Robot control methods, devices, robots and storage media

Abstract

This application discloses a robot control method, device, robot, and storage medium. The method includes detecting signals triggered by sensors during robot movement; when an abnormality is detected in the sensor-triggered signals, determining the type of difficult scenario the robot is currently in by combining real-time acquired surrounding environmental information; obtaining a corresponding escape strategy based on the difficult scenario type; and controlling the robot to execute escape actions according to the escape strategy corresponding to the difficult scenario type until the sensor-triggered signals are detected to return to normal. This application can identify different scenarios and use algorithms corresponding to different scenarios to help the robot escape from difficulties. The entire process is fast, efficient, and highly adaptable.

Description

Technical Field

[0001] This invention belongs to the field of robotics technology, and particularly relates to a robot control method, device, robot, and storage medium. Background Art

[0002] With the development of society and economy and the improvement of people's living standards, people have increasingly higher requirements for their living or working environment. In order to reduce people's workload in cleaning their homes or workplaces and alleviate the fatigue during the cleaning process, various floor cleaning products have emerged, such as sweeping machines.

[0003] When cleaning, robotic vacuum cleaners encounter various obstacles. They can effectively identify obstacles like tables, chairs, stairs, or cliffs, and navigate around them by going around or backing away. However, in real life, obstacles extend beyond stairs and cliffs. Other scenarios include sliding rail grooves, openwork furniture, various dark-colored carpets, and long-pile carpets. In situations where these areas are slightly higher than the robot's wheel height (e.g., grooves, openwork furniture, or tapered furniture), the robot may trigger a hovering alarm and move away from the current area. Additionally, it may falsely trigger alarms on dark-colored or long-pile carpets. Therefore, in these scenarios, existing robotic vacuum cleaner control algorithms often fail to correctly identify these obstacles, leading to misjudgments and affecting cleaning performance. Summary of the Invention

[0004] To address the problems of existing technologies, this invention proposes a robot control method, device, robot, and storage medium. This method can determine the current scene and control the robot to perform different obstacle-avoidance actions based on different scenes, thereby improving cleaning efficiency.

[0005] The specific technical solutions provided by the embodiments of the present invention are as follows:

[0006] Firstly, a robot control method is provided, the method comprising:

[0007] Detect signals triggered by sensors as the robot moves;

[0008] When an abnormal signal triggered by a sensor is detected, the robot determines the type of difficult scenario it is currently in by combining real-time information about the surrounding environment.

[0009] Based on the types of difficult scenarios, obtain the corresponding escape strategies;

[0010] The robot is controlled to perform escape actions according to the escape strategy corresponding to the type of difficult scenario until a sensor-triggered signal is detected and normal operation is restored.

[0011] In some embodiments, the difficult scenario type includes one or a combination of more than one of the following: a complete fall scenario, a partial fall scenario, a false trigger scenario, and an edge change scenario.

[0012] In some embodiments, the signals triggered by the sensor include the downward view height of the obstacle below the robot and fall position data indicating the robot's location;

[0013] The methods for determining the full drop scenario and the partial drop scenario include:

[0014] Obtain the comparison results of the downward view height with a first height threshold and a second height threshold respectively; wherein, the first height threshold is greater than or equal to the second height threshold;

[0015] When the downward viewing height is greater than the first height threshold, the current difficult scenario is determined to be a complete fall scenario by combining the fall location data and the surrounding environment information.

[0016] When the downward viewing height is less than the second height threshold, the current difficult scenario is determined to be a localized fall scenario by combining the fall location data and surrounding environment information.

[0017] In some embodiments, when the difficult scenario type is any single difficult scenario, controlling the robot to perform an escape action according to an escape strategy corresponding to the difficult scenario type until a sensor-triggered signal is detected and normal operation is restored includes:

[0018] If the difficult scenario is a complete fall scenario, control the robot to leave the current difficult scenario until a sensor-triggered signal is detected and normal operation is restored; and / or

[0019] If the difficult scenario is a partial fall scenario, assess the safety of the current difficult scenario. If it is determined to be unsafe, control the robot to leave the current difficult scenario until a sensor-triggered signal is detected and normal operation is restored; and / or

[0020] If the difficult scenario is a false trigger scenario or an edge change scenario, control the robot to rotate, move forward or backward until the sensor-triggered signal is detected and the robot returns to normal.

[0021] In some embodiments, when the difficult scenario type is a combination of multiple single difficult scenarios, controlling the robot to perform an escape action according to an escape strategy corresponding to the difficult scenario type until a sensor-triggered signal is detected and normal operation is restored includes:

[0022] The robot is controlled to perform escape actions corresponding to each difficult scenario in order of priority until a sensor-triggered signal is detected and normal operation is restored.

[0023] In some embodiments, if the difficult scenario type is a false trigger scenario, controlling the robot to rotate, move forward, or move backward until the sensor-triggered signal is detected and the system returns to normal includes:

[0024] The robot is controlled to rotate at a preset angle based on information about the surrounding environment.

[0025] The location of the sensor that triggered the abnormal signal is determined, and the robot is controlled to move forward or backward based on the location information until the signal triggered by the sensor is detected and the robot returns to normal.

[0026] In some embodiments, if the difficult scenario type is an edge-change scenario, controlling the robot to rotate, move forward, or move backward until the sensor-triggered signal is detected and returns to normal includes:

[0027] The robot is controlled to rotate at a preset angle based on information about the surrounding environment.

[0028] The location of the sensor that triggered the abnormal signal determines the suspended and non-suspended sides of the robot;

[0029] The robot is controlled to move forward or backward according to the speeds of the wheels on the suspended side and the wheels on the non-suspended side, until a sensor-triggered signal is detected and the robot returns to normal.

[0030] In some embodiments, controlling the robot to leave the current difficult scenario includes:

[0031] Determine whether the robot is capable of rotating;

[0032] When it is determined that the robot cannot rotate, acquire point cloud data on the safe side behind the robot;

[0033] The robot is controlled to retreat and leave the current difficult scene based on point cloud data from the safety side;

[0034] The safety side includes the boundary of the obstacle.

[0035] In some embodiments, controlling the robot to leave the current difficult scenario further includes:

[0036] When there is no safe side behind the robot, obtain the robot's historical trajectory when entering the current difficult scenario;

[0037] The robot is controlled to retreat and leave the current difficult scenario based on its historical trajectory.

[0038] Secondly, a robot control device is provided, the device comprising:

[0039] Detection module: Used to measure signals triggered by sensors when the robot is moving;

[0040] Processing module: When an abnormality is detected in the signal triggered by the sensor, it combines the real-time acquired information about the surrounding environment to determine the type of difficult scenario the robot is currently in.

[0041] Acquisition module: used to acquire corresponding escape strategies based on the type of difficult scenario;

[0042] Control module: Used to control the robot to perform escape actions according to the escape strategy corresponding to the type of difficult scenario until the sensor-triggered signal is detected and normal operation is restored.

[0043] Thirdly, a robot is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described in the first aspect.

[0044] Fourthly, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0045] The embodiments of the present invention have the following beneficial effects:

[0046] 1. This invention uses a sensor as a switch for detecting abnormal signals. When the sensor triggers an abnormal signal, it combines the surrounding environmental information to determine the current scene of the robot and determines the escape strategy corresponding to the current scene to complete the escape action. Compared with the prior art, this invention realizes the recognition of different scenes and helps the robot escape based on the algorithm corresponding to different scenes. The whole process is fast, efficient and highly adaptable.

[0047] 2. This invention can identify scenarios where stairs or cliffs may cause the robot to fall completely, scenarios where recessed or hollowed-out furniture may cause the robot to fall partially, scenarios where dark materials may cause sensors to be falsely triggered, and scenarios where changes at the edge of a carpet may cause the robot's bottom to roll up and become suspended in the air.

[0048] 3. This invention mainly distinguishes between complete fall scenarios and partial fall scenarios by using different height thresholds and combining information such as the fall location and surrounding environment, thus ensuring the correct identification of the two scenarios.

[0049] 4. This invention implements different escape methods for different scenarios. In the case of a complete fall, since it is impossible to cross, the robot is directly controlled to leave. In the case of a partial fall, it is necessary to analyze the surrounding environment information. If it is impossible to cross, the robot is controlled to leave. In the case of accidental triggering or edge change, the robot is controlled to rotate, move forward or backward to restore the accidental triggering signal to normal.

[0050] 5. This invention also enables control when multiple scenarios are combined, that is, it can execute the corresponding escape actions of each scenario according to the priority order of each scenario, thereby helping the robot to escape successfully;

[0051] 6. When encountering a scenario where the robot is falsely triggered by dark carpets or other objects, the present invention controls the robot to move forward or backward by rotating a preset angle and according to the position of the sensor that triggered the abnormal signal, thereby deactivating the false triggering of the sensor.

[0052] 7. When encountering scenarios with edge changes such as carpet edges, this invention determines the roll-up position based on the location of the sensor that triggers the abnormal signal, and controls the robot's two wheels through different control speeds until the robot safely returns to flat ground, making the whole process smoother.

[0053] 8. When controlling the robot to leave the current scene, the present invention first determines whether it can rotate. When the robot cannot rotate, it controls the robot to leave based on the point cloud data of the rear safety side. When there is no rear safety side, it controls the robot to leave through the historical trajectory, thereby ensuring that the robot can move backward smoothly. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is an exemplary flowchart of a robot control method according to an embodiment of the present disclosure;

[0056] Figure 2 This is a schematic diagram of a cliff scene according to an embodiment of the present disclosure;

[0057] Figure 3 This is a schematic diagram of a track groove scene according to an embodiment of the present disclosure;

[0058] Figure 4 This is a schematic diagram of a carpet edge scene according to an embodiment of the present disclosure;

[0059] Figure 5 This is a schematic diagram of the structure of a robot control device according to an embodiment of the present disclosure;

[0060] Figure 6 This is a schematic diagram of the structure of a robot according to an embodiment of the present disclosure. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0062] As described in the background section, currently, robotic vacuum cleaners encounter various obstacles during cleaning. They can typically identify and select appropriate escape methods when faced with obstacles such as tables, chairs, stairs, or cliffs. However, in real life, the cleaning areas in each household vary greatly, and are not limited to scenarios like stairs and cliffs. There are also situations involving sliding rails, openwork furniture, various dark carpets, and long-pile carpets. In these scenarios, existing technologies lack corresponding control algorithms to help the robot escape obstacles, thus affecting cleaning performance.

[0063] To address the aforementioned issues, the applicant has creatively conceived of categorizing challenging scenarios within the household cleaning area and setting corresponding solutions for each scenario. This allows the robot to quickly identify the scenario and implement appropriate escape methods during cleaning, reducing the risk of the robot getting stuck.

[0064] Figure 1 An exemplary flowchart of a robot control method according to an embodiment of the present disclosure is shown, and the robot control method is described in detail below:

[0065] Step 101: Detect the signals triggered by the sensors when the robot is moving.

[0066] The aforementioned sensors are used to collect various motion parameters and environmental spatial data of the robot. They can be one or more types of sensors, such as lidar, cameras, infrared sensors, and pressure sensors. It should be understood that the sensors are not limited to these, and those skilled in the art can select appropriate sensors according to actual needs. Furthermore, those skilled in the art can also install different sensors at different locations on the robot (such as the front, side, bottom, etc.) to obtain data from different directions / positions.

[0067] In this embodiment, to enable the robot to better recognize the scene, the following processing steps are included before step 101:

[0068] Obtain room partitioning information and build a map model.

[0069] Before the robot can clean, it is necessary to obtain the approximate zoning information of the home area and build a home map model. Specifically, the robot can run around each room / cleaning area to obtain relevant data and build a map model. The cleaning work can then be carried out better based on this model.

[0070] Step 102: When an abnormal signal is detected by the sensor, the robot is determined to be in a difficult scenario by combining the real-time acquired information about the surrounding environment.

[0071] In some embodiments, the signals triggered by the aforementioned sensors include the downward view height of the obstacle below the robot and fall position data used to indicate the robot's location.

[0072] The sensor used to detect the downward-looking height can be an infrared sensor, which is installed on the bottom of the robot and used to scan the height during robot movement. Specifically, the infrared sensor includes a transmitter and a receiver. The transmitter emits an infrared signal of a specific frequency, and the receiver receives this infrared signal. When the infrared detection direction encounters an obstacle, the infrared signal is reflected back and received by the receiver, allowing the robot's height from the obstacle to be calculated.

[0073] The number of infrared sensors installed can be determined based on the cleaning environment and the sensor's own structure. For example, four infrared sensors are usually installed on the bottom of the robot: two on the front sides and two on the rear. These four infrared sensors can detect various scenarios such as stairs and cliffs, helping the robot avoid falling in such situations.

[0074] In this embodiment, in order to better recognize various scenarios, in addition to the two infrared sensors on the front two sides and the rear two sides of the bottom, an infrared sensor is also installed on each of the two sides of the bottom of the robot, that is, a total of six infrared sensors are installed to obtain the height information of the robot from the bottom obstacle at different positions.

[0075] The aforementioned fall location data can be obtained by identifying and processing sensors that detect whether the robot is suspended or has been hit. These sensors can be mounted on the two wheels at the bottom of the robot to detect whether the wheels are under pressure.

[0076] For example, the sensors installed on the robot's wheels can be pressure sensors. When the robot is moving normally, its wheels are under pressure, and the pressure sensors will detect pressure data. When encountering scenarios such as cliffs or stairs, the robot's wheels may not be in contact with the ground, so the pressure sensors installed on the bottom of the robot will not detect pressure data. In this case, the robot's current position on the map can be quickly determined, i.e., the fall location data can be recorded. Subsequently, this data can be used to determine whether the robot is in a difficult scenario.

[0077] In addition to pressure sensors, the sensors mounted on the robot wheels can also be mechanical switches, such as limit switches. Limit switches include an operating head and a contact system. When the wheel is under pressure, the operating head is squeezed, causing the contact system to activate and output a connection signal. When the wheel is not under pressure, the circuit is broken. Once the circuit is broken, the fall position data can be recorded, and this data can then be used to determine whether the robot is in a difficult situation.

[0078] Besides pressure sensors and mechanical switches, other types of sensors can also be used to acquire fall location data; this solution does not restrict the type of sensor. When an abnormal signal triggered by a sensor is detected, the type of difficult scenario the robot is currently in can be determined by combining this information with surrounding environmental data.

[0079] In some embodiments, the surrounding environment information acquired in real time includes: map information, point cloud information of obstacles, room zoning information, etc.

[0080] Specifically, the map information and room zoning information mentioned above can be obtained before the robot's first cleaning. Since the room zoning information only describes the general situation of the room, allowing the robot to initially understand the cleaning area, but the robot does not know the situation of some furniture, decorations, etc. inside the room, it is also necessary to obtain the point cloud information of obstacles in real time during the cleaning process in order to achieve better cleaning.

[0081] In this embodiment, three different sensors are used to obtain detailed obstacle information: a LiDAR sensor mounted directly in front of the robot, an AI camera, and a line laser sensor.

[0082] The lidar is installed directly in front of the robot and continuously scans to acquire obstacle information. The specific process of acquiring point cloud data using lidar is as follows:

[0083] A lidar system consists of a laser and a receiving system. The laser generates and emits light pulses. When an obstacle is present, the light pulse strikes the obstacle's surface and reflects back, eventually being received by the receiver. The receiver can accurately measure the propagation time of the light pulse from emission to reflection. Given the speed of light, the distance to the obstacle can be calculated. Combined with the laser's height and scanning angle, the three-dimensional coordinates of each light spot on the obstacle's surface can be accurately calculated, i.e., point cloud data.

[0084] Due to limitations in the installation angle or structure of lidar, there may be blind spots in the scanning. To compensate for these blind spots, AI cameras and line laser sensors are also used as supplementary sensors.

[0085] AI sensors are also installed in front of the robot to capture images of the scene, such as real-time images of various furniture in the room, and then process them.

[0086] Line laser sensors can be installed at the front of the robot near the bottom to capture images of objects slightly higher than the robot's wheels, such as steps.

[0087] After obtaining the above information about the surrounding environment, the type of difficult scenario the robot is currently in can be determined.

[0088] In some embodiments, the difficult scenario type includes one or a combination of more than one of the following: a complete fall scenario, a partial fall scenario, a false trigger scenario, and an edge change scenario.

[0089] Among them, the complete fall scenario refers to a situation where stairs or cliffs could cause the robot to fall completely. (Refer to...) Figure 2 , Figure 2 This is a schematic diagram of a cliff scene;

[0090] Localized fall scenarios refer to situations where the robot might fall into recessed or openwork furniture, etc. (Refer to...) Figure 3 , Figure 3 This is a schematic diagram of a track groove scene;

[0091] False triggering scenarios refer to situations where the sensor is falsely triggered due to dark materials or long-pile carpets, etc.

[0092] Edge-changing scenarios include situations where changes at the edge of a carpet cause the robot's bottom to curl up, resulting in it being suspended in mid-air. (Refer to...) Figure 4 , Figure 4 This is a schematic diagram of the carpet edge scene.

[0093] When the sensor-triggered signals include the downward view height of obstacles below the robot and fall location data indicating the robot's position, the methods for determining the aforementioned full fall scenario and partial fall scenario include:

[0094] Obtain the comparison results of the downward view height with the first height threshold and the second height threshold respectively; wherein, the first height threshold is greater than or equal to the second height threshold;

[0095] When the current apparent height is greater than the first height threshold, the current difficult scenario is determined to be a complete fall scenario by combining the fall location data and surrounding environment information.

[0096] When the apparent height is less than the second height threshold, the current difficult scenario is determined to be a localized fall scenario by combining the fall location data and surrounding environmental information.

[0097] Generally speaking, robots cannot cross scenarios like stairs or cliffs. Once they fall, they cannot automatically get up, which is quite dangerous and can cause some impact to the robot, or even damage it. For scenarios like recessed or openwork furniture, which may cause the robot to fall partially, the height is usually lower than that of stairs or cliffs, and the robot may be able to cross them. Therefore, in order to correctly distinguish between the two different scenarios, different height thresholds can be set.

[0098] Figure 2 and Figure 3 The diagrams show a complete fall scenario and a partial fall scenario. When the robot is driving in the two scenarios, if it detects abnormalities in the downward view height and fall position data triggered by the sensor, it can first compare the downward view height with the first height threshold and the second height threshold, and then combine it with the surrounding environment data to determine which scenario is in front of it.

[0099] Step 103: Obtain the corresponding escape strategy based on the type of difficult scenario.

[0100] Since the robot controller contains the algorithm corresponding to each scenario, once the type of difficult scenario is determined, the escape algorithm can be obtained based on the correspondence table between the difficult scenario type and the escape strategy.

[0101] Step 104: Control the robot to perform the escape action according to the escape strategy corresponding to the type of difficult scenario until the sensor-triggered signal is detected and normal operation is restored.

[0102] Because real-life scenarios are complex, there may be single-scene situations (such as stairs or cliffs) or multiple-scene situations (such as laying a dark carpet on stairs). Therefore, different methods are used to handle single-scene and multiple-scene situations, as follows:

[0103] When the difficulty scenario type is any single difficulty scenario, step 104 includes:

[0104] If the difficult scenario is a complete fall scenario, control the robot to leave the current difficult scenario until a sensor-triggered signal is detected and normal operation is restored; and / or

[0105] If the difficult scenario is a partial fall scenario, assess the safety of the current difficult scenario. If it is determined to be unsafe, control the robot to leave the current difficult scenario until the sensor-triggered signal is detected and normal operation is restored. If it is determined to be safe, control the robot to cross the fall area until the sensor-triggered signal is detected and normal operation is restored; and / or

[0106] If the difficult scenario is a false trigger scenario or an edge change scenario, control the robot to rotate, move forward or backward until the sensor-triggered signal is detected and the robot returns to normal.

[0107] This invention implements different escape methods for different scenarios. In the case of a complete fall, since it is impossible to cross, the robot is directly controlled to leave. In the case of a partial fall, it is necessary to analyze the surrounding environment. If it is impossible to cross, the robot is controlled to leave. In the case of accidental triggering or edge change, the robot is controlled to rotate, move forward or backward to restore it to flat ground and restore the accidental triggering signal to normal.

[0108] In some embodiments, if the difficult scenario type is a false trigger scenario, controlling the robot to rotate, move forward, or move backward until the sensor-triggered signal is detected and the system returns to normal specifically includes:

[0109] The robot is controlled to rotate at a preset angle based on information about the surrounding environment.

[0110] The location of the sensor that triggered the abnormal signal is determined, and the robot is controlled to move forward or backward based on the location information until the signal triggered by the sensor is detected and the robot returns to normal.

[0111] When encountering scenarios where false triggers are caused by dark or long-pile carpets, the sensor signals may be abnormal due to color or material, potentially mistaking the location for a staircase or cliff. In such cases, first control the robot to rotate by a preset angle, then control the robot to move forward or backward. Specifically, if a rear sensor triggers an abnormal signal, the robot can be controlled to move forward a short distance; if a front sensor triggers an abnormal signal, the robot can be controlled to move backward a short distance. By rotating, moving forward, or moving backward, the false sensor triggers are resolved.

[0112] In some embodiments, if the difficult scenario type is an edge-change scenario, controlling the robot to rotate, move forward, or move backward until the sensor-triggered signal is detected and the system returns to normal includes:

[0113] The robot is controlled to rotate at a preset angle based on information about the surrounding environment.

[0114] The location of the sensor that triggered the abnormal signal determines the suspended and non-suspended sides of the robot;

[0115] The robot is controlled to move forward or backward according to the speeds of the wheels on the suspended side and the wheels on the non-suspended side, until a sensor-triggered signal is detected and the robot returns to normal.

[0116] When encountering scenarios with edge changes such as carpet edges, the robot determines the roll-up position based on the location of the sensor that triggers the abnormal signal, and controls the robot's two wheels by using different control speeds until the robot safely returns to flat ground, making the whole process smoother.

[0117] When the difficulty scenario type is a combination of multiple single difficulty scenarios, step 104 includes:

[0118] The robot is controlled to perform the escape actions corresponding to each difficult scenario in order of priority until a sensor-triggered signal is detected and normal operation is restored.

[0119] For example, if the robot encounters a combination of stairs and a dark carpet, it can first be made to rotate, move forward or backward based on the false trigger scenario. If the sensor signal is still abnormal after performing the above steps, the robot can be directly controlled to leave.

[0120] In some embodiments, controlling the robot to leave the current difficult scenario includes:

[0121] Determine if the robot can rotate;

[0122] When it is determined that the robot cannot rotate, acquire point cloud data from the safe side behind the robot;

[0123] The robot is controlled to retreat and leave the current difficult scenario based on point cloud data from the safety side;

[0124] When there is no safe side behind the robot, obtain the robot's historical trajectory when entering the current difficult scenario;

[0125] The robot is controlled to retreat and leave the current difficult scenario based on its historical trajectory.

[0126] The safety side includes the boundary of the obstacle.

[0127] Since most robots on the market lack a backward movement algorithm, when determining if a robot needs to leave, the first step is to determine if it can rotate. If it can rotate, it can rotate by the appropriate angle and then leave. However, besides circular robots, a significant number of robots on the market are irregularly shaped. For irregularly shaped robots, rotation is only possible when their rotation radius is smaller than the channel radius. Therefore, this solution adds a backward movement algorithm to control the robot's departure. Furthermore, during departure, the robot can move based on point cloud data from the safety side, ensuring the smoothness of its backward movement.

[0128] This invention uses sensors as switches for detecting abnormal signals. When a sensor triggers an abnormal signal, it combines the surrounding environmental information to determine the robot's current scene and then determines the escape strategy corresponding to the current scene to complete the escape action. Compared with the prior art, this invention realizes the recognition of different scenes and helps the robot escape based on algorithms corresponding to different scenes. The whole process is fast, efficient and highly adaptable.

[0129] See also Figure 5 As a response to the above Figure 1 The implementation of the method shown provides an embodiment of a robot control device, which is similar to... Figure 1 The method embodiments shown correspond to, for example, Figure 5 As shown, the robot control device in this embodiment includes:

[0130] Detection module 501: Used to measure signals triggered by sensors when the robot is moving;

[0131] Processing module 502: When an abnormality is detected in the signal triggered by the sensor, it combines the real-time acquired surrounding environmental information to determine the type of difficult scenario the robot is currently in.

[0132] Acquisition module 503: Used to acquire corresponding escape strategies based on the type of difficult scenario;

[0133] Control module 504: Used to control the robot to perform escape actions according to the escape strategy corresponding to the type of difficult scenario until the sensor-triggered signal is detected and normal operation is restored.

[0134] In some optional implementations of this embodiment, the aforementioned difficult scenario types include one or a combination of multiple types such as complete fall scenario, partial fall scenario, accidental trigger scenario, and edge change scenario.

[0135] In some optional implementations of this embodiment, the signals triggered by the aforementioned sensors include the downward view height of the obstacle below the robot and fall position data used to indicate the robot's location; the aforementioned processing module 502 is specifically used for:

[0136] Obtain the comparison results of the downward view height with the first height threshold and the second height threshold respectively; wherein, the first height threshold is greater than or equal to the second height threshold;

[0137] When the current apparent height is greater than the first height threshold, the current difficult scenario is determined to be a complete fall scenario by combining the fall location data and surrounding environment information.

[0138] When the apparent height is less than the second height threshold, the current difficult scenario is determined to be a localized fall scenario by combining the fall location data and surrounding environmental information.

[0139] In some optional implementations of this embodiment, the control module 504 is specifically used for:

[0140] If the difficult scenario is a complete fall scenario, control the robot to leave the current difficult scenario until a sensor-triggered signal is detected and normal operation is restored; and for / or for

[0141] If the difficult scenario is a partial fall scenario, assess the safety of the current difficult scenario, and if it is determined to be unsafe, control the robot to leave the current difficult scenario until a sensor-triggered signal is detected and normal operation is restored; and for / or for

[0142] If the difficult scenario is a false trigger scenario or an edge change scenario, control the robot to rotate, move forward or backward until the sensor-triggered signal is detected and the robot returns to normal.

[0143] In some optional implementations of this embodiment, the control module 504 is specifically used for:

[0144] When the difficult scenario type is a combination of multiple single difficult scenarios, the control robot executes the escape actions corresponding to each difficult scenario in order of priority until the sensor-triggered signal is detected and normal operation is restored.

[0145] In some optional implementations of this embodiment, the control module 504 is further configured to:

[0146] If the difficult scenario is a false trigger scenario, control the robot to rotate at a preset angle based on the surrounding environment information;

[0147] The location of the sensor that triggered the abnormal signal is determined, and the robot is controlled to move forward or backward based on the location information until the signal triggered by the sensor is detected and the robot returns to normal.

[0148] In some optional implementations of this embodiment, the control module 504 is further configured to:

[0149] If the difficult scenario is a scenario with edge changes, control the robot to rotate by a preset angle based on the surrounding environment information;

[0150] The location of the sensor that triggered the abnormal signal determines the suspended and non-suspended sides of the robot;

[0151] The robot is controlled to move forward or backward according to the speeds of the wheels on the suspended side and the wheels on the non-suspended side, until a sensor-triggered signal is detected and the robot returns to normal.

[0152] In some optional implementations of this embodiment, the control module 504 is further configured to:

[0153] Determine if the robot can rotate;

[0154] When it is determined that the robot cannot rotate, acquire point cloud data from the safe side behind the robot;

[0155] The robot is controlled to retreat and leave the current difficult scenario based on point cloud data from the safety side;

[0156] The safety side includes the boundary of the obstacle.

[0157] In some optional implementations of this embodiment, the control module 504 is further configured to:

[0158] When there is no safe side behind the robot, obtain the robot's historical trajectory when entering the current difficult scenario;

[0159] The robot is controlled to retreat and leave the current difficult scenario based on its historical trajectory.

[0160] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0161] Figure 6 A schematic diagram of a robot according to an embodiment of the present invention is disclosed. For example... Figure 6 As shown, the robot includes: a memory 61, a processor 62, and a computer program 63 stored in the memory 61 and executable on the processor 62, such as a program for a robot control method. When the processor 62 executes the computer program 63, it implements the steps described in the above-described embodiment of the robot control method, for example... Figure 1 Steps 101 to 103 are shown. Alternatively, when processor 62 executes computer program 63, it implements the functions of each module in the above-described embodiment of a robot control device, for example... Figure 5 The functions of modules 501 to 504 are shown. In addition, the robot also includes a measuring element 64 and a motion unit 65.

[0162] The measuring element 64 can be a radar, sensor, etc.; the radar can be a lidar or infrared radar, and the lidar can be a single-line radar or a multi-line radar.

[0163] The motion unit 65 is used to control the robot's movement.

[0164] The processor 62 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0165] The memory 61 can be an internal storage unit of a robot, such as a hard drive or memory. The memory 61 can also be an external storage device of the robot, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 61 can include both internal and external storage units. The memory 61 is used to store the computer program and other programs and data required by the robot. The memory 61 can also be used to temporarily store data that has been output or will be output.

[0166] Those skilled in the art will understand that Figure 6 This is merely an example of a robot and does not constitute a limitation on a robot. It may include more or fewer parts than illustrated, or combine certain parts, or different parts. For example, the robot may also include input / output devices, network access devices, buses, etc.

[0167] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0168] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0169] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0170] The program portion of a technology can be considered a "product" or "artifact" existing in the form of executable code and / or related data, and is involved in or implemented through a computer-readable medium. Tangible, permanent storage media can include memory or storage used by any computer, processor, or similar device or related module. For example, various semiconductor memories, tape drives, disk drives, or any similar device capable of providing storage functionality for software.

[0171] All software, or parts thereof, may sometimes communicate via networks, such as the Internet or other communication networks. Such communication can load software from one computer device or processor to another. Therefore, another medium capable of transmitting software elements can also be used as a physical connection between local devices, such as light waves, radio waves, electromagnetic waves, etc., propagated through cables, fiber optic cables, or air. Physical media used for carrier waves, such as cables, wireless connections, or fiber optic cables, can also be considered as media carrying software. In this context, unless limited to tangible "storage" media, the term "readable medium" for a computer or machine refers to the medium involved in the execution of any instructions by the processor.

[0172] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0173] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A robot control method, characterized in that, The method includes: Detect signals triggered by sensors as the robot moves; When an abnormal signal triggered by a sensor is detected, the robot determines the type of difficult scenario it is currently in by combining real-time information about the surrounding environment. Based on the types of difficult scenarios, obtain the corresponding escape strategies; The robot is controlled to perform escape actions according to the escape strategy corresponding to the type of difficult scenario until a sensor-triggered signal is detected and normal operation is restored. The difficult scenario types include one or a combination of complete fall scenarios and partial fall scenarios; When the difficulty scenario type is any single difficulty scenario, controlling the robot to execute an escape action according to the escape strategy corresponding to the difficulty scenario type until a sensor-triggered signal is detected and normal operation is restored includes: If the difficult scenario is a complete fall scenario, control the robot to leave the current difficult scenario until a sensor-triggered signal is detected and normal operation is restored; and / or If the difficult scenario is a partial fall scenario, assess the safety of the current difficult scenario. If it is determined to be unsafe, control the robot to leave the current difficult scenario until a sensor-triggered signal is detected and the robot returns to normal. Controlling the robot to leave the current difficult scenario includes: Determining whether the robot can rotate includes: determining whether the robot's rotation radius is less than the channel radius; When it is determined that the robot cannot rotate, acquire point cloud data on the safe side behind the robot; The robot is controlled to retreat and leave the current difficult scene based on point cloud data from the safety side; The safety side includes the boundary of the obstacle.

2. The method according to claim 1, characterized in that, The types of difficult scenarios also include accidental triggering scenarios and / or edge change scenarios; If the difficult scenario is a false trigger scenario or an edge change scenario, control the robot to rotate, move forward or backward until the sensor-triggered signal is detected and the robot returns to normal.

3. The method according to claim 1, characterized in that, The signals triggered by the sensor include the downward view height of the obstacle below the robot and fall position data used to indicate the robot's location; The methods for determining the full drop scenario and the partial drop scenario include: Obtain the comparison results of the downward view height with a first height threshold and a second height threshold respectively; wherein, the first height threshold is greater than or equal to the second height threshold; When the downward viewing height is greater than the first height threshold, the current difficult scenario is determined to be a complete fall scenario by combining the fall location data and the surrounding environment information. When the downward viewing height is less than the second height threshold, the current difficult scenario is determined to be a localized fall scenario by combining the fall location data and surrounding environment information.

4. The method according to claim 1, wherein when the difficult scenario type is a combination of multiple single difficult scenarios, controlling the robot to perform an escape action according to an escape strategy corresponding to the difficult scenario type until a sensor-triggered signal is detected and normal operation is restored, includes: The robot is controlled to perform escape actions corresponding to each difficult scenario in order of priority until a sensor-triggered signal is detected and normal operation is restored.

5. The method according to claim 2, characterized in that, If the difficult scenario type is a false trigger scenario, controlling the robot to rotate, move forward, or move backward until the sensor-triggered signal is detected and the system returns to normal includes: The robot is controlled to rotate at a preset angle based on information about the surrounding environment. The location of the sensor that triggered the abnormal signal is determined, and the robot is controlled to move forward or backward based on the location information until the signal triggered by the sensor is detected and the robot returns to normal.

6. The method according to claim 2, characterized in that, If the difficult scenario type is an edge-change scenario, controlling the robot to rotate, move forward, or move backward until the sensor-triggered signal is detected and the system returns to normal includes: The robot is controlled to rotate at a preset angle based on information about the surrounding environment. The location of the sensor that triggered the abnormal signal determines the suspended and non-suspended sides of the robot; The robot is controlled to move forward or backward according to the speeds of the wheels on the suspended side and the wheels on the non-suspended side, until a sensor-triggered signal is detected and the robot returns to normal.

7. The method according to claim 1, characterized in that, The method of controlling the robot to leave the current difficult scenario also includes: When there is no safe side behind the robot, obtain the robot's historical trajectory when entering the current difficult scenario; The robot is controlled to retreat and leave the current difficult scenario based on its historical trajectory.

8. A robot control device, characterized in that, The device includes: Detection module: Used to measure signals triggered by sensors when the robot is moving; Processing module: When an abnormality is detected in the signal triggered by the sensor, it combines the real-time acquired information about the surrounding environment to determine the type of difficult scenario the robot is currently in. Acquisition module: used to acquire corresponding escape strategies based on the type of difficult scenario; Control module: Used to control the robot to perform escaping actions according to the escaping strategy corresponding to the difficult scenario type until the sensor-triggered signal is detected and normal operation is restored; wherein, the difficult scenario type includes one or a combination of one or more of the following: a complete fall scenario and a partial fall scenario; when the difficult scenario type is any single difficult scenario, controlling the robot to perform escaping actions according to the escaping strategy corresponding to the difficult scenario type until the sensor-triggered signal is detected and normal operation is restored includes: if the difficult scenario type is a complete fall scenario, controlling the robot to leave the current difficult scenario until the sensor-triggered signal is detected and normal operation is restored; and / or if the difficult scenario type is a partial fall scenario, assessing the safety of the current difficult scenario, and controlling the robot to leave the current difficult scenario until the sensor-triggered signal is detected and normal operation is restored if it is determined to be unsafe; controlling the robot to leave the current difficult scenario includes: determining whether the robot can rotate, including: determining whether the robot's rotation radius is less than the channel radius; when it is determined that the robot cannot rotate, acquiring point cloud data of the safe side behind the robot; controlling the robot to retreat and leave the current difficult scenario based on the point cloud data of the safe side; wherein, the safe side includes the boundary of the obstacle.

9. A robot comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method as described in any one of claims 1 to 7.

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