CN114859380A

CN114859380A - Cliff detection method, driving device and storage medium

Info

Publication number: CN114859380A
Application number: CN202110528993.XA
Authority: CN
Inventors: 不公告发明人
Original assignee: Tonn Intelligent Technology Suzhou Co ltd
Current assignee: Tonn Intelligent Technology Suzhou Co ltd
Priority date: 2021-05-14
Filing date: 2021-05-14
Publication date: 2022-08-05

Abstract

The application relates to the technical field of intelligent robots, and discloses a cliff detection method, driving equipment and a storage medium, wherein the method comprises the following steps: acquiring a first coordinate value obtained by detecting an obstacle by the single-line laser radar, wherein the first coordinate value is determined by referring to a coordinate system of the single-line laser radar; performing coordinate conversion on the first coordinate value to obtain a second coordinate value, wherein the second coordinate value is determined by referring to a coordinate system of the driving equipment; it is determined whether the obstacle is a cliff obstacle based on the second coordinate value. Polar coordinate values of a plurality of detection points are acquired through a single-line laser radar, Cartesian coordinate values of a coordinate system of a reference driving device are obtained after the coordinate system is converted, then cliff points are screened based on the coordinate system to calculate the depth and width of a cliff, and whether the obstacle encountered by the robot is a cliff obstacle is determined; the method and the device can further determine the coverage range of the cliff, are favorable for improving the accurate obstacle avoidance capability of the robot, and have the advantages of high accuracy, strong sensitivity and low implementation cost.

Description

Cliff detection method, driving device and storage medium

Technical Field

The present application relates to the field of intelligent robot technology, and in particular, to a cliff detection method, a driving device, and a storage medium.

Background

Intelligent driving equipment, unmanned robot for example, can be used in each field such as patrolling and examining, sanitary cleanness, express delivery service, dining service, and the labour has been liberated greatly in unmanned robot's appearance, lets artificial intelligence bring more facilities for people's life.

In order to meet the requirements of various use scenarios, the unmanned robot generally needs to have various sensing capabilities, such as the capability of sensing obstacles to avoid obstacles during the traveling process.

The existing multi-line three-dimensional laser radar sensor generally extracts the characteristics of an obstacle based on three-dimensional point cloud information returned by the sensor through a Vector Field Histogram (VFH) method so as to determine the obstacle target to avoid the obstacle, but the obstacle avoiding technology cannot effectively identify the cliff obstacle, the cost of the multi-line three-dimensional laser radar is high, and the installation positions of most of the multi-line three-dimensional laser radar on a robot have detection blind areas of different degrees, so that the robot cannot effectively sense a low obstacle or the cliff obstacle. Especially for robots in basic service fields of logistics, sanitation and the like, generally only low-cost single-line laser radars are adopted for obstacle avoidance, but in the existing cliff detection scheme realized by the single-line laser radars, the single-line laser radars can only determine whether an obstacle is a cliff obstacle by judging whether a detected detection point at a fixed distance is lower than the ground, and when the robot traveling speed is high or the scanning frequency of the single-line laser radars is low, the existence of the cliff obstacle cannot be detected frequently, or misjudgment situations such as obstacle avoidance are determined by regarding a small ground ridge in the traveling direction of the robot as the cliff obstacle.

Disclosure of Invention

The embodiment of the application provides a cliff detection method, driving equipment and a storage medium, original point cloud polar coordinates of a plurality of detection points on the ground, obstacles and the like in the surrounding environment and in the advancing direction of the driving equipment such as a robot are acquired through a single-line laser radar, Cartesian coordinates of a reference driving equipment coordinate origin are obtained after coordinate system conversion and coordinate origin conversion, then cliff points are screened from the original point cloud based on the Cartesian coordinates obtained through conversion, and then whether the depth of the cliff is larger than a preset depth threshold value and the width of the cliff is larger than a preset width threshold value are calculated based on the screened coordinates of the cliff points, so that whether the obstacles encountered by the robot are obstacles on the cliff is determined. The cliff detection method can further calculate the coverage range of the cliff obstacle based on the Cartesian coordinates of each cliff point when the cliff obstacle encountered by the robot is determined to be the cliff obstacle, so that the robot can determine an obstacle avoidance scheme for the cliff obstacle by reference. The cliff detection method has the advantages that the single line laser radar adopted is simple in structure, when the number of the single line laser radars mounted on the robot is large, the cliff detection scheme can have high accuracy and sensitivity, the obstacle avoidance performance of the robot can be improved, and meanwhile, the single line laser radar is low in cost, and the production cost of the robot is controllable.

In a first aspect, an embodiment of the present application provides a cliff detection method, which is applied to a driving apparatus including a single line laser radar, and includes: acquiring a first coordinate value obtained by detecting an obstacle by the single-line laser radar, wherein the first coordinate value is determined by referring to a coordinate system of the single-line laser radar; performing coordinate conversion on the first coordinate value to obtain a second coordinate value, wherein the second coordinate value is determined by referring to a coordinate system of the driving equipment; determining whether the obstacle is a cliff obstacle based on the second coordinate value.

The original point cloud polar coordinates are formed by acquiring a plurality of detection points on the ground and the encountered obstacles in the traveling direction of the driving equipment or in the surrounding environment through the single-line laser radar. Wherein, the original point cloud is a set of all detection points. The single line laser radar can return to a plurality of detection point coordinates on the ground when scanning to the ground, the single line laser radar can return to a plurality of detection point coordinates on the surface of the obstacle when scanning to the obstacle, the plurality of detection point coordinates on the ground and the plurality of detection point coordinates on the surface of the obstacle jointly form an original point cloud coordinate, wherein the plurality of detection point coordinates on the surface of the obstacle are effective coordinates for determining whether the obstacle is a cliff obstacle, and the effective coordinates are the first coordinate values.

For example, if the obstacle detected by the single line laser radar is a cliff, the first coordinate values acquired by scanning the inner surface of the cliff by the single line laser radar are the original polar coordinate values of the detection points on the cliff surface, and the second coordinate values obtained by coordinate conversion are, for example, coordinate values in a cartesian coordinate system.

In one possible implementation of the first aspect, the determining whether the obstacle is a cliff based on the second coordinate value includes: determining a point of the second coordinate value, of which the z-axis coordinate value is smaller than the longitudinal coordinate value corresponding to the preset height threshold, as a cliff point; determining a cliff depth based on z-axis coordinate values of the cliff points and a cliff width based on x-axis coordinate values of the cliff points; and determining the obstacle to be a cliff obstacle when the cliff depth is greater than a preset depth threshold value and the cliff width is greater than a preset depth threshold value.

That is, the ordinate value in the three-dimensional coordinate system obtained by coordinate conversion of the probe point may be used as a basis for defining whether the probe point is a point on the ground, a cliff point lower than the ground, or an obstacle point higher than the ground. When the z-axis coordinate value of the detection point is smaller than the vertical coordinate value corresponding to the preset height threshold, the detection point can be determined as a cliff point, and the determined cliff point is used for analyzing and determining whether the obstacle encountered by the robot is a cliff obstacle.

For example, the second coordinate value is a cartesian coordinate value of the reference robot coordinate system, and the preset height threshold is, for example, the second height threshold z in the following embodiments _d According to whether the z-axis coordinate value in the Cartesian coordinate values of the reference robot coordinate system corresponding to each detection point is smaller than the second height threshold value z _d The corresponding ordinate value can determine whether the detection point is a cliff point, i.e. the z-axis coordinate value is less than the second height threshold value z _d The detection point of the corresponding ordinate value is marked as a cliff point, and further calculation based on the marked cliff point can determine whether the encountered obstacle is a cliff or notA disorder.

In a possible implementation of the first aspect, the coordinate transforming the first coordinate value to obtain a second coordinate value includes: converting a coordinate system based on the first coordinate value to obtain a third coordinate value, wherein the first coordinate value is determined by referring to a polar coordinate system of the single line laser radar, and the third coordinate value is determined by referring to a Cartesian coordinate system of the single line laser radar; and performing coordinate conversion based on the third coordinate value and a preset coordinate transformation matrix to obtain the second coordinate value. The coordinate system of the single-line laser radar comprises a polar coordinate system taking a laser emission point of the single-line laser radar as a coordinate origin and a Cartesian coordinate system taking the laser emission point of the single-line laser radar as the origin; the first coordinate value is a polar coordinate value under a polar coordinate system taking the laser emission point as a coordinate origin; the third coordinate value is a cartesian coordinate value in a cartesian coordinate system with the laser emission point as the origin of coordinates. The coordinate system of the driving equipment comprises a Cartesian coordinate system taking the center point of a rear wheel connecting shaft of the driving equipment as a coordinate origin; the second coordinate value is a Cartesian coordinate value in a Cartesian coordinate system with the center point of the rear wheel connecting shaft as the origin of coordinates.

That is, the first coordinate value is converted into the second coordinate value, and two steps of conversion of a coordinate system and conversion of a coordinate origin are required, for example, the first coordinate value is a polar coordinate value of a sensor coordinate system in which the barrier point reference single line laser radar laser emission point is the coordinate origin, the first coordinate value is converted into a cartesian coordinate value of a reference sensor coordinate system, that is, the third coordinate value, and then the coordinate origin is converted into the cartesian coordinate value of the robot coordinate system in which the center point of the robot rear wheel connecting shaft is used as the coordinate origin. The conversion of the coordinate origin can be realized through a coordinate conversion matrix, and the values of all matrix points in the preset coordinate conversion matrix are preset through experiments based on the relative positions of a single line laser radar laser emission point serving as the coordinate origin and the central point of a connecting shaft of a rear wheel of the robot and the included angles of all coordinate planes between a sensor coordinate system and a robot coordinate system.

In a possible implementation of the first aspect, the coordinate transformation matrix is determined based on a relative position between a laser emission point of the single line laser radar and a center point of a rear wheel connecting shaft of the device, and a deflection angle of a laser scanning plane of the single line laser radar with respect to a bottom surface or a side surface of the device.

In a possible implementation of the first aspect, obtaining the second coordinate value based on the third coordinate value and a preset coordinate transformation matrix includes: and obtaining the second coordinate value based on the third coordinate value multiplied by the coordinate transformation matrix.

In a possible implementation of the first aspect, the method further includes: determining the cliff obstacle coverage based on the second coordinate values of the cliff points.

The size value of the cliff coverage area is determined by calculating the length and width of a cliff obstacle encountered by the driving equipment according to the coordinate system Cartesian coordinate value of each cliff point reference driving equipment. For example, the cliff depth may be calculated from the absolute value of the difference between the z-axis coordinate value of each cliff point and the z-axis coordinate value of the ground point, or when the origin of the coordinate system of the drive device referred to by the cliff point is selected at the contact point between a component of the drive device and the ground, the cliff depth may be determined directly from the absolute value of the z-axis coordinate value of each cliff point, and after the obtained cliff depth corresponding to each cliff point, the maximum cliff depth value among them may be determined as the cliff depth of the cliff obstacle, or the mode value among them may be determined as the cliff depth of the cliff obstacle, without being limited thereto.

In a second aspect, an embodiment of the present application provides a driving apparatus, including: the system comprises a processor, a memory, a single-wire laser radar, a communication interface and a communication bus; the single-wire laser radar, the memory and the communication interface are connected through the communication bus; the memory is configured to store at least one instruction, and when the processor executes the at least one instruction stored in the memory, the driving apparatus is caused to execute the cliff detection method.

In a third aspect, an embodiment of the present application provides a computer-readable storage medium, where instructions are stored on the storage medium, and when executed on a computer, the instructions cause the computer to perform the cliff detection method.

Drawings

Fig. 1 is a schematic view illustrating an application scenario of the cliff detection method based on the single line laser radar according to an embodiment of the present application.

Fig. 2 shows a schematic structural diagram of a system 200 for driving a device according to an embodiment of the present application.

Fig. 3a is a schematic diagram illustrating an application scenario of the cliff detection method according to a first embodiment of the present application.

Fig. 3b is a schematic top view of the scene shown in fig. 3a according to the first embodiment of the present application.

Fig. 4 is a schematic flowchart illustrating a cliff detection method according to a first embodiment of the present application.

Fig. 5 is a schematic diagram illustrating a process of adjusting the installation angle of the single line laser radar 102 according to a first embodiment of the present application.

Fig. 6 is a schematic diagram illustrating another application scenario of the cliff detection method according to the second embodiment of the present application.

Fig. 7 is a schematic perspective view illustrating a mounting position of a single-line lidar in the scene shown in fig. 6 according to a second embodiment of the present application.

Fig. 8a to 8b are schematic diagrams illustrating changes in included angles between single line laser radars according to the second embodiment of the present application.

Detailed Description

The present application is further described with reference to the following detailed description and the accompanying drawings. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. The directional terms such as "upper", "lower", "front", "back", etc. are used for positional description corresponding to specific drawings, and are not used for limiting the position and orientation of specific structures. In addition, for convenience of description, only a part of structures or processes related to the present application, not all of them, is illustrated in the drawings. It should be noted that in this specification, like reference numerals and letters refer to like items in the following drawings.

The application provides a cliff detection scheme realized based on a single line laser radar, and particularly provides a cliff detection method, driving equipment and a storage medium.

Fig. 1 shows a schematic application scenario of the cliff detection method based on the single line laser radar of the present application.

As shown in fig. 1, the scenario includes a driving apparatus 101 and a single line lidar 102, wherein a laser scanning plane 1021 formed by laser light emitted by the single line lidar 102 can detect an obstacle 103 within a certain distance range. In general, the one-wire lidar 102 is used to detect an obstacle 103 in the traveling direction of the drive apparatus 101, and therefore, the one-wire lidar 102 is generally disposed on the front end surface 1011 of the drive apparatus 101.

In order to solve the problems of the prior art, the cliff detection method based on the single-line laser radar is provided by the application, collecting a plurality of detection points on the ground, the encountered obstacles 103 and the like in the traveling direction of the driving equipment 101 and in the surrounding environment by using the single-line laser radar 102 to form an original point cloud, referring a polar coordinate value under a sensor coordinate system taking a laser emission point of the single-line laser radar 102 as a coordinate origin to be used as an original point cloud polar coordinate, obtaining a Cartesian coordinate value of an original point cloud reference driving equipment coordinate system after coordinate system conversion and coordinate origin conversion, then screening out cliff points lower than a preset height threshold value from the corresponding original point cloud based on the converted Cartesian coordinate values, and further determining whether the encountered obstacle 103 is a cliff obstacle or not based on the cartesian coordinate values of the reference driving device coordinate system corresponding to the screened cliff point. Further, when the obstacle 103 is determined as a cliff obstacle, the present application is also able to determine the coverage of the cliff obstacle 103 based on the coordinate values of the respective cliff points, and the drive apparatus 101 may determine an obstacle avoidance scheme to pass through the cliff obstacle based on the determined coverage of the cliff obstacle. Therefore, the cliff detection method can accurately judge the cliff obstacle in the advancing direction of the driving device, can accurately calculate the coverage range of the cliff obstacle, can effectively avoid the situation that a ground shallow pit or a ditch ridge is mistakenly judged as the cliff obstacle, and can also improve the validity of cliff detection to avoid the situation that the driving device is dropped and damaged because the cliff is not detected. In addition, the cost of the single-line laser radar is low, so that the implementation cost of the technical scheme of the application is low, and the technical scheme of the application is beneficial to wide popularization and application on the unmanned driving device 101.

The single line laser radar 102 collects the original point cloud polar coordinates of a plurality of detection points on the ground and the encountered obstacles 103 in the traveling direction of the robot 101 and the surrounding environment, and the original point cloud polar coordinates are the polar coordinate values of the detection points returned by the sensor of the single line laser radar 102 when the single line laser radar 102 performs laser scanning on the ground and the surrounding ground in the traveling direction of the driving device 101.

It is to be understood that fig. 1 does not constitute a limitation on the arrangement position and the arrangement number of the single line laser radars 102, and in some embodiments, one or more single line laser radars 102 may be arranged on the driving device 101, which is not limited herein. It will be appreciated that, given that the singlet lidar 102 typically has a fixed scanning frequency, the detection frequency of the plurality of singlet lidar 102 is also typically higher than the detection frequency of the single singlet lidar 102, thereby making the drive apparatus 101 more sensitive to detect the obstacle 103.

Fig. 2 shows a schematic structural diagram of a system 200 of the driving apparatus 101 according to an embodiment of the present application.

As shown in fig. 2, system 200 may include one or more processors 204, system control logic 208 coupled to at least one of processors 204, system memory 212 coupled to system control logic 208, non-volatile memory (NVM)/storage 216 coupled to system control logic 208, network interface 210 coupled to system control logic 208, and single-wire lidar 102.

The processor 204 may include one or more single-core or multi-core processors. The processor 204 may include any combination of general-purpose processors and dedicated processors (e.g., graphics processors, application processors, baseband processors, etc.). In an embodiment of the present application, processor 204 may run a predetermined algorithm to perform coordinate transformation operations, filter cliff points, and calculate cliff depth, width, etc. based on coordinates of the cliff points to determine a size information operation of the coverage of the cliff.

System control logic 208 for an embodiment may include any suitable interface controllers to provide any suitable interface to at least one of processors 204 and/or any suitable device or component in communication with system control logic 208.

System control logic 208 for one embodiment may include one or more memory controllers to provide an interface to system memory 212. System memory 212 may be used to load and store data and/or instructions, for example, for system 200, memory 212 for an embodiment may comprise any suitable volatile memory, such as suitable Dynamic Random Access Memory (DRAM).

NVM/memory 216 may include one or more tangible, non-transitory computer-readable media for storing data and/or instructions. For example, NVM/memory 216 may include any suitable non-volatile memory, such as flash memory, and/or any suitable non-volatile storage device(s), such as one or more hard disk drives (hdd (s)), one or more Compact Disc (CD) drives, and/or one or more Digital Versatile Disc (DVD) drives.

NVM/memory 216 may comprise a portion of a storage resource on the apparatus on which system 200 is installed or it may be accessible by, but not necessarily a part of, a device. For example, NVM/storage 216 may be accessed over a network via network interface 210.

In particular, system memory 212 and NVM/storage 216 may each include: temporary and permanent copies of instructions 224. The instructions 224 may include: instructions that, when executed by at least one of processors 204, cause system 200 to implement the cliff detection method of the present application. In various embodiments, the instructions 224 or hardware, firmware, and/or software components thereof may additionally/alternatively be disposed in the system control logic 208, the network interface 210, and/or the processor 204. In the embodiment of the present application, an operation program for performing a coordinate transformation operation on the original point cloud coordinates acquired by the single line laser radar 102, screening cliff points, and calculating size information such as cliff depth and width based on the coordinates of the cliff points may be preset and stored in the NVM/memory 216 for the processor 204 to call.

Network interface 210 is used to provide a radio interface for system 200 to communicate with any other suitable devices (e.g., front end modules, antennas, etc.) over one or more networks. The network interface 210 may further include any suitable hardware and/or firmware to provide a multiple-input multiple-output radio interface. For example, network interface 210 for one embodiment may be a network adapter, a wireless network adapter, a telephone modem, and/or a wireless modem.

The single line laser radar 102 is used to detect the surrounding environment of the driving apparatus 101 to return the coordinate information (i.e., the original point cloud coordinates) of the detection point. The original point cloud coordinates are used to determine whether there is an obstacle 103 around the drive apparatus 101, whether the obstacle 103 is an obstacle to a cliff, calculate size information such as the depth of the cliff, the width of the cliff, and the like. The original point cloud coordinate acquired by the single line laser radar 102 is generally a coordinate value under a polar coordinate system, and the original point cloud coordinate acquired by the single line laser radar 102 is sent to the processor 204 for coordinate conversion, linear fitting analysis and other processing, or sent to the processor 204 through the system control logic 208 for coordinate conversion, linear fitting analysis and other processing. The processing such as coordinate conversion and linear fitting analysis performed on the processor 204 includes converting an original point cloud coordinate in a coordinate system of the acquired single line laser radar to a cartesian coordinate value of a coordinate origin of a coordinate system of the reference driving device 101 through a preset coordinate conversion matrix, and performing linear fitting analysis on an obstacle point of which the cartesian coordinate value meets a screening condition to determine whether the obstacle 103 encountered by the driving device 101 is a cliff obstacle, and if the obstacle is the cliff, further determining a coverage range of the cliff, and the like.

It is to be understood that the structure of the system 200 shown in fig. 2 is not a specific limitation on the system of the driving apparatus 101, and in other embodiments, the driving apparatus 101 may implement the cliff detection scheme of the present application through other forms of system structures.

It is understood that the driving device 101 provided in the present application may be various driving devices having an automatic driving and sensing function, for example, the driving device 101 may be a logistics distribution robot, an unmanned cleaning vehicle, a catering robot, an unmanned inspection robot, etc., without limitation. The following describes an implementation of the cliff detection scheme of the present application, taking the driving apparatus 101 as a logistics distribution robot (hereinafter referred to as robot 101) as an example.

The following describes in detail a specific implementation process of implementing the cliff detection scheme of the present application by arranging a single line laser radar on the robot 101 according to an embodiment.

Example one

In the present embodiment, two single line laser radars 102-1 and 102-2 are disposed on the robot 101, and the following describes in detail the installation positions of the single line laser radars 102-1 and 102-2 and the specific processes for implementing the cliff detection method of the present application based on the single line laser radars 102-1 and 102-2 with reference to the drawings.

Fig. 3a is a schematic view illustrating an application scenario of a cliff detection method according to an embodiment of the present application. As shown in FIG. 3a, a single line laser radar 102-1 and a single line laser radar 102-2 are respectively disposed at two ends of an edge, which is above a front end surface 1011 of the robot 101 and is connected to a right side of a top surface 1012, and a detection point acquired when the single line laser radar 102-1 and the single line laser radar 102-2 perform obstacle detection includes a Z shown in FIG. 3a ₁ 、Z ₂ 、Z ₃ 、Z ₄ In the process of determining whether an obstacle exists in the traveling direction of the robot 101, a coordinate system with the center point of the rear wheel connecting shaft on the robot 101 as the origin of coordinates is set as a robot coordinate system, for example, an XOZ coordinate plane shown in fig. 3a, and a first height threshold value above the ground is preset as z with reference to the robot coordinate system _u The second height threshold below the ground is z _d Then the height is greater than z _u Can be determined as an obstacle point with a height smaller than z _d Can be determined as a cliff point, e.g. Z ₂ 、Z ₃ 、Z ₄ Point, height in z _u And z _d The probe points in between can then be determined as ground points, e.g. Z ₁ And (4) point.

For ease of understanding, fig. 3b is a schematic view of a top view of the robot 101 from the top, corresponding to the installation position shown in fig. 3 a. As shown in FIG. 3b, the intersection of the laser scanning plane 102-1 'of the single line laser radar 102-1 and the laser scanning plane 102-2' of the single line laser radar 102-2 is O ₁ O ₂ 。

It is understood that if it is determined that there is a cliff point based on the coordinate information of the detection point returned by the single line laser radar 102 sensor, a cliff obstacle may exist in the traveling direction of the robot, and the cliff height and the cliff depth are further calculated based on the determined coordinate values of the cliff point to determine whether the obstacle 103 is a cliff obstacle. If the obstacle 103 is a cliff obstacle, the robot 101 may further determine the coverage area of the cliff obstacle 103, and further determine whether the robot 101 can smoothly pass through the cliff obstacle 103.

It is understood that the single line laser radars 102-1 and 102-2 may be disposed at any position on the front surface 1011 of the robot 101, for example, the single line laser radars 102-1 and 102-2 are respectively mounted at the center point of the upper edge of the front surface 1011 of the robot 101 shown in fig. 3a, and in other embodiments, the mounting position of the single line laser radar 102-1 or 102-2 may be other positions different from the position shown in fig. 3a or 3b, which is not limited herein.

Fig. 4 is a schematic flowchart illustrating a cliff detection method according to an embodiment of the present application. It will be appreciated that the steps in the flow chart shown in fig. 4 may be implemented by the processor 204 of the robot 101 by running a preset algorithm or software program.

Specifically, as shown in fig. 4, the cliff detection method provided in the embodiment of the present application includes the following steps:

step 401: and acquiring polar coordinate values of all the detection points in a reference sensor coordinate system as original point cloud coordinates.

For example, the detection point i reference acquired by the single line laser radar 102-1 or 102-2The polar coordinate values of the sensor coordinate system may be expressed as r _i ,θ _i Wherein, the detection point i is any detection point r collected by the single line laser radar 102-1 or 102-2 _i For scanning the laser beam over the radius of the spot i on the plane _i To detect the polar angle of point i on the laser scan plane.

Step 402: and converting the original point cloud coordinate into a Cartesian coordinate value of each detection point reference robot coordinate system.

For example, first, the polar coordinate value { r) of the detection point i of the sensor coordinate system is referred to _i ,θ _i Converting the coordinate values into Cartesian coordinate values { x } under the same coordinate system _si ，y _si ，z _si The conversion formula refers to the following formula (1):

as an example, when i is 1, r ₁ ＝1.58，θ ₁ When the value is-1.38, then x _s1 ＝1.58×sin(-1.38)＝-1.55，y _s1 ＝1.58× cos(-1.38)＝0.28，z _s1 The cartesian coordinate values of the 1-point reference sensor coordinate system are therefore { -1.55, 0.28, 0 }.

And then, carrying out coordinate conversion on the Cartesian coordinate value of the detection point i in the reference sensor coordinate system through a preset coordinate transformation matrix M to obtain the Cartesian coordinate value of the detection point i in the reference robot coordinate system. The coordinate transformation matrix M is used to transform cartesian coordinate values of the sensor coordinate system into a matrix of cartesian coordinate values of the robot coordinate system, and generally, values of matrix points of M are determined based on experiments and preset in the robot 101 system.

The process of performing coordinate conversion by the coordinate transformation matrix M may refer to the following formula (2):

where n is the sensor return of the laser-only radar 102-1 or 102-2The number of detection points, M is a predetermined coordinate transformation matrix, { x _i ,y _i ,z _i And f, referring to the Cartesian coordinate value of the robot coordinate system for the detection point i.

It is understood that in some embodiments, the coordinate origin of the robot coordinate system may refer to the center point of the rear wheel connecting shaft on the robot 101 in fig. 3a and the related description, and the XOZ coordinate plane in the cartesian coordinate system of the robot coordinate system may refer to the XOZ coordinate plane shown in fig. 3 a; in other embodiments, the coordinate origin of the robot coordinate system may also be another reference point, for example, the center of gravity of the robot 101 or the center point of the front wheel connecting shaft, and the XOZ coordinate plane in the cartesian coordinate system of the reference robot coordinate system may also be another coordinate plane that forms a certain included angle with the XOZ coordinate plane shown in fig. 3a, which is not limited herein.

As an example, taking the above-mentioned probe point i as 1, the cartesian coordinate values of the point reference sensor coordinate system calculated in the above-mentioned step 402 are { -1.55, 0.28, 0}, and the coordinate transformation process based on the preset coordinate transformation matrix M may refer to the following transformation process:

and finally, obtaining the Cartesian coordinate values of {2.23, 0.08, -0.001} of the coordinate system of the reference robot when the detection point i is 1.

It can be understood that the preset value of each matrix point in the coordinate transformation matrix M depends on the position and the installation angle of the single line laser radar 102 disposed on the robot 101, and the installation angle of the single line laser radar 102 determines the deflection angle of the laser scanning plane thereof. The process of adjusting the installation angle of the singlet lidar 102 can refer to fig. 5, for example, the singlet lidar is installed on the front end surface 1011 of the robot 101, and the laser scanning plane of the singlet lidar 102 can be adjusted to be parallel to the horizontal plane 601 during installation, or deflected upwards to an angle parallel to the plane 602, or deflected downwards to a position parallel to the plane 603, which is not limited herein. In actual practice, the optimal installation angle of the single line lidar 102 may be determined based on experimental data.

Step 403: and adding a classification mark to each detection point based on a preset Z value. Specifically, the preset Z value may refer to the above-mentioned first height threshold Z _u And a second height threshold z _d The preset value Z can also refer to the first height threshold value Z _u Corresponding ordinate value and second height threshold value z _d And setting corresponding ordinate values, and comparing the Z-axis coordinate value in the Cartesian coordinate values of the reference robot coordinate systems of the detection points obtained by conversion with a preset Z value or the ordinate value corresponding to the preset Z value so as to determine which type of the obstacle point, the ground point and the cliff point each detection point belongs to. Wherein the first height threshold value z _u Corresponding ordinate value and second height threshold value z _d The corresponding ordinate values refer to a cartesian coordinate system in the robot coordinate system.

It will be appreciated that points on the inner wall of the cliff obstacle encountered by the robot 101 during travel are generally located below the travel surface (i.e., the ground) of the robot 101. Referring to fig. 3a, the height of each probe point, for example, the height of the reference ground, may be calculated based on the cartesian coordinate values of the reference robot coordinate system of each probe point converted in step 402, and the absolute value of the difference between the z-axis coordinate value of each probe point and the z-axis coordinate value of the reference robot coordinate system of the point on the ground is calculated, so as to determine the height of each probe point. For the calculated height of each probe point, as described above, the height is less than z _d Is marked as a cliff point and has a height greater than z _u Can be marked as an obstacle point with a height z _u And z _d The probe points in between can then be labeled as ground points. In the process of the robot 101 moving, the blocking effect caused by the marked ground points is almost negligible, so if the detection points are all the ground points, the robot 101 moving direction detected by the surface single line laser radar 102 or the surrounding environment is free of blocking; if the detection point includes a cliff point, it indicates that the robot 101 detected by the single line laser radar 102 may be present in the traveling direction or in the surrounding environmentCliff obstacle; if the detection points include obstacle points, it indicates that there may be an obstacle higher than the ground in the traveling direction of the robot 101 or in the surrounding environment detected by the single line laser radar 102.

As described above, the probe point i refers to the z-axis coordinate value of the cartesian coordinate values of the robot coordinate system as z _i As an example, the point on the ground is referenced to z-axis coordinate value among cartesian coordinate values of the robot coordinate system as z ₀ The height of the detected point i can be calculated by the absolute value of the difference between the coordinate values, for example, the height of the detected point i is calculated by the formula | z _i -z ₀ Therefore, the judgment condition for adding the classification flag to each detection point may refer to the following judgment conditions:

when z _i -z ₀ |>z _u And judging that the detection point i is an obstacle point, and adding a classification mark corresponding to the obstacle point to the detection point i. For example, the classification mark corresponding to the obstacle point may refer to "obstacle point" or "obstacle point", and the like, which is not limited herein.

Z is when _i -z ₀ |≤z _u And | z _i -z ₀ |≥z _d And judging that the detection point i is a ground point, and adding a classification mark corresponding to the ground point to the detection point i. For example, the classification mark corresponding to the ground point may refer to "ground point" or "floor point", etc., and is not limited herein.

When z _i -z ₀ |<z _d And judging that the detection point i is a cliff point, and adding a classification mark corresponding to the cliff point to the detection point i. For example, the classification mark corresponding to a cliff point may refer to "cliff point" or "cliff point", etc., and is not limited herein.

For example, referring to FIG. 3a, described above, Z ₁ 、Z ₂ 、Z ₃ 、Z ₄ Of the four probing points, Z ₁ The height of the point is z _u And z _d Between can therefore be labeled as ground points; z ₂ 、Z ₃ 、Z ₄ Are all less than z _d And thus may be marked as a cliff point.

In other embodiments, aboveThe predetermined first height threshold value z _u A second height threshold z _d It is also possible to set the first height threshold z based on the ordinate values of the corresponding height position points, for example, in the cartesian coordinate system O-XYZ shown in fig. 3a with the center point of the connecting shaft of the rear wheel of the robot as the origin _u A second height threshold z _d If the corresponding ordinate values are negative numbers, the following determination conditions can be referred to add classification marks to the detection points:

referring to the ordinate value z of the probe point i of the above-mentioned O-XYZ coordinate system _i First height threshold z _u When the corresponding ordinate value is obtained, marking the detection point i as an obstacle point;

referring to the ordinate value z of the probe point i of the above-mentioned O-XYZ coordinate system _i Is less than or equal to the first height threshold value z _u Corresponding ordinate value and referring to the ordinate value z of the probe point i in the above-mentioned O-XYZ coordinate system _i Not less than the first height threshold z _d When the corresponding ordinate value is obtained, marking the detection point i as a ground point;

referring to the longitudinal coordinate value z of the detection point i in the O-XYZ coordinate system _i < first height threshold z _d And marking the detection point i as a cliff point when the corresponding ordinate value is obtained. And are not intended to be limiting herein.

Step 404: and calculating the cliff depth and the width of the cliff based on the Cartesian coordinate values of the cliff point. Specifically, the cliff depth may be calculated from the z-axis coordinate value and the cliff width may be calculated from the x-axis coordinate value among the coordinates of the respective probe points marked as the cliff points. It is understood that the z-axis coordinate value of the cliff point may be a negative value.

Regarding the calculation of cliff depth, for example, for the above example, referring to FIG. 3a, Z is labeled as the cliff point ₂ 、Z ₃ 、Z ₄ Among the points, the point at which the absolute value of the Z-axis coordinate value is the largest is Z ₂ 、Z ₃ And two points which are positioned at the bottom of the cliff as shown in the figure, wherein the absolute value of the coordinate value of the z axis of the two points can be used as the basis for determining the depth of the cliff.

In another embodiment, the ground point Z can be used ₁ As reference points, Z is calculated ₂ Or Z ₃ Z-axis coordinate value of point and Z ₁ And the absolute value of the difference of the coordinate values of the z axis of the point is the cliff depth. For the calculation of the cliff depth, in each application scenario, only the reference point or the reference height needs to be unified, and the cliff depth can be calculated based on the absolute value of the coordinate value or the absolute value of the difference, which is not limited herein. It will be appreciated that the calculated cliff depth from the absolute value of the difference is more referenceable in practical applications when the origin of the robot coordinate system to which each cliff point is referenced is not on the ground.

As can be understood from the drawings, the cliff depth calculated as described above partially determines whether the robot 101 can pass through the cliff, and depends on the climbing ability of the robot 101, and for example, when the cliff height is small, the robot 101 can pass through the cliff smoothly by its own climbing ability.

Regarding the calculation of cliff width, for example, for the above example, reference is made to Z, labeled cliff point, shown in FIG. 3a ₂ 、Z ₃ 、Z ₄ The absolute value of the difference between the x-axis coordinates of any two points is calculated, wherein the maximum value of the absolute values of the calculated x-axis coordinate differences can be determined as the width of the cliff, such as the Z-coordinate shown in fig. 3a ₂ And Z ₄ The absolute value of the difference between the points is the greatest, thus determining the width of the cliff as Z ₂ And Z ₄ The absolute value of the difference between the x-axis coordinates of the points. As can be understood from the drawings, the calculated width of the cliff determines whether the robot 101 can cross the cliff, and depends on the wheel size of the robot 101, and for example, when the wheel size of the robot 101 is large enough to cross the width of the cliff, the robot 101 can smoothly pass the cliff.

In addition, it can be understood that there is a certain error value between the calculated cliff width and the actual cliff width in this step, and when there are more cliff points collected by the single line laser radar, this error can be reduced.

Step 405: and judging whether the detected obstacle is the cliff or not based on the calculated cliff depth and the calculated cliff width. If yes, go on to step 406; if not, the process is ended.

Specifically, the processor 204 may compare the calculated cliff depth with a preset cliff depth, compare the calculated cliff width with a preset cliff width, and determine that the obstacle detected by the single-line laser radar is a cliff when the calculated cliff depth is greater than the preset cliff depth and the calculated cliff width is greater than the preset cliff width. For example, the preset cliff depth is 10cm and the preset cliff width is 5 cm. Then:

when the calculated cliff depth h is greater than 10cm and the calculated cliff width b is greater than 5cm, it is determined that the obstacle 103 encountered by the robot 101 is a cliff obstacle.

Step 406: calculating the size of the cliff and determining the coverage area of the cliff. Specifically, referring to fig. 3a, the cliff coverage is determined based on the absolute value of the difference between the Y-axis coordinates in the coordinates of the cliff point in combination with the calculated cliff depth in step 405, and the size of the cliff includes the characteristic values of the size, such as the width and length of the cliff, that can determine the cliff coverage.

In other embodiments, for example, as shown in fig. 3a, the XOY coordinate plane of the cartesian coordinate system of the reference robot coordinate system is not in the same plane or parallel to the plane where the ground is located, and the cliff coverage may be determined by calculating the projection point of the plane where the ground of the cliff point is located. And are not intended to be limiting herein.

It can be understood that, based on the determined cliff coverage, the robot 101 may accurately determine the obstacle avoidance route, for example, if the width of the edge of the cliff obstacle is small, the robot 101 may pass through the edge of the cliff without completely bypassing the cliff obstacle, so that the robot 101 may reduce the amount of change of the path, thereby facilitating the robot 101 to travel in the preset path direction as much as possible, improving the obstacle avoidance efficiency of the robot 101, and thus improving the accurate obstacle avoidance performance of the robot 101.

It is understood that the detection range based on the single line laser radar is limited, for example, the detection range is 5m, so that the cliff detection method implemented by the embodiment of the application can detect the cliff within a certain distance range in the specific implementation process.

Further, the x-axis coordinate value x of the projected point on the XOZ coordinate plane shown in fig. 3a based on the cliff point marked in the above step 403 _i The distance between the robot 101 and the cliff 103 can be determined according to the minimum value of the distance information, so that the robot 101 can generate a climbing or obstacle avoidance scheme in advance or in time by using the distance information, and the sensitivity of the robot 101 is improved.

It is understood that, in the present embodiment, the cliff detection method of the present application is implemented by two single line laser radars disposed on the robot 101, and as described above, the installation position of the single line laser radar shown in fig. 3a to 3b has certain limitations in cliff detection, for example, limited by the scanning frequency, the number of detected points collected by the single line laser radar is small, and the like. In order to overcome the limitation of installing two single line laser radars for cliff detection in this embodiment and achieve a better cliff detection effect, three or more single line laser radars may be provided on the robot 101.

The following describes a specific process of implementing the cliff detection method of the present application by providing three single line laser radars on the robot 101 by another embodiment.

Example two

The present embodiment describes a specific implementation procedure of the cliff detection method according to the present application by providing three single line laser radars on the robot 101.

Fig. 6 is a schematic diagram illustrating another application scenario of the cliff detection method applied to this embodiment. As shown in FIG. 6, a single line laser radar 102-1 is disposed above the center point of the lower edge line of the front end surface 1011 of the robot 101, and single line laser radars 102-2 and 102-3 are disposed at the two end positions of the upper edge of the front end surface 1011 of the robot 101, respectively. The single line laser radars 102-1, 102-2 and 102-3 detect the cliff on the ground in the traveling direction of the robot 101 by using the returned original point cloud coordinates, the processor 204 of the robot 101 determines whether the obstacle 103 encountered in front of the traveling direction of the robot 101 is the cliff based on the returned original point cloud coordinates, and if the obstacle 103 is the cliff 103, the robot 101 may further determine the slope or the slope angle α of the cliff 103 to determine whether the cliff 103 can be smoothly passed through.

Wherein, the laser scanning plane formed by the rotation of the laser emitted by the single-line laser radar 102-1 is 102-1 ', the laser scanning plane formed by the rotation of the laser emitted by the single-line laser radar 102-2 is 102-2 ', and the laser scanning plane formed by the rotation of the laser emitted by the single-line laser radar 102-3 is 102-3 '.

In order to more clearly describe the installation positions of the three single line lidar in the embodiment, fig. 7 shows a schematic perspective structure of the installation positions of the single line lidar in the scene shown in fig. 6. As shown in FIG. 7, a single line laser radar 102-1 is disposed above the center point of the lower edge line of the front end surface of the robot 101 in the traveling direction, and single line laser radars 102-2 and 102-3 are disposed at both ends of the upper edge of the front end surface 1011 of the robot 101, respectively.

The installation angles of the single line laser radars 102-2 and 102-3 are adjusted by referring to the method shown in fig. 5, taking the single line laser radar 102-3 as an example, as the schematic position of the coordinate axis shown in fig. 7, the initial laser scanning plane (hereinafter referred to as the sensor plane) of the single line laser radar 102-3 is parallel to the XOZ coordinate plane in the robot coordinate system shown in fig. 6, and an original robot coordinate system O-XYZ is formed. When the sensor is installed, the plane of the sensor can be rotated to an original X-axis position to an original Z-axis position by taking the Z-axis in the original robot coordinate system as a central axis to rotate an angle A ₁ At the axis position, the original Y-axis position is rotated to Y ₁ An axial position; when the sensor plane is at X ₁ The axis is a central axis rotating angle B, so that the sensor plane of the single line laser radar 102-3 rotates to the position of the laser scanning plane 102-3'. It is understood that in other embodiments, the installation angle of the singlet lidar 102-3 may be adjusted by other rotation methods to enable the singlet lidar 102-3 to be in the preferred scanning position, which is not limited herein.

In other embodiments, the three positions of the single line laser radars on the robot 101 may also be other positions on the front end surface of the robot 101, wherein the installation angles of the single line laser radars 102-2 and 102-3 may also be other installation angles, which is not limited herein. Referring to fig. 8a to 8b, which are schematic diagrams illustrating the change of the included angle between two types of single line laser radars applied to the present embodiment, the included angle C between the laser scanning plane 102-2 'of the single line laser radar 102-2 and the laser scanning plane 102-3' of the single line laser radar 102-3 may be larger (refer to fig. 8 a) or smaller (refer to fig. 8 b).

The detailed implementation process of the cliff detection method according to the present embodiment is described below with reference to the flow of steps shown in fig. 4 for implementing the cliff detection method according to the first embodiment based on the installation positions of the single line laser radars 102-1, 102-2, and 102-3 shown in fig. 6.

Referring to steps 401 to 406 in the first embodiment, the processing procedure of determining whether the obstacle 103 is the cliff obstacle 103 and determining the coverage of the cliff based on the coordinate information of each detection point in the original point cloud coordinates returned by the single line laser radar detection obstacle 103 by the processor 204 of the robot 101 is as follows:

referring to step 401, the processor 204 of the robot 101 acquires the polar coordinate values of the reference sensor coordinate system of each probe point returned when the single line laser radars 102-1, 102-2, and 102-3 perform cliff detection as the original point cloud coordinates. Referring to fig. 6, it can be understood that the polar coordinate values of the respective probe points with reference to the sensor coordinate system in the present embodiment include polar coordinate values of the sensor coordinate system with the laser emission point of the singlet lidar 102-1 as the origin of coordinates of the respective probe points returned by the singlet lidar 102-1, polar coordinate values of the sensor coordinate system with the laser emission point of the singlet lidar 102-2 as the origin of coordinates of the respective probe points returned by the singlet lidar 102-2, and polar coordinate values of the sensor coordinate system with the laser emission point of the singlet lidar 102-3 as the origin of coordinates of the respective probe points returned by the singlet lidar 102-3. Wherein, the sensor coordinate system of the single line laser radar 102-1 is X which takes the laser emission point of the single line laser radar 102-1 as the origin and the laser scanning plane 102-1' of the single line laser radar 102-1 as the coordinate plane ₁ O ₁ Y ₁ (ii) a The sensor coordinate system of the single line laser radar 102-2 is based on the laser emitting point of the single line laser radar 102-2 as the originThe laser scanning plane 102-2' of the single-line laser radar 102-2 is the X of the coordinate plane ₂ O ₂ Y ₂ (ii) a The sensor coordinate system of the single line laser radar 102-3 is X with the laser emitting point of the single line laser radar 102-3 as the origin and the laser scanning plane 102-3' of the single line laser radar 102-3 as the coordinate plane ₃ O ₃ Y ₃ . In this embodiment, the processor 204 of the robot 101 may acquire polar coordinate values of the respective probe points returned by the sensors of the two single line laser radars.

Referring to step 402, the processor 204 of the robot 101 converts the obtained polar coordinate values of the probe points returned by the two sensors of the single line laser radar into cartesian coordinate values in the robot coordinate system by running a corresponding algorithm. The coordinate transformation process refers to equations (1) to (2) in the first embodiment, and is not described herein again.

Referring to step 403, the processor 204 of the robot 101 runs a corresponding algorithm to compare the Z-coordinate value in the cartesian coordinate values of the reference robot coordinate system of each probe point obtained by the conversion with a preset Z value, and classifies and adds a classification mark to each probe point, where the classification of each probe point refers to the obstacle point, the ground point, and the cliff point in the first embodiment, and the preset Z value refers to the first height threshold Z value in the first embodiment _u And a second height threshold z _d And will not be described herein.

Referring to step 404, the processor 204 of the robot 101 calculates the cliff depth and the cliff width based on the coordinates of all the detected points marked as the cliff points by running a corresponding algorithm, and the specific calculation process refers to the related description in step 404 in the first embodiment, which is not described herein again.

Referring to step 405, the processor 204 of the robot 101, by running a corresponding algorithm, compares the cliff height and the cliff width obtained through the calculation with the preset cliff height and the preset cliff width, respectively, to determine whether the detected obstacle is a cliff obstacle, where the specific determination process may refer to the related description of step 405 in the first embodiment, and is not described herein again.

Referring to step 406, if it is determined in the above step that the obstacle detected by the robot 101 is a cliff obstacle, the processor 204 of the robot 101 further calculates the size of the cliff by running a corresponding algorithm, and further determines the coverage area of the cliff, and the specific process for determining the coverage area of the cliff may refer to the description related to step 406 in the first embodiment, which is not described herein again.

In addition, referring to step 404 in the first embodiment, based on the x-axis coordinate value x of the projection point of the obstacle point determined by the processor 204 of the robot 101 in the first embodiment in the step 404 on the XOZ coordinate plane shown in fig. 6 _i The distance between the robot 101 and the cliff 103 can be determined according to the minimum value of the distance information, so that the robot 101 can generate a climbing or obstacle avoidance scheme in advance or in time by using the distance information, and the sensitivity of the robot 101 is improved.

It can be understood that, in the present embodiment, the cliff detection method of the present application is implemented by two single line laser radars provided on the robot 101, and as described above, when the cliff detection is performed at the installation positions of the three single line laser radars shown in fig. 6, compared with the cliff detection schemes described in the first and second embodiments, the cliff detection method can achieve both a more comprehensive laser scanning range and timeliness of returning to a detection point. That is to say, in the implementation process of this embodiment, the robot 101 can sense a wider range of the surrounding environment, and can return to more dense original point cloud coordinates to timely and accurately determine whether an obstacle exists around the robot 101, whether the obstacle is a cliff obstacle, and the like, and the application can also determine the coverage area of the cliff, so that the robot 101 can further determine an accurate obstacle avoidance scheme for passing through the cliff obstacle, which is beneficial to improving the accurate obstacle avoidance capability of the robot 101.

In addition, because the cost of the single line laser radar is low, a few single line laser radars are adopted to realize cliff detection in the embodiments of the application, so that the production cost of the robot 101 can be greatly reduced, and meanwhile, the cliff detection method can achieve the technical effect superior to that of a multi-line laser radar for cliff detection, and is beneficial to wide popularization and application of the cliff detection scheme.

Embodiments of the mechanisms disclosed herein may be implemented in hardware, software, firmware, or a combination of these implementations. Embodiments of the application may be implemented as computer programs or program code executing on programmable systems comprising at least one processor, a storage system (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device.

The program code may be implemented in a high level procedural or object oriented programming language to communicate with a processing system. The program code can also be implemented in assembly or machine language, if desired. Indeed, the mechanisms described in this application are not limited in scope to any particular programming language. In any case, the language may be a compiled or interpreted language.

In some cases, the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried by or stored on one or more transitory or non-transitory machine-readable (e.g., computer-readable) storage media, which may be read and executed by one or more processors. For example, the instructions may be distributed via a network or via other computer readable media. Thus, a machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer), including, but not limited to, floppy diskettes, optical disks, read-only memories (CD-ROMs), magneto-optical disks, read-only memories (ROMs), Random Access Memories (RAMs), erasable programmable read-only memories (EPROMs), electrically erasable programmable read-only memories (EEPROMs), magnetic or optical cards, flash memory, or a tangible machine-readable memory for transmitting information (e.g., carrier waves, infrared digital signals, etc.) using the internet in an electrical, optical, acoustical or other form of propagated signal. Thus, a machine-readable medium includes any type of machine-readable medium suitable for storing or transmitting electronic instructions or information in a form readable by a machine (e.g., a computer).

In the drawings of embodiments of the present application, some features of structure or method may be shown in a particular arrangement and/or order. However, it is to be understood that such specific arrangement and/or ordering may not be required. Rather, in some embodiments, the features may be arranged in a manner and/or order different from that shown in the illustrative figures. In addition, the inclusion of a structural or methodological feature in a particular figure is not meant to imply that such feature is required in all embodiments, and in some embodiments may not be included or may be combined with other features.

It should be noted that, in the embodiments of the apparatuses in the present application, each unit/module is a logical unit/module, and physically, one logical unit/module may be one physical unit/module, or may be a part of one physical unit/module, and may also be implemented by a combination of multiple physical units/modules, where the physical implementation manner of the logical unit/module itself is not the most important, and the combination of the functions implemented by the logical unit/module is the key to solve the technical problem provided by the present application. Furthermore, in order to highlight the innovative part of the present application, the above-mentioned device embodiments of the present application do not introduce units/modules which are not so closely related to solve the technical problems presented in the present application, which does not indicate that no other units/modules exist in the above-mentioned device embodiments.

It is noted that, in the examples and descriptions of this patent, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the use of the verb "comprise a" to define an element does not exclude the presence of another, same element in a process, method, article, or apparatus that comprises the element.

While the present application has been shown and described with reference to certain preferred embodiments thereof, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present application.

Claims

1. A cliff detection method applied to driving equipment comprising a single line laser radar is characterized by comprising the following steps:

acquiring a first coordinate value obtained by detecting an obstacle by the single line laser radar, wherein the first coordinate value is determined by referring to a coordinate system of the single line laser radar;

performing coordinate conversion on the first coordinate value to obtain a second coordinate value, wherein the second coordinate value is determined by referring to a coordinate system of the driving equipment;

determining whether the obstacle is a cliff obstacle based on the second coordinate value.

2. The method of claim 1, wherein the determining whether the obstacle is a cliff based on the second coordinate value comprises:

determining a point of the second coordinate value, of which the z-axis coordinate value is smaller than the longitudinal coordinate value corresponding to the preset height threshold value, as a cliff point;

determining a cliff depth based on z-axis coordinate values of the cliff points and a cliff width based on x-axis coordinate values of the cliff points;

and determining the obstacle to be a cliff obstacle when the cliff depth is greater than a preset depth threshold value and the cliff width is greater than a preset width threshold value.

3. The method of claim 2, wherein the coordinate transforming the first coordinate value to a second coordinate value comprises:

converting a coordinate system based on the first coordinate value to obtain a third coordinate value, wherein the first coordinate value is determined by referring to a polar coordinate system of the single line laser radar, and the third coordinate value is determined by referring to a Cartesian coordinate system of the single line laser radar;

and performing coordinate conversion based on the third coordinate value and a preset coordinate transformation matrix to obtain the second coordinate value.

4. The method of claim 3, wherein the coordinate system of the singlet lidar comprises a polar coordinate system having the laser emission point of the singlet lidar as an origin of coordinates, and a Cartesian coordinate system having the laser emission point of the singlet lidar as an origin; and is

The first coordinate value is a polar coordinate value under a polar coordinate system taking the laser emission point as a coordinate origin;

the third coordinate value is a cartesian coordinate value in a cartesian coordinate system with the laser emission point as the origin of coordinates.

5. The method of claim 4, wherein: the coordinate system of the driving equipment comprises a Cartesian coordinate system taking the center point of a rear wheel connecting shaft of the driving equipment as a coordinate origin; and is

The second coordinate value is a Cartesian coordinate value in a Cartesian coordinate system with the center point of the rear wheel connecting shaft as the origin of coordinates.

6. The method of claim 5, wherein: the coordinate transformation matrix is determined based on the relative position between a laser emission point of the single line laser radar and the center point of a rear wheel connecting shaft of the equipment and the deflection angle of a laser scanning plane of the single line laser radar relative to the bottom surface or the side surface of the equipment.

7. The method of claim 3, wherein obtaining the second coordinate value based on the third coordinate value and a preset coordinate transformation matrix comprises:

and obtaining the second coordinate value based on the third coordinate value multiplied by the coordinate transformation matrix.

8. The method according to any one of claims 1 to 7, further comprising:

determining the cliff obstacle coverage based on the second coordinate values of the cliff points.

9. A drive apparatus, characterized by comprising: the system comprises a processor, a memory, a single-wire laser radar, a communication interface and a communication bus;

the single-wire laser radar, the memory and the communication interface are connected through the communication bus;

the memory is configured to store at least one instruction that, when executed by the processor, causes the driver apparatus to perform the cliff detection method of any one of claims 1 to 8.

10. A computer-readable storage medium having instructions stored thereon, which when executed on a computer cause the computer to perform the cliff detection method of any one of claims 1-8.