrier and the longitudinal height of the target barrier is calculated to be larger than the first preset electric wire height, if the robot executes bow-shaped walking at present, the robot is controlled to walk at a reduced speed, and when the depth distance between the robot and the target barrier is the first electric wire safety distance, the robot rotates 90 degrees towards the first preset hour hand direction, then the robot advances a fourth preset distance, then rotates 90 degrees towards the first preset hour hand direction, and then the robot advances so as to realize right-angle turning; after the target obstacle is classified as the electric wire type obstacle and the longitudinal height of the target obstacle is calculated to be larger than the first preset electric wire height, if the robot currently executes the global edgewise walking, the robot is controlled to walk at a reduced speed, and when the depth distance between the robot and the target barrier is the second electric wire safety distance, the robot rotates 90 degrees in the second preset hour hand direction and then moves forward by a fifth preset distance, then rotates 90 degrees in the direction opposite to the second preset hour hand direction, and then advances for a sixth preset distance, then, whether other obstacles exist in the current edge direction is detected by rotating a second observation angle, if so, the detected obstacles are bypassed by a third preset moving radian in an obstacle bypassing walking mode and then returned to the original global edge path, otherwise, the detected obstacles are bypassed by the target obstacles by a fourth preset moving radian and then returned to the original global edge path; the infrared sensor on the mobile robot detects the obstacles in real time in the process of executing the bow-shaped walking and the process of executing the global edgewise walking; the fourth preset distance, the fifth preset distance and the sixth preset distance are all related to the contour width of the target obstacle acquired by the TOF camera in real time; this profile width is: in the real-time view field area of the TOF camera, the horizontal distance between the leftmost side of the target barrier and the rightmost side of the target barrier is kept; the first wire safety distance is related to depth information measured in the process that the robot executes the bow-shaped walking; the second wire safety distance is related to depth information measured during the global edgewise walking performed by the robot.

According to the technical scheme, after the winding obstacles such as the electric wires are identified in the advancing direction of the robot, according to the current motion state of the robot, the obstacle avoidance strategy is flexibly adjusted in the deceleration walking process, the electric wires are avoided and touched in a right-angle turning mode after the bow-shaped deceleration walking is carried out for a certain safe distance, the electric wires are bypassed in a barrier walking mode after the edge deceleration walking is carried out for a certain safe distance, the robot is forbidden to touch the electric wires and even cross the electric wires, the robot is guaranteed to continue to return to the original walking mode after the robot is far away from the electric wires, and the influence of the obstacles such as the electric wires on the normal work of the robot is reduced.

Further, the first preset wire height is set to 5mm, wherein the wire type barrier comprises a winding. Effectively identifying a winding that is short and that can be spanned by the robot.

Further, the data stability statistical algorithm classifies depth information and longitudinal height information of the target obstacle based on a filtering and statistical algorithm to construct a three-dimensional profile of the target obstacle, and further classifies the target obstacle into a wall model, a toy model, a threshold model, a sofa model and a wire model. According to the technical scheme, the shape and the range of the target obstacle are analyzed on the basis of acquiring the depth information output by the TOF camera, so that the obstacle condition in front of the robot can be positioned, and the use of fitting operation is reduced. The accuracy of obstacle type identification is improved.

Drawings

Fig. 1 is a flowchart of an obstacle classification and avoidance control method based on depth information according to an embodiment of the present invention.

Fig. 2 is a flowchart of an obstacle classification and obstacle avoidance control method based on depth information according to a second embodiment of the present invention.

Fig. 3 is a flowchart of an obstacle classification and obstacle avoidance control method based on depth information according to a third embodiment of the present invention.

Fig. 4 is a flowchart of an obstacle classification and avoidance control method based on depth information according to a fourth embodiment of the present invention.

Fig. 5 is a flowchart of an obstacle classification and obstacle avoidance control method based on depth information according to a fifth embodiment of the present invention.

Fig. 6 is a flowchart of an obstacle classification and avoidance control method based on depth information according to a sixth embodiment of the present invention.

Fig. 7 is a flowchart of an obstacle classification and obstacle avoidance control method based on depth information according to a seventh embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be described in detail below with reference to the accompanying drawings in the embodiments of the present invention. It should be noted that, in the present application, the whole text of chinese patent CN111624997A is introduced into the text of the present application, so as to complete the description of calculating the relative position information of the obstacle region from the depth image acquired by the TOF camera and the description of the map calibration marking method.

The depth image is also called a distance image, and refers to an image in which the distance between each pixel point of the depth image and the actual measurement point of the corresponding obstacle is taken as a pixel value. Wherein the deflection angle between each pixel point and the corresponding measurement point is determined based on the setting parameters of the imaging device. The depth image directly reflects the geometric shape outline of the visible surface of each obstacle in the shot physical scene, and the depth image can be converted into spatial point cloud data through coordinate conversion. And all the obstacles described by the depth data in the depth image can be used as images of the obstacles to be identified for subsequent processing. Wherein the obstacle shall be taken to broadly include an object temporarily placed on a traveling plane and an object that is not easily moved. The traveling plane of the robot includes, but is not limited to, cement floor, painted floor, composite floor, solid wood floor, carpet floor, table top, glass surface, etc. according to the actual application environment. Examples of the object temporarily placed on the traveling plane include objects such as a doorsill (capable of crossing), a toy (collision prohibition), a wire (crossing prohibition), and the like; examples of objects that are not easily moved include sofas (the machine cannot be controlled to enter when the height of the sofa bottom is lower than the height of the machine), walls, etc.

As a first embodiment, a depth information-based obstacle classification and avoidance control method is disclosed, where an execution main body of the obstacle classification and avoidance control method is a robot whose front end of a body is equipped with a TOF camera and an infrared sensor, including but not limited to a sweeping robot, and as shown in fig. 1, the obstacle classification and avoidance control method includes:

step S1, combining the depth information of the target obstacle acquired by the TOF camera and the internal and external parameters of the TOF camera, calculating to acquire the longitudinal height information of the target obstacle, identifying and classifying the target obstacle into a wall type obstacle, a toy type obstacle, a threshold type obstacle, a sofa type obstacle and a wire type obstacle based on a data stability statistical algorithm, and then entering step S2. The acquired target obstacle is in the current view field area of the TOF camera and is positioned in front of the robot; in step S1, filtering and connected domain analysis are performed on depth image information acquired by the TOF camera to segment an image contour of the target obstacle, including a spatial contour feature of the target obstacle and a shape feature of the target obstacle, so as to analyze a shape tracking range of the obstacle; and then the actual physical size of the target obstacle, including the longitudinal height information of the target obstacle, is obtained by combining the depth information of the target obstacle acquired by the TOF camera and the internal and external parameters of the TOF camera. After the actual physical size of the target obstacle is obtained, the target obstacle is identified and classified into a wall type obstacle, a toy type obstacle, a threshold type obstacle, a sofa type obstacle and a wire type obstacle based on a data stability statistical algorithm, specifically, the depth information and the longitudinal height information of the target obstacle are classified and processed based on a filtering and statistical algorithm, in some embodiments, the type of the obstacle is also identified by utilizing gray data of the outline shape of the target obstacle to construct a three-dimensional outline of the target obstacle, and then the target obstacle is classified into a wall model, a toy model, a threshold model, a sofa model and a wire model. The surrounding 3-dimensional coordinate information can be detected, so that the condition of an obstacle in front of the robot can be positioned.

The related filtering algorithm of the depth image data comprises median filtering, Gaussian filtering, guided filtering, bilateral filtering, mean filtering, time domain median filtering, statistical filtering, straight-through filtering, radius filtering and voxel filtering; the connected domain analysis comprises Two of Two-pass and seed-filing.

It should be noted that TOF is an abbreviation of Time of Flight (TOF) technology, that is, a sensor emits modulated near-infrared light, which is reflected after encountering an object, and the sensor converts the distance of a shot scene by calculating the Time difference or phase difference between light emission and reflection to generate depth information.

Step S2, deciding a deceleration obstacle avoidance mode or a deceleration obstacle avoidance mode of the robot according to the current walking mode of the robot, the classification result and the longitudinal height information of the target obstacle in the corresponding type; the current walking mode of the robot comprises bow-shaped walking and global edgewise walking, and also comprises an equivalent walking path or a corresponding combined planning path. In step S2, the robot needs to determine a deceleration obstacle avoidance mode or a deceleration obstacle avoidance mode in combination with the work motion mode of the robot, the type characteristics of the target obstacle recognized in front of the body or in the current walking direction, and the occupied height space, so as to implement: when the robot approaches to the barrier, the robot can conveniently avoid the barrier in advance by using the advantage of the current walking mode to realize the collision-free function or bypass the barrier and then walk forwards; the machine can be prevented from avoiding the dangerous barrier when detecting the dangerous barrier, and the machine can be prevented from decelerating in time to avoid high-speed collision when encountering large objects such as furniture, walls and the like, thereby playing a role in protecting the furniture and the walls. The infrared obstacle avoidance mode is that the robot avoids the obstacle detected in the current walking direction based on the detection information of the infrared sensor.

Compared with the prior art, the method and the device have the advantages that a flexible and effective obstacle avoidance mode is adopted according to the type characteristics of the obstacles and the current motion state of the robot, the phenomenon that the robot collides with the obstacles at a high speed is avoided by using a deceleration obstacle avoidance or deceleration obstacle avoidance mode, the robot is suitable for walking without collision and with little collision in front of the obstacles with various heights, extra collision warning signals and early warning area prompt are not needed, the robot is suitable for walking with little collision in various working modes, and the interference of the obstacles is reduced.

In the specific implementation process, at least: the current movement state (normal linear walking, in-situ rotation, radian rotation and edgewise) of the robot and the type of the barrier determine to adjust the current pose of the robot, so that the robot can linearly cross the barrier before passing through the barrier and crossing the barrier, or the robot can walk around the barrier or linearly avoid the barrier before a small-sized barrier (including a small-sized winding object) without touching the barrier by adjusting the current pose of the mobile robot, or the robot can avoid the barrier edgewise when approaching a wall by adjusting the current pose of the mobile robot. This is of course also relevant for the shape features of the recognized obstacle, which are composed or abstracted geometries, geometry combinations, etc. based on contour lines and/or feature points for matching the respective obstacle type. Wherein the geometry, combination of geometries may be based on the full outline or partial representation of the outline of the identified obstacle. For example, the shape features provided based on the island type include one or more combinations of circles, spheres, arcs, squares, cubes, pi-shapes, and the like. For example, the shoe shape features comprise a plurality of arc shapes which are connected end to end, and the chair shape features comprise a pi shape, an eight-claw shape and the like. The shape characteristics provided based on the type of wrap include at least one or more of a combination of curvilinear shapes, serpentine shapes, and the like. The shape features provided based on the space division type include at least one or more combinations of a straight line shape, a broken line shape, a rectangle shape, and the like.

As shown in fig. 2, the method for controlling obstacle avoidance based on TOF camera classification includes the following specific steps:

step S201, in the current arch-shaped walking process of the robot, after detecting that the target obstacle in front of the robot body is classified as a toy type obstacle, the process goes to step S202. The front of the body is the front of the walking direction of the robot or in the overlapping area of the view angle range and the effective distance measuring range of the TOF camera, namely in the current view field area of the TOF camera.

Step S202, judging whether the longitudinal height of the target obstacle is larger than a first preset toy height, if so, entering step S203, otherwise, entering step S204. Preferably, the first preset toy height is set to 65 mm; wherein the toy type barrier comprises an island type barrier.

And S203, controlling the robot to walk in a decelerating manner along the current walking direction, so that the robot is decelerated and travels straight to be closer to a toy type obstacle, and meanwhile, the obstacle detected in the current walking direction is avoided by utilizing the detection information of the infrared sensor.

And step S204, controlling the robot to walk along the originally planned arch-shaped path in a decelerating way, and then, entering step S205.

Step S205, in the process that the robot walks along the originally planned zigzag path in a decelerating manner, judging whether the depth distance between the robot and the target obstacle is reduced to be a first toy safety distance or not, or judging whether the depth distance between the robot and the target obstacle is the first toy safety distance or within the error numerical range of the first toy safety distance or not, if yes, entering step S206, otherwise, returning to step S204 to keep the decelerating walking until the measured depth distance is the first toy safety distance. The first toy safety distance is related to depth information measured in the process that the robot executes the bow-shaped walking, the robot is limited not to collide with the target barrier before decelerating to zero, and the function of protecting the target barrier is achieved.

And S206, controlling the robot to rotate 90 degrees in the first preset hour hand direction, then to advance for a first preset distance, then to rotate 90 degrees in the first preset hour hand direction, and then to advance, so that the robot can turn around at a right angle, and before the robot tends to collide with an obstacle, the robot can avoid the dangerous obstacle in time. Wherein, first preset distance is relevant with the profile width of the same toy type barrier that the TOF camera was gathered, and this profile width is: the horizontal distances of the leftmost side and the rightmost side of the same toy type obstacle within the field of view region of the TOF camera are calculated and acquired in step S201 and step S202 in the present embodiment. In the visual angle range of the TOF camera, when the horizontal distance between the leftmost side of the obstacle of the same toy type and the center of the robot body is larger, the first preset distance for the robot to move straight after rotating to the left is larger; in the visual angle range of the TOF camera, when the horizontal distance between the rightmost side of the obstacle of the same toy type and the center of the robot body is larger, the first preset distance for the robot to move straight after rotating rightwards is larger; otherwise, the smaller the first preset distance is. So as to meet the requirement of obstacle avoidance range.

On the basis of the second embodiment, the third embodiment discloses an obstacle classification and obstacle avoidance control method in a working mode in which the robot walks along the edge globally, which specifically includes, as shown in fig. 3:

step S301, in the process that the robot walks along the edge currently, after detecting that the target obstacle in front of the robot body is classified as a toy type obstacle, the process goes to step S302. The front of the body is in the walking direction of the robot or in the overlapping area of the view angle range and the effective distance measuring range of the TOF camera.

Step S302, judging whether the longitudinal height of the target obstacle is larger than a first preset toy height, if so, entering step S303, otherwise, entering step S304. Preferably, the first preset toy height is set to 65 mm; wherein the toy type barrier comprises an island type barrier.

And S303, controlling the robot to walk along the current edgewise direction in a decelerating manner, so that the robot can walk in a straight decelerating manner to be closer to a toy type obstacle, and simultaneously avoiding the obstacle detected in the current walking direction by utilizing the detection information of the infrared sensor.

And S304, controlling the robot to walk at a reduced speed, walking at a reduced speed along a global edgewise path, not approaching the target obstacle quickly, judging whether the depth distance between the robot and the target obstacle is reduced to a second toy safety distance or not, or judging whether the depth distance between the robot and the target obstacle is the second toy safety distance or within an error numerical range of the second toy safety distance, if so, entering S305, and if not, continuing to maintain the reduced speed walking until the depth distance between the robot and the target obstacle is reduced to the second toy safety distance. The second toy safety distance is related to depth information measured in the process that the robot executes the edgewise walking, and can be a safety door limit value set based on the outline shape of the target obstacle, so that the robot is limited not to collide with the target obstacle before decelerating to zero, and the function of protecting the target obstacle is achieved.

Step S305, the robot is controlled to rotate 90 degrees in the second preset hour direction, then the robot is advanced by a second preset distance, namely the robot moves straight by the second preset distance in the current walking direction, then the robot rotates 90 degrees in the reverse direction of the second preset hour direction, then the robot is advanced by a third preset distance, namely the robot moves straight by a third preset distance in the current walking direction, and then the operation goes to step S306. It is noted that in step S305, the robot may or may not be decelerated, because after the depth distance between the robot and the target obstacle is the second toy safe distance, the robot has started to change the walking direction and may no longer tend to collide with the target obstacle. Wherein, the second preset distance and the third preset distance are both related to the contour width of the same target obstacle collected by the TOF camera of the robot on the zigzag path, and the contour width is as follows: in the field of view region of the TOF camera, the horizontal distances of the leftmost side and the rightmost side of the same target obstacle are calculated in step S301 and step S302 in this embodiment, and the depth data of the same target obstacle is also measured. In the visual angle range of the TOF camera, when the horizontal distance between the leftmost side of the obstacle of the same toy type and the center of the robot body is larger, the second preset distance for the robot to move straight after rotating to the left is larger; in the visual angle range of the TOF camera, when the horizontal distance between the rightmost side of the obstacle of the same toy type and the center of the robot body is larger, the second preset distance for the robot to move straight after rotating rightwards is larger; whereas the second predetermined distance is set smaller. No matter the robot rotates to the right or to the left, if the depth data of the same toy type barrier is larger, the third preset distance is set to be larger, otherwise, the third preset distance is smaller.

Step S306, the robot is controlled to rotate by a first viewing angle, and then the process proceeds to step S307. The rotation direction of the robot in this step may be a second preset hour direction or an opposite direction, so that the walking direction of the robot that advances a third preset distance from step S305 is turned to detect whether an obstacle exists on the global edgewise path in step S301, for example, whether an obstacle exists in front of a wall along which the original global edgewise walking.

Step S307, detecting whether other obstacles exist on the global edge path in step S301, if so, entering step S309, otherwise, entering step S308. Other obstacles here are obstacles within the current field of view area of the TOF camera of the robot, in addition to the aforementioned target obstacle.

And S309, bypassing the detected obstacle by a first preset movement radian in a barrier-bypassing walking mode, and returning to the original global edgewise path to enable the robot to return to the original global edgewise walking. The obstacles of this step include the obstacle detected in step S307 and the aforementioned target obstacle.

And S308, bypassing the target obstacle by a second preset moving radian and returning to the original global edge path, wherein the second preset moving radian is smaller than the first preset moving radian.

The second embodiment and the third embodiment control the robot to recognize the type of the obstacle in advance and to decelerate to move upward before the robot touches the higher toy obstacle, and simultaneously keep executing infrared obstacle avoidance and avoid the dangerous obstacle of the higher toy obstacle without collision. In the second embodiment and the third embodiment, before the robot touches a short and small toy obstacle, on the basis of speed reduction walking, the obstacle avoidance path is adjusted in advance according to the real-time walking mode and depth distance information of the robot, namely, the robot is prevented from colliding with the short toy obstacle by right-angle turning and obstacle avoidance walking, the robot is effectively prevented from crossing the short toy obstacle, the robot can be guaranteed to return to the originally planned working path after obstacle avoidance or obstacle avoidance, and the interference of the obstacle on the work of the robot is reduced.

As an embodiment, the step S2 further includes: if the robot currently executes the zigzag walking or the global edgewise walking, controlling the robot to walk at a reduced speed to cross the threshold after the target barrier is classified as the threshold type barrier; wherein the threshold type obstacle comprises an obstacle that can be crossed by the robot. Specifically, after the target obstacle is classified as a threshold type obstacle, if the robot currently executes zigzag walking, the robot is controlled to walk at a reduced speed, and the robot walks at a reduced speed along a zigzag path so as to cross the threshold; after the target obstacle is classified into a threshold type obstacle, if the robot currently executes global edgewise walking, controlling the robot to walk at a reduced speed to cross the threshold and walk along a global edgewise path; wherein the threshold type obstacle comprises an obstacle that can be crossed by the robot. After the threshold is identified, the robot decelerates to advance to cross the threshold, so that the robot is prevented from impacting the threshold at a high speed, and the function of protecting the threshold is achieved.

As an embodiment, the step S2 further includes: if the robot currently executes the zigzag walking, after the target barrier is classified into a wall type barrier, controlling the robot to keep executing an original zigzag walking mode, and simultaneously utilizing detection information of an infrared sensor to avoid the barrier detected in the current walking direction; the arch-shaped walking device can not touch the wall body in the arch-shaped walking process. And if the robot currently executes the global edgewise walking, controlling the robot to keep executing the original edgewise walking mode so as to realize the edgewise walking without infrared obstacle avoidance. The robot is controlled to adjust the optimal edgewise direction, so that the robot can adjust the current edgewise mode, but the robot cannot collide with a wall body in the process of walking along the edge. The robot is controlled not to execute the infrared obstacle avoidance in the wall walking process, and the infrared obstacle avoidance mode is selected under the condition that the robot does not walk along the wall, so that the robot is prevented from frequently colliding with the wall, and the effect of protecting the wall type of higher furniture is achieved.

In the fourth embodiment, the disclosed sofa obstacle classification obstacle avoidance control embodiment, as shown in fig. 4, specifically includes:

step S401, in the process that the robot currently executes the zigzag walking, after detecting that the target obstacle in front of the machine body is classified as the sofa type obstacle, the process goes to step S402. The front of the body is in the walking direction of the robot or in the overlapping area of the view angle range and the effective distance measuring range of the TOF camera.

Step S402, judging whether the longitudinal height of the target obstacle is smaller than or equal to a first preset sofa height, if so, entering step S404, otherwise, entering step S403. Preferably, the first preset sofa height is set to 50 mm; wherein the sofa-type barrier comprises a furniture barrier for the robot to traverse.

Step S403, determining that the longitudinal height of the target obstacle is less than or equal to a second preset sofa height, if so, going to step S406, otherwise, going to step S405. Namely, whether the longitudinal height of the target obstacle meets the following conditions is judged: is higher than the first preset sofa height and is less than or equal to the second preset sofa height. Preferably, the third preset sofa height is set to be 110mm, and the first preset sofa height is set to be 50 mm; wherein the sofa-type barrier comprises a furniture barrier for the robot to traverse. Thereby recognizing large obstacles that the robot is allowed to touch or even pass through.

And S404, when the robot executes the original bow-shaped walking and keeps the original walking mode, the obstacle detected in the current walking direction is avoided by using the detection information of the infrared sensor.

And S406, controlling the robot to walk in a decelerating manner along the current walking direction, and simultaneously avoiding the obstacles detected in the current walking direction by utilizing the detection information of the infrared sensor, including avoiding the classified target obstacles, so that the robot avoids the target obstacles in a collision-free manner through an infrared obstacle avoiding mode, and the subsequent recovery of the original walking mode is facilitated. Because the robot continuously walks along the current walking direction and collides with the target obstacle, the infrared obstacle avoidance walking is directly executed after the collision warning signal is triggered, and the robot is quickened to return to the original bow-shaped walking mode.

And S405, if the longitudinal height of the target barrier is judged to be greater than the second preset sofa height, controlling the robot to keep executing the original bow-shaped walking to enter the bottom of the sofa type barrier without speed reduction. It should be noted that, when the type of the target obstacle is furniture for the mobile robot to pass through, the mobile robot is controlled to maintain the current walking direction, but an obstacle avoidance action is also performed, so that the influence degree of obstacles except for sand hair on the normal working behavior of the robot in the process of passing through the bottom of the sofa is reduced to the minimum, but the robot cannot collide with the sofa in the bow-shaped walking process. The height of the second preset sofa is greater than the height of the robot body; the second preset sofa height is greater than the first preset sofa height.

Compared with the prior art, by executing the obstacle classification and obstacle avoidance control method described in the foregoing steps S401 to S405, in this embodiment, after the obstacle of the type that the sofa can pass through is recognized in the advancing direction of the robot, whether the robot enters the bottom of the sofa or not is determined according to the longitudinal height value range of the sofa, and a mode of deceleration and obstacle avoidance is performed, in which the infrared obstacle avoidance is performed directly to avoid collision when the sofa is small in height (the robot cannot enter the bottom of the sofa), the infrared obstacle avoidance is performed to slow down and advance when the sofa is moderate in height (a part of the robot can enter the bottom of the sofa), and the infrared obstacle avoidance is maintained to avoid high-speed collision with the sofa, and the sofa is directly entered into the sofa in the original walking mode without being decelerated when the sofa is large in height (the robot can completely enter the bottom of the sofa), so that the working.

On the basis of the fourth embodiment, the fifth embodiment discloses another classification obstacle avoidance control embodiment of the sofa obstacle, as shown in fig. 5, which specifically includes:

step S501, in the process that the robot executes the global edgewise walking currently, after the fact that the target obstacle in front of the robot body is classified into the sofa type obstacle is detected, the step S502 is carried out. The front of the body is in the walking direction of the robot or in the overlapping area of the view angle range and the effective distance measuring range of the TOF camera. Namely, after the robot is confirmed to walk along the edge globally, the following deceleration obstacle avoidance method is started.

Step S502, judging whether the longitudinal height of the target barrier is smaller than or equal to a third preset sofa height, if so, entering step S504, otherwise, entering step S503.

And S503, controlling the robot to walk along the outline of the target obstacle in a decelerating way, so that the robot is not clamped by the target obstacle when colliding with the target obstacle, wherein the machine is allowed to collide with the sofa occasionally, but the machine is not allowed to be clamped.

In some implementations, during the process of the robot passing through the bottom of the sofa furniture by walking along the edge, the robot may walk along the edge around the supporting portion of the bottom of the sofa-type obstacle, and then the robot is allowed to collide with the sofa for walking along the edge, and after entering the hollow portion of the bottom of the furniture and physically colliding with the supporting portion of the sofa-type obstacle, the position detection result or the obstacle type recognition result of the sofa-type obstacle may be corrected.

Step S504, controlling the robot to decelerate and walk along the edge, and simultaneously controlling the robot to determine the occupied area of the target obstacle through physical collision so that the robot is not clamped by the target obstacle when colliding with the target obstacle, thereby allowing the machine to occasionally collide with the sofa in some implementation scenes but not be clamped; the third preset sofa height is greater than the first preset sofa height, and the second preset sofa height is greater than the third preset sofa height. Preferably, the second preset sofa height is set to 90 mm.

Compared with the prior art, the embodiment allows the robot to collide with the sofa without being clamped into the bottom of the sofa when recognizing that the height of the sofa is moderate, and the robot collides with the sofa at a reduced speed, so that the sofa is protected, and meanwhile, the specific position of the sofa is determined through collision.

Sixth embodiment, as an embodiment in which a robot walks in a zigzag manner to avoid an electric wire type with an obstacle, as shown in fig. 6, the obstacle classification and obstacle avoidance control method based on the TOF camera includes the specific steps of:

step S601, in the current zigzag walking execution process of the robot, after detecting that the target obstacle in front of the body is classified as the electric wire type obstacle, the process proceeds to step S602. Here, the front of the body is the front of the walking direction of the robot or in the overlapping region of the view angle range and the effective distance measurement range of the TOF camera. Therefore, after the robot is confirmed to be walking in a bow-shaped mode, the following deceleration obstacle avoidance mode is started to be executed.

Step S602, determining whether the longitudinal height of the target obstacle is greater than a first preset wire height, if so, going to step S603. Preferably, the first preset wire height is set to 5mm, wherein the wire type barrier comprises a winding. It should be noted that the height of these windings is relatively small, and generally smaller than the height of the robot body, so that it is easy to guide the robot to cross the wire type obstacle under misjudgment conditions.

Step S603, when it is detected that the height of the wire type obstacle is sufficiently high, the robot is controlled to travel at a reduced speed, and the robot is controlled to travel at a reduced speed along the zigzag path to avoid a high speed collision with the wire type obstacle, and then the process proceeds to step S604.

Step S604, in the process that the robot decelerates along the zigzag path, judging whether the depth distance between the robot and the target obstacle is reduced to be a first electric wire safety distance or not, or judging whether the depth distance between the robot and the target obstacle is the first electric wire safety distance or within an error numerical range of the first electric wire safety distance or not, if so, entering step S606, otherwise, returning to step S603 to continue to decelerate along the zigzag path. Wherein the first wire safety distance is related to depth information measured during the robot performing the zigzag walking, and the robot is restricted from colliding with the wire type obstacle before decelerating to zero, and is not required to walk around the winding and is easily stuck in case of misdetecting the relative position of the winding.

And step S605, controlling the robot to rotate 90 degrees in the first preset hour hand direction, then advance for a fourth preset distance, then rotate 90 degrees in the first preset hour hand direction, and then advance so as to realize right-angle turning of the robot and avoid the electric wire type barrier in time before the robot tends to collide with the barrier. The fourth preset distance is related to the contour width of the same wire type obstacle acquired by the TOF camera and can be obtained by scaling; this profile width is: horizontal distance of the leftmost and rightmost sides of the same wire type barrier within the field of view region of the TOF camera. In the visual angle range of the TOF camera, when the horizontal distance between the leftmost side of the wire type barrier and the center of the robot body is larger, the fourth preset distance for the robot to move straight after rotating to the left is larger; in the visual angle range of the TOF camera, when the horizontal distance between the rightmost side of the wire type barrier and the center of the robot body is larger, the fourth preset distance for the robot to move straight after the robot rotates rightwards is larger; otherwise, the smaller the fourth preset distance is. To satisfy the technical effect of forbidding touching and even crossing winding obstacles.

On the basis of the sixth embodiment, the seventh embodiment discloses an embodiment in which the robot walks globally edgewise to avoid the wire type obstacle, as shown in fig. 7, specifically including:

step S701, in the process that the robot walks along the edge in the current execution global situation, after the target obstacle in front of the machine body is detected to be classified as the electric wire type obstacle, the step S702 is carried out. The front of the body is in the walking direction of the robot or in the overlapping area of the view angle range and the effective distance measuring range of the TOF camera.

Step S702, determining whether the longitudinal height of the target obstacle is greater than the first preset wire height, if so, going to step S703.

Step S703, when it is detected that the height of the wire type obstacle is high enough to be obvious, the robot is controlled to walk at a reduced speed, and the robot walks at a reduced speed along the global edgewise path to avoid crossing the wire type obstacle at a high speed, and then the process proceeds to step S704.

Step S704, determining whether the depth distance between the robot and the target obstacle is reduced to a second wire safety distance, or determining whether the depth distance between the robot and the target obstacle is within the second wire safety distance or an error numerical range of the second wire safety distance, if yes, entering step S705, otherwise, returning to step S703 to continue to walk along the global edgewise path at a reduced speed. It is noted that in step S704, the robot may or may not be decelerated, and the robot is allowed to walk without deceleration because the robot has started to change the walking direction after the depth distance from the target obstacle is the second wire safety distance and may no longer tend to collide with the target obstacle. Wherein the second wire safety distance is related to depth information measured during the robot performing global edgewise walking, and may be a safety door limit value set based on a contour shape of the wire type obstacle, limiting the robot from colliding with the wire type obstacle before decelerating to zero, without walking around the winding and being easily stuck in case of misdetecting a relative position of the winding.

Step S705, the robot is controlled to rotate 90 degrees in the second preset hour direction, then advance by a fifth preset distance (i.e., travel straight by the fifth preset distance along the current traveling direction), then rotate by 90 degrees in the opposite direction of the second preset hour direction, then advance by a sixth preset distance (i.e., travel straight by the sixth preset distance along the current traveling direction), so as to start obstacle-detouring traveling, and then the process goes to step S706.

Wherein, under the scene of the global edgewise walking of robot, fifth preset distance and aforementioned fourth preset distance all are relevant with the profile width of the same electric wire type barrier that the TOF camera was gathered, and this profile width is: in the field of view area of the TOF camera, the horizontal distances of the leftmost side and the rightmost side of the same wire type obstacle are obtained through calculation in step S701 and step S702 in the present embodiment, and meanwhile, the depth data of the same target obstacle is also measured. In the visual angle range of the TOF camera, when the horizontal distance between the leftmost side of the barrier of the same wire type and the center of the body of the robot is larger, the fifth preset distance for the robot to move straight after rotating to the left is larger; in the visual angle range of the TOF camera, when the horizontal distance between the rightmost side of the barrier of the same wire type and the center of the robot body is larger, the fifth preset distance for the robot to move straight after the robot rotates rightwards is larger; whereas the smaller the fifth preset distance is set. No matter the robot rotates to the right or to the left, if the depth data of the same wire type barrier is larger, the sixth preset distance is also set to be larger, otherwise, the sixth preset distance is smaller. So as to meet the obstacle avoidance requirement of the wire type obstacle.

Step S706, the robot is controlled to rotate by a second observation angle, and then the process proceeds to step S707. The rotation direction of the robot in this step may be a second preset hour direction or an opposite direction thereof, so that the robot performs a walking direction turning detection step S705 to advance a sixth preset distance to detect whether an obstacle exists on the global edgewise path in step S701, for example, whether an obstacle exists in front of a wall along which the original global edgewise walking is performed.

Step S707, detecting whether there are other obstacles on the global edge path in step S701, if yes, going to step S709, otherwise, going to step S710. Other obstacles here are obstacles other than the aforementioned wire type obstacles within the current field of view of the TOF camera of the robot.

And S708, bypassing the detected obstacle by a third preset movement radian in a barrier-bypassing walking mode, and returning to the original global edgewise path to enable the robot to return to the original global edgewise walking. The obstacles of this step include the obstacle detected in step S707 and the aforementioned electric wire type obstacle.

And step S709, bypassing the target obstacle by a fourth preset moving radian, and returning to the original global edgewise path, wherein the fourth preset moving radian is smaller than the third preset moving radian. The fourth preset distance and the fifth preset distance are used for limiting the robot not to touch the target obstacle in the process of walking along the edge or in the process of walking at a reduced speed, the fourth preset moving radian and the third preset moving radian are used for limiting the robot not to touch the target obstacle in the process of walking around the obstacle, and in the embodiment, the requirements of the obstacle of a matching type on collision and obstacle avoidance are met by setting different safety distances before the target obstacle identified in the visual angle range is approached, so that the obstacle-free passable area is pre-judged, and an effective obstacle avoidance path is conveniently planned in the follow-up process.

By combining the sixth embodiment and the seventh embodiment, it can be known that after a winding obstacle such as an electric wire is identified in the advancing direction of the robot, the obstacle avoidance strategy is flexibly adjusted in the process of deceleration walking according to the current motion state of the robot, so that the electric wire is avoided and touched in a right-angle turning way after the robot decelerates in a zigzag shape for a certain safe distance, and the electric wire is bypassed in a barrier-bypassing way after the robot decelerates for a certain safe distance along the edge, thereby prohibiting the robot from touching the electric wire and even crossing the electric wire, ensuring that the robot continues to return to the original walking mode after being far away from the electric wire, and reducing the influence of the obstacle such as the electric wire on the normal work of the robot.

It should be noted that, in the foregoing embodiment, the data stability statistical algorithm classifies depth information and longitudinal height information of the target obstacle based on a filtering and statistical algorithm to construct a three-dimensional contour of the target obstacle, and further classifies the target obstacle into a wall model, a toy model, a threshold model, a sofa model, and an electric wire model. The shape and the range of the target obstacle are analyzed by collecting the depth information output by the TOF camera, so that the obstacle condition in front of the robot can be positioned. The use of fitting operations is reduced. The accuracy of obstacle type identification is improved.

The embodiment of the invention also discloses a cleaning robot, which comprises an infrared sensor, a cleaning device, a TOF camera and a processing unit, wherein the TOF camera is arranged in front of the cleaning robot at a preset inclination angle, so that the detection view angle of the TOF camera covers a preset advancing plane in front of the cleaning robot; the infrared sensor is installed at a side surface of the cleaning robot, and is used for executing the infrared obstacle avoidance mode of the foregoing embodiment. A cleaning device for performing a cleaning action in a controlled obstacle avoidance mode; the processing unit is electrically connected to the TOF camera and the cleaning device, respectively, and is configured to execute the obstacle identification method according to the foregoing embodiment. In the present embodiment, the cleaning robot is provided with a 3d-tof camera that simultaneously takes a depth image and a brightness image. Wherein, the top or the body side of the cleaning robot is provided with a camera device comprising an infrared camera device and a surface array laser measurement. The schematic diagram of the hardware structure can refer to chinese patent CN 111624997A. The 3d-ToF camera device is a 3d-ToF sensor which obtains a depth image and an infrared image by using the flight time of infrared light, and the 3d-ToF sensor comprises an infrared light emitter and an infrared light receiver. The infrared light receiver generates a gray image and a depth image by using infrared light reflected by the surface of the obstacle. The cleaning robot disclosed by the embodiment integrates a plurality of types of obstacle recognition function algorithms, is suitable for cleaning operation in an indoor actual activity environment, executes a plurality of image feature points and has too large fitting classification training compared with the prior art, so that the production cost is reduced, and the operation load of the robot for recognizing obstacles is reduced.

It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.

Claims

1. A method for controlling obstacle avoidance based on depth information in an obstacle classification mode is characterized by comprising the following steps:

step 1, combining depth information of a target obstacle acquired by a TOF camera and internal and external parameters of the TOF camera, calculating to obtain longitudinal height information of the target obstacle, and identifying and classifying the target obstacle into a wall type obstacle, a toy type obstacle, a threshold type obstacle, a sofa type obstacle and a wire type obstacle based on a data stability statistical algorithm;

step 2, deciding a deceleration obstacle avoidance mode or a deceleration obstacle avoidance mode of the robot according to the current walking mode of the robot, the classification result and the longitudinal height information of the target obstacle in the corresponding type;

the execution main body of the obstacle classification obstacle avoidance control method is a robot with a TOF camera and an infrared sensor assembled at the front end of a body, and the target obstacle is in the current view field area of the TOF camera;

the current walking mode of the robot comprises bow-shaped walking and global edgewise walking.

2. The obstacle classification obstacle avoidance control method according to claim 1, wherein the step 2 includes:

after the target barrier is classified into a toy type barrier and the longitudinal height of the target barrier is calculated to be larger than a first preset toy height, if the robot currently executes bow-shaped walking or the robot currently executes global edgewise walking, the robot is controlled to walk at a reduced speed along the current walking direction, and meanwhile, the barrier detected in the current walking direction is avoided by utilizing the detection information of the infrared sensor;

the infrared sensor of the mobile robot detects the obstacles in real time in the process of executing the bow-shaped walking and the process of executing the global edgewise walking.

3. The obstacle classification obstacle avoidance control method according to claim 1 or 2, wherein the step 2 further comprises:

after the target barrier is classified into a toy type barrier and the longitudinal height of the target barrier is calculated to be smaller than or equal to a first preset toy height, if the robot executes bow-shaped walking at present, the robot is controlled to walk at a reduced speed, and when the depth distance between the robot and the target barrier is a first toy safety distance, the robot rotates 90 degrees in a first preset hour hand direction, then moves forward for a first preset distance, then rotates 90 degrees in the first preset hour hand direction, and then moves forward to realize right-angle turning;

after the target obstacle is classified as a toy type obstacle and the longitudinal height of the target obstacle is calculated to be less than or equal to a first preset toy height, if the robot currently executes global edgewise walking, the robot is controlled to walk at a reduced speed, and when the depth distance between the robot and the target obstacle is the second toy safety distance, the robot rotates 90 degrees in the second preset hour direction and then moves forward by the second preset distance, then rotates 90 degrees in the direction opposite to the second preset hour hand direction, and then advances for a third preset distance, then detecting whether other obstacles exist on the original path of the global edge walking by rotating a first observation angle, if so, bypassing the detected obstacles by a first preset movement radian in an obstacle bypassing walking mode and returning to the original path of the global edge walking, otherwise, bypassing the target obstacles by a second preset movement radian and returning to the original path of the global edge walking;

wherein, first preset distance, second preset distance and third preset distance all are relevant with the profile width of the same target obstacle that TOF camera was gathered in real time, and this profile width is: horizontal distances between the leftmost side and the rightmost side of the same target obstacle in a real-time view field region of the TOF camera;

wherein the first toy safety distance is related to depth information measured in the process that the robot executes the bow-shaped walking; the second toy safety distance is related to depth information measured during the execution of the global edgewise walking by the robot.

4. The obstacle classification obstacle avoidance control method according to claim 3, wherein the first preset toy height is set to 65 mm; wherein the toy type barrier comprises an island type barrier.

5. The obstacle classification obstacle avoidance control method according to claim 1, wherein the step 2 further comprises:

after the target barrier is classified into a threshold type barrier, if the robot performs zigzag walking at present, controlling the robot to walk at a reduced speed to cross the threshold;

after the target obstacle is classified into a threshold type obstacle, if the robot executes global edgewise walking currently, controlling the robot to walk at a reduced speed to cross a threshold;

wherein the threshold type obstacle comprises an obstacle that can be crossed by the robot.

6. The obstacle classification obstacle avoidance control method according to claim 1, wherein the step 2 further comprises:

after the target barrier is classified into a wall type barrier, if the robot currently executes the zigzag walking, controlling the robot to keep executing the zigzag walking, and simultaneously avoiding the barrier detected in the current walking direction by using the detection information of the infrared sensor;

and after the target barrier is classified as a wall type barrier, if the robot currently executes the global edgewise walking, controlling the robot to keep executing the global edgewise walking so as to realize the wall-wise walking.

7. The obstacle classification obstacle avoidance control method according to claim 1, wherein the step 2 further comprises:

if the walking mode currently executed by the robot is bow-shaped walking, the following deceleration obstacle avoidance modes exist:

when the target barrier is classified into a sofa type barrier and the longitudinal height of the target barrier is calculated to be smaller than or equal to a first preset sofa height, controlling the robot to keep executing the zigzag walking and avoiding the barrier detected in the current walking direction by utilizing the detection information of the infrared sensor;

when the target barrier is classified as a sofa type barrier and the longitudinal height of the target barrier is calculated to be larger than a first preset sofa height and smaller than or equal to a second preset sofa height, the vehicle decelerates to walk along the current walking direction, and simultaneously utilizes the detection information of the infrared sensor to avoid the barrier detected in the current walking direction;

when the target obstacle is classified as a sofa-type obstacle and the longitudinal height of the target obstacle is calculated to be larger than a second preset sofa height, controlling the robot to enter the bottom of the sofa-type obstacle along the current zigzag path; wherein, the second preset sofa height is larger than the height of the robot body;

if the walking mode currently executed by the robot is global edgewise walking, the following deceleration obstacle avoidance modes exist:

when the target obstacle is classified as a sofa type obstacle and the longitudinal height of the target obstacle is calculated to be smaller than or equal to a third preset sofa height, controlling the robot to walk along the outline of the target obstacle at a reduced speed so that the robot is not clamped by the target obstacle when colliding with the target obstacle;

when the target obstacle is classified into a sofa type obstacle and the longitudinal height of the target obstacle is calculated to be larger than a third preset sofa height, controlling the robot to walk along the side in a decelerating manner, and simultaneously controlling the robot to determine the occupied area of the target obstacle through collision so that the robot is not clamped by the target obstacle when colliding with the target obstacle; the third preset sofa height is greater than the first preset sofa height, and the second preset sofa height is greater than the third preset sofa height.

8. The obstacle classification obstacle avoidance control method according to claim 7, wherein the third preset sofa height is set to 110mm, the second preset sofa height is set to 90mm, and the first preset sofa height is set to 50 mm; wherein the sofa-type barrier comprises a furniture barrier for the robot to traverse.

9. The obstacle classification obstacle avoidance control method according to claim 1, wherein the step 2 further comprises:

after the target barrier is classified into the electric wire type barrier and the longitudinal height of the target barrier is calculated to be larger than the first preset electric wire height, if the robot executes bow-shaped walking at present, the robot is controlled to walk at a reduced speed, and when the depth distance between the robot and the target barrier is the first electric wire safety distance, the robot rotates 90 degrees towards the first preset hour hand direction, then the robot advances a fourth preset distance, then rotates 90 degrees towards the first preset hour hand direction, and then the robot advances so as to realize right-angle turning;

after the target obstacle is classified as the electric wire type obstacle and the longitudinal height of the target obstacle is calculated to be larger than the first preset electric wire height, if the robot currently executes the global edgewise walking, the robot is controlled to walk at a reduced speed, and when the depth distance between the robot and the target barrier is the second electric wire safety distance, the robot rotates 90 degrees in the second preset hour hand direction and then moves forward by a fifth preset distance, then rotates 90 degrees in the direction opposite to the second preset hour hand direction, and then advances for a sixth preset distance, then detecting whether other obstacles exist on the path of the global edgewise walking by rotating a second observation angle, if so, bypassing the detected obstacles by a third preset moving radian in an obstacle bypassing walking mode and returning to the original path of the global edgewise walking, otherwise, bypassing the target obstacles by a fourth preset moving radian and returning to the original path of the global edgewise walking;

the infrared sensor on the mobile robot detects the obstacles in real time in the process of executing the bow-shaped walking and the process of executing the global edgewise walking;

the fourth preset distance, the fifth preset distance and the sixth preset distance are all related to the contour width of the target obstacle acquired by the TOF camera in real time; this profile width is: in the real-time view field area of the TOF camera, the horizontal distance between the leftmost side of the target barrier and the rightmost side of the target barrier is kept;

the first wire safety distance is related to depth information measured in the process that the robot executes the bow-shaped walking; the second wire safety distance is related to depth information measured during the global edgewise walking performed by the robot.

10. The obstacle classification obstacle avoidance control method of claim 9, wherein the first preset wire height is set to 5mm, wherein the wire type obstacle comprises a winding.

11. The method for controlling classified obstacle avoidance according to any one of claims 1 to 10, wherein the statistical algorithm for data stability is based on a filtering and statistical algorithm to classify depth information and longitudinal height information of a target obstacle to construct a three-dimensional contour of the target obstacle, and further classify the target obstacle into a wall model, a toy model, a threshold model, a sofa model, and a wire model.