A robot cliff detection method, chip and robot

Abstract

The application discloses a robot cliff detection method, a chip and a robot, and the method comprises the following steps: step 1, acquiring cliff detection values collected by each cliff sensor carried on the robot in real time; step 2, based on the cliff detection values collected by each cliff sensor carried on the robot in real time, it is judged whether the scene where the robot is currently located meets a preset first cliff scene, if yes, step 5 is entered, if not, step 3 is entered; step 3, based on the cliff detection values collected by each cliff sensor carried on the robot in real time, a fused cliff detection value is calculated; step 4, according to the fused cliff detection value, it is judged whether the scene where the robot is currently located meets a preset second cliff scene, if yes, step 5 is entered, if not, step 1 is returned; and step 5, it is determined that there is a cliff near the robot. The application solves the problem that the cliff detection scene of the robot is single, and improves the comprehensiveness of the cliff detection of the robot.

Description

Technical Field

[0001] This application relates to the field of cliff detection, specifically to a robotic cliff detection method, chip, and robot. Background Technology

[0002] In the field of robotics, cliffs typically refer to scenarios where there is a significant height difference between the robot and the ground it is currently moving on, such as low-lying areas or stairwells. Current cliff detection methods for robots employing multiple cliff sensors typically utilize these sensors at different robot locations to detect the presence of cliffs in multiple directions near the robot. The method for determining the presence of a cliff using cliff sensors usually involves comparing the detected cliff values ​​with a preset cliff threshold. This threshold is obtained by calibrating the cliff detection values ​​at a specific depth. However, due to the limitation of this preset threshold, some cliffs with shallower depths are difficult to detect. Therefore, existing technologies for cliff detection in robots suffer from limitations in terms of simplistic detection methods and poor comprehensiveness. Summary of the Invention

[0003] This application provides a robot cliff detection method, chip, and robot, with the specific technical solution as follows: A method for detecting cliffs using a robot includes the following steps: Step 1: Acquire the cliff detection values ​​collected in real time by each cliff sensor mounted on the robot, and then proceed to Step 2; Step 2: Based on the cliff detection values ​​collected in real time by each cliff sensor mounted on the robot, determine whether the current scene of the robot matches a preset first cliff scene. If yes, proceed to Step 5; otherwise, proceed to Step 3; Step 3: Calculate a fused cliff detection value based on the cliff detection values ​​collected in real time by each cliff sensor mounted on the robot, and then proceed to Step 4; Step 4: Based on the fused cliff detection value, determine whether the current scene of the robot matches a preset second cliff scene. If yes, proceed to Step 5; otherwise, return to Step 1. Step 5: Determine that there is a cliff near the robot; the robot is equipped with at least two cliff sensors.

[0004] Furthermore, the robot cliff detection method further includes: if the cliff detection values ​​collected in real time by the various cliff sensors mounted on the robot determine that the current scene of the robot does not conform to the preset first cliff scene, then determine whether the robot is in a horizontal state. If yes, then execute step 3; otherwise, return to step 1.

[0005] Further, step 2, which involves iterating through the cliff detection values ​​collected in real time by the various cliff sensors mounted on the robot to determine whether the current scene of the robot conforms to the preset first cliff scene, specifically includes: determining whether at least one cliff detection value among the multiple cliff detection values ​​collected in real time by the various cliff sensors mounted on the robot is less than or equal to the preset first cliff threshold; if at least one cliff detection value is less than or equal to the preset first cliff threshold, then the current scene of the robot is determined to conform to the preset first cliff scene; if all cliff detection values ​​are greater than the preset first cliff threshold, then the current scene of the robot is determined to not conform to the preset first cliff scene.

[0006] Furthermore, step 2 also includes: determining whether the cliff detection values ​​collected in real time by each cliff sensor on the robot are less than or equal to a preset first cliff threshold in ascending order; when a cliff detection value is detected to be greater than the preset first cliff threshold, the traversal of determining whether multiple cliff detection values ​​are less than or equal to the preset first cliff threshold ends; when at least one cliff detection value is detected to be less than or equal to the preset first cliff threshold, it is determined that the current scene where the robot is located conforms to the preset first cliff scene.

[0007] Furthermore, step 2 also includes: when the detected cliff detection value is greater than the minimum value among multiple cliff detection values, the traversal ends and it is determined whether the multiple cliff detection values ​​are less than or equal to the preset first cliff threshold, thus determining that the current scene where the robot is located does not conform to the preset first cliff scene.

[0008] Further, step 3, which involves calculating the fused cliff detection value based on the cliff detection values ​​collected in real time by the various cliff sensors mounted on the robot, specifically includes: Step 31: Selecting the maximum cliff detection value from the multiple cliff detection values ​​collected in real time by the various cliff sensors mounted on the robot, and proceeding to step 32; Step 32: Selecting the minimum cliff detection value from the multiple cliff detection values ​​collected in real time by the various cliff sensors mounted on the robot, and proceeding to step 33; Step 33: Calculating the difference between the maximum and minimum cliff detection values, using it as the cliff detection difference, and proceeding to step 34; Step 34: Using the cliff detection difference as the dividend, using the minimum cliff detection value as the divisor, and using the quotient of the dividend and the divisor as the fused cliff detection value.

[0009] Further, step 4, which involves determining whether the current scene of the robot conforms to the preset second cliff scene based on the fusion cliff detection value, specifically includes: determining whether the fusion cliff detection value is greater than or equal to the preset second cliff threshold; if the fusion cliff detection value is greater than or equal to the preset second cliff threshold, then it is determined that the current scene of the robot conforms to the preset second cliff scene; if the fusion cliff detection value is less than the preset second cliff threshold, then it is determined that the current scene of the robot does not conform to the preset second cliff scene.

[0010] This application also discloses a chip that internally stores a computer program, which, when executed by a processor, performs the robot cliff detection method as described in any of the preceding claims.

[0011] This application also discloses a robot, characterized in that the robot includes: a plurality of cliff sensors for real-time acquisition of cliff detection values ​​to detect whether a cliff exists near the robot; a chip as described above for storing a computer program for running a robot cliff detection method; and a processor for running the computer program stored inside the chip to execute the robot cliff detection method as described in any of the preceding claims based on the cliff detection values ​​acquired in real-time by the cliff sensors.

[0012] Furthermore, the robot also includes a gyroscope sensor for acquiring the robot's three-axis angular velocity to determine whether the robot is in a horizontal position.

[0013] The robot cliff detection method, chip, and robot described in this application optimize the accuracy of cliff detection results by fusing and calculating cliff detection values ​​collected by multiple cliff sensors. This specifically addresses the problems of single cliff detection methods and lack of communication between multiple cliff sensors in robots equipped with multiple cliff sensors, thereby improving the comprehensiveness of robot cliff detection. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating a robot cliff detection method according to one embodiment of this application. Detailed Implementation

[0015] The embodiments of this application will now be described in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described below are for illustrative purposes only and are not intended to limit the scope of this application.

[0016] To address the limitations of existing technologies in terms of the simplistic nature of robotic cliff detection methods and the limited height of cliff detection, this application proposes a robotic cliff detection method. This method specifically addresses the issues of limited detection methods and lack of communication between multiple cliff sensors in robots equipped with two or more cliff sensors. This application optimizes the accuracy of cliff detection results by fusing and calculating cliff detection values ​​collected by multiple cliff sensors.

[0017] The cliff described in this application refers to a scenario where there is a significant height difference between the robot's current ground and the ground it is currently moving on, which could easily cause the robot to get stuck, fall, or be unable to pass. Examples include low-lying areas and stairwells with large elevation differences. Specifically, the robot cliff detection method, such as... Figure 1 As shown, the process includes the following steps: Step 1: Obtain the cliff detection values ​​collected in real time by each cliff sensor mounted on the robot, and then proceed to Step 2; Step 2: Based on the cliff detection values ​​collected in real time by each cliff sensor mounted on the robot, determine whether the current scene of the robot matches the preset first cliff scene. If yes, proceed to Step 5; otherwise, proceed to Step 3; Step 3: Calculate the fused cliff detection value based on the multiple cliff detection values ​​collected in real time by each cliff sensor mounted on the robot, and then proceed to Step 4; Step 4: Based on the fused cliff detection value, determine whether the current scene of the robot matches the preset second cliff scene. If yes, proceed to Step 5; otherwise, return to Step 1; Step 5: Determine that there is a cliff near the robot; wherein, the robot is equipped with at least two cliff sensors.

[0018] Specifically, the cliff sensor can be, but is not limited to, an infrared ranging sensor or other sensor with ground distance detection capabilities. It uses the detected ground distance as the cliff detection value to assist the robot in determining whether the cliff sensor has detected a preset first cliff scenario. The preset first cliff scenario and the preset second cliff scenario are two pre-set cliff existence scenarios. The preset first cliff scenario is matched by analyzing individual cliff detection values ​​one by one; the preset second cliff scenario is matched by fusing multiple cliff detection values. This application uses the method of analyzing individual cliff detection values ​​one by one to match the preset first cliff scenario. When none of the cliff detection values ​​match the preset first cliff scenario, it uses the method of fusing multiple cliff detection values ​​to further match and detect the preset second cliff scenario. This enriches the robot's cliff detection scenarios and improves cliff detection accuracy through both single cliff detection value analysis and fusing cliff detection value analysis, solving the problems of a single cliff detection method and a lack of connection between multiple cliff sensors.

[0019] As a preferred embodiment of this application, the robot cliff detection method further includes: if it is determined that the current scene of the robot does not conform to the preset first cliff scene based on the cliff detection values ​​collected in real time by the various cliff sensors mounted on the robot, then it is determined whether the robot is in a horizontal state. If yes, then step 3 is executed; if no, then step 1 is returned.

[0020] Specifically, a robot being in a horizontal state means that the entire robot body is on a level surface, without any significant height difference between parts of the robot body and the ground. For example, when a robot is crossing an obstacle, a significant height difference between parts of the robot body and the ground would cause the robot to be in a non-horizontal state. Methods for determining whether a robot is in a horizontal state can be, but are not limited to, using a level mounted on the robot, or using a gyroscope mounted on the robot to obtain the robot's three-axis angular velocities. When the robot is not in a horizontal state, the cliff detection values ​​detected by the multiple cliff sensors mounted on the robot body are affected by the robot's non-horizontal state and may easily deviate from the actual environmental scene. Therefore, this embodiment determines whether the multiple cliff detection values ​​collected in real time by the multiple cliff sensors mounted on the robot are reliable by judging whether the robot is in a horizontal state. When the robot is in a horizontal state, the multiple cliff detection values ​​are determined to be reliable, and the calculation of fused cliff detection values ​​can be performed. Conversely, when the robot is not in a horizontal state, at least one cliff detection value may be affected by the robot's non-horizontal state and have an error. In this case, the robot is controlled to reacquire the cliff detection value, and the influence of environmental ground changes during the robot's movement is included in the cliff detection. This effectively avoids the impact of the robot's temporary crossing of bumps and obstacles on the cliff detection results, improves the accuracy of the robot's cliff detection, and avoids the situation where the robot loses its ability to cross bumps or obstacles due to avoiding cliffs.

[0021] As a preferred embodiment of this application, step 2, which involves iterating through the cliff detection values ​​collected in real time by the various cliff sensors mounted on the robot to determine whether the scene conforms to the preset first cliff scenario, specifically includes: determining whether at least one cliff detection value among the cliff detection values ​​collected in real time by the various cliff sensors mounted on the robot is less than or equal to the preset first cliff threshold; if at least one cliff detection value is less than or equal to the preset first cliff threshold, then the scene where the robot is currently located is determined to conform to the preset first cliff scenario; if all cliff detection values ​​are greater than the preset first cliff threshold, then the scene where the robot is currently located is determined to not conform to the preset first cliff scenario.

[0022] The preset first cliff threshold refers to a cliff detection value at a pre-set specified cliff depth. For example, the cliff detection value when the cliff sensor detects a cliff with a depth of 5 / 10 / 15 / 20 cm is calibrated as the preset first cliff threshold. By comparing the cliff detection values ​​collected in real time by multiple cliff sensors with the preset first cliff threshold, the relative relationship between the cliff depth detected by each cliff sensor and the cliff depth calibrated by the preset first cliff threshold can be determined. The preset first cliff scenario refers to a situation where there is a cliff around the robot and the cliff depth is greater than the cliff depth calibrated by the preset first cliff threshold. It should be noted that in this application, the cliff detection value obtained by the cliff sensor is negatively correlated with the cliff depth; that is, the larger the cliff detection value obtained by the cliff sensor, the shallower the cliff depth, and vice versa.

[0023] If all cliff detection values ​​are greater than the preset first cliff threshold, it may be because the cliff depth corresponding to the preset first cliff threshold is too deep. It cannot be directly concluded that there is no cliff near the robot. In order to improve the accuracy of the robot's cliff detection and enable the robot to avoid cliffs more safely, this application determines that the current scene of the robot does not meet the preset first cliff scene. Steps 3 and 4 are used to fuse multiple cliff detection values ​​collected in real time by multiple cliff sensors to further analyze whether there is a cliff in the current scene of the robot, thereby improving the accuracy of cliff detection.

[0024] As a preferred embodiment of this application, step 2 further includes: determining whether the cliff detection values ​​collected in real time by each cliff sensor mounted on the robot are less than or equal to a preset first cliff threshold in ascending order; when a cliff detection value is detected to be greater than the preset first cliff threshold, the traversal of determining whether multiple cliff detection values ​​are less than or equal to the preset first cliff threshold ends; when at least one cliff detection value is detected to be less than or equal to the preset first cliff threshold, it is determined that the current scene where the robot is located conforms to the preset first cliff scene. Specifically, before comparing multiple cliff detection values ​​with a preset first cliff threshold, this application sorts and compares the cliff detection values ​​in ascending order. Since the purpose of the comparison is to detect whether there are any cliff detection values ​​collected by the cliff sensor that indicate a cliff depth greater than the calibration cliff depth corresponding to the preset first cliff threshold, based on the principle that cliff detection values ​​and cliff depth are negatively correlated, by sorting multiple cliff detection values ​​in ascending order, when a cliff detection value is detected to be greater than the preset first cliff threshold, it can be determined that all subsequent cliff detection values ​​to be judged are greater than the preset first cliff threshold. To save computing resources, the step of traversing and judging whether multiple cliff detection values ​​are less than or equal to the preset first cliff threshold is terminated. Meanwhile, when a cliff detection value is less than or equal to a preset first cliff threshold, it can be determined that there is a cliff around the robot and the cliff depth is greater than the cliff depth corresponding to the preset first cliff threshold. This is equivalent to determining that the current scene where the robot is located conforms to the preset first cliff scene. However, in order to further improve the robot's cliff detection accuracy, this embodiment does not limit the traversal judgment of whether multiple cliff detection values ​​are less than or equal to the preset first cliff threshold to when a cliff detection value is detected that is less than or equal to the preset first cliff threshold. The traversal judgment of cliff detection values ​​can continue to obtain the number of cliff sensors on the robot that have collected cliff detection values ​​less than or equal to the preset first cliff threshold more accurately. This helps the robot determine the direction of the cliff relative to the robot based on the setting position of the cliff sensors, making the robot's cliff avoidance behavior more targeted.

[0025] In a preferred embodiment of this application, step 2 further includes: when the detected cliff detection value is greater than the minimum value among multiple cliff detection values, the process of determining whether multiple cliff detection values ​​are less than or equal to the preset first cliff threshold is terminated, thus confirming that the current robot's scene does not conform to the preset first cliff scene. This embodiment is based on the principle that cliff detection values ​​are negatively correlated with cliff depth. By sorting multiple cliff detection values ​​in ascending order, when the minimum value among multiple cliff detection values ​​is detected to be greater than the preset first cliff threshold, it can be determined that all subsequent cliff detection values ​​to be judged are greater than the preset first cliff threshold. To save computational resources, the step of determining whether multiple cliff detection values ​​are less than or equal to the preset first cliff threshold is terminated, thereby improving the matching efficiency between the current robot's scene and the preset first cliff scene.

[0026] As a preferred embodiment of this application, step 3, which involves calculating the fused cliff detection value based on the cliff detection values ​​collected in real time by the various cliff sensors mounted on the robot, specifically includes: Step 31: Select the maximum cliff detection value from the cliff detection values ​​collected in real time by each cliff sensor on the robot, and proceed to Step 32. This step is used to select the maximum cliff detection value among all cliff detection values. The selection method can be, but is not limited to, randomly selecting two cliff detection values ​​from multiple cliff detection values ​​and comparing their sizes, keeping the larger one; selecting an uncompared cliff detection value from multiple cliff detection values ​​and comparing its size with the retained cliff detection value, keeping the larger one; repeating the above steps until all cliff detection values ​​have been compared and the final retained cliff detection value is determined to be the maximum cliff detection value among multiple cliff detection values.

[0027] Step 32: Select the minimum cliff detection value from the cliff detection values ​​collected in real time by each cliff sensor on the robot, and proceed to Step 33. This step is used to select the minimum cliff detection value from the multiple cliff detection values ​​acquired by each cliff sensor on the robot. The selection method can be, but is not limited to, randomly selecting two cliff detection values ​​from multiple cliff detection values ​​and comparing their sizes, keeping the smaller one; selecting an uncompared cliff detection value from multiple cliff detection values ​​and comparing its size with the retained cliff detection value, keeping the smaller one; repeating the above steps until all cliff detection values ​​have been compared and the final retained cliff detection value is determined to be the minimum cliff detection value among multiple cliff detection values.

[0028] Step 33: Calculate the difference between the maximum cliff detection value and the minimum cliff detection value, and use it as the cliff detection difference to proceed to step 34; This step calculates the difference between the maximum cliff detection value and the minimum cliff detection value among multiple cliff detection values ​​to achieve the maximum difference between the cliff detection values ​​obtained by each cliff detection sensor of the cliff detection difference feedback robot.

[0029] Step 34: Use the cliff detection difference as the dividend and the minimum cliff detection value as the divisor. The quotient of the dividend and the divisor is the fused cliff detection value. This step calculates the fused cliff detection value by dividing the cliff detection difference by the minimum cliff detection value, so that the fused cliff detection value can intuitively reflect the height difference detected by the robot's various cliff detection sensors.

[0030] As a preferred embodiment of this application, step 4, which involves determining whether the current scene of the robot conforms to the preset second cliff scene based on the fusion cliff detection value, specifically includes: determining whether the fusion cliff detection value is greater than or equal to the preset second cliff threshold; if the fusion cliff detection value is greater than or equal to the preset second cliff threshold, then it is determined that the current scene of the robot conforms to the preset second cliff scene; if the fusion cliff detection value is less than the preset second cliff threshold, then it is determined that the current scene of the robot does not conform to the preset second cliff scene.

[0031] Specifically, the preset second cliff threshold is adjusted based on the user's setting of the preset second cliff scenario, and may be, but is not limited to, setting the preset second cliff threshold to be equal to 1. In this application, the preset second cliff scenario refers to a situation where the cliff detection values ​​obtained by multiple cliff sensors of the robot do not meet the preset first cliff scenario, and the robot is in a horizontal state, but the fused cliff detection value corresponding to the cliff detection values ​​obtained by multiple cliff sensors meets the condition of being greater than or equal to the preset second cliff threshold. Therefore, it is determined that in the horizontal state, the difference between the maximum and minimum cliff detection values ​​among the multiple cliff sensors is large, which meets the condition that a cliff exists.

[0032] In a preferred embodiment of this application, a chip is provided that internally stores a computer program. When the computer program stored internally is run by a processor, it executes the robot cliff detection method as described in any of the embodiments. This embodiment, by storing the robot cliff detection method from the above embodiments as a computer program within the chip, allows users to flexibly apply the robot cliff detection method to various robots through flexible chip installation.

[0033] This application provides a preferred embodiment of a robot, comprising: a plurality of cliff sensors, a chip, and a processor; wherein, the plurality of cliff sensors are used to collect cliff detection values ​​in real time to detect whether a cliff exists near the robot, and may be, but is not limited to, depth sensors, infrared ranging sensors, etc., to collect cliff detection values. The chip is used to store a computer program for running the robot cliff detection method as described in any of the preceding embodiments; the processor is used to run the computer program stored in the chip, and execute the robot cliff detection method as described in any of the preceding embodiments based on the cliff detection values ​​collected in real time by the cliff sensors, controlling the robot to perform cliff avoidance actions when a cliff is detected.

[0034] In a preferred embodiment of this application, the robot further includes a gyroscope sensor for acquiring the robot's three-axis angular velocity to determine whether the robot is in a horizontal state. Specifically, the gyroscope sensor can acquire the robot's three-axis angular velocity, and by comparing the changes in the three-axis angular velocity, the robot's horizontal state can be detected. Based on the robot's horizontal state determined by the gyroscope sensor, if the cliff detection values ​​collected by multiple cliff sensors on the robot do not conform to a preset first cliff scenario, it can further determine whether the current scene where the robot is located conforms to a preset second cliff scenario, thus solving the problem of the single cliff detection method in existing robots and improving the accuracy of robot cliff detection.

[0035] It should be noted that any process or method description in the flowchart or otherwise described herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order described or discussed, including substantially simultaneously or in reverse order according to the functions involved, as should be understood by those skilled in the art to which the embodiments of this application pertain.

[0036] Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the invention. The scope of the invention is defined by the appended claims and their equivalents. The above descriptions are merely preferred embodiments of the invention and are not intended to limit the invention. Various modifications and variations can be made to the invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the invention should be included within the scope of protection of the invention.

Claims

1. A method for detecting cliffs using a robot, characterized in that, The robotic cliff detection method specifically includes: Step 1: Obtain the cliff detection values ​​collected in real time by each cliff sensor mounted on the robot, and then proceed to Step 2; Step 2: Based on the cliff detection values ​​collected in real time by the various cliff sensors on the robot, determine whether the current scene of the robot matches the preset first cliff scene. Determine whether at least one cliff detection value collected in real time by each cliff sensor on the robot is less than or equal to the preset first cliff threshold. If at least one cliff detection value is less than or equal to the preset first cliff threshold, it is determined that the current scene of the robot matches the preset first cliff scene, and proceed to Step 5. If all cliff detection values ​​are greater than the preset first cliff threshold, it is determined that the current scene of the robot does not match the preset first cliff scene, and proceed to Step 3. Step 3: Calculate the fused cliff detection value based on the cliff detection values ​​collected in real time by each cliff sensor mounted on the robot, including: Step 31: Filter out the maximum cliff detection value among the cliff detection values ​​collected in real time by each cliff sensor mounted on the robot, and proceed to Step 32; Step 32: Filter out the minimum cliff detection value among the cliff detection values ​​collected in real time by each cliff sensor mounted on the robot, and proceed to Step 33; Step 33: Calculate the difference between the maximum and minimum cliff detection values ​​as the cliff detection difference, and proceed to Step 34; Step 34: Use the cliff detection difference as the dividend, the minimum cliff detection value as the divisor, and the quotient of the dividend and the divisor as the fused cliff detection value, and then proceed to Step 4; Step 4: Determine whether the current scene where the robot is located conforms to the preset second cliff scene based on the fusion cliff detection value, and determine whether the fusion cliff detection value is greater than or equal to the preset second cliff threshold; if the fusion cliff detection value is greater than or equal to the preset second cliff threshold, it is determined that the current scene where the robot is located conforms to the preset second cliff scene, and proceed to Step 5; if the fusion cliff detection value is less than the preset second cliff threshold, it is determined that the current scene where the robot is located does not conform to the preset second cliff scene, and return to Step 1; Step 5: Confirm that there is a cliff near the robot; The robot is equipped with at least two cliff sensors.

2. The robot cliff detection method according to claim 1, characterized in that, The robot cliff detection method further includes: if the cliff detection values ​​collected in real time by the various cliff sensors mounted on the robot determine that the current scene of the robot does not conform to the preset first cliff scene, then determine whether the robot is in a horizontal state. If yes, then proceed to step 3; otherwise, return to step 1.

3. The robot cliff detection method according to claim 1, characterized in that, Step 2 also includes: The cliff detection values ​​collected in real time by the various cliff sensors on the robot are sorted in ascending order, and it is determined whether each cliff detection value is less than or equal to a preset first cliff threshold. When a cliff detection value is detected to be greater than the preset first cliff threshold, the traversal ends and it is determined whether multiple cliff detection values ​​are less than or equal to the preset first cliff threshold. If at least one cliff detection value is detected that is less than or equal to a preset first cliff threshold, then the current scene where the robot is located is determined to be a scene that conforms to the preset first cliff scene.

4. The robot cliff detection method according to claim 3, characterized in that, Step 2 also includes: When the detected cliff value is greater than the preset first cliff threshold, the minimum value among multiple cliff detection values ​​is obtained. Then the traversal ends and it is determined whether the multiple cliff detection values ​​are less than or equal to the preset first cliff threshold. If so, it is determined that the current scene where the robot is located does not conform to the preset first cliff scene.

5. A chip internally storing a computer program, characterized in that, The computer program stored inside the chip is executed by the processor to perform the robot cliff detection method as described in any one of claims 1 to 4.

6. A robot, characterized in that, The robot includes: Several cliff sensors are used to collect cliff detection values ​​in real time to detect whether there is a cliff near the robot; The chip as described in claim 5 is used to store a computer program for running a robotic cliff detection method; The processor is used to run a computer program stored inside the chip, and to execute the robotic cliff detection method as described in any one of claims 1 to 4 based on the cliff detection values ​​collected in real time by the cliff sensor.

7. The robot according to claim 6, characterized in that, The robot also includes a gyroscope sensor for acquiring the robot's three-axis angular velocity to determine whether the robot is in a horizontal position.

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