Rail robot obstacle avoidance method and device, rail robot and storage medium

Abstract

The embodiment of the invention provides a rail robot obstacle avoidance method and device, a rail robot and a storage medium. The method comprises the steps that the rail robot determines first obstacle information according to a preset safety obstacle avoidance area and TOF data of the rail robot; then, the minimum value of the braking distance and the preset detection distance of the rail robot is determined as a first distance, and the maximum value of the stopping distance and the preset distance of the rail robot is determined as a second distance; then, according to the ultrasonic data, the first distance and the second distance, second obstacle information is determined; and finally, according to the first obstacle information, the second obstacle information, the first field angle of the TOF sensor and the second field angle of the ultrasonic sensor, the credible type of each obstacle is determined, and according to the credible type of each obstacle, the orbital robot is controlled to operate. According to the technical scheme, the false detection rate of the sensor can be reduced, and the control accuracy of the rail robot is improved.

Description

Technical Field

[0001] The present application relates to the field of robotics technology, and in particular to a rail robot obstacle avoidance method, device, rail robot and storage medium. Background Art

[0002] The movement of rail robots depends on the real-time detection of the surroundings by sensors, and then the subsequent movement is determined according to the detection results. Therefore, it is very necessary for the sensors installed on rail robots to have high accuracy and fast response capabilities.

[0003] When rail robots use sensors to detect the environment, sensor misdetection is inevitable. At present, in scenarios where multiple sensors are used simultaneously, obstacles are mainly identified by combining the distance measurement information of time-of-flight (TOF) data and ultrasonic data to generate obstacle fusion point clouds in real time, thereby reducing sensor misdetection.

[0004] However, the application scenarios of existing technologies are relatively limited, and the sensor false detection rate is high, resulting in low accuracy in controlling the rail robot. Summary of the invention

[0005] The embodiments of the present application provide a rail robot obstacle avoidance method, a device, a rail robot and a storage medium, so as to achieve the effect of improving the control accuracy of the rail robot.

[0006] In a first aspect, an embodiment of the present application provides a rail robot obstacle avoidance method, which is applied to a rail robot, wherein the rail robot is deployed with a TOF sensor and an ultrasonic sensor, and the method includes:

[0007] Determining first obstacle information according to a preset safe obstacle avoidance area of ​​the rail robot and the TOF data, wherein the first obstacle information includes an obstacle position and a first obstacle type, and the first obstacle type includes a first type;

[0008] The minimum value between the braking distance of the rail robot and the preset detection distance is determined as the first distance, and the maximum value between the deceleration distance of the rail robot and the preset distance is determined as the second distance, wherein the deceleration distance is the distance traveled by the rail robot when it decelerates to a stop at a preset acceleration, and the preset distance is the preset minimum distance between the rail robot and the fixed obstacle;

[0009] Determine second obstacle information according to the ultrasonic data, the first distance, and the second distance, wherein the second obstacle information includes an obstacle distance and a second obstacle type, wherein the second obstacle type includes the first type and a second type, and the distance between an obstacle of the second type and the track robot is smaller than the distance between an obstacle of the first type and the track robot;

[0010] Determining a credible type of each obstacle according to the first obstacle information, the second obstacle information, a first field of view angle of the TOF sensor, and a second field of view angle of the ultrasonic sensor;

[0011] According to the credible type of each obstacle, the track robot is controlled to run.

[0012] In a second aspect, an embodiment of the present application provides a rail robot obstacle avoidance device, characterized in that it is applied to a rail robot, the rail robot is deployed with a TOF sensor and an ultrasonic sensor, and the device includes:

[0013] A determination module, configured to determine first obstacle information according to a preset safe obstacle avoidance area of ​​the rail robot and the TOF data, wherein the first obstacle information includes an obstacle position and a first obstacle type, and the first obstacle type includes a first type;

[0014] The determination module is further used to determine the minimum value of the braking distance of the rail robot and the preset detection distance as the first distance, and determine the maximum value of the deceleration distance of the rail robot and the preset distance as the second distance, wherein the deceleration distance is the distance traveled by the rail robot when it decelerates to a stop at a preset acceleration, and the preset distance is the preset minimum distance between the rail robot and the fixed obstacle;

[0015] The determination module is further used to determine second obstacle information according to the ultrasonic data, the first distance, and the second distance, the second obstacle information includes an obstacle distance and a second obstacle type, the second obstacle type includes the first type and a second type, and the distance between an obstacle of the second type and the track robot is smaller than the distance between an obstacle of the first type and the track robot;

[0016] The determination module is further used to determine the credible type of each obstacle according to the first obstacle information, the second obstacle information, the first field of view angle of the TOF sensor, and the second field of view angle of the ultrasonic sensor;

[0017] The control module is used to control the operation of the track robot according to the trustworthy type of each obstacle.

[0018] In a third aspect, an embodiment of the present application provides a rail robot, characterized in that it includes: a TOF sensor, an ultrasonic sensor, a memory, and a processor;

[0019] The memory stores computer-executable instructions;

[0020] The processor executes the computer-executable instructions stored in the memory, so that the processor performs the implementation of the first aspect as described above.

[0021] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores computer execution instructions, and when the computer execution instructions are executed by a processor, they are used to implement the implementation method of the first aspect as above.

[0022] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the implementation method of the first aspect as above.

[0023] The rail robot obstacle avoidance method, device, rail robot and storage medium provided in the embodiments of the present application determine the first obstacle information detected by the TOF sensor and the second obstacle information detected by the ultrasonic sensor based on the TOF data and ultrasonic data at the same time. Then, based on the first obstacle information and the second obstacle information, it is determined whether the obstacle in the overlapping detection area of ​​the TOF sensor and the ultrasonic sensor is detected by the two sensors at the same time to determine the credible type of the obstacle. At the same time, the urgency of the obstacle detected by the ultrasonic sensor is determined according to the dual threshold strategy. For the second type of obstacles with a higher degree of urgency, the sensor misdetection detection process is not performed on them, and they are directly determined as credible obstacles, which can ensure that the rail robot has reaction time to brake and stop, thereby avoiding the collision between the rail robot and the obstacle and improving the control accuracy of the rail robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0025] Figure 1 Schematic diagram of the structure of the track robot provided for this application Figure 1 ;

[0026] Figure 2 Schematic diagram of the process of the rail robot obstacle avoidance method provided in this application Figure 1 ;

[0027] Figure 3A schematic diagram of the projection components in the right-hand coordinate system provided for this application;

[0028] Figure 4 Schematic diagram of the process of the rail robot obstacle avoidance method provided in this application Figure 2 ;

[0029] Figure 5 Schematic diagram of the process of the rail robot obstacle avoidance method provided in this application Figure 3 ;

[0030] Figure 6 Schematic diagram of the process of the rail robot obstacle avoidance method provided in this application Figure 4 ;

[0031] Figure 7 Schematic diagram of the process of the rail robot obstacle avoidance method provided in this application Figure 5 ;

[0032] Figure 8 Schematic diagram of the process of the rail robot obstacle avoidance method provided in this application Figure 6 ;

[0033] Fig. 9 Schematic diagram of the process of the rail robot obstacle avoidance method provided in this application Figure 7 ;

[0034] Fig.10 Schematic diagram of the process of the rail robot obstacle avoidance method provided in this application Figure 8 ;

[0035] Fig.11 A schematic diagram of the structure of the rail robot obstacle avoidance device provided in this application;

[0036] Fig.12 Schematic diagram of the structure of the track robot provided for this application Figure 2 .

[0037] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0038] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0039] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0040] First, the terms used in this application are explained:

[0041] TOF sensor: It calculates the target distance between the TOF sensor and the target object by emitting infrared light signals to the target object and measuring the time difference between the infrared light signals from emission to return, generating three-dimensional dot matrix depth data.

[0042] Ultrasonic sensor: Utilizes the reflection characteristics of ultrasonic waves and measures the time difference from transmitting to receiving ultrasonic waves to calculate the target distance between the ultrasonic sensor and the target object. The detection field angle is approximately conical, and can only return a single distance information, without providing direction information.

[0043] Sensor misdetection: The sensor incorrectly identifies the target object, resulting in inaccurate data or system misjudgment, usually caused by environmental interference or sensor failure.

[0044] Track robot: An intelligent robot that can run along a specific track. It combines robotics technology, automatic control technology and track systems, and is usually designed to perform specific tasks, such as material transportation in industrial production, equipment inspection, environmental monitoring and many other fields.

[0045] Next, the scenarios involved in this application are explained:

[0046] Robot movement relies on real-time detection of the surroundings by sensors, and then the subsequent movement mode is determined based on the detection results. Especially for rail robots, since the movement path is fixed and the speed is fast, it must be able to accurately and quickly determine whether there are obstacles ahead based on the detection results, and then make a choice among the three movement states of stopping, decelerating and maintaining the current speed. This requires sensors with high precision and fast response capabilities.

[0047] In actual applications, a sensor is provided in front of the rail robot to detect in advance whether there are obstacles near the front track. At the same time, a sensor can also be provided at the rear of the rail robot so that when the rail robot needs to reverse or other equipment approaches the rail robot from behind, the rear situation can be detected in time to prevent rear-end collisions.

[0048] Among them, the sensors commonly used in rail robots include ultrasonic sensors and TOF sensors. Although TOF sensors have a simple structure, low cost, and sensitive response, they have certain requirements for ambient light and cannot detect the distance of objects that are close to black bodies or transparent objects. In addition, they are affected by the environment during use, and "ghost" false alarms may occur. Ultrasonic sensors have strong air penetration, are not easily affected by particles such as dust, are not sensitive to light and color, can identify transparent and dark objects, and are not easily disturbed by environmental electromagnetic fields. However, they will be disturbed by high-frequency noise and self-ultrasound, and can only measure distance without azimuth information. In addition, due to the diffusion angle of ultrasonic waves, false alarms are easily generated outside the theoretical field of view.

[0049] Figure 1 Schematic diagram of the structure of the track robot provided for this application Figure 1 ,like Figure 1 As shown, the track robot includes two ultrasonic sensors and two TOF sensors, wherein one ultrasonic sensor and one TOF sensor are arranged in front of the track robot, and another ultrasonic sensor and another TOF sensor are arranged in the rear of the track robot.

[0050] It should be understood that the front refers to the direction in which the track robot moves, and the rear refers to the direction in which the track robot moves backward.

[0051] It should be understood that the embodiment of the present application does not limit the number of sensors. That is, the rail robot may include one ultrasonic sensor and one TOF sensor, or may include multiple ultrasonic sensors and multiple TOF sensors.

[0052] In practical applications, other types of sensors may also be provided in the rail robot, such as laser sensors, visual sensors, etc., and the embodiments of the present application do not impose specific restrictions on this.

[0053] Therefore, in the process of rail robots using sensors to detect the environment, sensor misdetection is inevitable. Moreover, in the scenario where multiple sensors are used at the same time, the misdetection problems of different sensors will be superimposed, further increasing the risk of misdetection. Once a misdetection occurs, the rail robot may take unnecessary braking or deceleration measures, which not only affects the motion stability of the rail robot, but also reduces the inspection efficiency. Therefore, reducing the sensor misdetection rate is an issue that needs to be solved urgently.

[0054] At present, in the scenario where multiple sensors are used simultaneously, obstacles are mainly identified by combining the ranging information of TOF data and ultrasonic data to generate obstacle fusion point clouds in real time, thereby reducing sensor misdetection.

[0055] However, the existing technology is mainly used in scenes with transparent objects, mirror reflections and dark objects. The TOF sensor cannot detect the distance of nearly black bodies or transparent objects, but the ultrasonic sensor can recognize the characteristics of transparent and dark objects. By combining the distance measurement information of TOF data and ultrasonic data, the sensor false detection is reduced. However, there are many other scenarios where sensor false detection problems may exist, such as sensor failures. In these scenarios, the existing technology cannot solve the sensor false detection problem, resulting in a high sensor false detection rate, which in turn leads to low accuracy in controlling the track robot based on erroneous detection results.

[0056] Based on the above technical problems, the inventive concept of the present application is as follows: Considering that the TOF sensor and the ultrasonic sensor may have overlapping detection areas. Therefore, it is possible to determine whether the TOF sensor and the ultrasonic sensor have false detection problems by judging whether the obstacles existing in the overlapping detection area are detected by both sensors at the same time, and then accurately control the rail robot according to the detection results. At the same time, considering that the closer the obstacle is to the rail robot, the more accurate the sensor detection is. Therefore, when the ultrasonic sensor detects the existence of an obstacle at a position close to the rail robot, it controls the rail robot as a credible non-falsely detected obstacle, thereby avoiding the risk of collision between the rail robot and the obstacle, and further improving the accuracy of controlling the rail robot.

[0057] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0058] Figure 2 Schematic diagram of the process of the rail robot obstacle avoidance method provided in this application Figure 1 ,like Figure 2 As shown, the track robot obstacle avoidance method can be implemented by the following steps:

[0059] S21. Determine first obstacle information according to a preset safe obstacle avoidance area of ​​the rail robot and TOF data.

[0060] The execution subject of the embodiment of the present application is a track robot, which is equipped with a TOF sensor and an ultrasonic sensor. The structure of the track robot can refer to Figure 1 The embodiments shown are not described in detail here. The TOF sensor and the ultrasonic sensor are located on the same side. That is, the TOF sensor and the ultrasonic sensor can be located at the front side of the track robot at the same time, or at the back side of the track robot at the same time.

[0061] Among them, the preset safety obstacle avoidance area is a pre-set area, and objects in this area are at risk of colliding with the track robot.

[0062] The TOF data may be the original depth map collected by the TOF sensor at a certain moment, or the depth map obtained by aligning the original depth map collected by the TOF sensor and the original ultrasonic data collected by the sensor in time. That is, the TOF data may be the original depth map collected directly by the TOF sensor, or the depth map obtained by post-processing the original depth map, which may be determined according to actual conditions, and the embodiments of the present application do not impose specific restrictions on this.

[0063] The TOF data includes the distance and direction between an object within the acquisition range of the TOF sensor at a certain moment and the TOF sensor.

[0064] The first obstacle information includes the obstacle position and the first obstacle type, and the first obstacle type includes the first type. It should be understood that the obstacle position can be determined according to the distance and direction between the object and the TOF sensor in the TOF data.

[0065] Since TOF data is the straight-line distance between the object and the TOF sensor, and TOF data is an N×N depth map, based on the installation position of the TOF sensor and the position of each pixel in the depth map, the projection values ​​of each object in the three spatial directions in the right-handed coordinate system can be calculated with the geometric center of the TOF sensor as the origin.

[0066] In a possible implementation, the sensor can be used as the origin, and in the right-hand coordinate system, the line connecting each detection point (the object within the acquisition range of the TOF sensor) and the origin can be determined, and the angle between the line and the X-axis, Y-axis, and Z-axis can be determined based on the installation position of the TOF sensor when it is installed and the prior knowledge of the TOF pixel information. After that, the component of the line on the X-axis, Y-axis, and Z-axis is determined based on the length of the line and the angle between the line and the X-axis, Y-axis, and Z-axis. Finally, the spatial obstacle avoidance threshold of the preset safe obstacle avoidance area on the X-axis, Y-axis, and Z-axis is calculated based on the preset safe obstacle avoidance area and the installation position of the TOF sensor. If the components of the line on the X-axis, Y-axis, and Z-axis are all less than the corresponding spatial obstacle avoidance threshold, it is determined that the object is an obstacle, and the first obstacle type is the first type. If any component of the line on the X-axis, Y-axis, and Z-axis is not less than the corresponding spatial obstacle avoidance threshold, it is determined that the object is a non-obstacle.

[0067] The X-axis direction is the forward direction of the track robot, the Z-axis direction is the direction perpendicular to the ground and upward, and the Y-axis direction is the direction perpendicular to both the X-axis and the Z-axis.

[0068] Figure 3 This is a schematic diagram of the projection components in the right-handed coordinate system provided by this application. Figure 3 As shown, For the detection point, The angle with the X axis is , The angle with the Z axis is , The angle with the Y axis is , The degree of the line connecting the origin is d.

[0069] The relationship between the line connecting the origin and the components of the line on the X-axis, Y-axis, and Z-axis can be achieved by the following formula:

[0070]

[0071] in, for The length of the line connecting the origin, is the component of the line on the X-axis, is the component of the line on the Y axis, is the component of the line on the Z axis.

[0072] The projection length of the line on the XY axis can be achieved by the following formula:

[0073]

[0074] in, It is the projection length of the line on the XY axis.

[0075] The projection length of the line on the XZ axis can be achieved by the following formula:

[0076]

[0077] in, is the projection length of the line on the XZ axis.

[0078] The projection length of the line on the YZ axis can be achieved by the following formula:

[0079]

[0080] in, is the projection length of the line on the YZ axis.

[0081] The relationship between the angles with each coordinate axis and the components of the connecting line on each coordinate axis can be realized by the following formula:

[0082]

[0083]

[0084]

[0085] According to the above formula, the components of the connecting line on each coordinate axis have the following equivalent relationship:

[0086]

[0087] Furthermore, according to the above formula, , , It can be expressed by the following formula:

[0088]

[0089]

[0090]

[0091] S22: The minimum value between the braking distance of the rail robot and the preset detection distance is determined as the first distance, and the maximum value between the braking distance of the rail robot and the preset distance is determined as the second distance.

[0092] Among them, the braking distance is the distance that the rail robot travels when it decelerates to a stop by applying the brakes, the preset detection distance is the preset minimum ultrasonic detection distance that can cover the rail robot body, the deceleration distance is the distance that the rail robot travels when it decelerates to a stop at a preset acceleration, and the preset distance is the preset minimum distance between the rail robot and a fixed obstacle. In practical applications, the preset distance can be the minimum required distance from a fixed obstacle such as a wall in the installation specification of the rail robot.

[0093] It should be understood that in practical applications, the braking distance should be less than or equal to the deceleration distance, and therefore the first distance should be less than or equal to the second distance.

[0094] S23. Determine second obstacle information according to the ultrasonic data, the first distance, and the second distance.

[0095] Among them, the same as TOF data, it can be the original ultrasonic wave collected by the ultrasonic sensor at a certain moment, or it can be the ultrasonic data at a certain moment obtained after the original depth map collected by the TOF sensor and the original ultrasonic data collected by the sensor are aligned in time. That is, the ultrasonic data can be the original ultrasonic data directly collected by the ultrasonic sensor, or it can be the ultrasonic data obtained by post-processing the original ultrasonic data, which can be determined according to the actual situation, and the embodiment of the present application does not impose specific restrictions on this.

[0096] It should be understood that the ultrasonic data in this step and the TOF data in S21 are data at the same time.

[0097] The second obstacle information includes the obstacle distance and the second obstacle type. The obstacle distance refers to the straight-line distance between the obstacle and the ultrasonic sensor, which can be determined based on the ultrasonic data. The second obstacle type includes the first type and the second type. The distance between the obstacle of the second type and the track robot is smaller than the distance between the obstacle of the first type and the track robot.

[0098] In a possible implementation, if the ultrasonic data indicates that the object distance is less than or equal to the first distance, the second obstacle type is determined to be the second type. If the object distance is greater than the first distance and the object distance is less than the second distance, the second obstacle type is determined to be the first type. The second obstacle information is determined according to the object distance and the second obstacle type.

[0099] In practical applications, it can be determined whether the object distance indicated by the ultrasonic data is greater than or equal to the second distance. If it is greater than the second distance, the object corresponding to the object distance is determined as a non-obstacle; if the object distance is less than the second distance, the object corresponding to the object distance is determined as an obstacle, and the object distance is compared with the first distance and the second distance again, so as to determine the second obstacle type of the obstacle;

[0100] In this implementation, a dual threshold strategy (first distance and second distance) is used to determine the second obstacle type. Since the first distance is the minimum value of the braking distance of the rail robot and the preset detection distance, if the obstacle distance is less than the first distance, it means that the distance between the obstacle and the rail robot is very close at this time, and it is necessary to brake to avoid the rail robot from colliding with the obstacle, so the second obstacle type is determined to be the second type. The first distance is the maximum value of the reduced stop distance of the rail robot and the preset distance. If the obstacle distance is less than the first distance and greater than the second distance, it means that the obstacle is within the range that affects the travel of the rail robot, but the rail robot can be prevented from colliding with the obstacle by reducing the stop, etc., so the second obstacle type is determined to be the first type. The urgency of the obstacle is determined by the dual threshold strategy, so that the rail robot can be accurately controlled according to the urgency of the obstacle in the future.

[0101] S24: Determine a credible type of each obstacle according to the first obstacle information, the second obstacle information, the first field of view angle of the TOF sensor, and the second field of view angle of the ultrasonic sensor.

[0102] In a possible implementation, S24 may be implemented through the following steps A1-A5:

[0103] A1. Determine an overlapping detection area of ​​the TOF sensor and the ultrasonic sensor according to the first field of view angle and the second field of view angle.

[0104] In A1, the TOF sensor's installation position can be used as the origin, and the detection area of ​​the TOF sensor can be fitted according to the first field of view of the TOF sensor in the right-handed coordinate system. At the same time, the ultrasonic sensor's installation position can be used as the origin, and the detection area of ​​the ultrasonic sensor can be fitted according to the second field of view of the ultrasonic sensor in the right-handed coordinate system.

[0105] After the detection area of ​​the TOF sensor and the detection area of ​​the ultrasonic sensor are determined, the overlapping detection area of ​​the two can be determined as the overlapping detection area of ​​the TOF sensor and the ultrasonic sensor.

[0106] A2. If the obstacle of the first type is not within the overlapping detection area, the credible type of the obstacle is determined to be credible.

[0107] A3. If the obstacle of the first type is within the overlapping detection area, and the obstacle position corresponding to the obstacle in the first obstacle information matches the obstacle distance corresponding to the obstacle in the second obstacle information, the credible type of the obstacle is determined to be credible.

[0108] In actual applications, since the ultrasonic sensor can only detect the distance between itself and the object, it cannot know the direction of the object. Therefore, when the first type of obstacle is detected by the ultrasonic sensor, it is necessary to determine whether the position points corresponding to the obstacle distance of the TOF sensor to the obstacle are all in the overlap detection area. If so, it is determined that the first type of obstacle is in the overlap detection area; if any position point is not in the overlap detection area, it is determined that the obstacle is not in the overlap detection area.

[0109] When the first type of obstacle is detected by the TOF sensor, it can be directly determined whether the obstacle is in the overlapping detection area according to the obstacle position corresponding to the obstacle.

[0110] Among them, determining whether the obstacle position corresponding to the obstacle in the first obstacle information and the obstacle distance corresponding to the obstacle in the second obstacle information match can be achieved through the following process: according to the obstacle distance in the second obstacle information, multiple position points whose distance between the obstacle and the ultrasonic sensor is the obstacle distance are determined. Then, it is determined whether any position point is the same as the obstacle position in the second obstacle information. If so, it is determined that the obstacle position corresponding to the obstacle in the first obstacle information and the obstacle distance corresponding to the obstacle in the second obstacle information match, indicating that the obstacle is detected by two sensors at the same time; if all the position points are different from the obstacle position in the second obstacle information, it is determined that the obstacle position corresponding to the obstacle in the first obstacle information and the obstacle distance corresponding to the obstacle in the second obstacle information do not match.

[0111] For example, assuming that the distance between the first type of obstacle and the ultrasonic sensor is 20 cm, it is determined whether all the position points at a distance of 20 cm from the ultrasonic sensor are in the overlap detection area. If all the position points at a distance of 20 cm from the ultrasonic sensor are in the overlap detection area, it is determined that the obstacle is in the overlap detection area; if any position point at a distance of 20 cm from the ultrasonic sensor is not in the overlap detection area, it is determined that the first type of obstacle is not in the overlap detection area.

[0112] Further, after determining that the first type of obstacle is in the overlapping detection area, it is determined whether any of the above position points is the same as the obstacle position in the second obstacle information, and then it is determined whether the obstacle position corresponding to the obstacle in the first obstacle information and the obstacle distance corresponding to the obstacle in the second obstacle information match.

[0113] A4. If the obstacle of the first type is within the overlapping detection area, and the obstacle position corresponding to the obstacle in the first obstacle information and the obstacle distance corresponding to the obstacle in the second obstacle information do not match, the credible type of the obstacle is determined to be untrustworthy.

[0114] A5. Determine the credibility type of the second type of obstacle as credible.

[0115] In this implementation, when an obstacle is in the overlap detection area, the obstacle should be detected by two sensors at the same time in theory. Therefore, when it is determined that the first type of obstacle is in the overlap detection area, it can be determined whether the obstacle is detected by two sensors by judging whether the obstacle position corresponding to the obstacle in the first obstacle information and the obstacle distance corresponding to the obstacle in the second obstacle information match. If it is determined that the obstacle is detected by two sensors at the same time, it means that the obstacle is credible and there is no sensor misdetection; if it is determined that the obstacle is not detected by two sensors at the same time, it means that the obstacle is uncredible, and there is a problem in one of the sensors that causes the obstacle to be not detected or to be detected incorrectly. At the same time, since the credible type of the obstacle is determined in real time, and since the urgency of the second type of obstacle is high, in order to avoid the real obstacle being mistakenly determined as an uncredible obstacle at a certain moment due to some unexpected circumstances, even if the wrong conclusion of the previous moment is corrected at the next moment, the real obstacle is mistakenly determined as a credible obstacle, which may cause the track robot to have no time to stop and the two to collide. For this reason, the second type of obstacles are directly determined as credible obstacles to avoid the problem of collision between the rail robot and the obstacles, thereby further improving the control accuracy of the rail robot.

[0116] S25. Control the track robot to run according to the trustworthy type of each obstacle.

[0117] It should be understood that this step can be implemented by Figure 4 The embodiments shown are explained and will not be described in detail here.

[0118] The rail robot obstacle avoidance method provided by the embodiment of the present application is that the rail robot determines the first obstacle information according to the preset safe obstacle avoidance area and TOF data of the rail robot. After that, the minimum value of the rail robot's braking distance and the preset detection distance is determined as the first distance, and the maximum value of the rail robot's reduced stop distance and the preset distance is determined as the second distance. Then, the second obstacle information is determined according to the ultrasonic data, the first distance and the second distance. Finally, the credible type of each obstacle is determined according to the first obstacle information, the second obstacle information, the first field of view of the TOF sensor and the second field of view of the ultrasonic sensor, and the operation of the rail robot is controlled according to the credible type of each obstacle. Among them, the first obstacle information includes the obstacle position and the first obstacle type, and the first obstacle type includes the first type. The reduced stop distance is the distance traveled by the rail robot when it decelerates to stop at a preset acceleration, and the preset distance is the preset minimum distance between the rail robot and the fixed obstacle. The second obstacle information includes the obstacle distance and the second obstacle type, and the second obstacle type includes the first type and the second type. The distance between the obstacle in the second type and the rail robot is less than the distance between the obstacle in the first type and the rail robot.

[0119] In the present technical solution, sensor false detection can be detected in multiple scenarios, and the first obstacle information detected by the TOF sensor and the second obstacle information detected by the ultrasonic sensor can be determined based on the TOF data and ultrasonic data at the same time. Then, based on the first obstacle information and the second obstacle information, it is determined whether the obstacle in the overlapping detection area of ​​the TOF sensor and the ultrasonic sensor is detected by both sensors at the same time to determine the credible type of the obstacle. At the same time, the urgency of the obstacle detected by the ultrasonic sensor is determined based on the dual threshold strategy. For the second type of obstacles with a higher degree of urgency, the sensor false detection process is not performed on them, and they are directly determined as credible obstacles, which can ensure that the rail robot has reaction time to brake and stop, thereby avoiding the collision between the rail robot and the obstacle and improving the control accuracy of the rail robot.

[0120] Next, through Figure 4 The implementation process of S25 is explained.

[0121] Figure 4 Schematic diagram of the process of the rail robot obstacle avoidance method provided in this application Figure 2 .like Figure 4 As shown, S25 can be implemented by the following steps:

[0122] S41. Determine, according to the trust type of each obstacle, a trustworthy obstacle closest to the track robot and an untrustworthy obstacle closest to the track robot.

[0123] The distance between the obstacle and the track robot can be the straight-line distance between the obstacle and the track robot, or the X-direction projection distance of the straight-line distance between the obstacle and the track robot (only for TOF sensor).

[0124] S42, determining a first speed at which the track robot moves to a credible obstacle and a second speed at which the track robot moves to a non-credible obstacle.

[0125] The first speed and the second speed can be determined by the following formula:

[0126]

[0127]

[0128] in, is the first speed, is the second speed, is the current reduction ratio, is the current speed, is the distance between the credible obstacle (the credible obstacle closest to the track robot) and the track robot, is the distance between the untrusted obstacle (the untrusted obstacle closest to the track robot) and the track robot.

[0129] S43, determining the target speed of the track robot according to the preset speed, the first speed and the second speed.

[0130] Among them, the braking distance corresponding to the preset speed is close to 0.

[0131] In one implementation, if the first speed is less than or equal to the second speed, the first speed is determined as the target speed; if the first speed is less than or equal to the preset speed, the first speed is determined as the target speed; if the first speed is greater than the second speed and the second speed is greater than the preset speed, the second speed is determined as the target speed; if the first speed is greater than the second speed and the second speed is equal to the preset speed, the second speed is determined as the target speed; if the preset speed is greater than the second speed and the preset speed is less than the first speed, the preset speed is determined as the target speed.

[0132] In this implementation, when the first speed is less than or equal to the second speed, it means that the trusted obstacle is closer to the track robot than the untrusted obstacle, and the untrusted obstacle does not affect the movement of the track robot. At this time, it is necessary to determine the first speed as the target speed, and control the track robot to move forward with the target speed as the target to ensure that the track robot can stop in front of the trusted obstacle. When the first speed is less than or equal to the preset speed, it means that the first speed is a very small value at this time, so it is necessary to determine the first speed as the target speed. When the first speed is greater than the second speed, and the second speed is greater than the preset speed, and when the first speed is greater than the second speed, and the second speed is equal to the preset speed, it means that the untrusted obstacle is closer to the track robot than the trusted obstacle. In order to prevent the trusted obstacle from being mistakenly determined as an untrusted obstacle, the untrusted obstacle can be temporarily trusted, and the second speed corresponding to the untrusted obstacle is determined as the target speed. The trusted type of the untrusted obstacle is updated in real time during the process of moving forward. If the previously determined trusted type corresponding to the untrusted obstacle is accurate, then when the rail robot is very close to both the untrusted obstacle and the trusted obstacle, and the first speed is less than the preset speed, the rail robot will be controlled according to the first speed corresponding to the trusted obstacle. If the previously determined trusted type corresponding to the untrusted obstacle is inaccurate, that is, the untrusted obstacle is actually a trusted obstacle, then since the rail robot is now controlled according to the second speed corresponding to the obstacle, it can also be guaranteed that the rail robot stops in front of the obstacle.

[0133] S44, controlling the track robot to run at the target speed.

[0134] In this implementation, considering that there may be unexpected situations that cause the determined trust type of the obstacle to be wrong, and the characteristics that the detection capability and accuracy of the sensor will increase when the rail robot approaches the obstacle, the determined trust type of the obstacle may be updated. The speed control strategy is updated in real time to suppress the impact of the previous misjudgment of the obstacle type and the misjudgment of the trust type of the obstacle. By comparing the preset speed, the first speed and the second speed, the target speed of the rail robot is determined to ensure that the vehicle can stop in front of the obstacle in time to avoid collision with the obstacle, further improving the control accuracy of the rail robot.

[0135] It should be understood that in actual applications, the obstacle type and the credible type of the obstacle corresponding to a certain moment are determined based on the TOF data and ultrasonic data at that moment, and then the target speed is determined, and the rail robot is controlled to travel at the target speed. Afterwards, at the next moment, the obstacle type and the credible type of the obstacle corresponding to that moment are updated based on the TOF data and ultrasonic data at that moment, and then the target speed is updated, and the rail robot is controlled to travel at the updated target speed. At the same time, the rail robot will dynamically determine the target speed in real time until the target speed and the actual speed form a dynamic balance.

[0136] Through the above technical solution, the sensor false detection and speed control form a linkage mechanism, which can not only ensure that the rail robot does not affect the speed of the equipment when it is falsely detected at a long distance, but also ensure that it stops in time when a close obstacle is detected. Especially when the obstacle is close and the obstacle type changes, the impact on the speed of the rail robot is also small, thereby effectively enhancing the stability and safety of the robot's movement and reducing the missed detection rate of non-obstacles.

[0137] In practical applications, TOF sensors and ultrasonic data usually cannot be collected at the same time. Therefore, before S21, the first TOF data collected by the TOF sensor and the first ultrasonic data collected by the ultrasonic sensor can be aligned in time according to the running speed of the track robot to obtain TOF data and ultrasonic data.

[0138] It should be understood that the first TOF data is Figure 2 The original depth map in the illustrated embodiment, the first ultrasonic data is the original ultrasonic data.

[0139] In this implementation, by aligning the first TOF data and the first ultrasonic data in time, it is possible to ensure that relevant information of the obstacle is determined in the same time dimension, laying a foundation for subsequent determination of accurate control of the operation of the track robot.

[0140] According to the running speed of the track robot, the first TOF data collected by the TOF sensor and the first ultrasonic data collected by the ultrasonic sensor are aligned in time, and the TOF data and the ultrasonic data can be obtained by Figure 5 The illustrated embodiment is explained.

[0141] Figure 5 Schematic diagram of the process of the rail robot obstacle avoidance method provided in this application Figure 3 .like Figure 5As shown, according to the running speed of the track robot, the first TOF data collected by the TOF sensor and the first ultrasonic data collected by the ultrasonic sensor are aligned in time, and the TOF data and the ultrasonic data are obtained, which can be achieved by the following steps:

[0142] S51. According to the running speed of the track robot, the first TOF data collected by the TOF sensor and the first ultrasonic data collected by the ultrasonic sensor are aligned in time to obtain the second TOF data and the second ultrasonic data.

[0143] In a possible implementation, a certain sensor (sensor A) is used as a reference, and according to the running speed of the rail robot, the sensor data collected by the sensor at each moment is determined, and the theoretical position A of the rail robot corresponding to the moment is determined. Then, according to the collection time interval between another sensor (sensor B) and sensor A, according to the collection time interval and the running speed of the rail robot, the moving distance within the collection time interval is determined. Then, according to the moving distance, the sensor data collected by sensor B is reversed, and the sensor data corresponding to sensor B when the rail robot is at the theoretical position A is obtained.

[0144] S52 , performing time domain smoothing and noise elimination processing on the second TOF data and the second ultrasonic data to generate TOF data and ultrasonic data.

[0145] Among them, time domain smoothing is to smooth multiple frames of continuous data to eliminate occasional false detections and retain short-term determination of obstacles; noise elimination is based on filtering in the spatial and temporal domains to eliminate noise in sensor data.

[0146] S53: If it is determined according to the abnormal mark and / or the preset condition that any one of the TOF sensor and the ultrasonic sensor is faulty, the detection range of the other sensor is increased.

[0147] Among them, the abnormal mark is a mark generated by the sensor when it determines that it has a fault, and the preset condition is the state of the sensor when it has a fault determined based on prior knowledge. When it is determined that any sensor of the TOF sensor and the ultrasonic sensor has a fault based on the abnormal mark and / or the preset condition, the detection range of the other sensor is increased to ensure that the position that the rail robot needs to detect is safely covered without blind spots, thereby ensuring the safety of the movement process.

[0148] In the above technical solution, the original collected sensor data is smoothed in the time domain and noise is eliminated to remove erroneous information, thus laying the foundation for the subsequent accurate control of the track robot.

[0149] In order to explain the present application more clearly, it is explained below through specific examples.

[0150] Figure 6 Schematic diagram of the process of the rail robot obstacle avoidance method provided in this application Figure 4 .like Figure 6 As shown, the track robot obstacle avoidance method comprises the following steps:

[0151] S61 : Acquire first TOF data of the TOF sensor and first ultrasonic data of the ultrasonic sensor.

[0152] S62 , pre-processing the first TOF data and the first ultrasonic data respectively to generate TOF data and ultrasonic data at the same time.

[0153] It should be understood that the implementation method and principle of this step can refer to Figure 5 The embodiments shown will not be described in detail here.

[0154] S63: Perform polygon fitting on the detection areas of the TOF sensor and the ultrasonic sensor.

[0155] The installation position of the TOF sensor can be used as the origin, and the detection area of ​​the TOF sensor can be fitted using a polygon in the right-handed coordinate system according to the first field of view of the TOF sensor. At the same time, the installation position of the ultrasonic sensor can be used as the origin, and the detection area of ​​the ultrasonic sensor can be fitted using a polygon in the right-handed coordinate system according to the second field of view of the ultrasonic sensor.

[0156] S64: Determine first obstacle information according to the TOF data, and determine second obstacle information according to the ultrasonic data.

[0157] S65. Multi-sensor data fusion.

[0158] S66, controlling the track robot to run at a target speed.

[0159] Furthermore, Figure 7 Schematic diagram of the process of the rail robot obstacle avoidance method provided in this application Figure 5 .like Figure 7 As shown, determining the first obstacle information according to the TOF data in S64 can be achieved by the following steps:

[0160] S71. Determine the installation location of the TOF sensor and preset a safe obstacle avoidance area.

[0161] S72, calculating the spatial obstacle avoidance thresholds of the preset safe obstacle avoidance area on the X-axis, Y-axis, and Z-axis of the right-hand coordinate system.

[0162] S73: Determine a depth map corresponding to the TOF sensor.

[0163] S74. Determine the projection components of the object on the X-axis, Y-axis, and Z-axis of the right-hand coordinate system pixel by pixel.

[0164] S75. Determine whether the projection component of each axis is smaller than the corresponding spatial obstacle avoidance threshold.

[0165] If not, that is, the projection component of any axis is greater than the corresponding spatial obstacle avoidance threshold, then execute S76. If the projection component of each axis is less than the corresponding spatial obstacle avoidance threshold, then execute S77;

[0166] S76: Determine the object as a non-obstacle.

[0167] S77: Determine the object as an obstacle, and determine the obstacle type as initial obstacle 1.

[0168] It should be understood that the initial obstacle 1 is of the first type.

[0169] Furthermore, Figure 8 Schematic diagram of the process of the rail robot obstacle avoidance method provided in this application Figure 6 .like Figure 8 As shown, determining the second obstacle information according to the ultrasonic data in S64 can be achieved by the following steps:

[0170] S81. Determine the installation position, preset detection distance, and preset distance of the ultrasonic sensor.

[0171] S82. Calculate threshold 1 and threshold 2.

[0172] It should be understood that threshold 1 is the first distance and threshold 2 is the second distance.

[0173] S83: Is the object distance greater than threshold 1?

[0174] If yes, execute S84; if no, determine the object as an obstacle and determine the obstacle type as initial obstacle 2.

[0175] It should be understood that the initial obstacle 2 is of the second type.

[0176] S84: Whether the object distance is greater than threshold 2.

[0177] If so, the object is determined to be a non-obstacle; if not, the object is determined to be an obstacle, and the obstacle type is determined to be initial obstacle 1.

[0178] Fig. 9 Schematic diagram of the process of the rail robot obstacle avoidance method provided in this application Figure 7 .like Fig. 9 As shown, S65 can be implemented by the following steps:

[0179] S91. Obtain obstacle type.

[0180] S92: Determine whether the obstacle type is initial obstacle 1.

[0181] If not, the obstacle type is determined to be initial obstacle 2, and the obstacle is determined to be obstacle type 1; if so, S93 is executed.

[0182] S93: Determine the overlapping detection area of ​​the TOF sensor and the ultrasonic sensor.

[0183] S94: Determine whether the obstacle is in the overlapping detection area.

[0184] If yes, execute S95; if no, determine the obstacle as obstacle type 1.

[0185] S95. Determine whether the obstacle is detected by other sensors.

[0186] If yes, the obstacle is determined as obstacle type 2; if no, the obstacle is determined as obstacle type 3.

[0187] It should be understood that obstacle type 1 refers to the situation where the obstacle is located in the blind spot of other detectors, or is very close to the device, and emergency braking needs to be triggered. Such obstacles are considered to be trusted obstacles. Obstacle type 2 is an obstacle detected by two detectors at the same time, which can also be considered to be a trusted obstacle. Obstacle type 3 means that the obstacle is in the overlapping detection area, but other sensors corresponding to the overlapping detection area do not detect the obstacle, and the obstacle is considered to be an untrustworthy obstacle.

[0188] Among them, the obstacles corresponding to obstacle type 2 and obstacle type 1 are trusted obstacles, and the obstacles corresponding to obstacle type 3 are untrustworthy obstacles.

[0189] Fig.10 Schematic diagram of the process of the rail robot obstacle avoidance method provided in this application Figure 8 .like Fig.10 As shown, S66 can be implemented by the following steps:

[0190] S101, OK , , .

[0191] in, is the preset speed.

[0192] S102: Determine whether .

[0193] If so, then Determine if it is the target speed, if not, execute S103.

[0194] S103: Determine whether .

[0195] If so, then Determine if it is the target speed, if not, execute S104.

[0196] S104: Determine whether .

[0197] If so, then Determine the target speed. If not, Determine the target speed.

[0198] After that, the track robot is controlled to run at the target speed.

[0199] In summary, the technical solution provided by this application achieves real-time obstacle avoidance, false detection elimination and motion control when multiple sensors are combined through data fusion of multiple sensors, without the need for complex map construction and target trajectory calculation steps, thus ensuring the stability and safety of the rail robot during movement. At the same time, an ultrasonic dual-threshold obstacle avoidance strategy is proposed, which combines the preset detection distance with the robot speed and environment, making full use of the detection characteristics of the ultrasonic sensor, effectively reducing false detection caused by ultrasonic waves while ensuring safety, and enhancing the stability of the rail robot's movement.

[0200] The following is an embodiment of the device of the present application, which can be used to execute the embodiment of the method of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method of the present application.

[0201] Fig.11 The schematic diagram of the structure of the track robot obstacle avoidance device provided in this application is as follows: Fig.11 As shown, the rail robot obstacle avoidance device 110 provided in this embodiment includes:

[0202] A determination module 111 is used to determine first obstacle information according to a preset safe obstacle avoidance area of ​​the rail robot and the TOF data, where the first obstacle information includes an obstacle position and a first obstacle type, where the first obstacle type includes a first type;

[0203] The determination module 111 is further used to determine the minimum value of the braking distance of the rail robot and the preset detection distance as the first distance, and determine the maximum value of the deceleration distance of the rail robot and the preset distance as the second distance, the deceleration distance is the distance traveled by the rail robot when it decelerates to a stop at a preset acceleration, and the preset distance is the preset minimum distance between the rail robot and the fixed obstacle;

[0204] The determination module 111 is further used to determine second obstacle information according to the ultrasonic data, the first distance and the second distance, where the second obstacle information includes the obstacle distance and the second obstacle type, where the second obstacle type includes a first type and a second type, and the distance between an obstacle of the second type and the track robot is smaller than the distance between an obstacle of the first type and the track robot;

[0205] The determination module 111 is further used to determine the credible type of each obstacle according to the first obstacle information, the second obstacle information, the first field of view angle of the TOF sensor, and the second field of view angle of the ultrasonic sensor;

[0206] The control module 112 is used to control the operation of the track robot according to the trustworthy type of each obstacle.

[0207] In a possible implementation, the determination module 111 is specifically configured to:

[0208] If the object distance indicated by the ultrasonic data is less than or equal to the first distance, determining that the second obstacle type is the second type;

[0209] If the object distance is greater than the first distance and the object distance is less than the second distance, determining the second obstacle type is the first type;

[0210] The second obstacle information is determined according to the object distance and the second obstacle type.

[0211] In a possible implementation, the control module 112 is specifically configured to:

[0212] According to the trust type of each obstacle, determine the trustworthy obstacle closest to the track robot and the untrustworthy obstacle closest to the track robot;

[0213] Determine a first speed at which the track robot moves to a credible obstacle and a second speed at which the track robot moves to an uncredible obstacle;

[0214] According to the preset speed, the first speed and the second speed, the target speed of the track robot is determined, and the braking distance corresponding to the preset speed is close to 0;

[0215] Control the track robot to run at the target speed.

[0216] In a possible implementation, the determination module 111 is specifically configured to:

[0217] Determine an overlapping detection area of ​​the TOF sensor and the ultrasonic sensor according to the first field of view angle and the second field of view angle;

[0218] If the first type of obstacle is not within the overlap detection area, the credible type of the obstacle is determined to be credible;

[0219] If the obstacle of the first type is within the overlapping detection area, and the obstacle position corresponding to the obstacle in the first obstacle information matches the obstacle distance corresponding to the obstacle in the second obstacle information, then the credible type of the obstacle is determined to be credible;

[0220] If the obstacle of the first type is within the overlap detection area, and the obstacle position corresponding to the obstacle in the first obstacle information and the obstacle distance corresponding to the obstacle in the second obstacle information do not match, then the credible type of the obstacle is determined to be untrustworthy;

[0221] The trustworthy type of the second type of obstacle is determined to be trustworthy.

[0222] In a possible implementation, the determination module 111 is specifically configured to:

[0223] If the first speed is less than or equal to the second speed, the first speed is determined as the target speed;

[0224] If the first speed is less than or equal to the preset speed, determining the first speed as the target speed;

[0225] If the first speed is greater than the second speed, and the second speed is greater than the preset speed, the second speed is determined as the target speed;

[0226] If the first speed is greater than the second speed, and the second speed is equal to the preset speed, the second speed is determined as the target speed;

[0227] If the preset speed is greater than the second speed and the preset speed is less than the first speed, the preset speed is determined as the target speed.

[0228] In one possible implementation, before determining the first obstacle information based on the preset safe obstacle avoidance area of ​​the rail robot and the TOF data, the rail robot obstacle avoidance device also includes a preprocessing module for aligning the first TOF data collected by the TOF sensor and the first ultrasonic data collected by the ultrasonic sensor in time according to the running speed of the rail robot to obtain the TOF data and the ultrasonic data.

[0229] In a possible implementation, the preprocessing module is specifically used to:

[0230] According to the running speed of the track robot, the first TOF data and the first ultrasonic data collected by the TOF sensor are aligned in time to obtain the second TOF data and the second ultrasonic data.

[0231] Perform time domain smoothing and noise elimination processing on the second TOF data and the second ultrasonic data to generate TOF data and ultrasonic data;

[0232] If it is determined that any one of the TOF sensor and the ultrasonic sensor is faulty according to the abnormality mark and / or the preset condition, the detection range of the other sensor is increased.

[0233] The rail robot obstacle avoidance device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and this embodiment will not be described in detail here.

[0234] Fig.12 Schematic diagram of the structure of the track robot provided for this application Figure 2 .like Fig.12 As shown, the rail robot 120 provided in this embodiment includes: a TOF sensor 121, an ultrasonic sensor 122, at least one processor 123 and a memory 124.

[0235] Optionally, the rail robot 120 further includes a communication component 125 , wherein the processor 123 , the memory 124 and the communication component 125 are connected via a bus 126 .

[0236] In a specific implementation process, at least one processor 123 executes the computer-executable instructions stored in the memory 124, so that at least one processor 123 executes the above method.

[0237] The specific implementation process of the processor 123 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0238] In the above embodiments, it should be understood that the processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the invention can be directly implemented as a hardware processor, or can be implemented by a combination of hardware and software modules in the processor.

[0239] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (NVM), such as at least one disk storage.

[0240] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of this application is not limited to only one bus or one type of bus.

[0241] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0242] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0243] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0244] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0245] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0246] 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.

[0247] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0248] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0249] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0250] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A rail robot obstacle avoidance method, characterized in that: Applied to a rail robot, the rail robot is equipped with a time-of-flight TOF sensor and an ultrasonic sensor, and the method comprises: Determining first obstacle information according to a preset safe obstacle avoidance area of ​​the rail robot and the TOF data, wherein the first obstacle information includes an obstacle position and a first obstacle type, and the first obstacle type includes a first type; The minimum value between the braking distance of the rail robot and the preset detection distance is determined as the first distance, and the maximum value between the deceleration distance of the rail robot and the preset distance is determined as the second distance, wherein the deceleration distance is the distance traveled by the rail robot when it decelerates to a stop at a preset acceleration, and the preset distance is the preset minimum distance between the rail robot and the fixed obstacle; Determine second obstacle information according to the ultrasonic data, the first distance, and the second distance, wherein the second obstacle information includes an obstacle distance and a second obstacle type, wherein the second obstacle type includes the first type and a second type, and the distance between an obstacle of the second type and the track robot is smaller than the distance between an obstacle of the first type and the track robot; Determining a credible type of each obstacle according to the first obstacle information, the second obstacle information, a first field of view angle of the TOF sensor, and a second field of view angle of the ultrasonic sensor; According to the credible type of each obstacle, the track robot is controlled to run.

2. The method according to claim 1, characterized in that The determining the second obstacle information according to the ultrasonic data, the first distance, and the second distance includes: If the object distance indicated by the ultrasonic data is less than or equal to the first distance, determining that the second obstacle type is the second type; If the object distance is greater than the first distance and the object distance is less than the second distance, determining that the second obstacle type is the first type; The second obstacle information is determined according to the object distance and the second obstacle type.

3. The method according to claim 1 or 2, characterized in that: The step of controlling the operation of the track robot according to the trustworthy type of each obstacle comprises: According to the trust type of each obstacle, determine the trustworthy obstacle closest to the track robot and the untrustworthy obstacle closest to the track robot; Determine a first speed at which the track robot moves to the front of the credible obstacle and a second speed at which the track robot moves to the front of the uncredible obstacle; Determine a target speed of the track robot according to a preset speed, the first speed, and the second speed, wherein a braking distance corresponding to the preset speed approaches 0; The track robot is controlled to run at the target speed.

4. The method according to claim 2, characterized in that: The determining the credible type of each obstacle according to the first obstacle information, the second obstacle information, the first field of view angle of the TOF sensor, and the second field of view angle of the ultrasonic sensor includes: Determining an overlapping detection area of ​​the TOF sensor and the ultrasonic sensor according to the first field of view angle and the second field of view angle; If the obstacle of the first type is not within the overlap detection area, determining the credible type of the obstacle as credible; If the obstacle of the first type is within the overlap detection area, and the obstacle position corresponding to the obstacle in the first obstacle information matches the obstacle distance corresponding to the obstacle in the second obstacle information, determining that the credible type of the obstacle is credible; If the obstacle of the first type is within the overlap detection area, and the obstacle position corresponding to the obstacle in the first obstacle information and the obstacle distance corresponding to the obstacle in the second obstacle information do not match, determining that the credible type of the obstacle is untrustworthy; The plausible type of the second-type obstacle is determined to be plausible.

5. The method according to claim 3, characterized in that: Determining the target speed of the track robot according to the preset speed, the first speed and the second speed includes: If the first speed is less than or equal to the second speed, determining the first speed as the target speed; If the first speed is less than or equal to the preset speed, determining the first speed as the target speed; If the first speed is greater than the second speed, and the second speed is greater than the preset speed, determining the second speed as the target speed; If the first speed is greater than the second speed, and the second speed is equal to the preset speed, determining the second speed as the target speed; If the preset speed is greater than the second speed and the preset speed is less than the first speed, the preset speed is determined as the target speed.

6. The method according to claim 1 or 2, characterized in that: Before determining the first obstacle information according to the preset safe obstacle avoidance area of ​​the rail robot and the TOF data, the method includes: According to the running speed of the track robot, the first TOF data collected by the TOF sensor and the first ultrasonic data collected by the ultrasonic sensor are aligned in time to obtain the TOF data and the ultrasonic data.

7. The method according to claim 6, characterized in that The step of aligning the first TOF data collected by the TOF sensor and the first ultrasonic data collected by the ultrasonic sensor in time according to the running speed of the track robot to obtain the TOF data and the ultrasonic data includes: According to the running speed of the track robot, the first TOF data and the first ultrasonic data collected by the TOF sensor are aligned in time to obtain the second TOF data and the second ultrasonic data. Performing time domain smoothing and noise elimination processing on the second TOF data and the second ultrasonic data to generate the TOF data and the ultrasonic data; If it is determined according to the abnormal mark and / or the preset condition that any one of the TOF sensor and the ultrasonic sensor is faulty, the detection range of the other sensor is increased.

8. A rail robot obstacle avoidance device, characterized in that: Applied to a rail robot, the rail robot is equipped with a time-of-flight TOF sensor and an ultrasonic sensor, and the device comprises: A determination module, configured to determine first obstacle information according to a preset safe obstacle avoidance area of ​​the rail robot and TOF data, wherein the first obstacle information includes an obstacle position and a first obstacle type, and the first obstacle type includes a first type; The determination module is further used to determine the minimum value of the braking distance of the rail robot and the preset detection distance as the first distance, and determine the maximum value of the deceleration distance of the rail robot and the preset distance as the second distance, wherein the deceleration distance is the distance traveled by the rail robot when it decelerates to a stop at a preset acceleration, and the preset distance is the preset minimum distance between the rail robot and the fixed obstacle; The determination module is further used to determine second obstacle information according to the ultrasonic data, the first distance, and the second distance, the second obstacle information includes an obstacle distance and a second obstacle type, the second obstacle type includes the first type and a second type, and the distance between an obstacle of the second type and the track robot is smaller than the distance between an obstacle of the first type and the track robot; The determination module is further used to determine the credible type of each obstacle according to the first obstacle information, the second obstacle information, the first field of view angle of the TOF sensor, and the second field of view angle of the ultrasonic sensor; The control module is used to control the operation of the track robot according to the trustworthy type of each obstacle.

9. A rail robot, characterized in that: include: Time-of-flight TOF sensor, ultrasonic sensor, memory and processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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