An intelligent stair-climbing method for a stair-climbing robot based on deformable wheels

Abstract

An intelligent stair - climbing method for a stair - climbing robot based on deformable wheels, which relates to an intelligent stair - climbing method for a mobile robot based on deformable wheels. The present invention addresses some challenges faced by existing mobile robots when encountering stair - like or step - like obstacles, and constructs an intelligent stair - climbing algorithm system for the designed two - wheel mobile robot based on deformable wheels. The steps of the present invention include compensating for the motion distortion of the lidar point cloud using IMU information, projecting, plane - fitting, and dimension - marking the stair step point cloud; performing scan matching using the compensated point cloud; providing position information and stair dimension information for the wheel deformation time and deformation amount of the mobile robot; obtaining the radius of the deformable wheel and the rotation angle of the wheel rim required for steps of this size; transmitting the distance between the robot and the stairs to the trajectory planning algorithm; and planning the rotation angle of the wheels at any time and controlling the change in the rotation angle of the deformable wheels in the planning algorithm. The present invention belongs to the technical field of intelligent devices.

Description

Technical Field

[0001] The present invention relates to an intelligent stair - climbing method for a mobile robot based on deformable wheels, belonging to the technical field of intelligent devices. Background Art

[0002] Today, with the increasingly high labor costs, reducing labor costs can significantly improve operating profits. The wide application of mobile robots can not only reduce labor costs but also improve work efficiency. This phenomenon has attracted the attention of the industrial and academic circles in the field of mobile robots. A variety of mobile robots have emerged one after another. Mobile robots based on deformable wheels combine the advantages of wheeled robots and bionic legged robots and can efficiently pass through obstacles such as stairs, which has attracted the attention of many scholars in recent years. However, there is little research on the construction of an intelligent stair - climbing system for mobile robots based on deformable wheels, and there are many research gaps: First, there is almost no research on the control of mobile robots based on deformable wheels, mainly involving the deformation control of deformable wheels and the balance control of two - wheel mobile robots based on deformable wheels. Second, there is very little research on the trajectory optimization algorithm for deformable wheels to overcome steps. Finally, the research accuracy of the stair - scene environment perception algorithm cannot meet the deformation requirements when the deformable wheels overcome steps. To sum up, it is very necessary to build an intelligent stair - climbing algorithm system for the designed mobile robot based on deformable wheels, which includes environment perception, deformation control of wheels, balance control of two - wheel mobile robots based on deformable wheels, and trajectory optimization algorithm. Summary of the Invention

[0003] In order to solve some challenges faced by existing mobile robots when facing obstacles such as stairs or steps, the present invention builds an intelligent stair - climbing algorithm system for the designed two - wheel mobile robot based on deformable wheels. The system mainly includes an environment perception subsystem, deformation control of wheels, a balance control subsystem for two - wheel mobile robots based on deformable wheels, and a wheel centroid trajectory optimization subsystem. The intelligent stair - climbing algorithm including the above - mentioned subsystems improves the stability and trajectory smoothness of the robot when climbing stairs.

[0004] The technical solution adopted by the present invention to solve the above problems is as follows:

[0005] The intelligent stair - climbing method of a stair - climbing robot based on deformable wheels of the present invention specifically includes the following steps:

[0006] Step 1: Compensate the motion distortion of the lidar point cloud using IMU information to obtain a high - precision and high - reliability lidar point cloud;

[0007] Step 2: Through the Ceres optimizer and the mean filtering method, project, plane - fit, and dimension - label the stepped point cloud to obtain the height and depth information of each step;

[0008] Step 3: Use the compensated point cloud for scan matching, and check whether the point cloud data degenerates. If degeneration occurs, solve the degeneration problem, and finally generate the map of the stairs and the relative position of the robot.

[0009] Step 4: Provide the position information and the stair dimension information for the wheel deformation time and deformation amount of the mobile robot; the stair dimension information includes the step depth D and the step height H.

[0010] Step 5: Obtain the required deformed wheel radius r and the rim rotation angle θ for the steps of this dimension through inverse kinematic calculation.

[0011] Step 6: Transmit (r, θ) and the distance L between the robot and the stairs obtained from the stair detection algorithm to the trajectory planning algorithm.

[0012] Step 7: Plan the rotation angle of the wheel at any moment in the planning algorithm and the rotation angles q of the two BLDC motors that control the change of the deformed wheel (r, θ) des (q 1des , q 2des ).

[0013] Furthermore, the specific steps of the inverse kinematic calculation in Step 5 are as follows:

[0014] Step 501: Solve the deformed wheel radius r and the rim rotation angle θ required to overcome steps of any dimension.

[0015]

[0016] In formula (1), D represents the step depth, H represents the step height, and r 0 represents;

[0017] Step 502: According to the required deformed wheel radius r and the rim rotation angle θ, solve the rotation angles (q 1 , q 2 ) of the solid and dashed sun gears respectively:

[0018]

[0019] In formulas (2) and (3), the deformed wheel radius r and the rim rotation angle θ are functions of P 3y and ξ. Therefore, use (P 3y , ξ) to represent the two degrees of freedom (r, θ) of the deformed wheel.

[0020] Step 503: It can be obtained from formulas (2) and (3) that:

[0021]

[0022] Step 504, the angles (q 3y , q 1 , q 2 ) of the solid and dashed line sun gears corresponding to the desired (P

[0023]

[0024] Furthermore, the specific steps of the staircase detection algorithm in step 6 are as follows:

[0025] Step A: Use IMU information to perform motion distortion compensation on the lidar point cloud to obtain a lidar point cloud with high precision and high reliability;

[0026] Step B: The undistorted point cloud is used to establish a staircase point cloud map and detect the step size of the staircase respectively;

[0027] Step C: Through degradation detection, output the staircase mapping result;

[0028] Step D: Extract the staircase point cloud from the lidar point cloud, set a threshold for the horizontal angle of the lidar point cloud, and judge the point cloud within the threshold range as the point cloud in the region of interest;

[0029] Step E: Project the point cloud in the region of interest onto the Z-axis in the lidar coordinate system, perform point cloud XZ plane fitting, use the Ceres optimizer to solve the linear fitting parameters of the point cloud in the XZ plane. If the fitting parameters meet the threshold requirements, identify the point cloud in the region of interest as the staircase point cloud;

[0030] Step F: Perform size detection on the identified staircase point cloud.

[0031] Furthermore, the specific steps of the trajectory planning algorithm in step 6 are as follows:

[0032] Step a: Analyze the kinematics of the deformable wheel overcoming the step, mark several waypoints O i that the deformable wheel must pass through when overcoming the step, calculate the position expressions of each waypoint, and stipulate that the speed and acceleration of the deformable wheel when passing through the first and last waypoints are zero;

[0033] Step b: Connect adjacent waypoints with a fifth-order Bezier curve, and the optimizer solves the optimal trajectory under the constraint conditions;

[0034] Step c: Analyze the possible interferences during the process of the deformable wheel overcoming the step, and establish a mathematical model to avoid interferences, and constrain the motion and shape of the deformable wheel in the form of inequalities or equalities. Essentially, it is to constrain the rotation angle of the deformable wheel, as well as the radius r of the deformable wheel and the rotation angle θ of the wheel rim;

[0035] Step d: To make the trajectory of the deformable wheel overcoming the steps as smooth as possible and to constrain the reciprocating up-and-down movement of the centroid of the deformable wheel, an objective function regarding acceleration is established, with the expectation that the sum of the average acceleration and the maximum acceleration during the entire trajectory optimization process is minimized.

[0036] Step e: Using the particle swarm optimization algorithm as the optimizer, under the motion constraints, solve for the centroid trajectory of the wheel that can minimize the objective function, and simultaneously solve for the unknown parameters. In the particle swarm optimization algorithm, the number of unknown parameters represents the search dimension of the particle swarm algorithm.

[0037] The beneficial effects of the present invention are as follows:

[0038] 1. The present invention can not only efficiently perform tasks on flat ground, but also climb stairs by changing the shape of the wheels when encountering stairs and continue to perform tasks efficiently.

[0039] 2. The robot system of the present invention can, without changing the forward direction of the robot, only by changing the direction of the rim rotation angle of the wheels, achieve going up and down stairs, climbing steps and stair obstacles in both directions, thereby improving the efficiency of the robot in performing tasks.

[0040] 3. The stair detection algorithm of the present invention provides relatively high-precision stair step sizes for the wheel deformation of the robot, which helps the mobile robot system to overcome the steps and continue to perform tasks.

[0041] 4. The trajectory planning algorithm of the present invention plans a smooth trajectory for the deformable wheel to overcome steps or stairs, which helps to improve the stability and success rate of the robot in overcoming steps. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a schematic diagram of the inverse kinematics algorithm;

[0043] Figure 2 is a flowchart of the intelligent perception algorithm of the present invention;

[0044] Figure 3 is a control schematic diagram of the stair-climbing robot system based on the deformable wheel;

[0045] Figure 4 is a stair-climbing schematic diagram of the stair-climbing robot system based on the deformable wheel;

[0046] Figure 5 is a structural schematic diagram of the stair-climbing robot system based on the deformable wheel. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] DETAILED DESCRIPTION OF THE EMBODIMENT 1: As Figures 1 to 4 shown, an intelligent stair-climbing method for a stair-climbing robot based on a deformable wheel specifically includes:

[0048] Step 1: Compensate for the motion distortion of the lidar point cloud using IMU information to obtain high-precision and highly reliable lidar point cloud;

[0049] Step 2: Through the Ceres optimizer and the mean filtering method, project, plane fit, and dimension label the stepped point cloud to obtain the height and width information of each step;

[0050] Step 3: Use the compensated point cloud for scan matching, and check whether the point cloud data degenerates. If degeneration occurs, solve the degeneration problem, and finally generate the map of the stairs and the relative position of the robot;

[0051] Step 4: Provide position information and stair dimension information for the wheel deformation time and deformation amount of the mobile robot; the stair dimension information includes the step depth D and the step height H;

[0052] Step 5: Obtain the required deformed wheel radius r and the rim rotation angle θ for the steps of this size through inverse kinematics calculation;

[0053] Step 6: Transmit (r, θ) and the distance L of the robot relative to the stairs obtained in the stair detection algorithm to the trajectory planning algorithm;

[0054] Step 7: Plan the rotation angle of the wheel at any time in the planning algorithm and the rotation angles q of the two BLDC motors that control the change of the deformed wheel (r, θ) des (q 1des , q 2des ).

[0055] The rotation angle q of the deformed wheel des (q 1des , q 2des ) is the expected rotation angle in the deformation control of the deformed wheel. The rotation angle of the deformed wheel is controlled by a PD controller, and is the actual rotation angle feedback of the BLDC that controls the deformation of the deformed wheel to the PD controller.

[0056] The expected rotation angle of the wheel generated by the trajectory planning is the desired state of the wheel, and the rotation angle γ of the vehicle body around the y-axis and the rotation angle around the z-axis measured by the IMU are passed into the balance control algorithm as reference angles for controlling the balance and steering of the mobile robot. Different from the balance control algorithm of traditional two-wheel balanced mobile robots, after the wheel is deformed, the center of mass of the wheel is no longer perpendicular to the ground with the line connecting the rim and the ground contact point, so the actual also affects the balance of the mobile robot, so it needs to be transmitted to the balance control algorithm. Under the action of the balance control algorithm, the balance of the mobile robot during the stair climbing process is achieved.

[0057] As Figure 5 shown, the deformable wheel stair-climbing robot system includes a deformable wheel on each of the left and right sides; a rotational drive system for each deformable wheel; a battery for providing energy; a control board with an Orange Pi as the core; a radar and an IMU.

[0058] Specific Embodiment 2: As Figures 1 to 4 shown, the specific steps of the inverse kinematics calculation in Step 5 are as follows:

[0059] Step 501: Solve for the radius r of the deformable wheel and the rotation angle θ of the wheel rim required to overcome steps of any size;

[0060]

[0061] In formula (1), W represents the depth of the step, H represents the height of the step, and r 0 represents;

[0062] Step 502: According to the required radius r of the deformable wheel and the rotation angle θ of the wheel rim, solve for the rotation angles (q 1 , q 2 ) of the red and green sun gears:

[0063]

[0064] In formulas (2) and (3), the radius r of the deformable wheel and the rotation angle θ of the wheel rim are functions of P 3y and ξ. Therefore, use (P 3y , ξ) to represent the two degrees of freedom (r, θ) of the deformable wheel;

[0065] Step 503: It can be obtained from formulas (2) and (3) that:

[0066]

[0067] 3y , ξ) corresponding to the rotation angles q1 and q2 of the red and green sun gears:

[0068]

[0069] Specific Embodiment 3: As Figures 1 to 4 shown, the specific steps of the stair detection algorithm in Step 6 are as follows:

[0070] Step A: Use the IMU information to perform motion distortion compensation on the lidar point cloud to obtain a high-precision and highly reliable lidar point cloud;

[0071] Step B: The undistorted point clouds are respectively used to establish a staircase point cloud map and detect the step sizes of the staircase.

[0072] Step C: Through degeneracy detection, the staircase mapping result is output.

[0073] Step D: Extract the staircase point cloud from the lidar point cloud, set a threshold for the horizontal angle of the lidar point cloud, and determine the point cloud within the threshold range as the point cloud of the region of interest.

[0074] Step E: Project the point cloud within the region of interest onto the Z-axis in the lidar coordinate system, perform point cloud XZ plane fitting, use the Ceres optimizer to solve the linear fitting parameters of the point cloud in the XZ plane. If the fitting parameters meet the threshold requirements, identify the point cloud within the region of interest as the staircase point cloud.

[0075] Step F: Conduct size detection on the identified staircase point cloud.

[0076] Specific Embodiment 4: As Figures 1 to 4 shown, the specific steps of the trajectory planning algorithm in Step 6 are as follows:

[0077] Step a: Analyze the kinematics of the deformable wheel overcoming the step, mark several waypoints O i that the deformable wheel must pass through when overcoming the step, calculate the position expressions of each waypoint, and stipulate that the speed and acceleration of the deformable wheel when passing through the first and last waypoints are zero.

[0078] Step b: Connect adjacent waypoints with a fifth-order Bezier curve, and the optimizer solves the optimal trajectory under the constraint conditions.

[0079] Step c: Analyze the possible interferences during the process of the deformable wheel overcoming the step, and establish a mathematical model to avoid interferences, which constrains the movement and shape of the deformable wheel in the form of inequalities or equations. Essentially, it is to constrain the rotation angle of the deformable wheel, the radius r of the deformable wheel, and the rotation angle θ of the wheel rim.

[0080] Step d: To make the trajectory of the deformable wheel overcoming the step as smooth as possible, and at the same time constrain the up and down reciprocating movement of the centroid of the deformable wheel, establish an objective function regarding acceleration, expecting the sum of the average acceleration and the maximum acceleration during the entire trajectory optimization process to be the smallest.

[0081] Step e: Use the particle swarm optimization algorithm as the optimizer to solve the centroid trajectory of the wheel that can minimize the objective function under the movement constraints, and at the same time solve the unknown parameters. In the particle swarm optimization algorithm, the number of unknown parameters represents the search dimension of the particle swarm algorithm.

[0082] The above are only the preferred embodiments of the present invention, and do not impose any form of limitation on the present invention. Although the present invention has been disclosed above with the preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art, within the scope of the technical solution of the present invention, can make some changes or modifications to the above-disclosed technical content to obtain equivalent embodiments with equivalent changes. However, as long as it does not depart from the technical solution content of the present invention, and according to the technical essence of the present invention, any simple modification, equivalent replacement, and improvement made to the above embodiments within the spirit and principle of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. An intelligent stair climbing method for a stair climbing robot based on deformable wheels, characterized in that: Specifically include: Step 1: Use IMU information to compensate for the motion distortion of the lidar point cloud to obtain a high-precision and high-reliability lidar point cloud; Step 2: Project, plane fit and dimension the step point cloud through Ceres optimizer and mean filter method to obtain the height and depth information of each step; Step 3: Use the compensated point cloud to perform scan matching and check whether the point cloud data is degraded. If so, solve the degradation problem and finally generate a map of the stairs and the relative position of the robot. Step 4, providing position information and stair size information for the deformation time and deformation amount of the wheels of the mobile robot; the stair size information includes the step depth D and the step height H; Step 5: Obtain the radius of the deformation wheel required for the step of this size through inverse kinematics calculation and rim rotation angle ; Step 6: And the distance of the robot relative to the stairs obtained in the stair detection algorithm Passed to the trajectory planning algorithm; Step 7: Plan the rotation angle of the wheel at any time in the planning algorithm And control the deformation wheel Varying the rotation angle of the two BLDC motors .

2. The intelligent stair climbing method of a stair climbing robot based on deformable wheels according to claim 1, characterized in that: The specific steps of inverse kinematics calculation in step 5 are: Step 501: Determine the radius of the deformable wheel required to overcome steps of any size and the corner of the wheel rim ; （1）， In formula (1), Indicates the depth of the step, Indicates the height of the step, express; Step 502: according to the radius of the required deformation wheel and rim angle , solve for the rotation angle of the solid and dashed sun gears : （2）， （3）， In formulas (2) and (3), the radius of the deformed wheel is and the corner of the wheel rim About and function, so using Represents the two degrees of freedom of the deformation wheel ; Step 503: From formula (2) and formula (3), it can be obtained that: （4）， Step 504: Desired The corresponding solid and dashed sun gear rotation angles The formula is: （5）。 3. The intelligent stair climbing method of a stair climbing robot based on deformable wheels according to claim 1, characterized in that: The specific steps of the stair detection algorithm in step 6 are: Step A: Use IMU information to perform motion distortion compensation on the LiDAR point cloud, so as to obtain a LiDAR point cloud with high accuracy and high reliability; Step B: The dedistorted point cloud is used to establish a stair point cloud map and detect the size of the stair steps; Step C: Output the staircase mapping result through degradation detection; Step D, extracting stair point clouds from the laser radar point clouds, setting a threshold for the horizontal angle of the laser radar point clouds, and determining the point clouds that meet the threshold range as the point clouds of the area of ​​interest; Step E: Project the point cloud in the region of interest to the Z axis of the laser radar coordinate system, perform XZ plane fitting of the point cloud, and use the Ceres optimizer to solve the straight line fitting parameters of the point cloud in the XZ plane. If the fitting parameters meet the threshold requirements, the point cloud in the region of interest is identified as a stair point cloud. Step F: Perform size detection on the identified stair point cloud.

4. The intelligent stair climbing method of a stair climbing robot based on deformable wheels according to claim 1, characterized in that: The specific steps of the trajectory planning algorithm in step 6 are: Step a: Analyze the kinematics of the deformable wheel overcoming the step, and mark several landmarks that the deformable wheel must pass through to overcome the step , calculate the position expression of each landmark point, and stipulate that the speed and acceleration of the deformable wheel when passing the first and last landmark points are zero; Step b: connect two adjacent landmark points with a fifth-order Bezier curve, and the optimizer solves the optimal trajectory under the constraint conditions; Step c: Analyze the possible interference of the deformation wheel in overcoming the step process, and establish a mathematical model to avoid interference. Constrain the movement and shape of the deformation wheel in the form of inequalities or equations, which is essentially the rotation angle of the deformation wheel. And the radius of the deformation wheel and the corner of the wheel rim To impose restraints; Step d: In order to make the trajectory of the deformable wheel overcoming the step as smooth as possible and constrain the center of mass of the deformable wheel to reciprocate up and down, an objective function about acceleration is established, and it is expected that the sum of the average acceleration and the maximum acceleration in the entire trajectory optimization process is minimized; Step e: using the particle swarm optimization algorithm as the optimizer, solving the wheel center of mass trajectory that can minimize the objective function under the motion constraint, and solving the unknown parameters at the same time; in the particle swarm optimization algorithm, the number of unknown parameters is expressed as the search dimension of the particle swarm algorithm.

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