Obstacle avoidance method, device and system for unmanned cleaning equipment and storage medium

Abstract

The invention discloses an obstacle avoidance method, device and system for unmanned cleaning equipment and a storage medium, and relates to the technical field of obstacle avoidance, and the method comprises the steps: monitoring the concentration of dust particles around a laser radar sensor, calculating a dust coverage risk coefficient, and sending out a dust coverage early warning of the laser radar sensor; when a dust coverage early warning of the laser radar sensors is received, dust coverage risk coefficients of all the laser radar sensors of the unmanned cleaning equipment are obtained, an obstacle avoidance abnormal risk coefficient of the unmanned cleaning equipment is calculated, and an obstacle avoidance correction instruction is sent out; and after the obstacle avoidance correction instruction is received, a camera on the unmanned cleaning equipment is used for capturing a cleaning scene image, a dust correction obstacle avoidance distance is calculated, and the unmanned cleaning equipment performs obstacle avoidance according to the dust correction obstacle avoidance distance. The obstacle avoidance requirements under different dust coverage degrees can be dynamically met, and the stability and safety of the unmanned cleaning equipment in a complex environment are improved.

Description

Technical Field

[0001] The present invention relates to the field of obstacle avoidance technology, and in particular to an obstacle avoidance method, device, system and storage medium for unmanned cleaning equipment. Background Art

[0002] With the rapid development of unmanned driving technology, unmanned driving equipment has gradually been applied to various scenarios. In recent years, unmanned cleaning equipment has gradually developed to solve the cleaning problems in people's lives. These devices are usually equipped with sensors and computing units, which can autonomously navigate and perform cleaning tasks. However, existing unmanned cleaning equipment still has problems with insufficient efficiency and accuracy in obstacle avoidance, especially in complex and changing environments. How to effectively identify obstacles and plan a reasonable obstacle avoidance path is a problem that needs to be solved urgently by current technology.

[0003] In a Chinese invention application with application publication number CN113916246A, a method and system for unmanned driving obstacle avoidance path planning is disclosed, including a moving vehicle map server and a path planning platform. During driving, the moving vehicle detects obstacles in front of the lane in real time through a detection device, transmits the detected obstacle information to the map server through a communication layer device, and sends obstacle avoidance notification information to the communication layer device. The communication layer device sends a path planning request to the path planning platform; the path planning platform responds to the path planning request information to obtain obstacle map data and lane map data, determines whether the vehicle can avoid obstacles, calculates vehicle turning points, obstacle avoidance end points and obstacle avoidance passing points; calculates and determines a reference path, and locally adjusts the reference path to obtain an adjusted obstacle avoidance path, and then performs a safety check on the obstacle avoidance path to determine the final obstacle avoidance path.

[0004] In the above invention application, the obstacle in front of the lane is detected by the detection equipment, and the obstacle information is transmitted to the server. The server platform determines whether the vehicle can avoid the obstacle, calculates the vehicle turning, obstacle avoidance end point and obstacle avoidance passing point to determine the reference path, and determines the final obstacle avoidance path after analysis and processing. However, the unmanned cleaning equipment relies on various sensors (such as lidar, camera, ultrasonic sensor, etc.) to detect obstacles, but the sensors are easily covered by pollutants such as dust during use, resulting in performance degradation or even failure, causing the obstacle avoidance system to falsely report or miss obstacles.

[0005] To this end, the present invention provides an obstacle avoidance method, device, system and storage medium for unmanned cleaning equipment. Summary of the invention

[0006] 1. Technical issues to be resolved In view of the shortcomings of the prior art, the present invention provides an obstacle avoidance method, device, system and storage medium for unmanned cleaning equipment. The present invention calculates the dust coverage risk coefficient. Fx, Send out dust coverage warnings from lidar sensors and calculate the risk factor of obstacle avoidance anomalies for unmanned cleaning equipment By, Send out obstacle avoidance correction instructions and calculate the dust correction obstacle avoidance distance Cb The unmanned cleaning equipment avoids obstacles based on the dust-corrected obstacle avoidance distance Cb, can calculate the dust-corrected obstacle avoidance distance more accurately, improve the obstacle avoidance accuracy of the unmanned cleaning equipment, can dynamically adapt to the obstacle avoidance needs under different dust coverage levels, and improve the stability and safety of the unmanned cleaning equipment in complex environments, thereby solving the technical problems recorded in the background technology.

[0007] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: an obstacle avoidance method for unmanned cleaning equipment, comprising the following steps: Monitoring dust particle concentration around the lidar sensor , and periodically extract the intensity of the echo signal from the lidar data , combined with the last cleaning interval Sj , calculate the dust cover risk factor Fx, Send out dust cover warnings for lidar sensors; After receiving the dust coverage warning from the LiDAR sensor, obtain the dust coverage risk coefficient of all LiDAR sensors of the unmanned cleaning equipment , calculate the risk factor of obstacle avoidance abnormality for unmanned cleaning equipment By, Send out obstacle avoidance correction instructions; After receiving the obstacle avoidance correction instruction, the camera on the unmanned cleaning equipment is used to capture the cleaning scene image and calculate the corrected obstacle avoidance distance coefficient of each lidar , combined with real-time obstacle avoidance distance data and dust cover risk factor , calculate the dust-corrected obstacle avoidance distance Cb ,The unmanned cleaning equipment avoids obstacles based on the dust calibrated obstacle avoidance distance Cb.

[0008] Furthermore, a dust particle counter is installed near the lidar sensor of the unmanned cleaning equipment to periodically monitor the dust particle concentration around the lidar sensor. , and periodically extract the intensity of the echo signal from the lidar data .

[0009] Furthermore, query the log records of the unmanned cleaning equipment to obtain the most recent cleaning time interval of the lidar sensor Sj , combined with the dust particle concentration and the strength of the echo signal , calculate the dust cover risk factor Fx :

[0010] in, Indicates the historical maximum cleaning time interval. Indicates the maximum dust particle concentration in history.

[0011] Furthermore, when dust covers the risk factor Fx Greater than When , it indicates that the laser radar sensor is likely to be covered by dust, and a dust cover warning for the laser radar sensor is issued. represents the mean value of the historical dust cover risk coefficient, Represents the variance of the historical dust cover risk coefficient.

[0012] Furthermore, after receiving the dust coverage warning from the lidar sensor, the dust coverage risk coefficient of all lidar sensors of the unmanned cleaning equipment is obtained. , calculate the risk factor of obstacle avoidance abnormality for unmanned cleaning equipment By :

[0013] in, i Indicates the ID of all LiDAR sensors on the unmanned cleaning equipment. i =1, 2, …, n .

[0014] Furthermore, when the obstacle avoidance abnormal risk factor By When it is greater than 0, an obstacle avoidance correction instruction is issued and the obstacle avoidance abnormal risk coefficient is By Greater than When the number of laser radar sensors of the unmanned cleaning equipment is high, it indicates that the possibility of multiple laser radar sensors of the unmanned cleaning equipment being covered by dust is high, and a cleaning warning of the laser radar sensors of the unmanned cleaning equipment is issued. represents the mean value of the historical obstacle avoidance abnormal risk coefficient, Represents the variance of the historical obstacle avoidance anomaly risk coefficient.

[0015] Furthermore, after receiving the obstacle avoidance correction instruction, the camera on the unmanned cleaning equipment is used to capture the cleaning scene image, and the distance of the obstacle in the image is estimated by the principle of similar triangles or the principle of stereo vision, which is recorded as the image distance Tj, and obtain the distance data measured by all lidar sensors of the unmanned cleaning equipment for the same obstacle, recorded as the lidar distance .

[0016] Further, get the image distance Tj Distance from LiDAR , calculate the corrected obstacle avoidance distance coefficient for each lidar :

[0017] Corrected obstacle avoidance distance coefficient for each corresponding laser radar The calculation formula is as above.

[0018] Furthermore, obtain the real-time obstacle avoidance distance data of each lidar , combined with the corrected obstacle avoidance distance coefficient and dust cover risk factor , calculate the dust-corrected obstacle avoidance distance Cb :

[0019] Unmanned cleaning equipment calibrates obstacle avoidance distance based on dust Cb Perform obstacle avoidance operations.

[0020] Furthermore, it is characterized by comprising: a laser radar sensor, a camera, a data processor and an actuator.

[0021] Furthermore, the LiDAR dust coverage analysis module monitors the concentration of dust particles around the LiDAR sensor. , and periodically extract the intensity of the echo signal from the lidar data , combined with the last cleaning interval Sj , calculate the dust cover risk factor Fx, Send out dust cover warnings for lidar sensors; The cleaning equipment obstacle avoidance anomaly analysis module obtains the dust coverage risk coefficient of all LiDAR sensors of the unmanned cleaning equipment after receiving the dust coverage warning from the LiDAR sensor. , calculate the risk factor of obstacle avoidance abnormality for unmanned cleaning equipment By, Send out obstacle avoidance correction instructions; The obstacle avoidance distance correction module, after receiving the obstacle avoidance correction instruction, uses the camera on the unmanned cleaning equipment to capture the cleaning scene image and calculates the corrected obstacle avoidance distance coefficient of each lidar , combined with real-time obstacle avoidance distance data and dust cover risk factor , calculate the dust-corrected obstacle avoidance distance Cb ,The unmanned cleaning equipment avoids obstacles based on the dust calibrated obstacle avoidance distance Cb.

[0022] A storage medium for unmanned cleaning equipment stores a computer program.

[0023] (III) Beneficial effects The present invention provides an obstacle avoidance method, device, system and storage medium for unmanned cleaning equipment, which has the following beneficial effects: 1. Monitor the dust particle concentration around the lidar sensor , and periodically extract the intensity of the echo signal from the lidar data , combined with the last cleaning interval Sj , calculate the dust cover risk factor Fx, The dust coverage warning of the LiDAR sensor can be issued externally, which can timely detect dust coverage and issue a warning, improve the efficiency and accuracy of cleaning and maintenance, effectively reduce the impact of dust on the performance of the LiDAR sensor, and extend the service life and reliability of the equipment.

[0024] 2. After receiving the dust coverage warning from the LiDAR sensor, obtain the dust coverage risk coefficient of all LiDAR sensors of the unmanned cleaning equipment , calculate the risk factor of obstacle avoidance abnormality for unmanned cleaning equipment By, Issuing obstacle avoidance correction instructions can timely discover and deal with potential problems, improve the obstacle avoidance performance of unmanned cleaning equipment, and the generation and execution of obstacle avoidance correction instructions can help reduce obstacle avoidance errors caused by dust coverage, thereby enhancing the safety of unmanned cleaning equipment in complex environments.

[0025] 3. After receiving the obstacle avoidance correction instruction, use the camera on the unmanned cleaning equipment to capture the cleaning scene image and calculate the corrected obstacle avoidance distance coefficient of each lidar , combined with real-time obstacle avoidance distance data and dust cover risk factor , calculate the dust-corrected obstacle avoidance distance Cb The unmanned cleaning equipment avoids obstacles based on the dust-corrected obstacle avoidance distance Cb, can calculate the dust-corrected obstacle avoidance distance more accurately, improve the obstacle avoidance accuracy of the unmanned cleaning equipment, can dynamically adapt to the obstacle avoidance needs under different dust coverage levels, and improve the stability and safety of the unmanned cleaning equipment in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A schematic flow chart of an obstacle avoidance method for unmanned cleaning equipment according to the present invention; Figure 2It is a structural schematic diagram of an obstacle avoidance device for unmanned cleaning equipment of the present invention; Figure 3 This is a schematic diagram of the structure of an obstacle avoidance system for unmanned cleaning equipment of the present invention; Figure 4 The present invention is a schematic diagram of a storage medium structure of an unmanned cleaning device. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] See also Figure 1 The present invention provides an obstacle avoidance method for unmanned cleaning equipment, comprising the following steps: Step 1: Monitor the dust particle concentration around the lidar sensor , and periodically extract the intensity of the echo signal from the lidar data , combined with the last cleaning interval Sj , calculate the dust cover risk factor Fx, Send out dust cover warning of LiDAR sensors.

[0029] The step 1 includes the following contents: Step 101: Install a dust particle counter near the lidar sensor of the unmanned cleaning equipment to periodically monitor the dust particle concentration around the lidar sensor. , and periodically extract the intensity of the echo signal from the lidar data .

[0030] Step 102: Query the log records of the unmanned cleaning equipment to obtain the most recent cleaning time interval of the lidar sensor Sj , combined with the dust particle concentration and the strength of the echo signal , calculate the dust cover risk factor Fx :

[0031] in, Indicates the historical maximum cleaning time interval. Indicates the maximum dust particle concentration in history.

[0032] Step 103: When dust covers the risk factor Fx Greater than When , it indicates that the laser radar sensor is likely to be covered by dust, and a dust cover warning for the laser radar sensor is issued. represents the mean value of the historical dust cover risk coefficient, Represents the variance of the historical dust cover risk coefficient.

[0033] When using, combine the contents in steps 101 to 103: Monitoring dust particle concentration around the lidar sensor , and periodically extract the intensity of the echo signal from the lidar data , combined with the last cleaning interval Sj , calculate the dust cover risk factor Fx, The dust coverage warning of the LiDAR sensor can be issued externally, which can timely detect dust coverage and issue a warning, improve the efficiency and accuracy of cleaning and maintenance, effectively reduce the impact of dust on the performance of the LiDAR sensor, and extend the service life and reliability of the equipment.

[0034] Step 2: After receiving the dust coverage warning from the LiDAR sensor, obtain the dust coverage risk coefficient of all LiDAR sensors of the unmanned cleaning equipment , calculate the risk factor of obstacle avoidance abnormality for unmanned cleaning equipment By, Issue obstacle avoidance correction instructions externally.

[0035] The step 2 includes the following contents: Step 201: After receiving the dust coverage warning from the laser radar sensor, obtain the dust coverage risk coefficient of all laser radar sensors of the unmanned cleaning equipment , calculate the risk factor of obstacle avoidance abnormality for unmanned cleaning equipment By :

[0036] in, i Indicates the ID of all LiDAR sensors on the unmanned cleaning equipment. i =1, 2, …, n .

[0037] Step 202: When the risk factor of obstacle avoidance is abnormal By When it is greater than 0, an obstacle avoidance correction instruction is issued and the obstacle avoidance abnormal risk coefficient is By Greater than When the number of laser radar sensors of the unmanned cleaning equipment is high, it indicates that the possibility of multiple laser radar sensors of the unmanned cleaning equipment being covered by dust is high, and a cleaning warning of the laser radar sensors of the unmanned cleaning equipment is issued. represents the mean value of the historical obstacle avoidance abnormal risk coefficient, Represents the variance of the historical obstacle avoidance anomaly risk coefficient.

[0038] When using, combine the contents in steps 201 and 202: After receiving the dust coverage warning from the LiDAR sensor, obtain the dust coverage risk coefficient of all LiDAR sensors of the unmanned cleaning equipment , calculate the risk factor of obstacle avoidance abnormality for unmanned cleaning equipment By, Issuing obstacle avoidance correction instructions can timely discover and deal with potential problems, improve the obstacle avoidance performance of unmanned cleaning equipment, and the generation and execution of obstacle avoidance correction instructions can help reduce obstacle avoidance errors caused by dust coverage, thereby enhancing the safety of unmanned cleaning equipment in complex environments.

[0039] Step 3: After receiving the obstacle avoidance correction instruction, use the camera on the unmanned cleaning equipment to capture the cleaning scene image and calculate the corrected obstacle avoidance distance coefficient of each lidar , combined with real-time obstacle avoidance distance data and dust cover risk factor , calculate the dust-corrected obstacle avoidance distance Cb , unmanned cleaning equipment calibrates obstacle avoidance distance based on dust Cb Perform obstacle avoidance.

[0040] The step three comprises the following steps: Step 301: After receiving the obstacle avoidance correction instruction, use the camera on the unmanned cleaning device to capture the cleaning scene image, and estimate the distance of the obstacle in the image by the principle of similar triangles or stereoscopic vision principle, which is recorded as the image distance Tj , and obtain the distance data measured by all lidar sensors of the unmanned cleaning equipment for the same obstacle, recorded as the lidar distance .

[0041] Step 302: Obtain image distance Tj Distance from LiDAR , calculate the corrected obstacle avoidance distance coefficient for each lidar :

[0042] Corrected obstacle avoidance distance coefficient for each corresponding laser radar The calculation formula is as above.

[0043] Step 303: Obtain real-time obstacle avoidance distance data for each laser radar , combined with the corrected obstacle avoidance distance coefficient and dust cover risk factor , calculate the dust-corrected obstacle avoidance distance Cb :

[0044] The unmanned cleaning equipment performs obstacle avoidance operations based on the dust-corrected obstacle avoidance distance Cb.

[0045] When using, combine the contents in steps 301 to 303: After receiving the obstacle avoidance correction instruction, the camera on the unmanned cleaning equipment is used to capture the cleaning scene image and calculate the corrected obstacle avoidance distance coefficient of each lidar , combined with real-time obstacle avoidance distance data and dust cover risk factor , calculate the dust-corrected obstacle avoidance distance Cb The unmanned cleaning equipment avoids obstacles based on the dust-corrected obstacle avoidance distance Cb, can calculate the dust-corrected obstacle avoidance distance more accurately, improve the obstacle avoidance accuracy of the unmanned cleaning equipment, can dynamically adapt to the obstacle avoidance needs under different dust coverage levels, and improve the stability and safety of the unmanned cleaning equipment in complex environments.

[0046] See also Figure 2 The present invention provides an obstacle avoidance device for unmanned cleaning equipment, including: a laser radar sensor, a camera, a data processor and an actuator.

[0047] See also Figure 3 The present invention provides an obstacle avoidance system for unmanned cleaning equipment, comprising: LiDAR dust coverage analysis module, monitoring the concentration of dust particles around the LiDAR sensor , and periodically extract the intensity of the echo signal from the lidar data , combined with the last cleaning interval Sj , calculate the dust cover risk factor Fx, Send out dust cover warning of LiDAR sensors.

[0048] The cleaning equipment obstacle avoidance anomaly analysis module obtains the dust coverage risk coefficient of all LiDAR sensors of the unmanned cleaning equipment after receiving the dust coverage warning from the LiDAR sensor. , calculate the risk factor of obstacle avoidance abnormality for unmanned cleaning equipment By, Issue obstacle avoidance correction instructions externally.

[0049] The obstacle avoidance distance correction module, after receiving the obstacle avoidance correction instruction, uses the camera on the unmanned cleaning equipment to capture the cleaning scene image and calculates the corrected obstacle avoidance distance coefficient of each lidar , combined with real-time obstacle avoidance distance data and dust cover risk factor , calculate the dust-corrected obstacle avoidance distance Cb ,The unmanned cleaning equipment avoids obstacles based on the dust calibrated obstacle avoidance distance Cb.

[0050] See also Figure 4 , the present invention provides an obstacle avoidance storage medium for unmanned cleaning equipment, on which a computer program is stored.

[0051] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0052] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0053] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. An obstacle avoidance method for unmanned cleaning equipment, characterized in that: The steps include: Monitoring dust particle concentration around the lidar sensor , and periodically extract the intensity of the echo signal from the lidar data , combined with the last cleaning interval Sj , calculate the dust cover risk factor Fx, Send out dust cover warnings for lidar sensors; After receiving the dust coverage warning from the LiDAR sensor, obtain the dust coverage risk coefficient of all LiDAR sensors of the unmanned cleaning equipment , calculate the risk factor of obstacle avoidance abnormality for unmanned cleaning equipment By, Send out obstacle avoidance correction instructions; After receiving the obstacle avoidance correction instruction, the camera on the unmanned cleaning equipment is used to capture the cleaning scene image and calculate the corrected obstacle avoidance distance coefficient of each lidar , combined with real-time obstacle avoidance distance data and dust cover risk factor , calculate the dust-corrected obstacle avoidance distance Cb ,The unmanned cleaning equipment avoids obstacles based on the dust calibrated obstacle avoidance distance Cb.

2. The obstacle avoidance method for unmanned cleaning equipment according to claim 1, characterized in that: Query the log records of the unmanned cleaning equipment to obtain the most recent cleaning time interval of the lidar sensor Sj , combined with the dust particle concentration and the strength of the echo signal , calculate the dust cover risk factor Fx : in, Indicates the historical maximum cleaning time interval. Indicates the maximum dust particle concentration in history.

3. The obstacle avoidance method for unmanned cleaning equipment according to claim 2, characterized in that: When dust covers the risk factor Fx Greater than When the dust coverage warning of the laser radar sensor is issued, represents the mean value of the historical dust cover risk coefficient, Represents the variance of the historical dust cover risk coefficient.

4. The obstacle avoidance method for unmanned cleaning equipment according to claim 3, characterized in that: After receiving the dust coverage warning from the LiDAR sensor, obtain the dust coverage risk coefficient of all LiDAR sensors of the unmanned cleaning equipment , calculate the risk factor of obstacle avoidance abnormality for unmanned cleaning equipment By : in, i Indicates the ID of all LiDAR sensors on the unmanned cleaning equipment. i =1, 2, …, n .

5. The obstacle avoidance method for unmanned cleaning equipment according to claim 4, characterized in that: When the risk factor of obstacle avoidance is abnormal By When it is greater than 0, an obstacle avoidance correction instruction is issued and the obstacle avoidance abnormal risk coefficient is By Greater than When the laser radar sensor of the unmanned cleaning equipment is activated, a cleaning warning is issued; represents the mean value of the historical obstacle avoidance abnormal risk coefficient, Represents the variance of the historical obstacle avoidance anomaly risk coefficient.

6. The obstacle avoidance method for unmanned cleaning equipment according to claim 1, characterized in that: Get image distance Tj Distance from LiDAR , calculate the corrected obstacle avoidance distance coefficient for each lidar : Corrected obstacle avoidance distance coefficient for each corresponding laser radar The calculation formula is as above.

7. The obstacle avoidance method for unmanned cleaning equipment according to claim 6, characterized in that: Get real-time obstacle avoidance distance data for each lidar , combined with the corrected obstacle avoidance distance coefficient and dust cover risk factor , calculate the dust-corrected obstacle avoidance distance Cb : Unmanned cleaning equipment calibrates obstacle avoidance distance based on dust Cb Perform obstacle avoidance operations.

8. An obstacle avoidance device for unmanned cleaning equipment, used to implement the method according to any one of claims 1 to 7, characterized in that: include: LiDAR sensors, cameras, data processors and actuators.

9. An obstacle avoidance system for unmanned cleaning equipment, used to implement the method according to any one of claims 1 to 7, characterized in that: include: LiDAR dust coverage analysis module, monitoring the concentration of dust particles around the LiDAR sensor , and periodically extract the intensity of the echo signal from the lidar data , combined with the last cleaning interval Sj , calculate the dust cover risk factor Fx, Send out dust cover warnings for lidar sensors; The cleaning equipment obstacle avoidance anomaly analysis module obtains the dust coverage risk coefficient of all LiDAR sensors of the unmanned cleaning equipment after receiving the dust coverage warning from the LiDAR sensor. , calculate the risk factor of obstacle avoidance abnormality for unmanned cleaning equipment By, Send out obstacle avoidance correction instructions; The obstacle avoidance distance correction module, after receiving the obstacle avoidance correction instruction, uses the camera on the unmanned cleaning equipment to capture the cleaning scene image and calculates the corrected obstacle avoidance distance coefficient of each lidar , combined with real-time obstacle avoidance distance data and dust cover risk factor , calculate the dust-corrected obstacle avoidance distance Cb ,The unmanned cleaning equipment avoids obstacles based on the dust calibrated obstacle avoidance distance Cb.

10. A storage medium for an unmanned cleaning device, characterized in that: A computer program is stored thereon, and the computer program can be executed by a processor of the device where the storage medium is located to implement the obstacle avoidance method as described in any one of claims 1 to 7.

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