Robot obstacle crossing method and system and computer program

Abstract

The invention discloses a robot obstacle crossing method and system and a computer program, and relates to the field of robot obstacle crossing, and the method comprises the steps: obtaining the point cloud data in front of a robot, carrying out the recognition and judgment according to the point cloud data, and obtaining the front obstacle data; calibrating the pose of the robot according to the obstacle data; according to the robot parameters, traction force generated by a driving motor is obtained; the output power of the driving motor is optimized according to the traction force, so that the robot can cross obstacles; the driving motor is started, and the robot crosses obstacles. According to the technical scheme provided by the invention, the obstacle crossing success rate of the robot is improved by detecting the obstacle, calibrating the pose of the robot, calculating the traction force generated by the driving motor of the robot and optimizing the output power of the driving motor; by updating the obstacle data in real time and adjusting the wheel speed difference of the robot, the orientation of the robot is kept in the center line direction of the obstacle, the robot can quickly and smoothly cross the obstacle, and good adaptability and robustness are achieved.

Description

Technical Field

[0001] The present invention relates to the field of robot obstacle surmounting, and in particular to a robot obstacle surmounting method, system and computer program. Background Art

[0002] Although existing tracked robots can identify obstacles and overcome them to a certain extent, their obstacle-overcoming capabilities still need to be improved in complex and changing environments. Specifically, existing tracked robots mostly use fixed obstacle-overcoming strategies in terms of obstacle-overcoming control. When faced with complex terrains such as stairs, slopes, and gullies, it is difficult for them to autonomously adjust their posture and overcome obstacles according to actual conditions. Their obstacle-overcoming capabilities and stability often cannot meet actual needs, thus limiting the scope of application of tracked robots. Summary of the invention

[0003] In view of the above-mentioned deficiencies in the current technology, the present invention provides a robot obstacle surmounting method, which detects obstacles and calibrates the robot's posture, calculates the traction generated by the robot's driving motor, determines whether the robot can cross the obstacle, and improves the success rate of the robot's obstacle surmounting.

[0004] To achieve the above object, the embodiments of the present invention adopt the following technical solutions: A robot obstacle surmounting method comprises the following steps: Obtain the point cloud data in front of the robot, identify and judge based on the point cloud data, and obtain the obstacle data in front; Calibrate the robot’s posture based on obstacle data; According to the robot parameters, the traction force generated by its driving motor is obtained; Optimize the output power of the drive motor according to the traction force, so that the robot can cross obstacles; Start the drive motor and the robot will overcome the obstacle; Wherein, calibrating the robot posture according to the obstacle data includes: The robot moves to the obstacle-crossing starting point; Update obstacle data. According to the current obstacle data, adjust the robot's posture so that the robot's direction is aligned with the center line of the obstacle, making it easier for the robot to overcome the obstacle. Wherein, obtaining the traction force generated by the driving motor of the robot according to the robot parameters includes: The total resistance torque of the robot is obtained according to the meshing friction of the robot tracks, the resistance torque of the transmission chain and the friction torque of the bearings on each wheel axle; Obtain the driving torque generated by the robot driving motor; According to the total resistance torque and driving torque, the traction force generated by the robot driving motor is obtained based on the force balance equation and the torque balance equation.

[0005] According to one aspect of the present invention, the obstacle data includes: obstacle type, obstacle location and obstacle geometric parameters.

[0006] According to one aspect of the present invention, the robot obstacle surmounting method further comprises: Based on the obstacle data, determine whether the robot can pass through the obstacle.

[0007] According to one aspect of the present invention, comparing the traction force with a predetermined threshold to determine whether the robot can cross the obstacle includes: If the traction force is greater than or equal to a predetermined threshold, the robot can cross the obstacle; If the traction force is less than a predetermined threshold, the output power of the robot's drive motor is optimized through a PID or MPC controller to enable it to cross the obstacle.

[0008] According to one aspect of the present invention, the robot obstacle surmounting method further comprises: When the robot is crossing an obstacle, the obstacle data is updated in real time, and the wheel speed difference between the left and right sides of the robot is adjusted to keep the robot facing the center line of the obstacle.

[0009] According to one aspect of the present invention, the robot obstacle surmounting method further comprises: During the robot's obstacle traversal process, a real-time feedback control system is used to dynamically adjust the robot's wheel speed difference, allowing the robot to quickly and smoothly adjust to the correct path.

[0010] According to one aspect of the present invention, the robot obstacle surmounting method further comprises: The machine vision system is used to assist in detection and adjustment. By analyzing the images taken by the robot, the edges and directions of the stairs / slopes can be identified to further optimize the angle calculation and path adjustment strategy.

[0011] According to one aspect of the present invention, the robot obstacle surmounting method further comprises: During the robot's obstacle traversal process, obstacle data is updated in real time. When no obstacle is detected or the pitch angle of the robot's inertial measurement unit approaches 0°, the robot completes the obstacle traversal.

[0012] A robot obstacle surmounting system, based on the robot obstacle surmounting method as described above, comprises: The detection module is used to obtain the point cloud data in front of the robot, identify and judge based on the point cloud data, and obtain the obstacle data in front; The calibration module is used to calibrate the robot's posture according to obstacle data; A calculation module, used to obtain the traction force generated by the driving motor of the robot according to the robot parameters; The motor adjustment module is used to optimize the output power of the drive motor according to the traction force so that the robot can cross obstacles; The obstacle crossing module is used to start the drive motor and enable the robot to cross obstacles.

[0013] A computer program, which implements the steps of the robot obstacle surmounting method as described above when executed.

[0014] Advantages of the present invention: The present invention provides a robot obstacle surmounting method, which detects obstacles and calibrates the robot's posture, calculates the traction generated by the robot's driving motor, optimizes the output power of the driving motor, and improves the success rate of the robot's obstacle surmounting. By updating obstacle data in real time and adjusting the robot's wheel speed difference, the robot is oriented to maintain the direction of the obstacle centerline, and can quickly and smoothly surmount obstacles, with good adaptability and robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A flowchart of a robot obstacle surmounting method according to the present invention; Figure 2 The posture of the robot before crossing the obstacle of the present invention; Figure 3 This is a structural diagram of the mobile robot with a flip arm structure described in the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Embodiment 1

[0019] like Figure 1 As shown, a robot obstacle crossing method comprises the following steps: S1: Obtain the point cloud data in front of the robot, identify and judge based on the point cloud data, and obtain the obstacle data in front.

[0020] Specifically, the obstacle data includes: obstacle type, obstacle location and obstacle geometric parameters.

[0021] In practical applications, obstacle types include: stairs, slopes, gullies, etc.

[0022] Specifically, the obstacle geometric parameters include the slope of stairs or ramps, the length of stair treads, the height of stair risers, the width of gullies, etc.

[0023] In actual applications, the robot will be equipped with detection devices such as cameras or radars. During the movement, the robot will collect real-time visual images, radar images, point clouds and other data in front of it. The pre-built obstacle detection and recognition model can be used to detect and identify the collected data, obtain the obstacle type, location and geometric parameters of the obstacle in front, so as to facilitate the robot to cross the obstacle in the future.

[0024] Specifically, the obstacle detection and recognition model may be a neural network model based on convolutional neural network or semantic segmentation, etc. By pre-collecting obstacle data, building a sample data set, and training the neural network model, the obstacle detection and recognition model is obtained.

[0025] In practical applications, the geometric parameters of obstacles can be obtained by using point cloud data to identify obstacles.

[0026] In addition, various obstacle crossing scenarios can be simulated in detail based on multi-sensor fusion technology, including but not limited to complex obstacles such as stairs, slopes, and gullies, to obtain the geometric parameters of the obstacles. By integrating sensor data, the impact of different obstacles on the system can be accurately simulated, thereby improving the evaluation and optimization of obstacle crossing capabilities.

[0027] Preferably, the method further comprises: Based on the obstacle data, determine whether the robot can pass through the obstacle.

[0028] Specifically, before the robot can cross an obstacle, it needs to determine whether the robot can pass through the obstacle based on the geometric parameters of the obstacle. For example, if a gully exceeds a certain width and depth, or a staircase or slope exceeds a certain slope, the robot cannot pass through it.

[0029] S2: Calibrate the robot posture based on obstacle data.

[0030] Step S2 includes: S21: The robot moves to the obstacle crossing starting point.

[0031] In actual applications, when the robot detects an obstacle, it may still be a certain distance away from the obstacle, so it needs to move to the side of the obstacle first as the starting point for crossing the obstacle.

[0032] S22: Update obstacle data. According to the current obstacle data, adjust the robot posture so that the robot is aligned with the center line of the obstacle, so as to facilitate the robot to cross the obstacle.

[0033] In practical applications, when the robot reaches the starting point of the obstacle crossing, its orientation may be at a certain angle to the center line of the obstacle. At this time, the robot's posture is not convenient for obstacle crossing. Therefore, the robot posture is required to align the robot's orientation with the center line of the obstacle, such as Figure 2 As shown, it is convenient for subsequent obstacle crossing.

[0034] Preferably, step S2 may further include: S23: updating obstacle data, and adjusting the robot driving structure according to the current obstacle data to facilitate the robot to traverse obstacles.

[0035] In practical applications, some robots will have drive structures to assist robot movements, such as Figure 3 The mobile robot shown has a flip arm structure, which can be used to climb and climb over obstacles with the help of the flip arm, and has a strong obstacle-crossing ability. Before implementing the obstacle-crossing, the angle of the flip arm needs to be adjusted according to the geometric parameters of stairs, slopes, gullies, etc.

[0036] S3: According to the robot parameters, obtain the traction force generated by its driving motor.

[0037] Step S3 includes: S31: Obtain the total resistance torque of the robot based on the robot track meshing friction, the resistance torque of the transmission chain, and the friction torque of the bearings on each wheel axle. M f .

[0038] S32: Get the driving torque generated by the robot's driving motor M k .

[0039] Usually, the robot drive motor generates a rotating magnetic field through the energized coil and acts on the rotor to form a magneto-electrical rotation torque, thereby providing power for the robot. Different types of motors have different current and torque characteristics. Common motor types include DC motors, AC motors, and stepper motors. These motors will produce different torque outputs when driving robots.

[0040] The center of the robot's driving wheel is taken as the origin O of the coordinate system, and the direction parallel to the low orbit is taken as the X-axis. Based on classical mechanical analysis, the position coordinates of the robot's center of gravity (X G , Y G ) and the driving torque generated by the drive motor M k , total resistance torqueM f .

[0041] S33: According to the total resistance torque and driving torque, based on the force balance equation and torque balance equation, the traction force generated by the robot driving motor is obtained F k .

[0042] According to the force balance equation and torque balance equation, the traction force generated by the robot drive motor can be calculated from the drive torque and the total resistance torque:

[0043] Where R is the radius of the driving wheel.

[0044] In practical applications, the data obtained from the above operations can also be input into the robot's adaptive control algorithm, which can dynamically adjust the robot's posture according to the current obstacle crossing stage and environmental conditions to optimize the robot's center of gravity distribution and obstacle crossing strategy.

[0045] S4: Optimize the output power of the drive motor according to the traction force so that the robot can cross obstacles.

[0046] Step S4 includes: If the traction force is greater than or equal to the predetermined threshold F th , then the robot can cross obstacles; If the traction force is less than the predetermined threshold F th , the output power of the robot's drive motor is optimized through PID or MPC controller so that it can cross obstacles.

[0047] In practical applications, the threshold F th It can be determined by decomposing the force of the robot according to the specific type of obstacle, such as stairs, slopes, gullies, etc. For example, for stairs or slopes, the traction of the robot needs to be greater than the component of the robot's own gravity along the stairs or slope; for gullies, if the gully is narrow and much smaller than the length of the robot's track, the robot only needs the traction to move forward to pass through the gully; if the gully is wide, the method of calculating the traction required by the robot is similar to that of a slope.

[0048] In addition, the threshold F th It can also be set by conducting actual tests. For example, for stairs or slopes, you can test how much traction the robot needs at different inclination angles to successfully climb the stairs or slopes; for gullies, you can test how much traction the robot needs at least to successfully cross the gullies at different widths and depths.

[0049] The traction force is compared with a predetermined threshold F th The comparison is made to determine whether the current drive motor can provide enough torque to overcome the resistance caused by the obstacle. If the traction is not strong enough, the output power of the drive motor can be optimized through the PID (Proportional-Integral-Derivative) or MPC (Model Predictive Control) controller to adjust the input power and speed of the motor to achieve the required torque output.

[0050] The PID controller can adjust the duty cycle of the motor according to the deviation signal (i.e. the difference between the expected value and the actual value), thereby adjusting the motor speed and torque output. The MPC controller optimizes the motor input by predicting the system behavior in the next few control cycles so that it can smoothly cross obstacles. By monitoring the robot's dynamic state and environmental conditions in real time, we can dynamically adjust the robot's posture and the output power of the motor to optimize the robot's obstacle crossing performance.

[0051] S5: Start the drive motor and the robot starts to overcome obstacles.

[0052] Preferably, the method further comprises: The machine vision system is used to assist in detection and adjustment. By analyzing the images taken by the robot, the edges and directions of the stairs / slopes can be identified to further optimize the angle calculation and path adjustment strategy.

[0053] In practical applications, in order to improve the adaptability and robustness of robots in complex environments, machine vision systems can also be introduced to assist in detection and adjustment. By analyzing the images taken by the robot, the edges and directions of stairs / slopes can be identified, and angle calculation and path adjustment strategies can be further optimized. This method is particularly suitable for situations where environmental conditions vary greatly or lighting is unstable, and can significantly improve the autonomy and reliability of the robot.

[0054] Preferably, the method further comprises: S6: During the robot's obstacle crossing process, obstacle data is updated in real time. When no obstacle is detected or the pitch angle of the robot's inertial measurement unit approaches 0°, the robot completes the obstacle crossing.

[0055] The beneficial effect of this embodiment is that the method detects obstacles and calibrates the robot's posture, calculates the traction generated by the robot's drive motor, optimizes the output power of the drive motor, improves the robot's success rate in overcoming obstacles, and has good adaptability and robustness.

[0056] Embodiment 2

[0057] like Figure 1 As shown, a robot obstacle crossing method comprises the following steps: S1: Obtain the point cloud data in front of the robot, identify and judge based on the point cloud data, and obtain the obstacle data in front.

[0058] Specifically, the obstacle data includes: obstacle type, obstacle location and obstacle geometric parameters.

[0059] In practical applications, obstacle types include: stairs, slopes, gullies, etc.

[0060] Specifically, the obstacle geometric parameters include the slope of stairs or ramps, the length of stair treads, the height of stair risers, the width of gullies, etc.

[0061] In actual applications, the robot will be equipped with detection devices such as cameras or radars. During the movement, the robot will collect real-time visual images, radar images, point clouds and other data in front of it. The pre-built obstacle detection and recognition model can be used to detect and identify the collected data, obtain the obstacle type, location and geometric parameters of the obstacle in front, so as to facilitate the robot to cross the obstacle in the future.

[0062] Specifically, the obstacle detection and recognition model may be a neural network model based on convolutional neural network or semantic segmentation, etc. By pre-collecting obstacle data, building a sample data set, and training the neural network model, the obstacle detection and recognition model is obtained.

[0063] In practical applications, the geometric parameters of obstacles can be obtained by using point cloud data to identify obstacles.

[0064] In addition, various obstacle crossing scenarios can be simulated in detail based on multi-sensor fusion technology, including but not limited to complex obstacles such as stairs, slopes, and gullies, to obtain the geometric parameters of the obstacles. By integrating sensor data, the impact of different obstacles on the system can be accurately simulated, thereby improving the evaluation and optimization of obstacle crossing capabilities.

[0065] Preferably, the method further comprises: Based on the obstacle data, determine whether the robot can pass through the obstacle.

[0066] Specifically, before the robot can cross an obstacle, it needs to determine whether the robot can pass through the obstacle based on the geometric parameters of the obstacle. For example, if a gully exceeds a certain width and depth, or a staircase or slope exceeds a certain slope, the robot cannot pass through it.

[0067] S2: Calibrate the robot posture based on obstacle data.

[0068] Step S2 includes: S21: The robot moves to the obstacle crossing starting point.

[0069] In actual applications, when the robot detects an obstacle, it may still be a certain distance away from the obstacle, so it needs to move to the side of the obstacle first as the starting point for crossing the obstacle.

[0070] S22: Update obstacle data. According to the current obstacle data, adjust the robot posture so that the robot is aligned with the center line of the obstacle, so as to facilitate the robot to cross the obstacle.

[0071] In practical applications, when the robot reaches the starting point of the obstacle crossing, its orientation may be at a certain angle to the center line of the obstacle. At this time, the robot's posture is not convenient for obstacle crossing. Therefore, the robot posture is required to align the robot's orientation with the center line of the obstacle, such as Figure 2 As shown, it is convenient for subsequent obstacle crossing. Preferably, step S2 may also include: S23: updating obstacle data, and adjusting the robot driving structure according to the current obstacle data to facilitate the robot to overcome obstacles.

[0072] In practical applications, some robots will have drive structures to assist robot movements, such as Figure 3 The mobile robot shown has a flip arm structure, which can be used to climb and climb over obstacles with the help of the flip arm, and has a strong obstacle-crossing ability. Before implementing the obstacle-crossing, the angle of the flip arm needs to be adjusted according to the geometric parameters of stairs, slopes, gullies, etc.

[0073] S3: According to the robot parameters, obtain the traction force generated by its driving motor.

[0074] Step S3 includes: S31: Obtain the total resistance torque of the robot based on the robot track meshing friction, the resistance torque of the transmission chain, and the friction torque of the bearings on each wheel axle. M f .

[0075] S32: Get the driving torque generated by the robot's driving motor M k .

[0076] Usually, the robot drive motor generates a rotating magnetic field through the energized coil and acts on the rotor to form a magneto-electrical rotation torque, thereby providing power for the robot. Different types of motors have different current and torque characteristics. Common motor types include DC motors, AC motors, and stepper motors. These motors will produce different torque outputs when driving robots.

[0077] The center of the robot's driving wheel is taken as the origin O of the coordinate system, and the direction parallel to the low orbit is taken as the X-axis. Based on classical mechanical analysis, the position coordinates of the robot's center of gravity (X G , Y G ) and the driving torque generated by the drive motor M k , total resistance torque M f .

[0078] S33: According to the total resistance torque and driving torque, based on the force balance equation and torque balance equation, the traction force generated by the robot driving motor is obtained F k .

[0079] According to the force balance equation and torque balance equation, the traction force generated by the robot drive motor can be calculated from the drive torque and the total resistance torque:

[0080] Where R is the radius of the driving wheel.

[0081] In practical applications, the data obtained from the above operations can also be input into the robot's adaptive control algorithm, which can dynamically adjust the robot's posture according to the current obstacle crossing stage and environmental conditions to optimize the robot's center of gravity distribution and obstacle crossing strategy.

[0082] S4: Optimize the output power of the drive motor according to the traction force so that the robot can cross obstacles.

[0083] Step S4 includes: If the traction force is greater than or equal to the predetermined threshold F th , then the robot can cross obstacles; If the traction force is less than the predetermined threshold F th , the output power of the robot's drive motor is optimized through PID or MPC controller so that it can cross obstacles.

[0084] In practical applications, the threshold F th It can be determined by decomposing the force of the robot according to the specific type of obstacle, such as stairs, slopes, gullies, etc. For example, for stairs or slopes, the traction of the robot needs to be greater than the component of the robot's own gravity along the stairs or slope; for gullies, if the gully is narrow and much smaller than the length of the robot's track, the robot only needs the traction to move forward to pass through the gully; if the gully is wide, the method of calculating the traction required by the robot is similar to that of a slope.

[0085] In addition, the threshold Fth It can also be set by conducting actual tests. For example, for stairs or slopes, you can test how much traction the robot needs at different inclination angles to successfully climb the stairs or slopes; for gullies, you can test how much traction the robot needs at least to successfully cross the gullies at different widths and depths.

[0086] The traction force is compared with a predetermined threshold F th The comparison is made to determine whether the current drive motor can provide enough torque to overcome the resistance caused by the obstacle. If the traction is not strong enough, the output power of the drive motor can be optimized through the PID (Proportional-Integral-Derivative) or MPC (Model Predictive Control) controller to adjust the input power and speed of the motor to achieve the required torque output.

[0087] The PID controller can adjust the duty cycle of the motor according to the deviation signal (i.e. the difference between the expected value and the actual value), thereby adjusting the motor speed and torque output. The MPC controller optimizes the motor input by predicting the system behavior in the next few control cycles so that it can smoothly cross obstacles. By monitoring the robot's dynamic state and environmental conditions in real time, we can dynamically adjust the robot's posture and the output power of the motor to optimize the robot's obstacle crossing performance.

[0088] S5: Start the drive motor and the robot starts to overcome obstacles.

[0089] The method further comprises: When the robot is crossing an obstacle, the obstacle data is updated in real time, and the wheel speed difference between the left and right sides of the robot is adjusted to keep the robot facing the center line of the obstacle.

[0090] In actual applications, when the robot is crossing obstacles, its forward direction may deviate from the center line of the obstacle. In order to ensure that the robot can travel stably on stairs and slopes, it is necessary to calculate the angle between the robot's direction and the center line of the stairs and slopes. , can be calculated using the vector dot product formula:

[0091] Where n is the centerline vector of the stairs / ramp and r is the robot's heading vector.

[0092] Then we need to determine whether the current robot is within the allowed offset range. This range can be determined by a threshold. To define, that is: if , the robot remains on the center line; if the robot exceeds this threshold, , it is necessary to adjust the wheel speed difference on the left and right sides of the robot to return it to the center position. This can be achieved through a differential drive strategy, where the left wheel speed and right wheel speed Adjust according to the direction the robot needs to turn. Specifically, if it needs to turn left, reduce or increase ,vice versa.

[0093] Preferably, the method further comprises: During the robot's obstacle traversal process, a real-time feedback control system is used to dynamically adjust the robot's wheel speed difference, allowing the robot to quickly and smoothly adjust to the correct path.

[0094] In practical applications, the wheel speed difference can be dynamically adjusted through a real-time feedback control system, such as a PID controller, so that the robot can quickly and smoothly adjust to the correct path. By repeating this step continuously, the robot will be able to continuously stay on the center line of the stairs or slope, thus ensuring safe and efficient driving.

[0095] Preferably, the method further comprises: The machine vision system is used to assist in detection and adjustment. By analyzing the images taken by the robot, the edges and directions of the stairs / slopes can be identified to further optimize the angle calculation and path adjustment strategy.

[0096] In practical applications, in order to improve the adaptability and robustness of robots in complex environments, machine vision systems can also be introduced to assist in detection and adjustment. By analyzing the images taken by the robot, the edges and directions of stairs / slopes can be identified, and angle calculation and path adjustment strategies can be further optimized. This method is particularly suitable for situations where environmental conditions vary greatly or lighting is unstable, and can significantly improve the autonomy and reliability of the robot.

[0097] Preferably, the method further comprises: S6: During the robot's obstacle crossing process, the obstacle data is updated in real time. When no obstacle is detected or the pitch angle of the robot's inertial measurement unit approaches 0°, the robot completes the obstacle crossing.

[0098] The beneficial effect of this embodiment is that the method also adjusts the robot's wheel speed difference by updating the obstacle data in real time, so that the robot's direction remains in the direction of the obstacle center line, and the robot can quickly and smoothly cross the obstacle.

[0099] Embodiment 3

[0100] A robot obstacle surmounting system, based on the robot obstacle surmounting method as described in Embodiment 1 or 2, comprises: The detection module is used to obtain the point cloud data in front of the robot, identify and judge based on the point cloud data, and obtain the obstacle data in front; The calibration module is used to calibrate the robot's posture according to obstacle data; A calculation module, used to obtain the traction force generated by the driving motor of the robot according to the robot parameters; The motor adjustment module is used to optimize the output power of the drive motor according to the traction force so that the robot can cross obstacles; The obstacle crossing module is used to start the drive motor and enable the robot to cross obstacles.

[0101] Preferably, the system further comprises: The adjustment module is used to update obstacle data in real time during the robot's obstacle crossing process, adjust the wheel speed difference between the left and right sides of the robot, and keep the robot's direction in the direction of the obstacle centerline.

[0102] Preferably, the system further comprises: The adjustment module can also be used in the process of the robot overcoming obstacles. It uses a real-time feedback control system to dynamically adjust the robot's wheel speed difference so that the robot can quickly and smoothly adjust to the correct path.

[0103] Preferably, the system further comprises: Vision module, which is used to use machine vision system to assist detection and adjustment. By analyzing the images taken by the robot, it can identify the edges and directions of stairs / slopes and further optimize the angle calculation and path adjustment strategy. The completion module is used to update obstacle data in real time during the robot's obstacle crossing process. When no obstacle is detected or the pitch angle of the robot's inertial measurement unit approaches 0°, it is confirmed that the robot has completed the obstacle crossing.

[0104] Embodiment 4

[0105] A computer program, which, when executed, implements the steps of the robot obstacle surmounting method as described in Embodiment 1 or 2.

[0106] Embodiment 5

[0107] A readable storage medium stores a computer program as described in Example 4, and when the computer program is executed, the steps of the robot obstacle surmounting method as described in Example 1 or 2 are implemented.

[0108] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the art within the technical scope disclosed in the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A robot obstacle surmounting method, characterized in that: The following steps are involved: Obtain the point cloud data in front of the robot, identify and judge based on the point cloud data, and obtain the obstacle data in front; Calibrate the robot’s posture based on obstacle data; According to the robot parameters, the traction force generated by its driving motor is obtained; Optimize the output power of the drive motor according to the traction force, so that the robot can cross obstacles; Start the drive motor and the robot will overcome the obstacle; Wherein, calibrating the robot posture according to the obstacle data includes: The robot moves to the obstacle-crossing starting point; Update obstacle data. According to the current obstacle data, adjust the robot's posture so that the robot's direction is aligned with the center line of the obstacle, making it easier for the robot to overcome the obstacle. Wherein, obtaining the traction force generated by the driving motor of the robot according to the robot parameters includes: The total resistance torque of the robot is obtained according to the meshing friction of the robot tracks, the resistance torque of the transmission chain and the friction torque of the bearings on each wheel axle; Obtain the driving torque generated by the robot driving motor; According to the total resistance torque and driving torque, the traction force generated by the robot driving motor is obtained based on the force balance equation and the torque balance equation.

2. The robot obstacle surmounting method according to claim 1, characterized in that: The obstacle data includes: obstacle type, obstacle location and obstacle geometric parameters.

3. The robot obstacle surmounting method according to claim 1, characterized in that: The robot obstacle crossing method also includes: Based on the obstacle data, determine whether the robot can pass through the obstacle.

4. The robot obstacle surmounting method according to claim 1, characterized in that: Optimizing the output power of the driving motor according to the traction force so that the robot can cross obstacles includes: If the traction force is greater than or equal to a predetermined threshold, the robot can cross the obstacle; If the traction force is less than a predetermined threshold, the output power of the robot's drive motor is optimized through a PID or MPC controller to enable it to cross the obstacle.

5. The robot obstacle surmounting method according to claim 1, characterized in that: The robot obstacle crossing method also includes: When the robot is crossing an obstacle, the obstacle data is updated in real time, and the wheel speed difference between the left and right sides of the robot is adjusted to keep the robot facing the center line of the obstacle.

6. The robot obstacle surmounting method according to claim 1, characterized in that: The robot obstacle crossing method also includes: During the robot's obstacle traversal process, a real-time feedback control system is used to dynamically adjust the robot's wheel speed difference, allowing the robot to quickly and smoothly adjust to the correct path.

7. The robot obstacle surmounting method according to claim 1, characterized in that: The robot obstacle crossing method also includes: The machine vision system is used to assist in detection and adjustment. By analyzing the images taken by the robot, the edges and directions of the stairs / slopes can be identified to further optimize the angle calculation and path adjustment strategy.

8. The robot obstacle surmounting method according to claim 1, characterized in that: The robot obstacle crossing method also includes: During the robot's obstacle traversal process, obstacle data is updated in real time. When no obstacle is detected or the pitch angle of the robot's inertial measurement unit approaches 0°, the robot completes the obstacle traversal.

9. A robot obstacle crossing system, characterized in that: The robot obstacle surmounting method according to any one of claims 1 to 8 comprises: The detection module is used to obtain the point cloud data in front of the robot, identify and judge based on the point cloud data, and obtain the obstacle data in front; The calibration module is used to calibrate the robot's posture according to obstacle data; A calculation module, used to obtain the traction force generated by the driving motor of the robot according to the robot parameters; The motor adjustment module is used to optimize the output power of the drive motor according to the traction force so that the robot can cross obstacles; The obstacle crossing module is used to start the drive motor and enable the robot to cross obstacles; Wherein, calibrating the robot posture according to the obstacle data includes: The robot moves to the obstacle-crossing starting point; Update obstacle data. According to the current obstacle data, adjust the robot's posture so that the robot's direction is aligned with the center line of the obstacle, making it easier for the robot to overcome the obstacle. Wherein, obtaining the traction force generated by the driving motor of the robot according to the robot parameters includes: The total resistance torque of the robot is obtained according to the meshing friction of the robot tracks, the resistance torque of the transmission chain and the friction torque of the bearings on each wheel axle; Obtain the driving torque generated by the robot driving motor; According to the total resistance torque and driving torque, the traction force generated by the robot driving motor is obtained based on the force balance equation and the torque balance equation.

10. A computer program, characterized in that When the computer program is executed, the steps of the robot obstacle surmounting method according to any one of claims 1 to 8 are implemented.

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